Objective Development of a preoperative mesorectal lymph node metastasis (LNM) prediction model for rectal cancer (RC) based on intratumoral and multiregional peritumoral radiomics features extracted from super-resolution multiparametric MRI. Materials and methods This multicenter study included preoperative MRI data from 243 rectal cancer patients (194 from center A, 49 from center B) with SR reconstruction and scoring. Radiomic features were extracted from tumor, peri-3mm and peri-5mm on SR-DWI and SR-T2WI images. The least absolute shrinkage and selection operator (LASSO) and the maximum relevance minimum redundancy (mRMR) were used for feature selection and dimensionality reduction. DWI_T2WI_INTRA, DWI_T2WI_IntraPeri3mm, DWI_T2WI_InterPeri5mm models were developed employing Logistic regression. Independent clinical risk factors identified through univariate and multivariate stepwise regression analyses were used to construct a clinical model. The optimal IntraPeri model integrated with clinical model design the combined model. Predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Results Qualitative evaluation demonstrated superior scores for SR-T2WI across five metrics compared to original images (all p < 0.001). For DWI, SR images achieved significant improvements in all parameters (p < 0.001), except lesion conspicuity [median (IQR): 3 (1) vs. 3 (1)]. Comparative analysis revealed the DWI_T2WI_IntraPeri3mm model’s optimal predictive performance in training, validation, and test cohorts (AUCs: 0.880, 0.735, and 0.714, respectively). The AUC of the combined model, integrating radiomic (DWI_T2WI_IntraPeri3mm) model with clinical risk factors, was 0.933, 0.829, and 0.867 in each cohort, all exceeding those of the clinical and radiomic models. Conclusion Using GANs-based 3D-SR of multi-sequence MRI, our multiregional prediction model for preoperative mesorectal LNM in RC demonstrated good diagnostic performance. Critical relevance statement The integration of super-resolution-based tumor and peritumoral 3-mm predictive model with clinical risk factors enables performance in predicting mesorectal LNM, potentially aiding clinical therapeutic decision-making. Key Points <UnorderedList Mark="Bullet"> <ItemContent> How do tumor and peritumoral (3–5 mm) models-based SR images perform in predicting lymph node metastasis (LNM)? </ItemContent> <ItemContent> The DWI_T2WI_IntraPeri3mm model, when combined with clinical factors, improves diagnostic accuracy. </ItemContent> <ItemContent> Multiparametric, multiregional super-resolution (SR)-MRI radiomics models exhibit good performance for LNM. </ItemContent> </UnorderedList> Graphical Abstract

Generative adversarial networks: multiparametric, multiregion super-resolution MRI in predicting lymph node metastasis in rectal cancer

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2026年1月5日

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Yupeng Wu,
Tao Jiang,
Han Liu,
Shengming Shi,
Apekshya Singh,
Yuhang Wang,
Jiayi Xie,
Xiaofu Li

摘要

Objective

Development of a preoperative mesorectal lymph node metastasis (LNM) prediction model for rectal cancer (RC) based on intratumoral and multiregional peritumoral radiomics features extracted from super-resolution multiparametric MRI.

Materials and methods

This multicenter study included preoperative MRI data from 243 rectal cancer patients (194 from center A, 49 from center B) with SR reconstruction and scoring. Radiomic features were extracted from tumor, peri-3mm and peri-5mm on SR-DWI and SR-T2WI images. The least absolute shrinkage and selection operator (LASSO) and the maximum relevance minimum redundancy (mRMR) were used for feature selection and dimensionality reduction. DWI_T2WI_INTRA, DWI_T2WI_IntraPeri3mm, DWI_T2WI_InterPeri5mm models were developed employing Logistic regression. Independent clinical risk factors identified through univariate and multivariate stepwise regression analyses were used to construct a clinical model. The optimal IntraPeri model integrated with clinical model design the combined model. Predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).

Results

Qualitative evaluation demonstrated superior scores for SR-T2WI across five metrics compared to original images (all p < 0.001). For DWI, SR images achieved significant improvements in all parameters (p < 0.001), except lesion conspicuity [median (IQR): 3 (1) vs. 3 (1)]. Comparative analysis revealed the DWI_T2WI_IntraPeri3mm model’s optimal predictive performance in training, validation, and test cohorts (AUCs: 0.880, 0.735, and 0.714, respectively). The AUC of the combined model, integrating radiomic (DWI_T2WI_IntraPeri3mm) model with clinical risk factors, was 0.933, 0.829, and 0.867 in each cohort, all exceeding those of the clinical and radiomic models.

Conclusion

Using GANs-based 3D-SR of multi-sequence MRI, our multiregional prediction model for preoperative mesorectal LNM in RC demonstrated good diagnostic performance.

Critical relevance statement

The integration of super-resolution-based tumor and peritumoral 3-mm predictive model with clinical risk factors enables performance in predicting mesorectal LNM, potentially aiding clinical therapeutic decision-making.

Key Points

How do tumor and peritumoral (3–5 mm) models-based SR images perform in predicting lymph node metastasis (LNM)?

The DWI_T2WI_IntraPeri3mm model, when combined with clinical factors, improves diagnostic accuracy.

Multiparametric, multiregional super-resolution (SR)-MRI radiomics models exhibit good performance for LNM.

Graphical Abstract