Risk stratification in cervical cancer screening by complete screening history: Applying bioinformatics to a general screening population
PBN-AR
Instytucja
Instytut Podstaw Informatyki Polskiej Akademii Nauk
Informacje podstawowe
Główny język publikacji
angielski
Czasopismo
International Journal of Cancer (40pkt w roku publikacji)
ISSN
0020-7136
EISSN
1097-0215
Wydawca
WILEY-BLACKWELL
DOI
URL
Rok publikacji
2017
Numer zeszytu
1
Strony od-do
200-209
Numer tomu
141
Identyfikator DOI
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Autorzy
(liczba autorów: 5)
Pozostali autorzy
+ 4
Słowa kluczowe
angielski
bioinformatics
cervical cancer
screening
personalized medicine
machine learning
Streszczenia
Język
angielski
Treść
Women screened for cervical cancer in Sweden are currently treated under a one-size-fits-all programme, which has been suc-cessful in reducing the incidence of cervical cancer but does not use all of the participants’ available medical information.This study aimed to use women’s complete cervical screening histories to identify diagnostic patterns that may indicate anincreased risk of developing cervical cancer. A nationwide case-control study was performed where cervical cancer screeningdata from 125,476 women with a maximum follow-up of 10 years were evaluated for patterns of SNOMED diagnoses. The can-cer development risk was estimated for a number of different screening history patterns and expressed as Odds Ratios (OR),with a history of 4 benign cervical tests as reference, using logistic regression. The overall performance of the model wasmoderate (64% accuracy, 71% area under curve) with 61–62% of the study population showing no specific patterns associ-ated with risk. However, predictions for high-risk groups as defined by screening history patterns were highly discriminatorywith ORs ranging from 8 to 36. The model for computing risk performed consistently across different screening historylengths, and several patterns predicted cancer outcomes. The results show the presence of risk-increasing and risk-decreasingfactors in the screening history. Thus it is feasible to identify subgroups based on their complete screening histories. Severalhigh-risk subgroups identified might benefit from an increased screening density. Some low-risk subgroups identified couldlikely have a moderately reduced screening density without additional risk.
Inne
System-identifier
PX-598330acd5deb0738e572d7d
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