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Updated: May 6, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
A nomogram including baseline prognostic factors to estimate the activity of second-line therapy for advanced
Gregory R Pond1, Neeraj Agarwal, Joaquim Bellmunt
1Ontario Clinical Oncology Group, McMaster University, Hamilton, ON, Canada.
Objective:
To study the impact of the prognostic factors liver metastasis (LM), anaemia (haemoglobin [Hb] <10 g/dL), Eastern Cooperative Oncology Group performance status (ECOG-PS) ≥1 and time from previous chemotherapy (TFPC) on the activity of second-line therapy for advanced urothelial carcinoma (UC).
Patients And Methods:
Twelve phase II trials evaluating second-line chemotherapy and/or biological characteristics (n = 748) in patients with progressive disease were pooled. Progression-free survival (PFS) was defined as tumour progression or death from any cause. The PFS rate at 6 months (PFS6) was defined from the date of registration and calculated using the Kaplan-Meier method. Response rate (RR) was defined using Response Evaluation Criteria in Solid Tumours (RECIST) 1.0. A nomogram predicting PFS6 was constructed using the rms software package in R (http://www.r-project.org).
Results:
Data regarding progression, anaemia, LM, ECOG-PS and TFPC were available from 570 patients in nine phase II trials. The overall median PFS was 2.7 months, PFS6 was 22.2% (95% confidence interval 18.8-25.9) and the RR was 17.5% (95% CI: 14.5-20.9%). For every unit increase in risk group, the hazard of progression in 6 months increased by 41% and the odds of response decreased by 48%. A nomogram was constructed to predict PFS6 on an individual patient level. The model was internally validated and was shown to have acceptable calibration performance.
Conclusions:
The RR and PFS6 vary as a function of baseline prognostic factors in patients receiving second-line therapy for advanced UC. A nomogram incorporating prognostic factors facilitates the evaluation of outcomes across phase II trials enrolling heterogeneous populations and helps select suitable agents for phase III testing.

