Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model

• Published in: European radiology 2024-08, Vol.34 (8), p.5477-5486
• Main Authors: Tong, Yiwei, Hu, Zhaoyu, Wang, Haoyu, Huang, Jiahui, Zhan, Ying, Chai, Weimin, Deng, Yinhui, Yuan, Ying, Shen, Kunwei, Wang, Yuanyuan, Chen, Xiaosong, Yu, Jinhua
• Format: Article
• Language: English
• Subjects: Adult; Aged; Antibodies, Monoclonal, Humanized; Antineoplastic Agents, Immunological - pharmacology; Antineoplastic Agents, Immunological - therapeutic use; Breast; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - drug therapy; Deep Learning; Diagnostic Radiology; Effectiveness; ErbB-2 protein; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Invasiveness; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Patients; Predictions; Radiology; Radiomics; Receptor, ErbB-2 - antagonists & inhibitors; Receptor, ErbB-2 - metabolism; Retrospective Studies; Risk; Risk Assessment; Risk groups; Risk reduction; Strategy; Survival; Therapy; Trastuzumab; Trastuzumab - therapeutic use; Treatment Outcome; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-024-10609-7

Objectives Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy. Methods A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy. Results DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab ( p  = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab ( p  = 0.048), but did not obtain additional improvements from pertuzumab ( p  = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade ( p  = 0.045), suggesting an anti-HER2 escalation. Conclusions DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation. Clinical relevance statement We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients. Key Points • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.