ArticleInsights into imaging2023
Automatic analysis framework based on 3D-CT multi-scale features for accurate prediction of Ki67 expression levels in substantial renal cell carcinoma.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Deep learning model based on CT images to predict Ki-67 expression in renal carcinoma.Oncology letters · 2026Article
- CT-Based Radiomics for Prediction of Molecular Markers in Clear Cell Renal Cell Carcinoma: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2026Review
- Machine learning-based multi-class classification of bladder pathologies using fused 3D CT radiomic and 3D auto-encoder deep features.European journal of radiology open · 2026Article
- Integration of multi-scale radiomics and deep learning for Ki-67 prediction in clear cell renal carcinoma.NPJ precision oncology · 2025Article
- Automated quantification of Ki-67 expression in breast cancer from H&E-stained slides using a transformer-based regression model.Breast cancer research : BCR · 2025Article
- Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.Frontiers in oncology · 2025Article
- A quantitative analysis framework of placenta accreta spectrum: placenta subtype, intraoperative bleeding, and hysterectomy risk evaluation based on magnetic resonance imaging-anatomical-clinical features.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
11 authors at 5 institutions in 1 country.
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Abstract
purposeTo investigate the effectiveness of an automatic analysis framework based on 3D-CT multi-scale features in predicting Ki67 expression levels in substantial renal cell carcinoma (RCC).
methodsThis retrospective study was conducted using multi-center cohorts consisting of 588 participants with pathologically confirmed RCC. The participants were divided into an internal training set (n = 485) and an external testing set (n = 103) from four and one local hospitals, respectively. The proposed automatic analytic framework comprised a 3D kidney and tumor segmentation model constructed by 3D UNet, a 3D-CT multi-scale features extractor based on the renal-tumor feature, and a low or high Ki67 prediction classifier using XGBoost. The framework was validated using a fivefold cross-validation strategy. The Shapley additive explanation (SHAP) method was used to determine the contribution of each feature.
resultsIn the prediction of low or high Ki67, the combination of renal and tumor features achieved better performance than any single features. Internal validation using a fivefold cross-validation strategy yielded AUROC values of 0.75 ± 0.1, 0.75 ± 0.1, 0.83 ± 0.1, 0.77 ± 0.1, and 0.87 ± 0.1, respectively. The optimal model achieved an AUROC of 0.87 ± 0.1 and 0.82 ± 0.1 for low vs. high Ki67 prediction in the internal validation and external testing sets, respectively. Notably, the tumor first-order-10P was identified as the most influential feature in the model decision.
conclusionsOur study suggests that the proposed automatic analysis framework based on 3D-CT multi-scale features has great potential for accurately predicting Ki67 expression levels in substantial RCC. CRITICAL RELEVANCE STATEMENT: Automatic analysis framework based on 3D-CT multi-scale features provides reliable predictions for Ki67 expression levels in substantial RCC, indicating the potential usage of clinical applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.