Scholarlink

Purpose <p>Our purpose was to develop and validate an integrated clinical deep learning radiomics (DLR) nomogram for differentiation of fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC) in diagnostically challenging cases and to systematically compare spatial-input strategies for optimal model performance.</p> Methods <p>This retrospective study enrolled 469 patients with pathologically confirmed renal masses (fp-AML or ccRCC). A stratified cohort partitioning strategy was employed: the training cohort comprised cases with consistent radiological–pathological diagnoses, and the independent test cohort was purposely enriched with "gray-zone" cases (indeterminate imaging or prior discordant interpretations). Four spatial-input architectures for DLR feature extraction were systematically compared: region of interest (ROI) only, 1mm ROI expansion, uncropped full-slice, and conventional cropping. After rigorous feature selection, seven machine learning algorithms were evaluated to identify the optimal model. A nomogram was constructed and validated using receiver operating characteristics analysis, calibration curves, and decision curve analysis.</p> Results <p>The ROI-only DLR model demonstrated superior discriminative performance among all spatial-input strategies. In the independent "stress-test" cohort, it achieved an area under the curve (AUC) of 0.820, significantly outperforming standalone radiomics (AUC 0.735), deep learning (AUC 0.778), and other DLR variants (AUCs 0.736–0.785). The nomogram further improved diagnostic performance with an AUC of 0.840. Decision curve analysis confirmed the superior net benefit of the nomogram across a wide range of threshold probabilities, and calibration curves demonstrated good agreement between predicted and observed outcomes.</p> Conclusion <p>The ROI-only DLR nomogram, specifically validated on diagnostically challenging cases, provides a robust and non-invasive decision-support tool for differentiating fp-AML from ccRCC, potentially reducing unnecessary surgeries and improving preoperative management of indeterminate renal masses.</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

Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram

  • 期刊论文

  • 发表日期：

    2026年5月25日

查看全文
  • Yue Xiao,
  • Huchao Mao,
  • Haifeng Fan,
  • Xupeng Ye,
  • Hang Huang,
  • Mei Yang,
  • Yan Zhang

摘要

Purpose

Our purpose was to develop and validate an integrated clinical deep learning radiomics (DLR) nomogram for differentiation of fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC) in diagnostically challenging cases and to systematically compare spatial-input strategies for optimal model performance.

Methods

This retrospective study enrolled 469 patients with pathologically confirmed renal masses (fp-AML or ccRCC). A stratified cohort partitioning strategy was employed: the training cohort comprised cases with consistent radiological–pathological diagnoses, and the independent test cohort was purposely enriched with "gray-zone" cases (indeterminate imaging or prior discordant interpretations). Four spatial-input architectures for DLR feature extraction were systematically compared: region of interest (ROI) only, 1mm ROI expansion, uncropped full-slice, and conventional cropping. After rigorous feature selection, seven machine learning algorithms were evaluated to identify the optimal model. A nomogram was constructed and validated using receiver operating characteristics analysis, calibration curves, and decision curve analysis.

Results

The ROI-only DLR model demonstrated superior discriminative performance among all spatial-input strategies. In the independent "stress-test" cohort, it achieved an area under the curve (AUC) of 0.820, significantly outperforming standalone radiomics (AUC 0.735), deep learning (AUC 0.778), and other DLR variants (AUCs 0.736–0.785). The nomogram further improved diagnostic performance with an AUC of 0.840. Decision curve analysis confirmed the superior net benefit of the nomogram across a wide range of threshold probabilities, and calibration curves demonstrated good agreement between predicted and observed outcomes.

Conclusion

The ROI-only DLR nomogram, specifically validated on diagnostically challenging cases, provides a robust and non-invasive decision-support tool for differentiating fp-AML from ccRCC, potentially reducing unnecessary surgeries and improving preoperative management of indeterminate renal masses.