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Objective <p>To develop and validate a simplified CT prediction model (SCTM) based on the 2019 Bosniak classification for predicting malignancy in cystic renal masses (CRMs), and to create a secondary model to distinguish indolent from aggressive CRMs.</p> Materials and methods <p>This retrospective study included the consecutive patients who were pathologically confirmed to have malignant and benign CRMs at four institutions from January 2013 to December 2022. Two radiologists evaluated all CRMs using the Bosniak classification. Multivariable logistic regression and XGBoost models were used to create prediction models. The models were developed, and validated in an independent internal cohort and four independent external cohorts. Model performance was assessed for differentiation ability and clinical utility.</p> Results <p>In total, 1138 CRM patients were analyzed across training (<i>n</i> = 655), internal validation (<i>n</i> = 165), and external tests (<i>n</i> = 146, <i>n</i> = 69, <i>n</i> = 103). Benign and malignant CRMs accounted for 710 (62.39%) and 428 (37.61%), respectively. The SCTM, which combined the number of septa and the angle of convex protrusion, showed excellent differentiation in the validation (AUC 0.99), test 1 (AUC 0.89), test 2 (AUC 0.96), and test 3 (AUC 0.95) sets. The XGBoost model, which integrated age, sex, BMI, location, size, and SCTM probability to differentiate indolent from aggressive CRMs, showed good performance with AUCs of 0.92, 0.80, and 0.87 in the validation, test 1, and combined validation and test sets.</p> Conclusion <p>SCTM was a simplified, accurate, non-invasive CT model that effectively differentiated benign from malignant CRMs, and the XGBoost model refined the identification of indolent CRMs, optimizing treatments and reducing unnecessary surgeries.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can an SCTM accurately predict malignancy in CRMs and identify aggressive lesions to guide personalized clinical decisions</i>?</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>The SCTM and XGBoost models achieved high diagnostic accuracy in distinguishing malignant and aggressive CRMs across internal and external cohorts</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The SCTM, utilizing key 2019 Bosniak features, differentiates benign from malignant CRMs. Combined with the XGBoost model, refining indolent vs aggressive CRM identification, this achieves precise renal cystic tumor stratification, aiding in tailored treatment decisions, interventions, and reducing unnecessary surgeries</i>.</p> Graphical Abstract <p></p>

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Development and validation of a CT prediction model for precise stratification of renal cystic tumors: a multicenter retrospective study based on the BOSNIAK classification

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Qinqin Kang,
  • Na Li,
  • Chengwei Chen,
  • Jun Huang,
  • Jiajun Liu,
  • Guangyu Wu,
  • Xuedong Yang,
  • Yongjie Zhang,
  • Bin Xia,
  • Fang Liu,
  • Xu Fang,
  • Jing Li,
  • Tiegong Wang,
  • Jianping Lu,
  • Li Wang,
  • Chengwei Shao,
  • Yun Bian

摘要

Objective

To develop and validate a simplified CT prediction model (SCTM) based on the 2019 Bosniak classification for predicting malignancy in cystic renal masses (CRMs), and to create a secondary model to distinguish indolent from aggressive CRMs.

Materials and methods

This retrospective study included the consecutive patients who were pathologically confirmed to have malignant and benign CRMs at four institutions from January 2013 to December 2022. Two radiologists evaluated all CRMs using the Bosniak classification. Multivariable logistic regression and XGBoost models were used to create prediction models. The models were developed, and validated in an independent internal cohort and four independent external cohorts. Model performance was assessed for differentiation ability and clinical utility.

Results

In total, 1138 CRM patients were analyzed across training (n = 655), internal validation (n = 165), and external tests (n = 146, n = 69, n = 103). Benign and malignant CRMs accounted for 710 (62.39%) and 428 (37.61%), respectively. The SCTM, which combined the number of septa and the angle of convex protrusion, showed excellent differentiation in the validation (AUC 0.99), test 1 (AUC 0.89), test 2 (AUC 0.96), and test 3 (AUC 0.95) sets. The XGBoost model, which integrated age, sex, BMI, location, size, and SCTM probability to differentiate indolent from aggressive CRMs, showed good performance with AUCs of 0.92, 0.80, and 0.87 in the validation, test 1, and combined validation and test sets.

Conclusion

SCTM was a simplified, accurate, non-invasive CT model that effectively differentiated benign from malignant CRMs, and the XGBoost model refined the identification of indolent CRMs, optimizing treatments and reducing unnecessary surgeries.

Key Points

Question Can an SCTM accurately predict malignancy in CRMs and identify aggressive lesions to guide personalized clinical decisions?

Findings The SCTM and XGBoost models achieved high diagnostic accuracy in distinguishing malignant and aggressive CRMs across internal and external cohorts.

Clinical relevance The SCTM, utilizing key 2019 Bosniak features, differentiates benign from malignant CRMs. Combined with the XGBoost model, refining indolent vs aggressive CRM identification, this achieves precise renal cystic tumor stratification, aiding in tailored treatment decisions, interventions, and reducing unnecessary surgeries.

Graphical Abstract