Summary
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1 Sample Definition And Size
The study retrospectively analyzed patients from The Third Affiliated Hospital of Chongqing Medical University between 2022 and 2024. It included individuals diagnosed with colorectal cancer (CRC) and those with benign colorectal diseases. After excluding invalid or non-numerical records and retaining only the first diagnostic test result per parameter per patient, 78 laboratory indicators remained from an initial 371. From these, eight key parameters were selected to construct the ColoLDB model. The exact number of patients is not specified in the accessible metadata.
2 Study Type
Observational retrospective diagnostic study using machine learning model development and validation (random forest, LightGBM, logistic regression, XGBoost), following TRIPOD reporting guidelines.
3 Conflicts Of Interest
One author, C.Z., is an employee of Roche Diagnostics Ltd. The other authors declared no conflicts of interest. ([pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC12972017/?utm_source=openai))
4 Results Summary
The random forest (RF) ColoLDB model, based on eight parameters (specific gravity, CA19‑9, CEA, age, albumin, CYFRA21‑1, HDL‑C, CA72‑4), achieved in the test set: AUC 0.863 (95% CI: 0.792–0.922), accuracy 0.900, sensitivity 0.225, specificity 0.997, PPV 0.917, NPV 0.900. When specificity was set at 0.903, sensitivity increased to 0.694. In comparison, a model combining CEA and CA19‑9 had AUC 0.688, sensitivity 0.429, specificity 0.947. ([pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC12972017/?utm_source=openai))