Sanchez, Violeta M.

Abstract Background Aortic valve calcification (AVC), as measured by gold-standard computed tomography (CT) Agatston score, provides an anatomic assessment of aortic stenosis (AS) severity and is a key predictor of AS progression and need for valve replacement. AVC detection and quantification from transthoracic echocardiography (TTE) could expand AS early diagnosis and risk stratification, currently limited by CT availability and radiation exposure. Methods A multi-view video-based deep learning framework was developed using 1166 TTE aortic valve videos from 187 TTE studies acquired in 110 patients with available AVC score by CT. EchoAVC architecture includes a feature extraction model followed by a quality-aware model that aggregates video-level information to obtain patient-level predictions for AVC detection and quantification (score). The framework was validated internally with 173 TTE studies from 86 patients across seven centres, and externally using 430 TTE studies from 280 patients across four different centres. The associations between EchoAVC estimations and AS severity, progression, and need for aortic valve replacement were examined. Results EchoAVC demonstrated excellent performance in AVC detection (AUROC 0.98, accuracy 94.4%), and quantification (R = 0.64) in the external multi-centre testing set. EchoAVC score was correlated with echocardiographic AS severity descriptors, including aortic valve mean pressure gradient (ρ = 0.749) and peak velocity (ρ = 0.757), and predicted future increase in mean pressure gradient (ρ = 0.382), peak velocity (ρ = 0.433) and calcium score by CT (ρ = 0.650). In 361 patients followed for a median of 3.8 years, 139 underwent aortic valve replacement. Baseline presence and extent of AVC as predicted by EchoAVC showed strong risk-stratification power for aortic valve replacement, remarkably in line with those obtained by CT, and incremental over TTE AS descriptors. EchoAVC was further tested in routine clinical practice images, confirming strong associations with AS severity and progression, including stratification for incident AS in previously unaffected individuals (p<0.001). Conclusions EchoAVC enables accurate and non-invasive detection and quantification of AVC, offering substantial diagnostic and prognostic value for aortic stenosis progression and need for valve replacement. This technique holds promise as a scalable tool for early detection and clinical management of aortic valve stenosis. Clinical Perspective What Is New? * Aortic valve calcification can be detected and quantified on standard, two-dimensional transthoracic echocardiography * EchoAVC score is associated with echocardiographic metrics of aortic stenosis severity * EchoAVC predicts progressive increase in aortic stenosis severity and need for aortic valve replacement What Are the Clinical Implications? * This openly available deep learning framework can be used to estimate aortic valve calcium in patients at risk of or presenting aortic stenosis to predict aortic stenosis progression * EchoAVC may help identify patients likely to have high aortic valve calcium and prioritize them for confirmatory CT assessment, thereby supporting earlier identification of calcific aortic valve disease in clinical pathways.

Abstract Purpose: Preclinical data demonstrating androgen receptor (AR)–positive (AR+) triple-negative breast cancer (TNBC) cells are sensitive to AR antagonists, and PI3K inhibition catalyzed an investigator-initiated, multi-institutional phase Ib/II study TBCRC032. The trial investigated the safety and efficacy of the AR-antagonist enzalutamide alone or in combination with the PI3K inhibitor taselisib in patients with metastatic AR+ (≥10%) breast cancer. Patients and Methods: Phase Ib patients [estrogen receptor positive (ER+) or TNBC] with AR+ breast cancer received 160 mg enzalutamide in combination with taselisib to determine dose-limiting toxicities and the maximum tolerated dose (MTD). Phase II TNBC patients were randomized to receive either enzalutamide alone or in combination with 4 mg taselisib until disease progression. Primary endpoint was clinical benefit rate (CBR) at 16 weeks. Results: The combination was tolerated, and the MTD was not reached. The adverse events were hyperglycemia and skin rash. Overall, CBR for evaluable patients receiving the combination was 35.7%, and median progression-free survival (PFS) was 3.4 months. Luminal AR (LAR) TNBC subtype patients trended toward better response compared with non-LAR (75.0% vs. 12.5%, P = 0.06), and increased PFS (4.6 vs. 2.0 months, P = 0.082). Genomic analyses revealed subtype-specific treatment response, and novel FGFR2 fusions and AR splice variants. Conclusions: The combination of enzalutamide and taselisib increased CBR in TNBC patients with AR+ tumors. Correlative analyses suggest AR protein expression alone is insufficient for identifying patients with AR-dependent tumors and knowledge of tumor LAR subtype and AR splice variants may identify patients more or less likely to benefit from AR antagonists.