Abstract PO4-07-06: Development of MRI-based Radiomics Nomogram for the Prediction of Recurrence in Patients with Luminal-type Breast Cancer: A Nested Case-Control Study

Abstract

Abstract Purpose: The aim of this study was to develop a radiomics nomogram using breast MRI data and preoperative pathologic information to predict oncotype DX recurrence score (RS) in patients with estrogen receptor-positive early-stage breast cancer (EBC). Materials and methods: In this retrospective study, a total of 493 patients with EBC, diagnosed through core needle biopsy and who underwent preoperative breast MRI at a single institution between 2011 and 2017, were included. The patients were categorized based on RS, age at surgery, and nodal status, as suggested by the TAILORX trial, and out of the included patients, 249 were identified as likely to benefit from chemotherapy, while 244 were deemed safe to forgo chemotherapy. Radiomic features were extracted from three-dimensional segmentations of each tumor, and computer-extracted image phenotypes (CEIP) were generated from early post-contrast T1-weighted images, percent enhancement (PE) maps, and signal enhancement ratio (SER) maps. The patient cohort was divided into a training set (n=329) and a validation set (n=164). Feature selection and radiomics score construction were performed using elastic net, and a prediction model was developed using multivariate logistic regression analysis. The radiomics score, along with independent pathologic risk factors, was incorporated into a radiomics nomogram. Internal validation was conducted using an independent validation set (n=164). Results: The radiomics score, composed of 24 selected CEIPs, demonstrated a significant association with recurrence prediction (C-index, 0.769 for training set and 0.745 for validation set). The independent pathologic predictors included in the nomogram were histology, estrogen receptor status, progesterone receptor status, nuclear grade, histologic grade, HER1, CK56, P53, and Ki67 status, with a C-index of 0.858 for the training set and 0.774 for the validation set. Adding the radiomics score to the pathologic nomogram resulted in incremental values of 0.054 and 0.092, respectively. The radiomics nomogram showed favorable prediction of low-risk RS, with a C-index of 0.912 for the training set and 0.866 for the validation set. Conclusion: This study demonstrated that an MRI-based radiomics nomogram incorporating the radiomics score and preoperatively obtained pathologic data can effectively predict oncotype DX RS and the benefit of adjuvant chemotherapy in EBC patients. The model improves risk assessment availability and timeliness for patients with EBC. Citation Format: Minji Song, Hee Jung Shin. Development of MRI-based Radiomics Nomogram for the Prediction of Recurrence in Patients with Luminal-type Breast Cancer: A Nested Case-Control Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-06.