Abstract LB070: Predicting distant recurrences in invasive breast carcinoma patients using their clinicopathological profiles

Abstract Purpose: To determine whether the classification-based machine learning (ML) or artificially intelligent (AI) techniques can predict distant recurrence flags (yes or no) in invasive breast cancer patients using the data comprising several clinicopathological measurements such as pathological staging of tumor and surrounding nodes deemed both pre and post neoadjuvant (i.e. either chemo, radiation or hormone based) therapy including imaging based therapy-response as well and the status of adjuvant therapy (Chemo or Anti-Her2/Neu antibody) therapy were administered to patients post resection of their tumor to minimize the possibility of diseases-recurrence in them. Method: The clinicopathological data from a retrospective study of 900 breast cancer patients at Duke university (posted at The Cancer Imaging Archive, i.e. TCIA) was tapped for our study. These patients received neoadjuvant therapies such as Chemo-, Radiation or Endocrine Hormone based therapy, had their responses evaluated through imaging and pathological staging (both tumor and nodal), and received Adjuvant therapies (such as Chemo or Anti-Her2/Neu antibody therapy) post tumor resection. The patient entries pertaining to ungraded tumor responses from therapies or those labelled as ‘Not applicable (NA)’ for any of the aforementioned clinicopathological parameters were filtered out retaining 161 patient-entries which were further split into train and test sets in 90:10 ratio where 90% were used for training (ntrain=144) and 10% for testing (ntest=17) of several ML models. Classification based machine learning models such as Random Forest (RF), C-Support Vector Classification (SVC) and Supervised Neural Network (aka Multi-Layer Perceptron, i.e. MLP) were employed to train and test aforementioned clinicopathological parameters to predict distant recurrence flags or labels (i.e. Yes or No). The training of the model was conducted using ImaGene software. Out of the three aforementioned models, RF seemed to have performed best with grid search activated over tree-depth hyperparameter to predict distant recurrence flags in test patients (ntest=17) at AUC=1.0 (p<0.002). Further, the validation of the model was conducted using external clinicopathological dataset of 20 patients from Dartmouth-Hitchcock (DH) Medical center which yielded AUC>0.75. Conclusion: Random Forest model trained using grid search through tree-depth hyperparameter can predict distant recurrence flags (Yes or No) in invasive breast cancer patients at AUC in the range of 0.75 1.0 across multiple institutions, thereby aiding portability of models and advancing the idea of multi-instutional ML/AI model validations in Invasive Breast Carcinoma subtype of Breast Cancer. Citation Format: Shrey S. Sukhadia, Kristen Muller, Adrienne Workman, Shivashankar Nagaraj, Olivier Gevaert. Predicting distant recurrences in invasive breast carcinoma patients using their clinicopathological profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB070.