ArticleBMC medical imaging2025
A nomogram based on dual-layer spectral detector CT-derived 40KeV virtual monoenergetic images for preoperative prediction of simultaneous distant metastasis in colorectal cancer.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A nomogram based on dual-layer spectral detector CT-derived 40-KeV virtual monoenergetic images and iodine maps for preoperative prediction of regional lymph node metastasis in colorectal cancer.BMC medical imaging · 2026Article
- The diagnostic efficacy of venous-phase spectral CT combined with Node-RADS for differentiating enlarged lymph nodes in common solid tumors: a preliminary exploratory study.Frontiers in oncology · 2026Article
- Deep learning radiomics models based on contrast-enhanced transrectal ultrasound for predicting distant metastasis in rectal cancer.Frontiers in oncology · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundTo establish and validate a nomogram combining dual-layer spectral detector CT(DLSCT)-derived 40KeV virtual monoenergetic images (VMI) of radiomics features, spectral parameters and clinical features for preoperative prediction of simultaneous distant metastases (SDM) in colorectal cancer (CRC).
methodsWe retrospectively included 137 patients [SDM
resultsIn the training group, the nomogram (AUC = 0.938) was remarkably better than that of the radiomics models. In the external validation group, the nomogram (AUC = 0.930) was remarkably superior to that of the DP model and the clinical model. In the vast majority of threshold probabilities, the nomogram had a better critical net benefit than the other four models in predicting SDM of CRC.
conclusionsThe nomogram incorporating radiomics features of DLSCT-derived 40KeV-VMI, spectral parameters and clinical features showed excellent predictive performance in preoperatively predicting SDM in CRC, which can help clinicians make accurate individualized treatment plans.
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