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
Ruiz Borrego, M.; Cortes, J.; Stradella, A.; Bermejo, B.; Escriva de Romani, S.; Reboredo, C.; Albacar, C.; Garrigos, L.; Kummel, S.; Colleoni, M.; Gebhart, G.; Dalenc, F.; Kerrou, K.; Braga, S.; Schmid, P.; Marme, F.; Di Cosimo, S.; Notini, G.; Martinez Garcia, E.; Amaya-Garrido, A.; Alcala Lopez, D.; Mina, L.; Rodriguez Morato, J.; Tang, C.; Biswas, D.; Machura, B.; Witowski, J.; Geras, K. J.; Park, J.; Mancino, M.; Perez Garcia, J. M.; Llombart Cussac, A.
Show 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 (H&E) 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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- RNA Sequencing-Based Single Sample Predictors of Molecular Subtype and Risk of Recurrence for Clinical Assessment of Early-Stage Breast Cancer 93%
- A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death 93%
- An updated PREDICT breast cancer prognostic model including the benefits and harms of radiotherapy 92%
Similar papers in this journal
- Clinical Validation of Digital PCR-based ctDNA detection for risk stratification in residual triple negative breast cancer: TRICIA trial results 94%
- Clinicogenomic characterization of inflammatory breast cancer 91%
- Association between early discontinuation of endocrine therapy and recurrence of breast cancer among premenopausal women in a Danish population-based cohort 91%
Similar papers in this journal
- Criteria-based curation of a therapy-focused compendium to support treatment recommendations in precision oncology 92%
- Survival-Inferred Fragility of Statistical Significance in Phase III Oncology Trials 92%
- Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from Histopathology 92%
Similar papers in this journal
- Use of natural language understanding to facilitate surgical de-escalation of axillary staging in patients with breast cancer 93%
- Histology-based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning 90%
- Towards Predicting 30-Day Readmission among Oncology Patients: Identifying Timely and Actionable Risk Factors 90%
Similar papers in this journal
- Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer 93%
- A prospective study on tumour response assessments methods after neoadjuvant endocrine therapy in early oestrogen receptor positive breast cancer 92%
- COVID-19 in breast cancer patients: a cohort at the Institut Curie hospitals in the Paris area 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.