ArticleBMC cancer2024
A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- CT-based radiomics signature for optimizing adjuvant chemotherapy decisions in stage II colorectal cancer with proficient mismatch repair.Abdominal radiology (New York) · 2026Article
- CT-Based Radiomic Signatures Associated with Serum CEA Status in Colon Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Interpretable machine learning model for predicting 5-Year postoperative recurrence risk in patients with stage III colon cancer using preoperative laboratory tests: a two-centre study.BMC gastroenterology · 2026Article
- Predictive value of computed tomography radiomics for lymphatic-vascular space infiltration in colon cancer.American journal of cancer research · 2026Article
- A novel MRI-based habitat analysis and deep learning for predicting perineural invasion in prostate cancer: a two-center study.BMC cancer · 2025Article
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10 authors.
Funding
Abstract
backgroundColon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effective predictor of stage II-III colon cancer. Nonetheless, the lack of effective and facile predictive methodologies for detecting PNI prior operation in colon cancer remains a persistent challenge.
methodPre-operative computer tomography (CT) images and clinical data of patients diagnosed with stage II-III colon cancer between January 2015 and December 2023 were obtained from two sub-districts of Sun Yat-sen Memorial Hospital (SYSUMH). The LASSO/RF/PCA filters were used to screen radiomics features and LR/SVM models were utilized to construct radiomics model. A comprehensive model, shown as nomogram finally, combining with radiomics score and significant clinical features were developed and validated by area under the curve (AUC) and decision curve analysis (DCA).
resultThe total cohort, comprising 426 individuals, was randomly divided into a development cohort and a validation cohort as a 7:3 ratio. Radiomics scores were extracted from LASSO-SVM models with AUC of 0.898/0.726 in the development and validation cohorts, respectively. Significant clinical features (CA199, CA125, T-stage, and N-stage) were used to establish combining model with radiomics scores. The combined model exhibited superior reliability compared to single radiomics model in AUC value (0.792 vs. 0.726, p = 0.003) in validation cohorts. The radiomics-clinical model demonstrated an AUC of 0.918/0.792, a sensitivity of 0.907/0.813 and a specificity of 0.804/0.716 in the development and validation cohorts, respectively.
conclusionThe study developed and validated a predictive nomogram model combining radiomics scores and clinical features, and showed good performance in predicting PNI pre-operation in stage II-III colon cancer patients.
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