Multimodal Artificial Intelligence Predicts Pathological Response and Prognosis in HER2-Positive Early Breast Cancer Treated with Chemotherapy De-escalation Strategies: Analyses from PHERGain and PHERGain-2 Trials
Year: 2026
Type: preprint
Abstract: ABSTRACT Background Chemotherapy (CT) de-escalation strategies based on dual HER2 blockade with trastuzumab and pertuzumab (HP) have shown promising efficacy in HER2-positive (HER2+) early breast cancer (EBC), but biomarkers to guide patient selection are lacking. The Ataraxis Breast platform (ATX) uses artificial intelligence (AI) models integrating hematoxylin-eosin (HCE) whole-slide images and clinical variables to predict pathological complete response (pCR) and long-term outcomes. We evaluated ATX in the phase II PHERGain ( NCT03161353 ) and PHERGain-2 ( NCT04733118 ) trials. Methods The ATX NEO model was assessed for pCR prediction in PHERGain (n=306 evaluable patients) and PHERGain-2 (n=383 evaluable patients). The ATX RISK model was evaluated for prognosis in PHERGain (n=309 evaluable patients). Prespecified cutoffs classified patients into low-, medium-, and high-probability groups for pCR and low-or high-risk groups for recurrence. Associations with pCR were assessed using logistic regression. Prognostic performance was evaluated using Cox proportional hazards models, Kaplan-Meier estimates, and Harrell’s C-index. Results Overall pCR rates were 40.2% in PHERGain and 60.3% in PHERGain-2. Continuous NEO scores were significantly associated with pCR in both PHERGain (odds ratio [OR], 1.28; 95% CI, 1.11-1.48; P<0.001) and PHERGain-2 (OR, 1.30; 95% CI, 1.13-1.52; P<0.001). Using prespecified cutoffs, pCR rates increased across low-, medium-, and high-probability groups (19.2%, 41.3%, and 48.3% in PHERGain; 29.5%, 61.6%, and 70.1% in PHERGain-2). In PHERGain, the RISK score was significantly associated with event-free survival (hazard ratio per 1-SD increase, 1.56; 95% CI, 1.17-2.08; P=0.002; C-index, 0.66). Five-year event-free survival was 93.9% in the low-risk group and 83.1% in the high-risk group. Conclusions NEO score was consistently associated with pCR across two independent HER2+ EBC trials, while RISK score provided prognostic information for long-term outcomes in PHERGain. Prespecified score categories identified clinically distinct response and risk groups. These findings support further evaluation of AI-based biomarkers to refine patient selection for CT de-escalation strategies in HER2+ EBC.
Source: medRxiv
Authors: Manuel Ruiz Borrego, Javier Cortés, Agostina Stradella, Begoña Bermejo, Santiago Escrivá-de-Romaní, Cristina Reboredo, Cinta Albacar, Laia Garrigós, Sherko Kümmel, Marco Colleoni, Geraldine Gebhart, Florence Dalenc, Khaldoun Kerrou, Sofia Braga, Peter Schmid, Frederik Marmé, Serena Di Cosimo, Giulia Notini, Elena Martínez-García, Ana Amaya-Garrido, Daniel Alcalá-López, Leonardo Mina, Jose Rodriguez-Morató, Cerise Tang, Dhruva Biswas, Bartosz Machura, Jan Witowski, Krzysztof J. Geras, Jungkyu Park, Mario Mancino, Jose Manuel Pérez-García, Antonio Llombart-Cussac
Institutions: Hospital Universitario Virgen del Rocío, MedSIR (Spain), Universidad Europea de Madrid, Institut d'Investigació Biomédica de Bellvitge, Institut Català d'Oncologia, Universitat de València, Instituto de Salud Carlos III, Hospital Clínico Universitario de Valencia, INCLIVA Health Research Institute, Centro de Investigación Biomédica en Red de Cáncer, Vall d'Hebron Hospital Universitari, Vall d'Hebron Institute of Oncology, Complexo Hospitalario Universitario A Coruña, Institut de Recerca Biomèdica Catalunya Sud, Hospital Universitari Sant Joan de Reus, Hospital Universitario Dexeus, Kliniken Essen-Mitte, European Institute of Oncology, Université Libre de Bruxelles, Institut Jules Bordet, Institut universitaire du cancer de Toulouse Oncopole, Sorbonne Université, Assistance Publique – Hôpitaux de Paris, Hôpital Tenon, University of London, Queen Mary University of London, Barts Health NHS Trust, Barts Cancer Institute, Heidelberg University, Medizinische Fakultät Mannheim, Fondazione IRCCS Istituto Nazionale dei Tumori, Vita-Salute San Raffaele University, IRCCS Ospedale San Raffaele, Universidad Cardenal Herrera CEU, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana, Hospital Arnau de Vilanova
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221058815