Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation

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. K E Y W O R D Salgorithm, Artificial Neural Network (ANN), breast cancer, predictive models, Support Vector Machine (SVM)

Construction and Validation of a Newly Prognostic Signature for CRISPR-Cas9-Based Cancer Dependency Map Genes in Breast Cancer

“…As breast cancer is characterized by tumor heterogeneity [ 42 , 43 ], which is also the main reason that leads to the complexity of treatment [ 44 ], a more reliable model with prediction value is urgently needed. Although there have been several signatures constructed for predicting the prognosis of patients with breast cancer [ 45 – 47 ], the relationship between these indicators and breast cancer remains unclear. Then, using the six identified CDM genes, we established a risk model with prognostic value for patients with breast cancer by combining the data from the TCGA training set.…”

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

“…Previous machine learning models of BC prognosis prediction were developed using baseline clinical and pathologic information (30)(31)(32). Most of those studies used pathologic information and additional molecular information, such as the intrinsic subtype at BC diagnosis.…”

Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia

“…In recent years, machine learning models of prognosis and outcomes have become popular methods to identify new candidate biomarkers in cancer [16] , [17] , [18] , [19] . Diverse machine learning methods, including support vector machines [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , Bayesian networks [27] , [28] , [29] , and neural networks [30] , [31] , [32] , have used molecular and/or clinical markers to predict cancer prognosis. Because machine learning models are robust to a variety of challenges faced by traditional statistical models, they have the potential to enable more nuanced therapeutic decision-making than has been previously possible [17] , [18] , [33] , [34] , [35] , [36] .…”