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Objectives <p>This study aimed to analyze preoperative multimodal magnetic resonance images of patients with rectal cancer using habitat-based, intratumoral, peritumoral, and combined radiomics models for non-invasive prediction of perineural invasion (PNI) status.</p> Methods <p>Data were collected from 385 pathologically confirmed rectal cancer cases across two centers. Patients from Center 1 were randomly assigned to training and internal validation groups at an 8:2 ratio; the external validation group comprised patients from Center 2. Tumors were divided into three subregions via K-means clustering. Radiomics features were isolated from intratumoral and peritumoral (3&#xa0;mm beyond the tumor) regions, as well as subregions, to form a combined dataset based on T2-weighted imaging and diffusion-weighted imaging. The support vector machine algorithm was used to construct seven predictive models. intratumoral, peritumoral, and subregion features were integrated to generate an additional model, referred to as the Total model. For each radiomics feature, its contribution to prediction outcomes was quantified using Shapley values, providing interpretable evidence to support clinical decision-making.</p> Results <p>The Total combined model outperformed other predictive models in the training, internal validation, and external validation sets (area under the curve values: 0.912, 0.882, and 0.880, respectively).</p> Conclusions <p>The integration of intratumoral, peritumoral, and subregion features represents an effective approach for predicting PNI in rectal cancer, providing valuable guidance for rectal cancer treatment, along with enhanced clinical decision-making precision and reliability.</p>

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MRI-based habitat, intra-, and peritumoral machine learning model for perineural invasion prediction in rectal cancer

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Junyuan Zhong,
  • Teng Huang,
  • Rongjian Jiang,
  • Qiangqiang Zhou,
  • Gongfa Wu,
  • Yuping Zeng

摘要

Objectives

This study aimed to analyze preoperative multimodal magnetic resonance images of patients with rectal cancer using habitat-based, intratumoral, peritumoral, and combined radiomics models for non-invasive prediction of perineural invasion (PNI) status.

Methods

Data were collected from 385 pathologically confirmed rectal cancer cases across two centers. Patients from Center 1 were randomly assigned to training and internal validation groups at an 8:2 ratio; the external validation group comprised patients from Center 2. Tumors were divided into three subregions via K-means clustering. Radiomics features were isolated from intratumoral and peritumoral (3 mm beyond the tumor) regions, as well as subregions, to form a combined dataset based on T2-weighted imaging and diffusion-weighted imaging. The support vector machine algorithm was used to construct seven predictive models. intratumoral, peritumoral, and subregion features were integrated to generate an additional model, referred to as the Total model. For each radiomics feature, its contribution to prediction outcomes was quantified using Shapley values, providing interpretable evidence to support clinical decision-making.

Results

The Total combined model outperformed other predictive models in the training, internal validation, and external validation sets (area under the curve values: 0.912, 0.882, and 0.880, respectively).

Conclusions

The integration of intratumoral, peritumoral, and subregion features represents an effective approach for predicting PNI in rectal cancer, providing valuable guidance for rectal cancer treatment, along with enhanced clinical decision-making precision and reliability.