Deep learning with interactive segmentation for risk stratification of cystic renal lesions on tri-phase CT: a multicenter study
6 affiliations
- 1Department of Urology, The First Affiliated Hospital of Chongqing Medical University , ,
- 2Department of Urology, Yongchuan Hospital of Chongqing Medical University , ,
- 3Department of Urology, The Second Affiliated Hospital of Chongqing Medical University , ,
- 4Department of Urology, Chongqing University Fuling Hospital , ,
- 5Department of Radiology, The First Affiliated Hospital of Chongqing Medical University , ,
- 6College of Artificial Intelligence Medicine, Chongqing Medical University , ,
Abstract
Background: Accurate preoperative risk stratification of cystic renal lesions (CRLs) remains a clinical challenge, primarily due to morphological overlap which often leads to the overtreatment of indolent cysts. To address this, we developed and validated an integrated deep learning framework that combines interactive segmentation with tri-phase CT feature integration to refine malignancy prediction.
Methods: This retrospective multicenter study enrolled 434 patients with CRLs from four institutions, partitioned into training (n=195), validation (n=100), and external test (n=139) cohorts. The reference standard was histopathological diagnosis or ≥4 years of radiological stability. For volumetric segmentation, we implemented an interactive ‘human-in-the-loop’ Swin UNETR model. To integrate complementary information across the three CT phases, we then deployed a Tri-Phase-Channel (TPC) Perceiver architecture to integrate features extracted by parallel SEResNet50 backbones from unenhanced, corticomedullary, and nephrographic CT phases. Diagnostic performance was evaluated against the single-phase SEResNet50 models and the Bosniak classification version 2019 using the Area Under the Receiver Operating Characteristic Curve (AUC) and Decision Curve Analysis (DCA).
Results: In the external test cohort, the interactive segmentation model yielded a Dice score of 0.890. For malignancy prediction, the TPC-Perceiver attained an AUC of 0.969 (95% CI: 0.911–1.000), significantly outperforming the Bosniak classification version 2019 (AUC 0.884; p =0.016 ). At the fixed probability threshold of 0.5, the model achieved a sensitivity of 94.4% and a specificity of 96.7%, compared with 100.0% and 76.9%, respectively, for the clinical standard. Furthermore, decision curve analysis indicated that the deep learning framework conferred greater net benefit across a broad range of threshold probabilities.
Conclusion: The integrated framework showed favorable performance for cystic renal lesion risk stratification. Exploratory pairwise AUC comparisons favored the TPC Perceiver over the single-phase models and the clinical Bosniak v2019 assessment, while its AUC was comparable to that of the previous SETD model. The sensitivity-specificity profile observed at the fixed threshold suggests potential value in reducing false-positive risk stratification. Larger prospective multicenter studies are required before clinical implementation.
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BibTeX
@article{yuan2026deep,
title = {{Deep learning with interactive segmentation for risk stratification of cystic renal lesions on tri-phase CT: a multicenter study}},
author = {Wenlong Yuan and Pinglin He and Quanhao He and Xuan Zhang and Qing Jiang and Siwen Yin and Fajin Lv and Jie Liu and Weiyang He and Mingzhao Xiao},
journal = {Frontiers in oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1832969},
url = {https://europepmc.org/article/PMC/PMC13671972},
note = {CC BY},
}
RIS
TY - JOUR
TI - Deep learning with interactive segmentation for risk stratification of cystic renal lesions on tri-phase CT: a multicenter study
AU - Wenlong Yuan
AU - Pinglin He
AU - Quanhao He
AU - Xuan Zhang
AU - Qing Jiang
AU - Siwen Yin
AU - Fajin Lv
AU - Jie Liu
AU - Weiyang He
AU - Mingzhao Xiao
PY - 2026
DA - 2026/10/02
JO - Frontiers in oncology
DO - 10.3389/fonc.2026.1832969
UR - https://europepmc.org/article/PMC/PMC13671972
AB - Background: Accurate preoperative risk stratification of cystic renal lesions (CRLs) remains a clinical challenge, primarily due to morphological overlap which often leads to the overtreatment of indolent cysts. To address this, we developed and validated an integrated deep learning framework that combines interactive segmentation with tri-phase CT feature integration to refine malignancy prediction. Methods: This retrospective multicenter study enrolled 434 patients with CRLs from four institutions, partitioned into training (n=195), validation (n=100), and external test (n=139) cohorts. The reference standard was histopathological diagnosis or ≥4 years of radiological stability. For volumetric segmentation, we implemented an interactive ‘human-in-the-loop’ Swin UNETR model. To integrate complementary information across the three CT phases, we then deployed a Tri-Phase-Channel (TPC) Perceiver architecture to integrate features extracted by parallel SEResNet50 backbones from unenhanced, corticomedullary, and nephrographic CT phases. Diagnostic performance was evaluated against the single-phase SEResNet50 models and the Bosniak classification version 2019 using the Area Under the Receiver Operating Characteristic Curve (AUC) and Decision Curve Analysis (DCA). Results: In the external test cohort, the interactive segmentation model yielded a Dice score of 0.890. For malignancy prediction, the TPC-Perceiver attained an AUC of 0.969 (95% CI: 0.911–1.000), significantly outperforming the Bosniak classification version 2019 (AUC 0.884; p =0.016 ). At the fixed probability threshold of 0.5, the model achieved a sensitivity of 94.4% and a specificity of 96.7%, compared with 100.0% and 76.9%, respectively, for the clinical standard. Furthermore, decision curve analysis indicated that the deep learning framework conferred greater net benefit across a broad range of threshold probabilities. Conclusion: The integrated framework showed favorable performance for cystic renal lesion risk stratification. Exploratory pairwise AUC comparisons favored the TPC Perceiver over the single-phase models and the clinical Bosniak v2019 assessment, while its AUC was comparable to that of the previous SETD model. The sensitivity-specificity profile observed at the fixed threshold suggests potential value in reducing false-positive risk stratification. Larger prospective multicenter studies are required before clinical implementation.
SN - 2234-943X
ER -