Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation. (10th March 2020)

• Record Type: Journal Article
• Authors: Boeri, Carlo; Chiappa, Corrado; Galli, Federica; De Berardinis, Valentina; Bardelli, Laura; Carcano, Giulio; Rovera, Francesca
• Is Part Of: Cancer medicine. Volume 9:Number 9(2020)
• Page Start: 3234
• Page End: 3243
• Publication Date: 2020-03-10
• Subjects: algorithm -- Artificial Neural Network (ANN) -- breast cancer -- predictive models -- Support Vector Machine (SVM); 616.994005
• DOI: 10.1002/cam4.2811
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow‐up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco‐regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%‐96.86%), sensitivity (=0.35‐0.64), specificity (=0.97‐0.99), and AUC (=0.804‐0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow‐up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging. Abstract : Machine Learning (ML) allows us to discover relations between prognostic factors and to predict breast cancer prognosis. These models might become an additional resource in our daily clinical practice.