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

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发表日期：

2024年3月21日

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Luca Boldrini,
Giuditta Chiloiro,
Davide Cusumano,
Poonam Yadav,
Gao Yu,
Angela Romano,
Antonio Piras,
Claudio Votta,
Lorenzo Placidi,
Sara Broggi,
Francesco Catucci,
Jacopo Lenkowicz,
Luca Indovina,
Michael F. Bassetti,
Yingli Yang,
Claudio Fiorino,
Vincenzo Valentini,
Maria Antonietta Gambacorta

摘要

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_TCP) 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_TCP 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_TCP 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_TCP alone (0.94 in training and 0.89 in validation set).

Conclusion

The integration of the radiomic analysis with ERI_TCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.