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Updated: Oct 27, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Predicting survival in colorectal carcinoma after curative resection: a new prognostic nomogram
Mahiar Mahjoub1, Loretta Sioson2, Amy Sheen2
1Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia; NSW Health Pathology, Department of Anatomical Pathology, Royal North Shore Hospital, Sydney, NSW, Australia; Sydney Medical School, University of Sydney, Sydney, NSW, Australia.
Abstract:
Several prognostic nomograms designed to predict survival after curative resection for colorectal cancer (CRC) have been proposed. Recently, routine pathological assessment has evolved with subtle changes to the AJCC staging system, and routine screening for mismatch repair deficiency (MMRd). Therefore we sought to develop and validate a new prognostic nomogram. All cause survival data from 4517 consecutive patients with primary CRC were used as independent training and validation cohorts to develop a final model including only: age, sex, tumour stage, nodal status, number of lymph nodes resected, apical node status, distant metastases, thin-walled vascular invasion, and MMR status. Patients were stratified into four risk groups to assess model discrimination and calibration. To assess discrimination, the area-under-the-curve (AUC) of a receiver-operator-curve (ROC), concordance-index (C-index), and D-index were calculated. The model was compared to the Memorial Sloan Kettering Cancer Center (MSKCC) CRC nomogram and the AJCC TNM staging. Based on the 5-year ROC analysis, the AUC for our model was 0.81 (0.79 and 0.74 for MSKCC and AJCC, respectively). Moreover, our model demonstrated a concordance index of 0.77 (95% CI 0.70-0.82) compared to 0.75 (95% CI 0.68-0.81) for MSKCC and 0.73 (95% CI 0.65-0.79) for AJCC. In conclusion, our new prognostic nomogram incorporates a larger number of clinically relevant prognostic markers, including MMR status, and therefore demonstrates improved predictive capability. As these factors are routinely assessed, it is hoped that this model will inform prognostication and difficult management decisions, such as patient selection for adjuvant therapy.
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