Improving radiologists' diagnostic accuracy for lymphovascular invasion in colorectal cancer: insights from a multicenter CT-based study.
Background: The current standard of subjective assessment by radiologists for lymphovascular invasion (LVI) in colorectal cancer (CRC) using CT images often falls short in diagnostic accuracy. This study introduces an advanced CT-based prediction model aimed at providing support for radiologists' as...
| Publicado en: | Abdominal Radiology Vol. 50; no. 10; pp. 4541 - 4553 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2025
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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=188150367&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188150367 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Oct2025 vid: 50 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188150367 184365814 10.1007/s00261-025-04884-1 188150367 ppf: 4541 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving radiologists' diagnostic accuracy for lymphovascular invasion in colorectal cancer: insights from a multicenter CT-based study. aug: au: Diao, Wenjun Hou, Kaiqi Chen, Xiaobo Han, Chaokang Li, Suyun Wang, Zhishan Xu, Ruxin Liao, Jiayi Yang, Liuyang Gu, Ruozhen Zhang, Ge Liu, Zaiyi Huang, Yanqi affil: https://ror.org/01vjw4z39 Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China sug: ab: Background: The current standard of subjective assessment by radiologists for lymphovascular invasion (LVI) in colorectal cancer (CRC) using CT images often falls short in diagnostic accuracy. This study introduces an advanced CT-based prediction model aimed at providing support for radiologists' assessment to accurately diagnose LVI. Methods: We conducted a retrospective analysis of 1409 patients with pathologically confirmed CRC from four institutions. Radiomics features were extracted from tumor areas on CT images, and Deep Residual Shrinkage Networks with Channel-wise Thresholds (DRSN-CW) algorithm was utilized to build prediction model. We assessed the model's impact on enhancing radiologists' diagnostic performance and employed Shapley Additive Explanation (SHAP) to interpret the influence of key features on predictions. Results: The prediction model achieved strong prediction performance with AUCs of 0.896 (95% CI: 0.866–0.923), 0.849 (0.782–0.908), 0.845 (0.782–0.901) and 0.799 (0.709–0.881) in the training and validation cohorts. Crucially, when informed by the model, radiologists demonstrated a significant improvement in diagnosing LVI. SHAP analysis provided detailed insights into the model's decision-making process, enhancing its clinical relevance. We also observed that patients predicted to be LVI-negative by the model had significantly longer overall survival (OS) compared to those LVI-positive (training cohort, p = 0.012; internal validation cohort, p = 0.046). Conclusions: This study introduces a CT-based prediction model that significantly enhances radiologists' ability to accurately diagnose LVI in CRC. By improving diagnostic accuracy and demonstrating the association between LVI predictions and OS, the model provides a valuable tool for clinical decision-making, with the potential to improve patient outcomes. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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