Background: Cancer prevalence is heterogeneous because it includes individuals who are undergoing initial treatment and those who are in remission, experiencing relapse, or cured. The proposed statistical approach describes the health status of this group by estimating the probabilities of death among prevalent cases. The application concerns colorectal, lung, breast, and prostate cancers and melanoma in France in 2017.
Methods: Excess mortality was used to estimate the probabilities of death from cancer and other causes.
Results: For the studied cancers, most deaths from cancer occurred during the first 5 years after diagnosis. The probability of death from cancer decreased with increasing time since diagnosis except for breast cancer, for which it remained relatively stable. The time beyond which the probability of death from cancer became lower than that from other causes depended on age and cancer site: for colorectal cancer, it was 6 years after diagnosis for women (7 years for men) aged 75-84 and 20 years for women (18 years for men) aged 45-54 years, whereas cancer was the major cause of death for women younger than 75 years whatever the time since diagnosis for breast and for all patients younger than 75 years for lung cancer. In contrast, deaths from other causes were more frequent in all the patients older than 75 years. Apart from breast cancer in women younger than 55 years and lung cancer in women older than 55 years and men older than 65 years, the probability of death from cancer among prevalent cases fell below 1%, with varying times since diagnosis.
Conclusions: The authors' approach can be used to better describe the burden of cancer by estimating outcomes in prevalent cases.
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http://dx.doi.org/10.1002/cncr.34413 | DOI Listing |
Lung Cancer
January 2025
Dept. of Medical Oncology, Princess Margaret Cancer Center, Toronto, ON, Canada.
Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.
View Article and Find Full Text PDFLung Cancer
January 2025
Internal Medicine III, Wakayama Medical University, Wakayama, Japan.
Objectives: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through comprehensive gene expression analysis using machine learning (ML).
Methods: A prospective multicenter cohort of patients with ES-SCLC who received first-line chemo-immunotherapy was analyzed.
J Geriatr Oncol
January 2025
Hellenic Oncology Research Group (HORG), 55, Lomvardou str, 11470 Athens, Greece.
Introduction: The use of taxanes in the adjuvant setting of early breast cancer (BC) confers survival benefits, however, their role in older patients merits further study. This retrospective pooled analysis of randomized controlled trials conducted by the Hellenic Oncology Research Group (HORG) aims to assess the efficacy and safety of taxane-based adjuvant chemotherapy in older women with BC.
Materials And Methods: Five phase III trials containing a taxane, conducted by HORG between 1995 and 2013, were included in a patient-data pooled analysis.
In 2012, a social issue arose concerning a high incidence of cholangiocarcinoma (bile duct cancer) among printing workers. The cause was prolonged exposure to high concentrations of 1,2-dichloropropane that was included in the ink cleaning agent. Until then, it was not known that this substance could cause cancer in humans.
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