What question did this study set out to answer?

This research aims to develop nomograms for diagnosing and prognostically stratifying CEA-negative colorectal cancer patients.

June 19, 2026Open Access

Serological signatures and pathological features for the management of CEA-negative colorectal cancer: development of combined diagnostic and prognostic nomograms

ZFZhongmao FuShanghai Jiao Tong University ZLZai LuoShanghai Jiao Tong University CHChao HanShanghai Jiao Tong University

Key Points

This research aims to develop nomograms for diagnosing and prognostically stratifying CEA-negative colorectal cancer patients.
Retrospective analysis of data from 553 CEA-negative CRC and 200 benign colorectal disease patients.
Identification of predictors through multivariable logistic and Cox regression methods.
Model performance evaluated using C-indices, calibration plots, DCA, and CIC.
The diagnostic model achieved C-indices of 0.886 for training and 0.895 for validation.
The prognostic model yielded C-indices of 0.749 for training and 0.829 for validation.
DCA and CIC indicated superior clinical net benefit for both models compared to the AJCC 8th TNM staging system.

Abstract

Colorectal cancer (CRC) remains a leading cause of malignancy-related mortality, with prognosis heavily contingent upon early detection. Notably, a significant proportion of CRC patients present with normal preoperative Carcinoembryonic Antigen (CEA) levels, creating a diagnostic blind spot where malignant lesions may be indistinguishable from benign colorectal disease (BCD). This study aimed to develop and validate multidimensional nomograms to accurately diagnose and prognostically stratify patients with CEA-negative CRC. We retrospectively analyzed data from 553 CEA-negative CRC patients and 200 BCD patients. Optimal biomarker thresholds were determined via ROC analysis. Diagnostic and prognostic predictors were identified using multivariable logistic and Cox regressions. Model performance was evaluated using C-indices, calibration plots, Decision Curve Analysis (DCA), and Clinical Impact Curves (CIC). Of 895 screened CRC patients, 61.8% were CEA-negative. An eight-variable diagnostic model (age, sex, CA50, CA125, CA19-9, WBC, PLR, FAR) demonstrated strong discriminative power, yielding C-indices of 0.886 (training) and 0.895 (validation). For overall survival, a prognostic signature incorporating age, tumor location, Lymph Node Ratio (LNR), Monocyte-to-Lymphocyte ratio (MLR), and Tumor-Stroma Ratio (TSP) achieved C-indices of 0.749 (training) and 0.829 (validation), showing favorable prognostic stratification compared to the AJCC 8th TNM staging system. DCA and CIC confirmed superior clinical net benefit for both models. Integrating systemic inflammatory indices and stromal features provides a potentially useful adjunctive tool for cancer detection and risk assessment. These user-friendly nomograms may assist in personalized prognostication, potentially refining clinical decision-making and patient stratification for therapeutic trials. However, independent external validation is required before clinical implementation due to the single-center design and internally derived cut-offs.

Ask AI

Helpful

Bookmark

View Full Paper