ArticleInternational journal of clinical oncology2025
Personalized prognostic model for colorectal cancer in the era of precision medicine: a dynamic approach based on real-world data.
Article in International journal of clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Construction and validation of a prognostic model for 1 year all-cause mortality risk in patients with colorectal cancer liver metastases after HIFU treatment.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Sample size and power analysis for ROC AUC differences in diagnostic tests: a methodological evaluation of the Obuchowski-McClish and Hanley-McNeil methods.BMC medical research methodology · 2026Article
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16 authors.
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Abstract
backgroundPredicting individual prognosis is required for patients with colorectal cancer in the era of precision medicine. However, this may be challenging for the conventional survival analysis such as the Cox proportional hazards model. This study aims to develop a personalized prognostic prediction that incorporates longitudinal data to improve predictions for colorectal cancer patients.
methodsPatients with advanced or recurrent colorectal cancer, who received treatment at Kyoto University Hospital between April 2015 and December 2021, were retrospectively analyzed. The Joint model is one of the dynamic prediction models. Using longitudinal clinical data, a carcinoembryonic antigen (CEA) prediction equation was developed for each patient. Additionally, a personalized prognostic prediction model was created using the Joint model. The prediction accuracy of the Joint model was compared with one of the Cox proportional hazards model.
resultsAmong the 1010 patients, 614 patients were enrolled. The median frequency of tumor marker measurement (per patient) was 20 times (range: 3-117 times). CEA values could be predicted accurately and the Pearson's correlation coefficient between measured CEA and predicted CEA was 0.931. In the Joint model, the significant prognostic factors were baseline age (HR, 1.039; 95% CI, 1.025-1.054), poor-differentiated tumor (HR, 2.600; 95% CI 1.446-4.675) and log
conclusionThe Joint model may accurately predict personalized prognosis that reflects changes in longitudinal tumor marker values.
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