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Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study.

Authors :
Boldrini, Luca
Chiloiro, Giuditta
Cusumano, Davide
Yadav, Poonam
Yu, Gao
Romano, Angela
Piras, Antonio
Votta, Claudio
Placidi, Lorenzo
Broggi, Sara
Catucci, Francesco
Lenkowicz, Jacopo
Indovina, Luca
Bassetti, Michael F.
Yang, Yingli
Fiorino, Claudio
Valentini, Vincenzo
Gambacorta, Maria Antonietta
Source :
La Radiologia Medica; Apr2024, Vol. 129 Issue 4, p615-622, 8p
Publication Year :
2024

Abstract

Purpose: The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERI<subscript>TCP</subscript>) to evaluate treatment response in LARC patients treated with MRIgRT. Methods: Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERI<subscript>TCP</subscript> with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. Results: A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERI<subscript>TCP</subscript> at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERI<subscript>TCP</subscript> alone (0.94 in training and 0.89 in validation set). Conclusion: The integration of the radiomic analysis with ERI<subscript>TCP</subscript> improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00338362
Volume :
129
Issue :
4
Database :
Complementary Index
Journal :
La Radiologia Medica
Publication Type :
Academic Journal
Accession number :
176627861
Full Text :
https://doi.org/10.1007/s11547-024-01761-7