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Article Open Access

Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer

  • Authors:
    • Huanhui Liu
    • Hanjing Zhang
    • Qian Zou
    • Jianquan Yang
  • View Affiliations / Copyright

    Affiliations: Department of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, P.R. China
    Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 10
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    Published online on: October 29, 2025
       https://doi.org/10.3892/ol.2025.15363
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Abstract

The histopathological differentiation of rectal cancer (RC) is a key determinant of treatment strategy and prognosis, as tumors with varying differentiation demonstrate notable differences in therapeutic approach selection and survival outcomes. Consequently, accurate preoperative prediction of tumor differentiation is clinically essential for formulating initial surgical plans and adjuvant therapy strategies. The aim of the present study was to develop a magnetic resonance imaging (MRI)‑based radiomics strategy that integrates features from both intratumoral and peritumoral (margin, 5 mm) regions to construct a non‑invasive predictive model capable of distinguishing well differentiated RC (glandular structures ≥95%) from non‑well differentiated RC. A retrospective analysis was performed using data from 224 patients with RC who underwent preoperative MRI. Radiomic features were extracted from T1‑weighted images (T1WI), T2‑weighted images (T2WI) and diffusion‑weighted imaging (DWI) of the tumor and peritumoral 5 mm. The adaptive synthetic technique was used to address class imbalance, and ReliefF was applied to select 20 features. A total of three machine learning algorithms, logistic regression, Light Gradient‑Boosting Machine (LightGBM) classifier and Gaussian Naive Bayes, were used to build MRI radiomic models combining the tumor and the peritumoral areas to predict pathological differentiation of RC. The models were evaluated by comparing the area under the curve (AUC) and binary classification metrics, with the best performing T1‑LightGBM model selected. Subsequently, intratumoral and peritumoral and intratumoral (T1‑LightGBM) models were established. Discriminative ability, calibration and clinical applicability were assessed using receiver operating characteristic curves, calibration curves and decision curves, with Shapley Additive explanations (SHAP) analysis employed for interpretation. Among the nine models constructed using T1WI, T2WI and DWI from the tumor and 5‑mm surrounding region, the AUC range was 0.510‑0.756 across all validation sets. The T1‑LightGBM model incorporating both tumor and peritumoral areas demonstrated the best performance for predicting pathological differentiation in RC, with an AUC of 0.756, accuracy of 0.700 and sensitivity of 0.707. SHAP analysis identified wavelet_HHH_glszm_Size_Zone_Non_Uniformity_Normalized as the most significant feature for predicting pathological differentiation and evaluating non‑well differentiated patients. In conclusion, the integrated model employing the LightGBM algorithm on T1WI, which combined both intratumoral and 5‑mm peritumoral features, demonstrated promising predictive potential for individualized RC differentiation.
View Figures

Figure 1

ROC curves for the peritumoral +
intratumoral prediction model for pathological differentiation of
rectal cancer. ROC (A) training and (B) validation curves for T1WI
imaging. ROC (C) training and (D) validation curves for T2WI
imaging. ROC (E) training and (F) validation curves for
diffusion-weighted imaging. ROC, receiver operating characteristic;
WI, weighted image; AUC, area under the curve; LightGBM, Light
Gradient Boosting Machine; GNB, Gaussian Naive Bayes.

Figure 2

ROC curves of the intratumoral +
peritumoral 5-mm T1-LightGBM model for the training, validation and
test groups. ROC curves and AUC values for the (A) training set,
(B) validation set and (C) test set. (D) Learning curves. The red
dashed line represents the training set, whilst the blue dashed
line represents the validation set. ROC, receiver operating
characteristic; LightGBM, Light Gradient-Boosting Machine; AUC,
area under the curve; CI, confidence interval.

Figure 3

ROC curves of the intratumoral
T1-LightGBM model for the training, validation and test groups. ROC
curves and AUC values for the (A) training set, (B) validation set
and (C) test set. (D) Learning curves. LightGBM, Light
Gradient-Boosting Machine; ROC, receiver operating characteristic;
AUC, area under the curve; CI, confidence interval.

Figure 4

Calibration plots and DCA for the
intratumoral + peritumoral 5-mm T1-LightGBMmodel for predicting
pathological differentiation. (A) Calibration curve depicts the
extent of consistency between the predicted probability and
observed probability. (B) DCA of the LightGBM model. DCA, decision
curve analysis; LightGBM, Light Gradient-Boosting Machine; CI,
confidence interval.

Figure 5

Calibration plots and DCA of the
intratumoral T1-LightGBM model for predicting pathological
differentiation. (A) Calibration curve depicts the extent of
consistency between the predicted probability and observed
probability. (B) DCA of the LightGBM model. DCA, decision curve
analysis; LightGBM, Light Gradient-Boosting Machine; CI, confidence
interval.
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Copy and paste a formatted citation
Spandidos Publications style
Liu H, Zhang H, Zou Q and Yang J: Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer. Oncol Lett 31: 10, 2026.
APA
Liu, H., Zhang, H., Zou, Q., & Yang, J. (2026). Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer. Oncology Letters, 31, 10. https://doi.org/10.3892/ol.2025.15363
MLA
Liu, H., Zhang, H., Zou, Q., Yang, J."Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer". Oncology Letters 31.1 (2026): 10.
Chicago
Liu, H., Zhang, H., Zou, Q., Yang, J."Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer". Oncology Letters 31, no. 1 (2026): 10. https://doi.org/10.3892/ol.2025.15363
Copy and paste a formatted citation
x
Spandidos Publications style
Liu H, Zhang H, Zou Q and Yang J: Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer. Oncol Lett 31: 10, 2026.
APA
Liu, H., Zhang, H., Zou, Q., & Yang, J. (2026). Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer. Oncology Letters, 31, 10. https://doi.org/10.3892/ol.2025.15363
MLA
Liu, H., Zhang, H., Zou, Q., Yang, J."Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer". Oncology Letters 31.1 (2026): 10.
Chicago
Liu, H., Zhang, H., Zou, Q., Yang, J."Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer". Oncology Letters 31, no. 1 (2026): 10. https://doi.org/10.3892/ol.2025.15363
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