[20° Forum SIS] Avviso di seminario :: DiSerio a DSS (Scienze Statistiche, Sapienza)

[Pierpaolo Brutti]

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 A v v i s o   d i   S e m i n a r i o
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Venerdì 13 Dicembre, ore 11am
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Stanza 34

Dipartimento di Scienze Statistiche

Sapienza Università di Roma

CLELIA DI SERIO

(Centro Universitario di Statistica per le Scienze Biomediche,
Università Vita-Salute San Raffaele)

terrà un seminario dal titolo

INVESTIGATING TUMOR DORMANCY WITH FRAILTY

tutti gli interessati sono invitati a partecipare.

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Maggiori informazioni sui seminari presso il DSS sono

consultabili a quest'indirizzo: http://goo.gl/Y6OQYm

Saluti

Pierpaolo Brutti - Fulvio De Santis

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Summary

A non-negligible percentage of cancer patients relapse after years or
decades after surgical removal of the primary tumor. This phenomenon
is not following a random memory-less process and its timing is
reflected in hazard rates, with respect to both relapse and mortality,
showing multimodal shapes. From a biomedical perspective, such a
behavior may be explained by tumor dormancy, i.e. for some patients
microscopic tumor foci may remain asymptomatic for a prolonged time
interval and, when they escape from dormancy, micrometastatic growth
results in a clinical disease appearance. The activation of the growth
phase at different metastatic states would explain the occurrence of
metastatic recurrences and mortality at different times (multimodal
hazard). We place this phenomenon in the framework of survival frailty
models. We propose a new statistical approach which models the risk
function by considering possible effects of Gamma frailty and compound
Poisson fraity in acting on components of the hazard function. Thus,
the individual hazard rate is the product of a random frailty variable
and the sum of basic hazard rates. The basic hazard rates correspond
to micrometastatic developments starting from different initial
states. The frailty variable represents the heterogeneity between
patients with respect to relapse, which might be related to unknown
mechanisms that regulate tumor dormancy. The model is applied to
analyze the mortality of patients in a large breast cancer dataset. We
show how this framework can appropriately model the tumor dormancy
phenomenon and possibly improve the understanding of this biological
process.
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