A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results

The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we...

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Main Authors: Raffaella Massafra, Agnese Latorre, Annarita Fanizzi, Roberto Bellotti, Vittorio Didonna, Francesco Giotta, Daniele La Forgia, Annalisa Nardone, Maria Pastena, Cosmo Maurizio Ressa, Lucia Rinaldi, Anna Orsola Maria Russo, Pasquale Tamborra, Sabina Tangaro, Alfredo Zito, Vito Lorusso
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-03-01
Series:Frontiers in Oncology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.576007/full
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spelling doaj-765b017212f0470285d2743268f0470f2021-03-11T06:11:18ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-03-011110.3389/fonc.2021.576007576007A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary ResultsRaffaella Massafra0Agnese Latorre1Annarita Fanizzi2Roberto Bellotti3Vittorio Didonna4Francesco Giotta5Daniele La Forgia6Annalisa Nardone7Maria Pastena8Cosmo Maurizio Ressa9Lucia Rinaldi10Anna Orsola Maria Russo11Pasquale Tamborra12Sabina Tangaro13Alfredo Zito14Vito Lorusso15Struttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyUnitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyStruttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyDipartimento di Fisica, Universitá degli Studi “Aldo Moro” e Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, ItalyStruttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyUnitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyStruttura Semplice Dipartimentale di Radiologia Senologica, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyUnitá Opertiva Complessa di Radioterapia, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyUnitá Opertiva Complessa di Anatomia Patologica, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyUnitá Opertiva Complessa di Chirurgia Plastica e Ricostruttiva, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyStruttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyDipartimento di Oncologia Medica, Universitá degli Studi di Napoli, Napoli, ItalyStruttura Semplice Dipartimentale di Fisica Sanitaria, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy0Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi “Aldo Moro” e Istituto Nazionale di Fisica Nucleare - Sezione di Bari, Bari, ItalyUnitá Opertiva Complessa di Anatomia Patologica, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyUnitá Opertiva Complessa di Oncologia Medica, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, ItalyThe mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.https://www.frontiersin.org/articles/10.3389/fonc.2021.576007/fullinvasive breast cancercancer recurrencelate recurrencefeature importancemachine learningprognosis
collection DOAJ
language English
format Article
sources DOAJ
author Raffaella Massafra
Agnese Latorre
Annarita Fanizzi
Roberto Bellotti
Vittorio Didonna
Francesco Giotta
Daniele La Forgia
Annalisa Nardone
Maria Pastena
Cosmo Maurizio Ressa
Lucia Rinaldi
Anna Orsola Maria Russo
Pasquale Tamborra
Sabina Tangaro
Alfredo Zito
Vito Lorusso
spellingShingle Raffaella Massafra
Agnese Latorre
Annarita Fanizzi
Roberto Bellotti
Vittorio Didonna
Francesco Giotta
Daniele La Forgia
Annalisa Nardone
Maria Pastena
Cosmo Maurizio Ressa
Lucia Rinaldi
Anna Orsola Maria Russo
Pasquale Tamborra
Sabina Tangaro
Alfredo Zito
Vito Lorusso
A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results
Frontiers in Oncology
invasive breast cancer
cancer recurrence
late recurrence
feature importance
machine learning
prognosis
author_facet Raffaella Massafra
Agnese Latorre
Annarita Fanizzi
Roberto Bellotti
Vittorio Didonna
Francesco Giotta
Daniele La Forgia
Annalisa Nardone
Maria Pastena
Cosmo Maurizio Ressa
Lucia Rinaldi
Anna Orsola Maria Russo
Pasquale Tamborra
Sabina Tangaro
Alfredo Zito
Vito Lorusso
author_sort Raffaella Massafra
title A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results
title_short A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results
title_full A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results
title_fullStr A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results
title_full_unstemmed A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results
title_sort clinical decision support system for predicting invasive breast cancer recurrence: preliminary results
publisher Frontiers Media S.A.
series Frontiers in Oncology
issn 2234-943X
publishDate 2021-03-01
description The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.
topic invasive breast cancer
cancer recurrence
late recurrence
feature importance
machine learning
prognosis
url https://www.frontiersin.org/articles/10.3389/fonc.2021.576007/full
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