Primary tumor-derived, multiparametric MRI-based deep learning-radiomics-clinical model for predicting lymph node
Yu Hao Bao1, Yan Chen1, Mei Ling Xiao1,2
1Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Objectives:
To develop and validate a primary tumor-derived, multiparametric MRI-based deep learning-radiomics-clinical (DLRC) model for predicting pelvic lymph node metastasis (LNM) in early-stage cervical cancer.
Materials And Methods:
This retrospective five-center study selected 1095 patients (Jan 2020-Dec 2022), divided into training (n = 481), internal validation (n = 204), and external validation (n = 410) cohorts. Radiomics and deep learning (DL) features were extracted from the volumetric segmentations of the primary cervical tumors on three MRI sequences (CE-T1WI, DWI, FS-T2WI). After constructing individual radiomics and DL models, the DLRC model was developed by integrating the radiomics_score, optimal DL model, and significant clinical features. Model performance was evaluated using ROC analysis, calibration curves, and decision curve analysis.
Results:
The DLRC model demonstrated superior predictive performance, achieving AUCs of 0.807 (95% CI: 0.766-0.849) in the training cohort, 0.789 (95% CI: 0.721-0.857) in the internal validation cohort, and 0.807 (95% CI: 0.761-0.853) in the external validation cohort. It significantly outperformed both the radiomics model and the optimal DL model (all p < 0.001) in the external validation cohort. The calibration curves indicated good agreement between predictions and observations. The decision curve analysis showed that the DLRC model provided the highest net clinical benefit across most decision thresholds.
Conclusions:
The DLRC model, which integrates tumor-derived multiparametric MRI features with clinical features, represents a robust and generalizable tool for the preoperative prediction of LNM. Its comparable accuracy to standardized radiological assessment and clinical utility shows potential to aid in personalized treatment planning for patients with early-stage cervical cancer.
Critical Relevance Statement:
The combined model (DLRC) integrating deep learning and radiomics features from the primary tumor with clinical characteristics enables preoperative LNM risk stratification, supporting personalized surgical planning and reducing unnecessary lymphadenectomy.
Key Points:
Accurate preoperative prediction of lymph node metastasis in early-stage cervical cancer remains a significant clinical challenge. The model integrating deep learning and radiomics features derived from the primary tumor with clinical features achieved robust and generalizable predictive performance. The accuracy of a deep learning-radiomics-clinical nomogram for lymph node metastasis risk stratification in early-stage cervical cancer is comparable to standardized radiological assessment.
Related Concept Videos
01:23Detailed Structure and Function of Lymph Nodes
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...
02:30Metastasis
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
02:27Predicting Molecular Geometry
02:40Cancer Stem Cells and Tumor Maintenance
Cancer stem cells are thought to originate from tissue-specific normal stem cells or progenitor cells. The normal stem cells usually reside in...
01:14Base Quantities and Derived Quantities
The International Organization for...
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.