2023
Conference article
Open Access
On the applicability of prototypical part learning in medical images: breast masses classification using ProtoPNet
Carloni G., Berti A., Iacconi C., Pascali M. A., Colantonio S.
Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research. However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two considered aspects were the classification capabilities and the validity of explanations. We looked for the optimal model's hyperparameter configuration via a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.Source: ICPR 2022 - International Conference on Pattern Recognition - ICPR 2022 International Workshops and Challenges, pp. 539–557, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37660-3_38
Metrics:
Carloni G., Berti A., Iacconi C., Pascali M. A., Colantonio S.
Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research. However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two considered aspects were the classification capabilities and the validity of explanations. We looked for the optimal model's hyperparameter configuration via a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.Source: ICPR 2022 - International Conference on Pattern Recognition - ICPR 2022 International Workshops and Challenges, pp. 539–557, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37660-3_38
Metrics:
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ISTI Repository 
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| CNR ExploRA
2023
Journal article
Open Access
Raman spectroscopy and topological machine learning for cancer grading
Conti F., D'Acunto M., Caudai C., Colantonio C., Gaeta R., Moroni D., Pascali M. A.
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-34457-5
DOI: 10.1038/s41598-023-34457-5
Metrics:
Conti F., D'Acunto M., Caudai C., Colantonio C., Gaeta R., Moroni D., Pascali M. A.
In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-34457-5
DOI: 10.1038/s41598-023-34457-5
Metrics:
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ISTI Repository | www.nature.com | CNR ExploRA
2023
Conference article
Open Access
Analysis of sea surface temperature maps via topological machine learning
Conti F., Papini O., Moroni D., Pieri G., Reggiannini M., Pascali M. A.
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.Source: ITNT 2023 - IX International Conference on Information Technology and Nanotechnology, Samara, Russia, 17-21/04/2023
DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
Conti F., Papini O., Moroni D., Pieri G., Reggiannini M., Pascali M. A.
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.Source: ITNT 2023 - IX International Conference on Information Technology and Nanotechnology, Samara, Russia, 17-21/04/2023
DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Report
Open Access
Alzheimer disease detection from Raman spectroscopy of the cerebrospinal fluid via topological machine learning
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (> 87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.Source: ISTI Working paper, 2309.03664, pp.1–7, 2023
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (> 87%). Although our results are preliminary, they indicate that RS and topological analysis together may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps will include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to understand if topological data analysis could support the characterization of AD subtypes.Source: ISTI Working paper, 2309.03664, pp.1–7, 2023
See at:
arxiv.org | CNR ExploRA
2023
Journal article
Open Access
Alzheimer disease detection from Raman spectroscopy of the cerebrospinal fluid via topological machine learning
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls was collected and analyzed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (>87%). Although our results are preliminary, they indicate that RS and topological analysis may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to investigate whether topological data analysis could support the characterization of AD subtypes.Source: Engineering proceedings (Basel) 51 (2023). doi:10.3390/engproc2023051014
DOI: 10.3390/engproc2023051014
Metrics:
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls was collected and analyzed by Raman spectroscopy (RS). We investigated whether the raw and preprocessed Raman spectra could be used to distinguish AD from controls. First, we applied standard Machine Learning (ML) methods obtaining unsatisfactory results. Then, we applied ML to a set of topological descriptors extracted from raw spectra, achieving a very good classification accuracy (>87%). Although our results are preliminary, they indicate that RS and topological analysis may provide an effective combination to confirm or disprove a clinical diagnosis of AD. The next steps include enlarging the dataset of CSF samples to validate the proposed method better and, possibly, to investigate whether topological data analysis could support the characterization of AD subtypes.Source: Engineering proceedings (Basel) 51 (2023). doi:10.3390/engproc2023051014
DOI: 10.3390/engproc2023051014
Metrics:
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www.mdpi.com | CNR ExploRA
2023
Conference article
Open Access
Exploring the potentials and challenges of AI in supporting clinical diagnostics and remote assistance for the health and well-being of individuals
Berti A., Buongiorno R., Carloni G., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settingsSource: Ital-IA 2023 - Italia Intelligenza Artificiale. Thematic Workshops of the 3rd CINI National Lab AIIS Conference on Artificial Intelligence - 2023, Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I
Berti A., Buongiorno R., Carloni G., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settingsSource: Ital-IA 2023 - Italia Intelligenza Artificiale. Thematic Workshops of the 3rd CINI National Lab AIIS Conference on Artificial Intelligence - 2023, Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
AI trustworthiness in prostate cancer imaging: a look at algorithmic and system transparency
Colantonio S., Berti A., Buongiorno R., Del Corso G., Pachetti E., Pascali M. A., Kalantzopoulos C., Kalokyri V., Kondylakis H., Tachos N., Fotiadis D., Giannini V., Mazzetti S., Regge D., Papanikolaou N., Marias K., Tsiknakis M.
A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favourable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.Source: IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7-9/12/2023
Project(s): ProCAncer-I
Colantonio S., Berti A., Buongiorno R., Del Corso G., Pachetti E., Pascali M. A., Kalantzopoulos C., Kalokyri V., Kondylakis H., Tachos N., Fotiadis D., Giannini V., Mazzetti S., Regge D., Papanikolaou N., Marias K., Tsiknakis M.
A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favourable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.Source: IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7-9/12/2023
Project(s): ProCAncer-I
See at:
ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Horticultural therapy may reduce psychological and physiological stress in adolescents with anorexia nervosa: a pilot study
Curzio O., Billeci L., Belmonti V., Colantonio S., Cotrozzi L., De Pasquale C. F., Morales M. A., Nali C., Pascali M. A., Venturi F., Tonacci A., Zannoni N., Maestro S.
Studies in psychiatric populations have found a positive effect of Horticultural therapy (HCT) on reductions in stress levels. The main objective of the present pilot study was to evaluate the impact of the addition of HCT to conventional clinical treatment (Treatment as Usual, TaU) in a sample of six female adolescents with anorexia nervosa restricting type (AN-R), as compared to six AN-R patients, matched for sex and age, under TaU only. This is a prospective, non-profit, pilot study on patients with a previous diagnosis of AN-R and BMI < 16, recruited in 2020 in clinical settings. At enrolment (T0) and after treatment completion (TF), psychiatric assessment was performed. At T0, all the patients underwent: baseline electrocardiogram acquisition with a wearable chest strap for recording heart rate and its variability; skin conductance registration and thermal mapping of the individual's face. An olfactory identification test was administered both to evaluate the olfactory sensoriality and to assess the induced stress. One-way analyses of variance (ANOVAs) were performed to analyze modifications in clinical and physiological variables, considering time (T0, TF) as a within-subjects factor and group (experimental vs. control) as between-subjects factors. When the ANOVA was significant, post hoc analysis was performed by Paired Sample T-tests. Only in the HCT group, stress response levels, as measured by the biological parameters, improved over time. The body uneasiness level and the affective problem measures displayed a significant improvement in the HCT subjects. HCT seems to have a positive influence on stress levels in AN-R.Source: Nutrients 14 (2022). doi:10.3390/nu14245198
DOI: 10.3390/nu14245198
Metrics:
Curzio O., Billeci L., Belmonti V., Colantonio S., Cotrozzi L., De Pasquale C. F., Morales M. A., Nali C., Pascali M. A., Venturi F., Tonacci A., Zannoni N., Maestro S.
Studies in psychiatric populations have found a positive effect of Horticultural therapy (HCT) on reductions in stress levels. The main objective of the present pilot study was to evaluate the impact of the addition of HCT to conventional clinical treatment (Treatment as Usual, TaU) in a sample of six female adolescents with anorexia nervosa restricting type (AN-R), as compared to six AN-R patients, matched for sex and age, under TaU only. This is a prospective, non-profit, pilot study on patients with a previous diagnosis of AN-R and BMI < 16, recruited in 2020 in clinical settings. At enrolment (T0) and after treatment completion (TF), psychiatric assessment was performed. At T0, all the patients underwent: baseline electrocardiogram acquisition with a wearable chest strap for recording heart rate and its variability; skin conductance registration and thermal mapping of the individual's face. An olfactory identification test was administered both to evaluate the olfactory sensoriality and to assess the induced stress. One-way analyses of variance (ANOVAs) were performed to analyze modifications in clinical and physiological variables, considering time (T0, TF) as a within-subjects factor and group (experimental vs. control) as between-subjects factors. When the ANOVA was significant, post hoc analysis was performed by Paired Sample T-tests. Only in the HCT group, stress response levels, as measured by the biological parameters, improved over time. The body uneasiness level and the affective problem measures displayed a significant improvement in the HCT subjects. HCT seems to have a positive influence on stress levels in AN-R.Source: Nutrients 14 (2022). doi:10.3390/nu14245198
DOI: 10.3390/nu14245198
Metrics:
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ISTI Repository | www.mdpi.com | CNR ExploRA
2022
Journal article
Open Access
Machine and deep learning prediction of prostate cancer aggressiveness using multiparametric MRI
E. Bertelli, L. Mercatelli, C. Marzi, E. Pachetti, M. Baccini, A. Barucci, S. Colantonio, L. Gherardini, L. Lattavo, M. A. Pascali, S. Agostini, V. Miele
Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score >= 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.Source: Frontiers in oncology 11 (2022). doi:10.3389/fonc.2021.802964
DOI: 10.3389/fonc.2021.802964
Project(s): ProCAncer-I
Metrics:
E. Bertelli, L. Mercatelli, C. Marzi, E. Pachetti, M. Baccini, A. Barucci, S. Colantonio, L. Gherardini, L. Lattavo, M. A. Pascali, S. Agostini, V. Miele
Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score >= 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.Source: Frontiers in oncology 11 (2022). doi:10.3389/fonc.2021.802964
DOI: 10.3389/fonc.2021.802964
Project(s): ProCAncer-I
Metrics:
See at:
ISTI Repository | www.frontiersin.org | CNR ExploRA
2022
Conference article
Open Access
Augmented reality, artificial intelligence and machine learning in Industry 4.0: case studies at SI-Lab
Bruno A, Coscetti S, Leone G. R., Germanese D., Magrini M., Martinelli M., Moroni D., Pascali M. A., Pieri G., Reggiannini M., Tampucci M.
In recent years, the impressive advances in artificial intelligence, computer vision, pervasive computing, and augmented reality made them rise to pillars of the fourth industrial revolution. This short paper aims to provide a brief survey of current use cases in factory applications and industrial inspection under active development at the Signals and Images Lab, ISTI-CNR, Pisa.Source: Ital-IA 2022 - Convegno nazionale CINI sull'Intelligenza Artificiale, Torino, Italy, 9-11/02/2022
DOI: 10.5281/zenodo.6322733
Metrics:
Bruno A, Coscetti S, Leone G. R., Germanese D., Magrini M., Martinelli M., Moroni D., Pascali M. A., Pieri G., Reggiannini M., Tampucci M.
In recent years, the impressive advances in artificial intelligence, computer vision, pervasive computing, and augmented reality made them rise to pillars of the fourth industrial revolution. This short paper aims to provide a brief survey of current use cases in factory applications and industrial inspection under active development at the Signals and Images Lab, ISTI-CNR, Pisa.Source: Ital-IA 2022 - Convegno nazionale CINI sull'Intelligenza Artificiale, Torino, Italy, 9-11/02/2022
DOI: 10.5281/zenodo.6322733
Metrics:
See at:
ISTI Repository | www.ital-ia2022.it | CNR ExploRA
2022
Conference article
Open Access
Exploring UAVs for structural health monitoring
Germanese D., Moroni D., Pascali M. A., Tampucci M., Berton A.
The preservation and maintenance of architectural heritage on a large scale deserve the design, development, and exploitation of innovative methodologies and tools for sustainable Structural Heritage Monitoring (SHM). In the framework of the Moscardo Project (https://www.moscardo.it/), the role of Unmanned Aerial Vehicles (UAVs) in conjunction with a broader IoT platform for SHM has been investigated. UAVs resulted in significant aid for a safe, fast and routinely operated inspection of buildings in synergy with data collected in situ thanks to a network of pervasive wireless sensors (Bacco et al. 2020). The main idea has been to deploy an acquisition layer made of a network of low power sensors capable of collecting environmental parameters and building vibration modes. This layer has been connected to a service layer through gateways capable of performing data analysis and presenting aggregated results thanks to an integrated dashboard. In this architecture, the UAV has emerged as a particular network node for extending the acquisition layer by adding several imaging capabilities.Source: D-SITe 2022 - Drones. Systems of Information on culTural hEritage. For a spatial and social investigation, pp. 640–643, Pavia, Italy, 16-18/06/2022
Germanese D., Moroni D., Pascali M. A., Tampucci M., Berton A.
The preservation and maintenance of architectural heritage on a large scale deserve the design, development, and exploitation of innovative methodologies and tools for sustainable Structural Heritage Monitoring (SHM). In the framework of the Moscardo Project (https://www.moscardo.it/), the role of Unmanned Aerial Vehicles (UAVs) in conjunction with a broader IoT platform for SHM has been investigated. UAVs resulted in significant aid for a safe, fast and routinely operated inspection of buildings in synergy with data collected in situ thanks to a network of pervasive wireless sensors (Bacco et al. 2020). The main idea has been to deploy an acquisition layer made of a network of low power sensors capable of collecting environmental parameters and building vibration modes. This layer has been connected to a service layer through gateways capable of performing data analysis and presenting aggregated results thanks to an integrated dashboard. In this architecture, the UAV has emerged as a particular network node for extending the acquisition layer by adding several imaging capabilities.Source: D-SITe 2022 - Drones. Systems of Information on culTural hEritage. For a spatial and social investigation, pp. 640–643, Pavia, Italy, 16-18/06/2022
See at:
ISTI Repository | ISTI Repository | www.dsiteconference.com | CNR ExploRA
2022
Contribution to book
Open Access
On the effectiveness of 3D vision transformers for the prediction of prostate cancer aggressiveness
Pachetti E., Colantonio S., Pascali M. A.
Prostate cancer is the most frequent male neoplasm in European men. To date, the gold standard for determining the aggressiveness of this tumor is the biopsy, an invasive and uncomfortable procedure. Before the biopsy, physicians recommend an investigation by multiparametric magnetic resonance imaging, which may serve the radiologist to gather an initial assessment of the tumor. The study presented in this work aims to investigate the role of Vision Transformers in predicting prostate cancer aggressiveness based only on imaging data. We designed a 3D Vision Transformer able to process volumetric scans, and we optimized it on the ProstateX-2 challenge dataset by training it from scratch. As a term of comparison, we also designed a 3D Convolutional Neural Network, and we optimized it in a similar fashion. The results obtained by our preliminary investigations show that Vision Transformers, even without extensive optimization and customization, can ensure an improved performance with respect to Convolutional Neural Networks and might be comparable with other more fine-tuned solutions.Source: Image Analysis and Processing, edited by Mazzeo P.L., Frontoni E., Sclaroff S., Distante C., pp. 317–328. Switzerland: Springer International Publishing, 2022
DOI: 10.1007/978-3-031-13324-4_27
Project(s): ProCAncer-I
Metrics:
Pachetti E., Colantonio S., Pascali M. A.
Prostate cancer is the most frequent male neoplasm in European men. To date, the gold standard for determining the aggressiveness of this tumor is the biopsy, an invasive and uncomfortable procedure. Before the biopsy, physicians recommend an investigation by multiparametric magnetic resonance imaging, which may serve the radiologist to gather an initial assessment of the tumor. The study presented in this work aims to investigate the role of Vision Transformers in predicting prostate cancer aggressiveness based only on imaging data. We designed a 3D Vision Transformer able to process volumetric scans, and we optimized it on the ProstateX-2 challenge dataset by training it from scratch. As a term of comparison, we also designed a 3D Convolutional Neural Network, and we optimized it in a similar fashion. The results obtained by our preliminary investigations show that Vision Transformers, even without extensive optimization and customization, can ensure an improved performance with respect to Convolutional Neural Networks and might be comparable with other more fine-tuned solutions.Source: Image Analysis and Processing, edited by Mazzeo P.L., Frontoni E., Sclaroff S., Distante C., pp. 317–328. Switzerland: Springer International Publishing, 2022
DOI: 10.1007/978-3-031-13324-4_27
Project(s): ProCAncer-I
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2022
Journal article
Open Access
A topological machine learning pipeline for classification
Conti F., Moroni D., Pascali M. A.
In this work, we develop a pipeline that associates Persistence Diagrams to digital data via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation methods and parameters. The development of such a topological pipeline for Machine Learning involves two crucial steps that strongly affect its performance: firstly, digital data must be represented as an algebraic object with a proper associated filtration in order to compute its topological summary, the Persistence Diagram. Secondly, the persistence diagram must be transformed with suitable representation methods in order to be introduced in a Machine Learning algorithm. We assess the performance of our pipeline, and in parallel, we compare the different representation methods on popular benchmark datasets. This work is a first step toward both an easy and ready-to-use pipeline for data classification using persistent homology and Machine Learning, and to understand the theoretical reasons why, given a dataset and a task to be performed, a pair (filtration, topological representation) is better than another.Source: Mathematics 10 (2022). doi:10.3390/math10173086
DOI: 10.3390/math10173086
Metrics:
Conti F., Moroni D., Pascali M. A.
In this work, we develop a pipeline that associates Persistence Diagrams to digital data via the most appropriate filtration for the type of data considered. Using a grid search approach, this pipeline determines optimal representation methods and parameters. The development of such a topological pipeline for Machine Learning involves two crucial steps that strongly affect its performance: firstly, digital data must be represented as an algebraic object with a proper associated filtration in order to compute its topological summary, the Persistence Diagram. Secondly, the persistence diagram must be transformed with suitable representation methods in order to be introduced in a Machine Learning algorithm. We assess the performance of our pipeline, and in parallel, we compare the different representation methods on popular benchmark datasets. This work is a first step toward both an easy and ready-to-use pipeline for data classification using persistent homology and Machine Learning, and to understand the theoretical reasons why, given a dataset and a task to be performed, a pair (filtration, topological representation) is better than another.Source: Mathematics 10 (2022). doi:10.3390/math10173086
DOI: 10.3390/math10173086
Metrics:
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ISTI Repository | www.mdpi.com | CNR ExploRA
2022
Conference article
Open Access
Data models for an imaging bio-bank for colorectal, prostate and gastric cancer: the NAVIGATOR project
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Contribution to journal
Open Access
On some scientific results of the IMTA-VIII-2022: 8th International Workshop "Image Mining: Theory and Applications"
Gurevich I. B., Moroni D., Pascali M. A., Yashina V. V.
Source: Pattern recognition and image analysis 32 (2022): 460–465. doi:10.1134/S1054661822030312
DOI: 10.1134/s1054661822030312
Metrics:
Gurevich I. B., Moroni D., Pascali M. A., Yashina V. V.
Source: Pattern recognition and image analysis 32 (2022): 460–465. doi:10.1134/S1054661822030312
DOI: 10.1134/s1054661822030312
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2022
Report
Open Access
Studio e analisi delle architetture di reti convolutive
Moroni D., Papini O., Pascali M. A., Pieri G., Reggiannini M.
Questo rapporto tecnico di progetto è il risultato del contributo fornito dal Laboratorio Segnali e Immagini dell'ISTI-CNR per il documento di progetto RTOD-SYS-SDD-010-INT per il progetto RTOD (Real-Time Object Detection mediante Machine Learning basato su tecnologia Low-Power GPU). In particolare, il rapporto studia e discute delle varie possibilità di architetture di reti convolutive che sono state valutate e che potranno essere utilizzate nel contesto del progetto per effettuare delle categorizzazioni di immagini mediante algoritmi di machine learning.Source: ISTI Technical Report, ISTI-2022-TR/022, 2022
DOI: 10.32079/isti-tr-2022/022
Metrics:
Moroni D., Papini O., Pascali M. A., Pieri G., Reggiannini M.
Questo rapporto tecnico di progetto è il risultato del contributo fornito dal Laboratorio Segnali e Immagini dell'ISTI-CNR per il documento di progetto RTOD-SYS-SDD-010-INT per il progetto RTOD (Real-Time Object Detection mediante Machine Learning basato su tecnologia Low-Power GPU). In particolare, il rapporto studia e discute delle varie possibilità di architetture di reti convolutive che sono state valutate e che potranno essere utilizzate nel contesto del progetto per effettuare delle categorizzazioni di immagini mediante algoritmi di machine learning.Source: ISTI Technical Report, ISTI-2022-TR/022, 2022
DOI: 10.32079/isti-tr-2022/022
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Report
Unknown
DEL-27: Nota tecnica sulle metodologie per la validazione di algoritmi di Machine Learning
D. Moroni, O. Papini, M. A. Pascali, G. Pieri, M. Reggiannini
La presente nota tecnica rappresenta l'output del work package 3102 dal titolo "Identificazione delle metodologie per la validazione di algoritmi di Machine Learning". L'obiettivo chiave è quello di identificare delle metodologie adatte alle reti neurali già studiate e proposte nel corso del progetto RTOD in modo da studiarne l'affidabilità e abilitarne il loro impiego anche in scenari operativi.Source: ISTI Project report, RTOD-TN-ML-027, 2022
D. Moroni, O. Papini, M. A. Pascali, G. Pieri, M. Reggiannini
La presente nota tecnica rappresenta l'output del work package 3102 dal titolo "Identificazione delle metodologie per la validazione di algoritmi di Machine Learning". L'obiettivo chiave è quello di identificare delle metodologie adatte alle reti neurali già studiate e proposte nel corso del progetto RTOD in modo da studiarne l'affidabilità e abilitarne il loro impiego anche in scenari operativi.Source: ISTI Project report, RTOD-TN-ML-027, 2022
See at: CNR ExploRA
2022
Journal article
Open Access
NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients
Borgheresi R., Barucci A., Colantonio S., Aghakhanyan G., Assante M., Bertelli E., Carlini E., Carpi R., Caudai C., Cavallero D., Cioni D., Cirillo R., Colcelli V., Dell'Amico A., Di Gangi D., Erba P. A., Faggioni L., Falaschi Z., Gabelloni M., Gini R., Lelii L., Liò P., Lorito A., Lucarini S., Manghi P., Mangiacrapa F., Marzi C., Mazzei M. A., Mercatelli L., Mirabile A., Mungai F., Miele V., Olmastroni M., Pagano P., Paiar F., Panichi G., Pascali M. A., Pasquinelli F., Shortrede J. E., Tumminello L., Volterrani L., Neri E., On Behalf Of The Navigator Consortium Group
NAVIGATOR is an Italian regional project to boost precision medicine in oncology with the aim to make it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e. standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.Source: European radiology experimental Online 6 (2022). doi:10.1186/s41747-022-00306-9
DOI: 10.1186/s41747-022-00306-9
Metrics:
Borgheresi R., Barucci A., Colantonio S., Aghakhanyan G., Assante M., Bertelli E., Carlini E., Carpi R., Caudai C., Cavallero D., Cioni D., Cirillo R., Colcelli V., Dell'Amico A., Di Gangi D., Erba P. A., Faggioni L., Falaschi Z., Gabelloni M., Gini R., Lelii L., Liò P., Lorito A., Lucarini S., Manghi P., Mangiacrapa F., Marzi C., Mazzei M. A., Mercatelli L., Mirabile A., Mungai F., Miele V., Olmastroni M., Pagano P., Paiar F., Panichi G., Pascali M. A., Pasquinelli F., Shortrede J. E., Tumminello L., Volterrani L., Neri E., On Behalf Of The Navigator Consortium Group
NAVIGATOR is an Italian regional project to boost precision medicine in oncology with the aim to make it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e. standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.Source: European radiology experimental Online 6 (2022). doi:10.1186/s41747-022-00306-9
DOI: 10.1186/s41747-022-00306-9
Metrics:
See at:
eurradiolexp.springeropen.com | ISTI Repository | CNR ExploRA
2022
Report
Open Access
SI-Lab annual research report 2021
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Unknown
Studio PINK - Protocollo di studio per la linea di ricerca su Imaging e Radiomica
Pieroni S., Franchini M., Salvatori M., Anastasi G., Colantonio S., Pascali M. A., Trombadori C., Palma S., Belli P., Nicolucci A.
L'approccio personalizzato a prevenzione, diagnosi e cura rappresenta il caposaldo della medicina di precisione e si integra, come elemento fondante, nella medicina multiomica, l'ultima frontiera delle scienze mediche. Si tratta di un approccio basato sulla modellazione delle caratteristiche dei singoli e della variabilità di queste, in contrapposizione all'approccio classico basato su protocolli generalizzati ricavati da valutazioni (medie) su intere popolazioni. Seguire un approccio personalizzato presuppone la necessità di integrare dati eterogenei su singolo paziente con un intento olistico (e.g., dati clinici, stile di vita, alimentazione, esposizione ambientali ecc.), e possibilmente in un'ottica omica (i.e., attraverso tecnologie di analisi che consentono la produzione di informazioni/dati, in numero molto elevato e nel tempo, per la descrizione e l'interpretazione di processi o sistemi). Il disegno dello studio PINK è coerente con questa impostazione, consapevole dell'enorme potenziale che i dati e le informazioni raccolte fino ad oggi abbiano per la ricerca sul cancro al seno. In questo contesto, il tavolo di approfondimento su imaging e radiomica si colloca come un elemento centrale per la creazione di un ambiente di ricerca che, integrando i dati provenienti dalla piattaforma di inserimento implementata dall'impianto principale dello studio PINK, i dati di imaging e i questionari sullo stile vita, permetterà, attraverso differenti metodi di analisi (e.g., tecniche di analisi di immagini, Big Data analytics e intelligenza artificiale) di perseguire molteplici scenari di approfondimento relativi all'intero processo patologico del tumore mammario, dalla stima del rischio individuale, alla diagnosi precoce fino al monitoraggio della evoluzione della malattia.
Pieroni S., Franchini M., Salvatori M., Anastasi G., Colantonio S., Pascali M. A., Trombadori C., Palma S., Belli P., Nicolucci A.
L'approccio personalizzato a prevenzione, diagnosi e cura rappresenta il caposaldo della medicina di precisione e si integra, come elemento fondante, nella medicina multiomica, l'ultima frontiera delle scienze mediche. Si tratta di un approccio basato sulla modellazione delle caratteristiche dei singoli e della variabilità di queste, in contrapposizione all'approccio classico basato su protocolli generalizzati ricavati da valutazioni (medie) su intere popolazioni. Seguire un approccio personalizzato presuppone la necessità di integrare dati eterogenei su singolo paziente con un intento olistico (e.g., dati clinici, stile di vita, alimentazione, esposizione ambientali ecc.), e possibilmente in un'ottica omica (i.e., attraverso tecnologie di analisi che consentono la produzione di informazioni/dati, in numero molto elevato e nel tempo, per la descrizione e l'interpretazione di processi o sistemi). Il disegno dello studio PINK è coerente con questa impostazione, consapevole dell'enorme potenziale che i dati e le informazioni raccolte fino ad oggi abbiano per la ricerca sul cancro al seno. In questo contesto, il tavolo di approfondimento su imaging e radiomica si colloca come un elemento centrale per la creazione di un ambiente di ricerca che, integrando i dati provenienti dalla piattaforma di inserimento implementata dall'impianto principale dello studio PINK, i dati di imaging e i questionari sullo stile vita, permetterà, attraverso differenti metodi di analisi (e.g., tecniche di analisi di immagini, Big Data analytics e intelligenza artificiale) di perseguire molteplici scenari di approfondimento relativi all'intero processo patologico del tumore mammario, dalla stima del rischio individuale, alla diagnosi precoce fino al monitoraggio della evoluzione della malattia.
See at: CNR ExploRA