Non-invasive prediction of nuclear grade in renal cell carcinoma using CT-Based radiomics: a systematic review and meta-analysis.
Background and purpose: Renal cell carcinoma (RCC) represents the most prevalent malignant neoplasm of the kidney, with a rising global incidence. Tumor nuclear grade is a crucial prognostic factor, guiding treatment decisions, but current histopathological grading via biopsy is invasive and prone t...
| Publicado en: | Abdominal Radiology Vol. 51; no. 1; pp. 151 - 171 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191138660&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191138660 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2026 vid: 51 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191138660 185830645 10.1007/s00261-025-05056-x 191138660 ppf: 151 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Non-invasive prediction of nuclear grade in renal cell carcinoma using CT-Based radiomics: a systematic review and meta-analysis. aug: au: Salimi, Mohsen Hajikarimloo, Bardia Vadipour, Pouria Abdolizadeh, Ali Fayedeh, Farzad Seifi, Sharareh affil: https://ror.org/034m2b326 Research Center of Thoracic Oncology (RCTO), National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran sug: ab: Background and purpose: Renal cell carcinoma (RCC) represents the most prevalent malignant neoplasm of the kidney, with a rising global incidence. Tumor nuclear grade is a crucial prognostic factor, guiding treatment decisions, but current histopathological grading via biopsy is invasive and prone to sampling errors. This study aims to assess the diagnostic performance and quality of CT-based radiomics for preoperatively predicting RCC nuclear grade. Materials and methods: A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science to identify relevant studies up until 19 April 2025. Quality was assessed using the QUADAS-2 and METRICS tools. A bivariate random-effects meta-analysis was performed to evaluate model performance, including sensitivity, specificity, and Area Under the Curve (AUC). Results from separate validation cohorts were pooled, and clinical and combined models were analyzed separately in distinct analyses. Results: A total of 26 studies comprising 1993 individuals in 10 external and 16 internal validation cohorts were included. Meta-analysis of radiomics models showed pooled AUC of 0.88, sensitivity of 0.78, and specificity of 0.82. Clinical and combined (clinical-radiomics) models showed AUCs of 0.73 and 0.86, respectively. QUADAS-2 revealed significant risk of bias in the Index Test and Flow and Timing domains. METRICS scores ranged from 49.7 to 88.4%, with an average of 66.65%, indicating overall good quality, though gaps in some aspects of study methodologies were identified. Conclusion: This study suggests that radiomics models show great potential and diagnostic accuracy for non-invasive preoperative nuclear grading of RCC. However, challenges related to generalizability and clinical applicability remain, as further research with standardized methodologies, external validation, and larger cohorts is needed to enhance their reliability and integration into routine clinical practice. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|