Related Experiment Video
Updated: May 1, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Multivariate prognostic factors analysis for second-line chemotherapy in advanced biliary tract cancer
L Fornaro1, S Cereda2, G Aprile3
1Department of Oncology, Unit of Medical Oncology, Azienda USL2 Lucca Via dell'Ospedale 1, 55100 Lucca, Italy.
Background:
The role of second-line chemotherapy (CT) is not established in advanced biliary tract cancer (aBTC). We investigated the outcome of aBTC patients treated with second-line CT and devised a prognostic model.
Methods:
Baseline clinical and laboratory data of 300 consecutive aBTC patients were collected and association with overall survival (OS) was investigated by multivariable Cox models.
Results:
The following parameters resulted independently associated with longer OS: Eastern Cooperative Oncology Group performance status of 0 (P<0.001; hazard ratio (HR), 0.348; 95% confidence interval (CI) 0.215-0.562), CA19.9 lower than median (P=0.013; HR, 0.574; 95% CI 0.370-0.891), progression-free survival after first-line CT ≥ 6 months (P=0.027; HR, 0.633; 95% CI 0.422-0.949) and previous surgery on primary tumour (P=0.027; HR, 0.609; 95% CI 0.392-0.945). We grouped the 249 patients with complete data available into three categories according to the number of fulfilled risk factors: median OS times for good-risk (zero to one factors), intermediate-risk (two factors) and poor-risk (three to four factors) groups were 13.1, 6.6 and 3.7 months, respectively (P<0.001).
Conclusions:
Easily available clinical and laboratory factors predict prognosis of aBTC patients undergoing second-line CT. This model allows individual patient-risk stratification and may help in treatment decision and trial design.
More Related Videos
07:32Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018