Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis.
Background: Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for predicting PNI. This systematic review and meta-anal...
| Publicado en: | Abdominal Radiology Vol. 50; no. 8; pp. 3415 - 3435 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| 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=186678305&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186678305 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Aug2025 vid: 50 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 186678305 182360598 10.1007/s00261-024-04713-x 186678305 ppf: 3415 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics for prediction of perineural invasion in colorectal cancer: a systematic review and meta-analysis. aug: au: Tang, Ning Pan, Shicen Zhang, Qirong Zhou, Jian Zuo, Zhiwei Jiang, Rui Sheng, Jinping affil: The General Hospital of Western Theater Command, Chengdu, Sichuan 610083, China, Chengdu, China sug: ab: Background: Perineural invasion (PNI) in colorectal cancer (CRC) is a significant prognostic factor associated with poor outcomes. Radiomics, which involves extracting quantitative features from medical imaging, has emerged as a potential tool for predicting PNI. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of radiomics models in predicting PNI in CRC. Methods: A comprehensive literature search was conducted across PubMed, Embase, and Web of Science for studies published up to July 28, 2024. Inclusion criteria focused on studies using radiomics models to predict PNI in CRC with sufficient data to construct diagnostic accuracy metrics. The quality of the included studies was assessed using QUADAS-2 and METRICS tools. Pooled estimates of sensitivity, specificity, and area under the curve (AUC) were calculated. Subgroup analyses were performed based on imaging modalities, segmentation methods, and other variables. Results: Twelve studies comprising 2853 patients were included in the systematic review, with ten studies contributing to the meta-analysis. The pooled sensitivity and specificity for radiomics models in predicting PNI were 0.74 (95% CI: 0.63–0.82) and 0.85 (95% CI: 0.79–0.90), respectively, in the training cohorts. In the validation cohorts, the sensitivity was 0.65 (95% CI: 0.57–0.72), and specificity was 0.85 (95% CI: 0.81–0.89). The AUC was 0.87 (95% CI: 0.63–0.82) for the training cohorts and 0.84 (95% CI: 0.81–0.87) for the validation cohorts, indicating good diagnostic accuracy. The METRICS scores for the included studies ranged from 65.8 to 85.1%, with an overall average score of 67.25%, reflecting good methodological quality. However, significant heterogeneity was observed across studies, particularly in sensitivity and specificity estimates. Conclusion: Radiomics models show promise as a non-invasive tool for predicting PNI in CRC, with moderate to good diagnostic accuracy. However, the current study's limitations, including reliance on retrospective data, geographic concentration in China, and methodological variability, suggest that further research is needed. Future studies should focus on prospective designs, standardization of methodologies, and the integration of advanced machine-learning techniques to improve the clinical applicability and reliability of radiomics models in CRC management. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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