Length time bias in surveillance for hepatocellular carcinoma and how to avoid it

Hepatology Research : the Official Journal of the Japan Society of Hepatology
Alessandro CucchettiItalian Liver Cancer (ITA.LI.CA) group

Abstract

Length time bias is a selection bias which can lead to an overestimation of survival of screening-detected cases caused by the relative excess of slower-growing tumors detected with respect to symptomatic cases. This leads to the incorrect perception that screening improves outcomes when it only selects tumors with a favorable biology. Data regarding this bias in surveillance for hepatocellular carcinoma (HCC) have never been provided. A semi-Markov model was developed to investigate this issue. An exponential tumor growth was applied. During its growth, tumor diagnosis "at surveillance appointments" was made when tumor attained a size equal to or above the size of tumors diagnosed in surveilled patients obtained from pertinent published reports, or "in-between appointments" (due to the development of symptoms) if tumor size attained the size of symptomatic diagnosis, derived from published reports; otherwise the tumor continued to grow until the time horizon had been reached. Tumor doubling time (DT) values were recorded according to the method of diagnosis. In a theoretical cohort of 1000 patients submitted to semiannual surveillance, 72.5% will be diagnosed at a surveillance appointment and 18% because of symptom development...Continue Reading

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Citations

Nov 1, 2017·Current Hepatology Reports·Hager F Ahmed Mohammed, Lewis R Roberts
Mar 9, 2021·Cancer Letters·Maomao CaoWanqing Chen
Mar 7, 2021·Cancers·Filippo Pelizzaro On Behalf Of Ita Li Ca Study Group
Sep 29, 2021·Digestive and Liver Disease : Official Journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver·Filippo PelizzaroUNKNOWN Italian Liver Cancer (ITA.LI.CA) Study Group
Dec 20, 2021·Hepatology : Official Journal of the American Association for the Study of Liver Diseases·Nicole E Rich, Amit G Singal

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