CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset

CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset

Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral cen...

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Bibliographic Details
Main Authors: Giacomo Avesani, Huong Elena Tran, Giulio Cammarata, Francesca Botta, Sara Raimondi, Luca Russo, Salvatore Persiani, Matteo Bonatti, Tiziana Tagliaferri, Miriam Dolciami, Veronica Celli, Luca Boldrini, Jacopo Lenkowicz, Paola Pricolo, Federica Tomao, Stefania Maria Rita Rizzo, Nicoletta Colombo, Lucia Manganaro, Anna Fagotti, Giovanni Scambia, Benedetta Gui, Riccardo Manfredi
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Cancers
Subjects:
ovarian cancer
radiomics
computed tomography
machine learning
Online Access:https://www.mdpi.com/2072-6694/14/11/2739

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