A Novel Machine Learning Approach for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer: Integration of Multimodal Radiomics With Clinical and Molecular Subtype Markers

• Published in: IEEE access Vol. 12; pp. 104983 - 105003
• Main Authors: Gamal, Abdelrahman, Sharafeldeen, Ahmed, Alnaghy, Eman, Alghandour, Reham, Saleh Alghamdi, Norah, Ali, Khadiga M., Shamaa, Sameh, Aboueleneen, Amal, Elsaid Tolba, Ahmed, Elmougy, Samir, Ghazal, Mohammed, Contractor, Sohail, El-Baz, Ayman
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Breast Cancer; Chemotherapy; DW-MRI; Feature extraction; Genetic algorithms; Health services; Image enhancement; Machine learning; Magnetic resonance imaging; Medical imaging; Medical treatment; MRI; Neoadjuvant Chemotherapy; Pathology; Radiomics; Systems design; Texture; Treatment Response Prediction; Tumor Clinical Markers; Tumors
• DOI: 10.1109/ACCESS.2024.3432459
• ISSN: 2169-3536

The primary objective of this paper is to develop a machine learning-based approach capable of predicting the treatment response of neoadjuvant chemotherapy (NAC) to enhance breast cancer treatment management. The proposed system aims to predict NAC outcomes across three categories: pathological complete response (CR), partial response (PR), and stable disease (SD), by analyzing multimodal magnetic resonance images with clinical and molecular subtype markers. To ensure the comprehensiveness of our system design, texture radiomics were extracted from T1, T2, and STIR MRI modalities, along with functional radiomics from diffusion-weighted MRI at various b-values. The main rationale behind employing multiple b-values in collecting DW-MRI is to effectively capture the complexities of blood diffusion within the tumor microstructure. The proposed system comprises several key steps: (i) extracting texture and functional radiomics from T1, T2, STIR MRI, and DW-MRI data; (ii) identifying the most significant radiomics correlated with NAC treatment using a genetic algorithm; (iii) initially predicting the PR from alternative treatment responses utilizing the extracted textures and functional radiomics; and (iv) subsequently integrating clinical and molecular subtype markers with imaging radiomics to differentiate between CR and SD. Our proposed system is trained and validated through the utilization of a leave-one-subject-out (LOSO) cross-validation approach on various MRI scans from 109 subjects, of whom 27 had complete responses, 54 had partial responses, and 28 had no responses. The performance of the proposed system was assessed through the utilization of Cohen's Kappa and accuracy metrics, achieving 81.31% and 88.07%, respectively. Our various experiments showed that integrating clinical and molecular subtype markers with radiomics highlights the proposed system's efficiency in evaluating the tumor's response to NAC efficiently, outperforming predictions based solely on individual radiomics.INDEX TERMS Breast cancer, neoadjuvant chemotherapy, MRI, DW-MRI, radiomics, tumor clinical markers, machine learning, treatment response prediction.