Marc Colonna

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.

With improvements in acute myeloid leukemia (AML) diagnosis and treatment, more patients are surviving for longer periods. A French population of 9453 AML patients aged ≥15 years diagnosed from 1995 to 2015 was studied to quantify the proportion cured (P), time to cure (TTC) and median survival of patients who are not cured (MedS). Net survival (NS) was estimated using a flexible model adjusted for age and sex in sixteen AML subtypes. When cure assumption was acceptable, the flexible cure model was used to estimate P, TTC and MedS for the uncured patients. The 5-year NS varied from 68% to 9% in men and from 77% to 11% in women in acute promyelocytic leukemia (AML-APL) and in therapy-related AML (t-AML), respectively. Major age-differenced survival was observed for patients with a diagnosis of AML with recurrent cytogenetic abnormalities. A poorer survival in younger patients was found in t-AML and AML with minimal differentiation. An atypical survival profile was found for acute myelomonocytic leukemia and AML without maturation in both sexes and for AML not otherwise specified (only for men) according to age, with a better prognosis for middle-aged compared to younger patients. Sex disparity regarding survival was observed in younger patients with t-AML diagnosed at 25 years of age (+28% at 5 years in men compared to women) and in AML with minimal differentiation (+23% at 5 years in women compared to men). All AML subtypes included an age group for which the assumption of cure was acceptable, although P varied from 90% in younger women with AML-APL to 3% in older men with acute monoblastic and monocytic leukemia. Increased P was associated with shorter TTC. A sizeable proportion of AML patients do not achieve cure, and MedS for these did not exceed 23 months. We identify AML subsets where cure assumption is negative, thus pointing to priority areas for future research efforts.

Cure models have been widely developed to estimate the cure fraction when some subjects never experience the event of interest. However these models were rarely focused on the estimation of the time–to–cure i.e. the delay elapsed between the diagnosis and ”the time from which cure is reached”, an important indicator, for instance to address the question of access to insurance or loans for subjects with personal history of cancer. We propose a new excess hazard regression model that includes the time–to–cure as a covariate dependent parameter to be estimated. The model is written similarly to a Beta probability distribution function and is shown to be a particular case of the non‐mixture cure models. Parameters are estimated through a maximum likelihood approach and simulation studies demonstrate good performance of the model. Illustrative applications to three cancer data sets are provided and some limitations as well as possible extensions of the model are discussed. The proposed model offers a simple and comprehensive way to estimate more accurately the time–to–cure. This article is protected by copyright. All rights reserved

Survival rates of lung cancer remains poor and the impact of comorbidities on the prognosis is discussed. The objective of this study was to assess if the Charlson Comorbidity Index (CCI) was associated with 8-year survival rates by histological type. A cohort study was conducted using randomly selected cases from 10 French cancer registries. Net survival rates were computed using the Pohar-Perme estimator of the net cumulative rate. Three Cox models were independently built for adenocarcinomas, squamous cell and small cell cancers to estimate prognostic factors including CCI grade. A total of 646 adenocarcinomas, 524 squamous cell and 233 small cell cancers were included in the analysis. The net 8-year survival rate ranged from 12.6% (95% CI: 9.8–15.4%) for adenocarcinomas and 13.4% (95% CI: 10.1–16.7%) for squamous cell carcinomas, to 3.7% (95% CI: 1.1–6.3%) for small cell cancers. Observed and net survival rates decreased for CCI grades ≥3 for all histological group considered. After adjustment for sex, age group, stage and diagnostic mode, CCI grades 1 (HR = 1.6 [95% CI: 1.1–2.3]), 2 (HR = 1.7 [95% CI: 1.1–2.7]) and ≥ 3 (HR = 2.7 [95% CI: 1.7–4.4]) were associated with lower survival rates only for small cell cancers. After adjustment for age, sex, stage and diagnostic mode, the presence of comorbidity based on CCI grades 1–2 and ≥ 3 was associated with lower survival rates for small cell cancers whereas no differences were observed for adenocarcinomas and squamous cell cancers.