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September 20, 2024SHILAP Revista de lepidopterologíaOpen Access

Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features

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Authors

SQSiyuan QinKLKe LiuYCYongye Chen

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Overview

Cohort study demonstrates combined tumor and mesorectal MRI radiomics predict therapy response and nodal metastasis in rectal cancer, highlighting improved noninvasive staging accuracy.

Key Points

  • Develop predictive models for pathological response and lymph node metastasis in locally advanced rectal cancer using pre- and post-chemoradiotherapy tumor and mesorectal MRI radiomics.
  • Analyzed 209 patients with locally advanced rectal cancer receiving neoadjuvant chemoradiotherapy, partitioned into a training set (n = 146) and a test set (n = 63).
  • Extracted radiomic and delta-radiomic features from tumor and mesorectal compartment regions on pre- and post-treatment MRI, applying correlation filtering, univariate selection, and LASSO logistic regression.
  • Across 209 patients, 44 achieved pathological complete response (pCR), 118 achieved pathological good response (pGR), and 40 had lymph node metastasis (LNM).
  • Combined pre- and delta-radiomics models yielded AUCs of 0.874 for pCR, 0.801 for pGR, and 0.826 for LNM, significantly outperforming conventional MRI tumor regression grading (AUCs of 0.800, 0.715, and 0.603, respectively).

Cite This Study

Qin et al. (2024) studied this question.

synapsesocial.com/papers/69d878bcd56ca42147d18977https://doi.org/10.1038/s41598-024-72916-9
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer2024 · 13 citations
  2. 2Magnetic resonance imaging‐based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study2024 · 15 citations
  3. 3Predicting the prognosis of locally advanced rectal cancers after neoadjuvant chemoradiotherapy using quantitative MRI features and machine learning: A retrospective study2026
  4. 4Developing a predictive model for the efficacy of neoadjuvant chemoradiotherapy in locally advanced rectal cancer using multiparametric magnetic resonance imaging: An innovative approach2025
  5. 5MRI-Based Radiomics to Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Retrospective Study2026