ArticleNPJ precision oncology2025
Integration of multi-scale radiomics and deep learning for Ki-67 prediction in clear cell renal carcinoma.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictors of pathological upstaging to pT3a after nephrectomy for cT1 renal tumors: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- MRI-based habitat radiomics for assessing synchronous metastatic risk in renal cell carcinoma: a multicenter study.Abdominal radiology (New York) · 2026Article
- CT-Based Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction.Journal of clinical medicine · 2026Review
- CT-Based Radiomics for Prediction of Molecular Markers in Clear Cell Renal Cell Carcinoma: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2026Review
- Heterogeneity Analyzed by CT-Based Habitat Analysis for Clinical Management of Cancers: A Narrative Review.The Kaohsiung journal of medical sciences · 2026Review
- Biomarkers in axial spondyloarthritis diagnosis: from clinical signs to multi-omics integration.Frontiers in immunology · 2026Review
- A radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High Ki-67 expression in clear cell renal cell carcinoma (ccRCC) predicts poor prognosis but requires postoperative assessment. In a multicenter retrospective study of 627 ccRCC patients, we developed and validated a multi-modal model, integrating multi-scale radiomics and deep learning (DL) features, for non-invasive, preoperative Ki-67 prediction. Using ensemble machine learning algorithms, unimodal models were constructed from preoperative CT-derived multi-scale radiomics (intratumoral, habitat, peritumoral), 2D/3D DL, and clinical features. A stacking strategy was used to fuse the best-performing unimodal models. The fusion model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.756 (95% CI 0.692-0.821) in the external test set. The model demonstrated excellent calibration and the highest clinical net benefit, with habitat radiomics identified as the dominant predictive component via SHAP analysis. Our validated multi-modal model significantly improves the preoperative prediction of Ki-67 expression compared to unimodal approaches, offering a promising tool to guide individualized surgical and surveillance strategies.
Identifiers
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Registered trials
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.