Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study

Shaish, Hiram; Aukerman, Andrew; Vanguri, R.; Spinelli, Antonino; Armenta, Paul M.; Jambawalikar, Sachin; Makkar, Jasnit; Bentley–Hibbert, Stuart; Portillo, Armando Del; Kiran, Ravi P.; Monti, L; Bonifacio, Cristiana; Kirienko, Margarita; Gardner, Kevin; Schwartz, Lawrence H.; Keller, Deborah S.

doi:10.1007/s00330-020-06968-6

Cited by 89 publications

(77 citation statements)

References 27 publications

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“…In three patients, a sustained complete clinical response (with repeated negative MRI examinations and endoscopy with or without biopsy) was considered surrogate for a complete response; the follow-up (mean ± SD) was 47 ± 11 months. www.nature.com/scientificreports/ degree of mesorectal compartment, our results confirm that also perirectal tissues contain useful information for the prediction of treatment response, as recently showed by Shaish et al 55 .…”

Section: Discussionsupporting

confidence: 90%

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

Pizzi

Chiarelli

Chiacchiaretta

et al. 2021

Sci Rep

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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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Section: Discussionsupporting

confidence: 90%

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

Pizzi

Chiarelli

Chiacchiaretta

et al. 2021

Sci Rep

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“…To the best of our knowledge our study, apart for the added value of radiomic features to standard MRI-based clinical metrics, is the rst demonstrating the synergic role between feature extracted from the tumor core and the tumor border in the early prediction of response to therapy. Since the tumor border naturally include a certain degree of mesorectal compartment, our results con rm that also perirectal tissues contain useful information for the prediction of treatment response, as recently showed by Shaish et al 52 .…”

Section: Discussionsupporting

confidence: 87%

From the Core to the Border of Locally Advanced Rectal Cancer: A Novel MRI-based Clinical-Radiomic Model Early Predicts Treatment Response

Pizzi¹,

Chiarelli²,

Chiacchiaretta³

et al. 2020

Preprint

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Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (³ 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 (3.0T, 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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“…The contrast-enhanced T1-WI sequence is not routinely included in the MRI protocol for rectal cancer staging. The potential of radiomics features extracted from MRI T2-weighted images for predicting a pathological complete response of rectal cancer was demonstrated in several recent studies, which reported promising results of their radiomics models with AUCs ranging from 0.69 to 0.93 [ 51 , 52 , 57 , 69 , 70 , 71 ]. In contrast to MRI, a recent study had demonstrated that radiomics features extracted from CT images showed no predictive power for complete pathological response in LARC [ 72 ], while another research showed that MRI T2-WI radiomics model performed better than CT radiomics model for predicting the LARC response to nCRT [ 73 ].…”

Section: Discussionmentioning

confidence: 99%

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Petresc

Lebovici

Caraiani

et al. 2020

Cancers

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Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

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Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study

Cited by 89 publications

References 27 publications

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

From the Core to the Border of Locally Advanced Rectal Cancer: A Novel MRI-based Clinical-Radiomic Model Early Predicts Treatment Response

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

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