A novel non-invasive machine learning model for predicting tertiary lymphoid structures and treatment response to neoadjuvant therapy in triple-negative breast cancer: A multicenter retrospective study.

Abstract

e13596 Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer (BC) and neoadjuvant therapy (NAT) has become an essential treatment strategy. Recent studies have showed tertiary lymphoid structures (TLSs) is associated with better treatment response to NAT in BC. However, there is still a lack of non-invasive biomarkers to predict the presence of TLSs in TNBC. This study aimed to develop a machine learning model for early identification and prediction of the presence of TLSs and treatment response to NAT in TNBC, which were essential for timely adjustments in treatment strategies for TNBC. Methods: In this study, 698 patients from multicenter were divided into a training cohort (n = 137), the TLSs validation cohort (n = 63) and the NAT response validation cohorts (n = 561). A total of 4788 radiomic features per patient were extracted from the intratumoral and peritumoral regions of DCE-MR. After extracting the optimal features, multiple machine learning models were developed to predict the presence of TLSs and were subsequently applied to predict the treatment response to NAT in NAT response validation cohorts. The performance of the models was assessed by area under curve values (AUC), and prognostic analysis were performed to evaluate its predictive value. Finally, the correlation between key radiomic features and key pathomic features was further analyzed to reveal the tumor heterogeneity of TNBC from a pathological perspective. Results: The XGBoost model, which outperformed all other predictive models, was selected as the radiomics-based TLS (rTLS) predictive model. It achieved AUCs of 0.922 in the training cohort, 0.852 in the TLS validation cohort, 0.724 in the Chinese-TNBC cohort, 0.919 in the DUKE cohort, and 0.883 in the I-SPY2 cohort. The rTLS predictive model demonstrated robust predictive performance, including across various patient subgroups defined by age, menopausal status, Ki67 level, cT stage, cN stage, and MammaPrint gene status. In YNCH cohort and DUKE cohort, prognostic analysis showed that low rTLS predictive score was significantly correlated with better disease-free survival in TNBC receiving NAT, and Cox regression analysis also confirmed the rTLS predictive score was a strong independent prognostic factor. Pathomic features further explained the pathological heterogeneity of TNBC with different responses to NAT. Conclusions: The rTLS predictive model, which accurately predicted the presence of TLSs and treatment response to NAT in TNBC, held promise for future clinical application in formulating personalized and effective treatment strategies for TNBC, ultimately improving prognosis.