ArticleOncotarget2017
Magnetic resonance tumor regression grade (MR-TRG) to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Radiopathological Correlation in Locally Advanced Rectal Cancer After Neoadjuvant Treatment.Cancers · 2025Article
- Correlation Between MRI and Histopathology in Assessing Treatment Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- Evaluating the predictive value of endoscopic findings for residual colorectal cancer following neoadjuvant combination immunotherapy.World journal of gastrointestinal surgery · 2025Article
- Predictors of pathologic complete response in patients with residual flat mucosal lesions after neoadjuvant chemoradiotherapy for locally advanced rectal cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2022Article
- Article
- T2-weighted, apparent diffusion coefficient andTechniques in coloproctology · 2021Article
- MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).La Radiologia medica · 2020Article
- Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.Magnetic resonance imaging · 2019Article
- Non-operative management of rectal cancer.Seminars in colon & rectal surgery · 2019Article
- Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to evaluate the feasibility of a magnetic resonance (MR) automatic method for quantitative assessment of the percentage of fibrosis developed within locally advanced rectal cancers (LARC) after neoadjuvant radiochemotherapy (RCT). A total of 65 patients were enrolled in the study and MR studies were performed on 3.0 Tesla scanner; patients were followed-up for 30 months. The percentage of fibrosis was quantified on T2-weighted images, using automatic K-Means clustering algorithm. According to the percentage of fibrosis, an optimal cut-off point for separating patients into favorable and unfavorable pathologic response groups was identified by ROC analysis and tumor regression grade (MR-TRG) classes were determined and compared to histopathologic TRG. An optimal cut-off point of 81% of fibrosis was identified to differentiate between favorable and unfavorable pathologic response groups resulting in a sensitivity of 78.26% and a specificity of 97.62% for the identification of complete responders (CRs). Interobserver agreement was good (0.85). The agreement between P-TRG and MR-TRG was excellent (0.923). Significant differences in terms of overall survival (OS) and disease free survival (DFS) were found between favorable and unfavorable pathologic response groups. The automatic quantification of fibrosis determined by MR is feasible and reproducible.
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