Moglioni M., Christine Kraan A., Baroni G., Battistoni G., Belcari N., Berti A., Carra P., Cerello P., Ciocca M., De Gregorio A., De Simoni M., Del Sarto D., Donetti M., Dong Y., Embriaco A., Fantacci M. E., Ferrero V., Fiorina E., Fischetti M., Franciosini G., Giraudo G., Laruina F., Maestri D., Magi M., Magro G., Malekzadeh E., Marafini M., Mattei I., Mazzoni E., Mereu P., Mirandola A., Morrocchi M., Muraro S., Orlandi E., Patera V., Pennazio F., Pullia M., Retico A., Rivetti A., Da Rocha Rolo M. D., Rosso V., Sarti A., Schiavi A., Sciubba A., Sportelli G., Tampellini S., Toppi M., Traini G., Trigilio A., Valle S. M., Valvo F., Vischioni B., Vitolo V., Wheadon R., Bisogni M. G.
Proton therapy In-beam PET imaging In-vivo treatment verification Morphological changes Inter-fractional range differences Clinical trial
Morphological changes that may arise through a treatment course are probably one of the most significant sources of range uncertainty in proton therapy. Non- invasive in-vivo treatment monitoring is useful to increase treatment quality. The INSIDE in-beam Positron Emission Tomography (PET) scanner performs in-vivo range monitoring in proton and carbon therapy treatments at the National Center of Oncological Hadrontherapy (CNAO). It is currently in a clinical trial (ID: NCT03662373) and has acquired in-beam PET data during the treatment of various patients. In this work we analyze the in-beam PET (IB-PET) data of eight patients treated with proton therapy at CNAO. The goal of the analysis is twofold. First, we assess the level of experimental fluctuations in inter-fractional range differences (sensitivity) of the INSIDE PET system by studying patients without morphological changes. Second, we use the obtained results to see whether we can observe anomalously large range variations in patients where morphological changes have occurred. The sensitivity of the INSIDE IB-PET scanner was quantified as the standard deviation of the range difference distributions observed for six patients that did not show morphological changes. Inter- fractional range variations with respect to a reference distribution were estimated using the Most-Likely-Shift (MLS) method. To establish the efficacy of this method, we made a comparison with the Beam's Eye View (BEV) method. For patients showing no morphological changes in the control CT the average range variation standard deviation was found to be 2.5 mm with the MLS method and 2.3 mm with the BEV method. On the other hand, for patients where some small anatomical changes occurred, we found larger standard deviation values. In these patients we evaluated where anomalous range differences were found and compared them with the CT. We found that the identified regions were mostly in agreement with the morphological changes seen in the CT scan.
Source: Frontiers in oncology (2022). doi:10.3389/fonc.2022.929949
Publisher: Frontiers Editorial Office,, Lausanne , Svizzera
@article{oai:it.cnr:prodotti:471573, title = {In-vivo range verification analysis with in-beam PET data for patients treated with proton therapy at CNAO}, author = {Moglioni M. and Christine Kraan A. and Baroni G. and Battistoni G. and Belcari N. and Berti A. and Carra P. and Cerello P. and Ciocca M. and De Gregorio A. and De Simoni M. and Del Sarto D. and Donetti M. and Dong Y. and Embriaco A. and Fantacci M. E. and Ferrero V. and Fiorina E. and Fischetti M. and Franciosini G. and Giraudo G. and Laruina F. and Maestri D. and Magi M. and Magro G. and Malekzadeh E. and Marafini M. and Mattei I. and Mazzoni E. and Mereu P. and Mirandola A. and Morrocchi M. and Muraro S. and Orlandi E. and Patera V. and Pennazio F. and Pullia M. and Retico A. and Rivetti A. and Da Rocha Rolo M. D. and Rosso V. and Sarti A. and Schiavi A. and Sciubba A. and Sportelli G. and Tampellini S. and Toppi M. and Traini G. and Trigilio A. and Valle S. M. and Valvo F. and Vischioni B. and Vitolo V. and Wheadon R. and Bisogni M. G.}, publisher = {Frontiers Editorial Office,, Lausanne , Svizzera}, doi = {10.3389/fonc.2022.929949}, journal = {Frontiers in oncology}, year = {2022} }