Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form

• Published in: PloS one 2024-11, Vol.19 (11), p.e0312036
• Main Authors: Fanizzi, Annarita, Bove, Samantha, Comes, Maria Colomba, Di Benedetto, Erika Francesca, Latorre, Agnese, Giotta, Francesco, Nardone, Annalisa, Rizzo, Alessandro, Soranno, Clara, Zito, Alfredo, Massafra, Raffaella
• Format: Article
• Language: English
• Subjects: Adjuvants; Adult; Aged; Artificial intelligence; Artificial neural networks; Biology and Life Sciences; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Cancer therapies; Chemotherapy; Clinical medicine; Computer and Information Sciences; Datasets; Decision making; Decision support systems; Diagnosis; Endocrine therapy; Epidermal growth factor; Female; Health aspects; Humans; Information processing; Invasiveness; Machine Learning; Malignancy; Medical diagnosis; Medical imaging; Medical prognosis; Medicine and Health Sciences; Mental task performance; Metastases; Metastasis; Middle Aged; Missing data; Neoplasm Invasiveness; Neoplasm Recurrence, Local; Neural networks; Neural Networks, Computer; Patients; Prognosis; Risk factors; Support Vector Machine; Support vector machines; Surgery; Transfer learning; Tumors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0312036

Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem. In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Disease Event at both 5-year and 10-year follow-ups of 696 female patients with a first invasive breast cancer diagnosis enrolled at IRCCS "Giovanni Paolo II" in Bari, Italy. After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. Then, we classified breast cancer patients in the two classes, namely, Invasive Disease Event and non-Invasive Disease Event, via a Support Vector Machine classifier trained on a subset of significative features previously identified. Both 5-year and 10-year models resulted particularly accurate in predicting breast cancer recurrence event, achieving an AUC value of 92.07% and 92.84%, an accuracy of 88.71% and 88.82%, a sensitivity of 86.83% and 88.06%, a specificity of 89.55% and 89.3%, a precision of 71.93% and 84.82%, respectively. This is the first study proposing an approach which converts clinical information into an image-form to develop a decision support system for identifying patients at high risk of occurrence of an Invasive Disease Event, and then defining personalized oncological therapeutic treatments for breast cancer patients.