MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

Abstract Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI...

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Autores principales: Andrea Delli Pizzi, Antonio Maria Chiarelli, Piero Chiacchiaretta, Martina d’Annibale, Pierpaolo Croce, Consuelo Rosa, Domenico Mastrodicasa, Stefano Trebeschi, Doenja Marina Johanna Lambregts, Daniele Caposiena, Francesco Lorenzo Serafini, Raffaella Basilico, Giulio Cocco, Pierluigi Di Sebastiano, Sebastiano Cinalli, Antonio Ferretti, Richard Geoffrey Wise, Domenico Genovesi, Regina G. H. Beets-Tan, Massimo Caulo
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/e15e3b93b771497aa142f4bcc55d02ff
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Sumario:Abstract Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

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