학술논문
Complete prevalence and indicators of cancer cure: enhanced methods and validation in Italian population-based cancer registries
Document Type
article
Author
Federica Toffolutti; Stefano Guzzinati; Angela De Paoli; Silvia Francisci; Roberta De Angelis; Emanuele Crocetti; Laura Botta; Silvia Rossi; Sandra Mallone; Manuel Zorzi; Gianfranco Manneschi; Ettore Bidoli; Alessandra Ravaioli; Francesco Cuccaro; Enrica Migliore; Antonella Puppo; Margherita Ferrante; Cinzia Gasparotti; Maria Gambino; Giuliano Carrozzi; Fabrizio Stracci; Maria Michiara; Rossella Cavallo; Walter Mazzucco; Mario Fusco; Paola Ballotari; Giuseppe Sampietro; Stefano Ferretti; Lucia Mangone; Roberto Vito Rizzello; Michael Mian; Giuseppe Cascone; Lorenza Boschetti; Rocco Galasso; Daniela Piras; Maria Teresa Pesce; Francesca Bella; Pietro Seghini; Anna Clara Fanetti; Pasquala Pinna; Diego Serraino; Luigino Dal Maso; AIRTUM Working Group; Fabiola Giudici; Ellina Evdokimova; Elena Demuru; Gemma Gatta; Paolo Contiero; Giovanna Tagliabue; Riccardo Capocaccia; Massimo Rugge; Teresa Intrieri; Martina Taborelli; Lucia Bisceglia; Stefano Rosso; Claudia Casella; Antonietta Torrisi; Giovanni Maifredi; Monica Lanzoni; Alessio Gili; Sergio Mazzola; Maria Francesca Vitale; Erica Giacomazzi; Silvia Ghisleni; Maria Adalgisa Gentilini; Fabio Vitadello; Concetta Patrizia Rollo; Stefano Marguati; Luciana Del Riccio; Maria Rotella; Alessandra Sessa; Antonino Colanino Ziino; Ivan Cometti; Roberta Bosu
Source
Frontiers in Oncology, Vol 13 (2023)
Subject
Language
English
ISSN
2234-943X
Abstract
ObjectivesTo describe the procedures to derive complete prevalence and several indicators of cancer cure from population-based cancer registries.Materials and methodsCancer registry data (47% of the Italian population) were used to calculate limited duration prevalence for 62 cancer types by sex and registry. The incidence and survival models, needed to calculate the completeness index (R) and complete prevalence, were evaluated by likelihood ratio tests and by visual comparison. A sensitivity analysis was conducted to explore the effect on the complete prevalence of using different R indexes. Mixture cure models were used to estimate net survival (NS); life expectancy of fatal (LEF) cases; cure fraction (CF); time to cure (TTC); cure prevalence, prevalent patients who were not at risk of dying as a result of cancer; and already cured patients, those living longer than TTC at a specific point in time. CF was also compared with long-term NS since, for patients diagnosed after a certain age, CF (representing asymptotical values of NS) is reached far beyond the patient’s life expectancy.ResultsFor the most frequent cancer types, the Weibull survival model stratified by sex and age showed a very good fit with observed survival. For men diagnosed with any cancer type at age 65–74 years, CF was 41%, while the NS was 49% until age 100 and 50% until age 90. In women, similar differences emerged for patients with any cancer type or with breast cancer. Among patients alive in 2018 with colorectal cancer at age 55–64 years, 48% were already cured (had reached their specific TTC), while the cure prevalence (lifelong probability to be cured from cancer) was 89%. Cure prevalence became 97.5% (2.5% will die because of their neoplasm) for patients alive >5 years after diagnosis.ConclusionsThis study represents an addition to the current knowledge on the topic providing a detailed description of available indicators of prevalence and cancer cure, highlighting the links among them, and illustrating their interpretation. Indicators may be relevant for patients and clinical practice; they are unambiguously defined, measurable, and reproducible in different countries where population-based cancer registries are active.