Academic radiology · 2026
Radiologist-Informed Radiomics: Improving the Accuracy of Preoperative Assessment for Lymph Node Metastasis in Rectal Cancer.
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Fu Chunl-Ong, Zhou Long, Yang Ze-Bin, Shan Kang-Fei, Xu Jiao-Jiao, Pang Zhen-Zhu, Ma Chi-Jun, Xu Jie-Ping, Zhu Wei-Hua, Sun Ji-Hong, Zhao Fen-Hua
Аннотация
RATIONALE AND OBJECTIVE: The study aimed to develop and validate a multimodal radiomics model that integrates radiologist-informed feature augmentation leveraging expert-selected suspicious lymph nodes (LNs) based on ESGAR criteria to improve the accuracy of preoperative lymph node metastasis (LNM) prediction in patients with rectal cancer (RC).
MATERIALS AND METHODS: This retrospective study included 563 eligible patients with RC. From each patient's high-resolution T2-weighted imaging (HRT2WI) and diffusion-weighted imaging (DWI) sequences, we extracted radiomic features from three distinct regions: the primary tumor, the entire mesorectal nodal region, and suspicious mesorectal nodes identified by radiologists. Clinical factors associated with LNM were identified through univariate and multivariate logistic regression analyses to establish a clinical prediction model. Finally, we constructed an integrated predictive model by combining these clinical factors with multimodal radiomic features, followed by a comprehensive comparison and evaluation of the predictive performance across all developed models.
RESULTS: The integrated model, incorporating radiomic features derived from DWI sequences of the entire mesorectal nodal region and radiologist-annotated suspicious LNs, along with clinical factors, achieved optimal performance in predicting LNM. It yielded an area under the curve of 0.87 (95% confidence interval [CI]: 0.83-0.90) in the internal validation cohort and 0.83 (95% CI: 0.78-0.89) in the external validation cohort.
CONCLUSION: Our findings show that the multimodal radiomics model integrating radiologists' prior knowledge offers potential for improving preoperative LNM assessment in RC, particularly in internal validation, and may provide supportive information for personalized treatment strategies in clinical practice. However, the incremental benefit of the radiologist-informed component was not consistently demonstrated in external validation, and further multi-center prospective studies are warranted.
MATERIALS AND METHODS: This retrospective study included 563 eligible patients with RC. From each patient's high-resolution T2-weighted imaging (HRT2WI) and diffusion-weighted imaging (DWI) sequences, we extracted radiomic features from three distinct regions: the primary tumor, the entire mesorectal nodal region, and suspicious mesorectal nodes identified by radiologists. Clinical factors associated with LNM were identified through univariate and multivariate logistic regression analyses to establish a clinical prediction model. Finally, we constructed an integrated predictive model by combining these clinical factors with multimodal radiomic features, followed by a comprehensive comparison and evaluation of the predictive performance across all developed models.
RESULTS: The integrated model, incorporating radiomic features derived from DWI sequences of the entire mesorectal nodal region and radiologist-annotated suspicious LNs, along with clinical factors, achieved optimal performance in predicting LNM. It yielded an area under the curve of 0.87 (95% confidence interval [CI]: 0.83-0.90) in the internal validation cohort and 0.83 (95% CI: 0.78-0.89) in the external validation cohort.
CONCLUSION: Our findings show that the multimodal radiomics model integrating radiologists' prior knowledge offers potential for improving preoperative LNM assessment in RC, particularly in internal validation, and may provide supportive information for personalized treatment strategies in clinical practice. However, the incremental benefit of the radiologist-informed component was not consistently demonstrated in external validation, and further multi-center prospective studies are warranted.