Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr:185:112005.
doi: 10.1016/j.ejrad.2025.112005. Epub 2025 Feb 15.

What is the predictive value of pretreatment MRI characteristics for achieving a complete response after total neoadjuvant treatment in locally advanced rectal cancer?

Affiliations

What is the predictive value of pretreatment MRI characteristics for achieving a complete response after total neoadjuvant treatment in locally advanced rectal cancer?

Duygu Karahacioglu et al. Eur J Radiol. 2025 Apr.

Abstract

Objectives: To investigate the value of pretreatment magnetic resonance imaging (MRI) features in predicting a complete response to total neoadjuvant treatment (TNT) in locally advanced rectal cancer (LARC).

Methods: The data of patients who received TNT were analyzed retrospectively. MRI features, including T stage, morphology, length, and volume; the presence of MR-detected extramural venous invasion (mrEMVI), the number of mrEMVI, and the diameter of the largest invaded vein; main vein mrEMVI; presence of MR-detected tumor deposits (mrTDs), the number of mrTDs, and the size of the largest mrTD; MR-detected lymph node status (mrLN); tumor distance from the anal verge; mesorectal fascia involvement (mrMRF + ); and mean apparent diffusion coefficient (ADC) values were recorded. Patients were classified as complete (CRs) or noncomplete responders (non-CRs) according to the pathological/clinical outcomes. For patients managed nonoperatively, a sustained clinical complete response for > 2 years was deemed a surrogate endpoint for complete response. The MRI parameters were categorized into three distinct groups: baseline, advanced, and quantitative features, and were analyzed using multivariable stepwise logistic regression. The ability to predict complete response was evaluated by comparing different combinations of MRI parameters, and performance on an "independent" dataset was estimated using bootstrapped leave-one-out cross-validation (LOOCV).

Results: The data of 84 patients were evaluated (CRs, n = 44; non-CRs, n = 40). The optimal model, which included baseline and quantitative MRI features, achieved an area under the curve of 0.837 for predicting complete response. Selected predictors were T stage and ADC mean value. Advanced MRI features did not improve the performance of the model.

Conclusion: A multivariable model combining T stage and the ADC mean value can help identify LARC patients who are likely to a achieve complete response before the initiation of TNT.

PubMed Disclaimer

Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

LinkOut - more resources