Scholarlink

Objectives <p>The World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading of clear cell renal cell carcinoma (ccRCC) is crucial for prognosis and treatment planning. This study aims to predict the grade using intratumoral and peritumoral subregional CT radiomics analysis for better clinical interventions.</p> Methods <p>Data from two hospitals included 513 ccRCC patients, who were divided into training (70%), validation (30%), and an external validation set (testing) of 67 patients. Using ITK-SNAP, two radiologists annotated tumor regions of interest (ROI) and extended surrounding areas by 1&#xa0;mm, 3&#xa0;mm, and 5&#xa0;mm. The K-means clustering algorithm divided the tumor region into three sub-regions, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression identified the most predictive features. Various machine learning models were established, including radiomics models, peritumoral radiomics models, models based on intratumoral heterogeneity (ITH) score, clinical models, and comprehensive models. Predictive ability was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, DeLong tests, calibration curves, and decision curves.</p> Results <p>The combined model showed strong predictive power with an AUC of 0.852 (95% CI: 0.725–0.979) on the test data, outperforming individual models. The ITH score model was highly precise, with AUCs of 0.891 (95% CI: 0.854–0.927) in training, 0.877 (95% CI: 0.814–0.941) in validation, and 0.847 (95% CI: 0.725–0.969) in testing, proving its superior predictive ability across datasets.</p> Conclusion <p>A comprehensive model combining Habitat, Peri1mm, and salient clinical features was significantly more accurate in predicting ccRCC pathologic grading.</p> Key points <p><b>Question</b>: Characterize tumor heterogeneity to non-invasively predict WHO/ISUP pathological grading preoperatively.</p> <p><b>Findings</b>: An integrated model combining subregion characterization, peritumoral characteristics, and clinical features can predict ccRCC grade preoperatively.</p> <p><b>Clinical relevance</b>: Subregion tumor characterization outperforms the single-entity approach. The integrated model, compared with the radiomics model, boosts grading and prognostic accuracy for more targeted clinical actions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月3日

查看全文
  • Zhihui Chen,
  • Hongqing Zhu,
  • Hongmin Shu,
  • Jianbo Zhang,
  • Kangchen Gu,
  • Wenjun Yao

摘要

Objectives

The World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading of clear cell renal cell carcinoma (ccRCC) is crucial for prognosis and treatment planning. This study aims to predict the grade using intratumoral and peritumoral subregional CT radiomics analysis for better clinical interventions.

Methods

Data from two hospitals included 513 ccRCC patients, who were divided into training (70%), validation (30%), and an external validation set (testing) of 67 patients. Using ITK-SNAP, two radiologists annotated tumor regions of interest (ROI) and extended surrounding areas by 1 mm, 3 mm, and 5 mm. The K-means clustering algorithm divided the tumor region into three sub-regions, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression identified the most predictive features. Various machine learning models were established, including radiomics models, peritumoral radiomics models, models based on intratumoral heterogeneity (ITH) score, clinical models, and comprehensive models. Predictive ability was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, DeLong tests, calibration curves, and decision curves.

Results

The combined model showed strong predictive power with an AUC of 0.852 (95% CI: 0.725–0.979) on the test data, outperforming individual models. The ITH score model was highly precise, with AUCs of 0.891 (95% CI: 0.854–0.927) in training, 0.877 (95% CI: 0.814–0.941) in validation, and 0.847 (95% CI: 0.725–0.969) in testing, proving its superior predictive ability across datasets.

Conclusion

A comprehensive model combining Habitat, Peri1mm, and salient clinical features was significantly more accurate in predicting ccRCC pathologic grading.

Key points

Question: Characterize tumor heterogeneity to non-invasively predict WHO/ISUP pathological grading preoperatively.

Findings: An integrated model combining subregion characterization, peritumoral characteristics, and clinical features can predict ccRCC grade preoperatively.

Clinical relevance: Subregion tumor characterization outperforms the single-entity approach. The integrated model, compared with the radiomics model, boosts grading and prognostic accuracy for more targeted clinical actions.