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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Nature Portfolio
2021
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oai:doaj.org-article:e15e3b93b771497aa142f4bcc55d02ff2021-12-02T13:19:22ZMRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer10.1038/s41598-021-84816-32045-2322https://doaj.org/article/e15e3b93b771497aa142f4bcc55d02ff2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84816-3https://doaj.org/toc/2045-2322Abstract 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.Andrea Delli PizziAntonio Maria ChiarelliPiero ChiacchiarettaMartina d’AnnibalePierpaolo CroceConsuelo RosaDomenico MastrodicasaStefano TrebeschiDoenja Marina Johanna LambregtsDaniele CaposienaFrancesco Lorenzo SerafiniRaffaella BasilicoGiulio CoccoPierluigi Di SebastianoSebastiano CinalliAntonio FerrettiRichard Geoffrey WiseDomenico GenovesiRegina G. H. Beets-TanMassimo CauloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) |
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EN |
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Medicine R Science Q |
| spellingShingle |
Medicine R Science Q 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 MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Andrea Delli Pizzi |
| title |
MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer |
| title_short |
MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer |
| title_full |
MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer |
| title_fullStr |
MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer |
| title_full_unstemmed |
MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer |
| title_sort |
mri-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/e15e3b93b771497aa142f4bcc55d02ff |
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