Citations

Abstract Background Artificial intelligence (AI)–based, mammography breast cancer risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of 3 clinical breast cancer risk models: Gail, Tyrer–Cuzick v8, and Breast Cancer Surveillance Consortium v3 with the MIRAI AI-risk model. Methods Digital mammograms were ascertained from a screening mammography cohort of 12 308 women within the Mayo Clinic Biobank with 250 incident breast cancers (176 invasive) within 5 years. We predicted 5-year breast cancer risk, estimated discriminatory accuracy (C-index), and calibration (observed-to-expected ratio) of both overall and invasive breast cancer and compared estimates using bootstrapping approaches. Results MIRAI demonstrated similar or improved discriminatory accuracy of overall breast cancer (C-index = 0.71, 95% confidence interval [CI] = 0.68 to 0.74) and invasive breast cancer (C-index = 0.71, 95% CI = 0.67 to 0.75) compared with clinical models (overall breast cancer: C-index = 0.59-0.68; invasive breast cancer: C-index = 0.60-0.68). MIRAI’s calibration for risk of overall breast cancer (observed-to-expected ratio = 0.96, 95% CI = 0.85 to 1.08) was improved compared with Gail (observed-to-expected ratio = 1.22, 95% CI = 1.07 to 1.38) and Breast Cancer Surveillance Consortium (observed-to-expected ratio = 1.38, 95% CI = 1.22 to 1.56) but similar to TC with volumetric percent density and polygenic risk score (observed-to-expected ratio = 0.99, 95% CI = 0.87 to 1.13). However, for low-risk women (approximately 50%), MIRAI overestimated risk of overall breast cancer. MIRAI also overestimated risk of invasive breast cancer across the risk spectrum (observed-to-expected ratio = 0.68, 95% CI = 0.58 to 0.78), whereas clinical models had good calibration (observed-to-expected ratio = 0.86–0.99). Conclusion MIRAI demonstrated stronger discriminatory accuracy than clinical models for 5-year overall and invasive breast cancer risk prediction but overestimated risk for both breast cancer endpoints. AI-based risk models should consider discriminatory accuracy and calibration for invasive cancer before implementation.

Abstract Background: Artificial intelligence (AI) algorithms based on deep learning approaches show promise in improving breast cancer (BC) detection on mammography and may also improve prediction of future BC risk compared with clinical risk prediction models. Historical clinical risk prediction models underperform among women with BBD; however, AI models for BC detection on mammography have not been tested among women at elevated BC risk due to benign breast disease (BBD). We evaluated if an AI cancer detection algorithm can aid BC risk prediction among women with BBD. Methods: We examined women without a prior BC who had an initial BBD biopsy at Mayo Clinic, Rochester, between 2002 and 2013. Incident BC occurring after BBD was identified using the Mayo Tumor Registry and supplemented by follow-up questionnaires. Only women with a full field digital mammogram at least 2 years prior to BC diagnosis were eligible. A breast pathologist evaluated BBD according to increasing severity, as non-proliferative (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH). AI malignancy scores derived from the Transpara detection algorithm (1-10) and volumetric percent density (VPD) from Volpara (per one standard deviation, SD) were assessed from mammograms close to BBD diagnosis. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) with 95% Confidence Intervals (CIs), adjusted for age and BMI. C-statistics were estimated to assess the contribution of each factor to BC risk prediction. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance with the addition of AI malignancy score. Results: The BBD cohort with mammography consisted of 3,125 women followed for a median 12.9 years, with 250 incident BC. Of these, 221 BC occurred at least two years after the mammogram and were used in analyses. As expected, increased BC risk was associated with BBD severity [for AH HR=3.18 (95%CI: 2.04, 4.95) and for PDWA HR=1.59 (95%CI: 1.17, 2.14) compared to NP] and with higher VPD [HR=1.24 per SD (95% CI: 1.05, 1.45)] (Table ). The AI-malignancy score alone was also associated with BC risk [HR=1.16 per 1 unit score (95%CI: 1.09,1.23)] and showed similar discriminatory accuracy [C-statistic=0.626 (95%CI: 0.586, 0.665)] as the model with BBD severity and VPD combined [C-statistic=0.627, (95%CI: 0.585, 0.669)] (Table ). In models with BBD severity and VPD, the AI-malignancy score was an independent risk factor for BC [HR=1.14 (95%CI: 1.07, 1.21); PLRT<0.001] but only achieved a marginally significant improvement in discriminatory accuracy [C-statistic=0.651 (0.609, 0.693), ΔC-statistic=0.024 (95% CI: 0.000, 0.047)]. Conclusion: In this preliminary study, an AI BC detection algorithm achieved similar performance in predicting future BC risk compared to established risk factors. Citation Format: C. M. Vachon, S. Winham, M. Jensen, D. Hursh, L. Pacheco-Spann, A. Norman, J. Fischer, S. Schrup, L. Seymour, D. Gehling, S. Nyante, M. Troester, N. Karssemeijer, R. Vierkant, D. Radisky, C. Scott, S. I. Maimone, A. Degnim, M. Sherman. An AI algorithm for breast cancer detection improves future BC risk prediction among women with benign breast disease [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD4-02.

Background: Indications for TAVR explant have been established, although limited data exist regarding pre-TAVR baseline characteristics that predict eventual explantation. Methods: The TriNetX network, a database comprising medical records from over 105 institutions, was used. Two cohorts were created: those who underwent TAVR without explant and those requiring subsequent TAVR explant and SAVR. Predictors of explantation were analyzed by multivariate models. Results: Among the 63,377 patients undergoing TAVR, 273 (0.4%) required explantation. Patients in the explant group were younger (69.1 ± 11.3 vs. 78.1 ± 8.8 years; p < 0.001), more likely to have a thoracic aortic aneurysm (TAA) (10.6% vs. 4.7%; p < 0.001) and had higher LDL levels (88.2 ± 41.3 vs. 80.7 ± 34.6 mg/dL; p = 0.011). They also had increased post-TAVR rates of acute kidney injury (9.2% vs. 5.2%; p = 0.004) and paravalvular leak (5.9% vs. 0.9%; p < 0.001). Age at TAVR (HR 1.04; CI 1.03–1.06), baseline TAA (HR 1.69; CI 1.09–2.63), history of infective endocarditis (HR 1.92; CI 1.10–3.35), and higher LDL (HR 1.02; 95% CI 1.00–1.03) were independent predictors for explantation. Conclusions: Younger age at TAVR, TAA, history of endocarditis, and elevated baseline LDL were notable predictors of explantation. These findings highlight the necessity of pre-procedural assessment and follow-up in high-risk patients to optimize TAVR durability.

Background: Severe mitral regurgitation (MR) in cardiogenic shock (CS) presents unique therapeutic challenges, especially in patients with prohibitive surgical risk. Transcatheter edge-to-edge repair (TEER) with MitraClip offers a less invasive alternative, yet optimal strategies for hemodynamic support in this setting remain unclear. Methods: We conducted a retrospective study of patients undergoing MitraClip implantation for severe MR and CS between 2018 and 2024 at a tertiary academic center. Of 440 screened cases, 47 met CS criteria and were stratified based on receipt of periprocedural Impella support. Clinical, procedural, and survival outcomes were compared between Impella-supported and unsupported cohorts using appropriate statistical methods. Results: Among the 47 patients, 24 received Impella support and 23 did not. The Impella group had greater illness severity, including higher rates of preprocedural intubation (75.0% vs. 43.5%) and SCAI stage D shock (50.0% vs. 13.0%). Despite these differences, procedural success was 100% in both groups. In-hospital (8.3% vs. 8.7%), 30-day (12.5% vs. 8.7%), and 1-year mortality (25.0% vs. 26.0%) were similar between Impella and non-Impella patients, respectively. Impella recipients had longer hospital stays (median 18 vs. 11 days; p=0.032) and higher 1-year readmission rates (55.0% vs. 21.7%; p=0.024), reflecting baseline acuity. There were no differences in major procedural complications or re-intervention rates. Conclusions: MitraClip implantation in CS patients supported with Impella is feasible and safe. Despite significantly worse hemodynamic profiles, outcomes were comparable to those without mechanical support. These findings suggest a potential role for tailored Impella use in expanding TEER to critically ill patients and underscore the need for prospective studies to guide support strategies in high-risk MR intervention.

Introduction: Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide. In the quest for improved therapeutic strategies, the addition of Sodium Glucose Transporter Inhibitors (SGLTis) to the treatment regimen has garnered considerable attention. This meta-analysis endeavors to shed light on the efficacy of SGLTis in short-term cardio-renal protection post-AMI. By synthesizing evidence from diverse studies. Methods: Adhering to the PRISMA guidelines, a meticulous systematic review and meta-analysis were conducted. Comprehensive literature searches across PubMed, Web of Science, Scopes, and Google Scholar were performed. Inclusion criteria comprised four Randomized Controlled Trials (RCTs), one retrospective cohort, and one observational study, investigating the effects of SGLTis versus placebo on AMI patients, irrespective of diabetic status. Our analysis included the following outcomes: acute kidney injury, all-cause death, cardiovascular mortality, cardiac death, composite of cardiovascular death/hospitalization for heart failure, and NT-proBNP levels. Results: Our analysis encompassed six scientific articles involving 7238 patients. SGLTIs exhibited a notable favorable effect solely on acute kidney injury (P value=0.001, 95% CI [0.46-0.82]). Conversely, no significant benefit was observed in other short-term outcomes, including all-cause death, cardiovascular mortality, cardiac death, composite of cardiovascular death/hospitalization for heart failure, and NT-proBNP levels. These results align with previous studies suggesting the renal protective effects of SGLTIs but highlight the need for further research to clarify their overall cardiovascular benefits in AMI patients. Conclusion: Incorporating SGLTis into the therapeutic armamentarium of AMI patients, irrespective of diabetic status, demonstrated a discernible advantage solely concerning acute kidney injury. However, no appreciable benefit was discerned across other assessed metrics. These findings underscore the need for further exploration and tailored therapeutic strategies in the management of post-AMI patients.

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT &lt; 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values &lt; 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-06.

Abstract Artificial intelligence (AI) models applied to mammography have been shown to improve breast cancer (BC) detection and risk estimation. The Transpara AI detection system has been associated with short and longer-term risk of invasive BC. Whether Transpara can identify changes in the breast associated with tamoxifen (TAM) and aromatase inhibitor (AIs) treatment is unknown. We compare changes in the Transpara malignancy score in the unaffected breast of BC cases using TAM or AIs to those of untreated women without cancer. Cases and controls were sampled from the Mayo Clinic Rochester Breast Screening Practice. Eligible cases were women with breast cancer treated with TAM and/or AIs for at least 8 months who had full field digital (FFDM) mammograms within two years prior to start of treatment (pretreatment mammogram). Controls did not have BC, were not using TAM, AIs, or postmenopausal hormones, and had at least two FFDM screening mammograms. Transpara scores (1-10) were calculated for the contralateral breast in cases and a randomly selected side for controls. Two controls were matched to each case on menopausal status at both mammograms, time between mammograms (&lt; 6 months) and baseline Transpara score (±2 units). Conditional logistic regression was used to assess the association of treatment (case (TAM/AIs) vs. control) with change in Transpara score between the pretreatment mammogram and the mammogram taken closest to 13 months later. Change was classified as having a decrease in Transpara score between the two mammograms vs. no change or an increase. Models were adjusted for age and Transpara score at pretreatment mammogram, and time between the two mammograms. We performed analyses on all women combined and stratified by time on treatment, menopausal status, and treatment type (TAM vs. AIs). A total of 134 cases were identified, with 53 on TAM (21 premenopausal and 32 postmenopausal) and 81 on AIs (postmenopausal); 268 controls were matched to the cases. Characteristics of the study population are summarized (Table). Most case mammograms were obtained within six months prior to treatment start [median 2.63 months (interquartile range (IQR), 1.62, 5.72)] and median time on TAM/AIs was 10.9 months (IQR, 8.03, 16.6) at time of second mammogram. The median absolute change in Transpara score between the two mammograms was similar in cases [0 (IQR, -2, 1)] vs. controls [0 (IQR, -1.25, 1)]; 40.3% of cases and 39.2% of controls had any decrease. Conditional logistic models found no evidence for an association between TAM/AIs and decrease in Transpara score [odds ratio (OR) = 1.07; 95% confidence interval (CI), 0.65, 1.78]. When stratified by median time on treatment (10.9 months), there was suggestion of a stronger association for those on treatment for a longer duration (OR = 1.21; 95% CI, 0.61, 2.40 for &gt;10.9 months vs. OR = 0.96; 95% CI, 0.43, 2.13 for &lt; 10.9 months) but these results were not statistically significant. Similarly, odds ratios were stronger in postmenopausal (OR = 1.18; 95% CI, 0.67, 2.07) compared to premenopausal (OR = 0.91; 95% CI, 0.24, 3.45) women, and for AIs (OR = 1.21, 95% CI, 0.64, 2.29) vs. TAM (OR = 0.93; 95% CI, 0.39, 2.22) but none of these results reached statistical significance. In summary, we found no association between treatment with TAM/AIs and change in Transpara score. However, our study had limited power. Also, longer time on treatment as well as investigation of AI-based scores trained directly to assess BC risk may be necessary to identify relevant changes. Citation Format: Yvonne Wang, Christopher Scott, Matthew Jensen, Dan Hursh, Kathleen Brandt, Aaron Norman, Fang Fang Wu, Stacey Winham, Karla Kerlikowske, Celine Vachon. Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-09-04.