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PURPOSE Patients with breast cancer requiring postmastectomy radiation therapy (PMRT) often desire breast reconstruction. Reconstructions can be adversely affected by PMRT, and fear of reconstruction complications has discouraged adoption of short-course radiation therapy used after lumpectomy. To fill the evidence gap, we conducted a noninferiority trial of hypofractionated versus conventional PMRT in patients undergoing breast reconstruction. The primary outcome was reconstruction complication rate, a composite of wound healing, readmission, capsular contracture, unplanned reoperation, and reconstruction failure. METHODS Women with unilateral invasive breast cancer with planned immediate or delayed reconstruction and PMRT were randomly assigned (1:1) to 50 Gy (25 fractions/5 weeks) or 42.56 Gy (16 fractions/3 weeks). This study was designed with a noninferiority margin of 10%, assuming a 2-year reconstruction complication rate of 25% with conventional PMRT. Randomization was stratified by immediate versus delayed and autologous versus implant-only reconstruction. RESULTS Between 2018 and 2021, 898 patients enrolled with a median follow-up of 3.9 years; 825 patients were evaluable for the primary end point. The 24-month reconstruction complication rates were 12.3% (52/422) with hypofractionated PMRT and 10.2% (41/403) with conventional PMRT, corresponding to an estimated difference of -2.1% (95% CI, -6.5 to 2.2). The complication rate was higher (regardless of arm) with implant-only versus autologous reconstruction (odds ratio = 1.65). There was no statistically significant difference in 36-month locoregional recurrence rates: 1.5% (95% CI, 0.7 to 3.2) with hypofractionated and 2.2% (95% CI, 1.1 to 4.3) with conventional PMRT. Acute and late toxicity rates were not statistically different. CONCLUSION A 16-fraction course of hypofractionated PMRT is safe and noninferior to conventional PMRT in women undergoing breast reconstruction after mastectomy.

Introduction Antibody–drug conjugates (ADCs) are now widely used in breast cancer across multiple disease subtypes. With increasing clinical use, treatment-related toxicities have become an important factor in therapeutic decision-making. However, comparative safety data among ADCs are limited because direct head-to-head trials are lacking. Methods We conducted a systematic review and Bayesian network meta-analysis of randomized controlled trials evaluating ADCs in breast cancer, with the literature search updated through March 31, 2025. Treatment-related adverse events (TRAEs) and serious adverse events (SAEs) were classified according to the Common Terminology Criteria for Adverse Events. A Bayesian random-effects network meta-analysis was used to compare toxicity risks across regimens. Odds ratios (ORs) with 95% credible intervals (CrIs) were estimated, and surface under the cumulative ranking curve (SUCRA) values were used as descriptive ranking summaries interpreted alongside the corresponding comparative estimates. The protocol was registered in PROSPERO (CRD42024606194). Results Seventeen randomized trials including 8,946 patients and five ADCs—trastuzumab emtansine (T-DM1), sacituzumab govitecan (SG), trastuzumab deruxtecan (T-DXd), datopotamab deruxtecan (Dato-DXd), and ARX788—were analyzed. SG and T-DXd showed the least favorable hematologic safety profiles, with SUCRA values for anemia/neutropenia of 22.6%/17.4% and 12.0%/27.0%, respectively. T-DM1-based regimens had the highest risk of thrombocytopenia. For gastrointestinal toxicities, SG ranked worst for diarrhea (SUCRA 6.0%), whereas T-DXd was associated with higher risks of nausea (2.2%), vomiting (7.9%), and decreased appetite (8.6%). In analyses of SAEs, SG had the highest rates of anemia (11.7%) and neutropenia (8.5%), while gastrointestinal SAEs occurred more frequently with T-DXd. Conclusions This network meta-analysis highlights clinically meaningful differences in ADC safety profiles and may help guide individualized toxicity management in breast cancer.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

Importance Breast cancer is the most common cancer diagnosed in women. Therapies for breast cancer are evolving rapidly, and several are associated with cardiovascular adverse effects, can exacerbate cardiovascular risk factors, or can increase the risk of cardiovascular disease. Observations Advances in screening and treatment have led to earlier diagnosis and improved disease-free survival. For long-term survivors of breast cancer, cardiovascular adverse effects can pose a greater threat to overall long-term health than the cancer itself. Additionally, the increasing incidence of breast cancer in younger, premenopausal women has created a distinct demographic with unique medical needs. Conclusions and Relevance Cardiology practitioners need to remain updated on rapidly evolving breast cancer therapies to care for this growing, unique patient population. The field of cardio-oncology has evolved to address the cardiovascular needs of patients and survivors.

ABSTRACT Purpose We studied histologic Ewing sarcoma (ES) tumor response at local control surgery to explore associations between histologic response and chemotherapy practice changes over time and confirm its prognostic significance in the modern era. Methods We reviewed clinical data for 126 patients with ES treated at Mayo Clinic from 1977 to 2022, analyzing the percentage necrosis as a prognostic factor across chemotherapy regimens (including modern interval‐compressed VDC‐IE). Results In the historical cohort, 5‐year event‐free survival (EFS) and overall survival (OS) for patients with ≤5% versus greater than 5% viable tumor were 76% versus 59% ( p = 0.01) and 83% versus 62% ( p = 0.01). Review of 104 specimens by two pathologists identified the percentage remaining viable tumor as more reproducible than the percentage necrosis and/or fibrosis. On multivariable analysis of patients with localized disease ( n = 87), pelvic tumors and greater than 5% viable tumor remained independent risk factors for poor survival (EFS: hazard ratio [HR] 3.1, p < 0.01; OS: HR 2.1, p = 0.048). The modern cohort consisted of 22 patients. No patients with tumor size less than 8 cm at diagnosis and 100% necrosis after surgery had events; four of seven with tumors greater than 8 cm had events despite 100% necrosis. Conclusions Our results validate the historical threshold of greater than 5% viable tumor with older chemotherapy regimens but not with modern interval‐compressed VDC‐IE. In addition, inter‐rater reliability for the percentage viable is more reproducible than the percentage necrosis. Although numbers are small, tumors less than 8 cm with no viable tumor appear to be a favorable subgroup and suggest risk stratification using a combination of tumor size and any viable tumor should be evaluated in ES.

Abstract Background Breast cancer is etiologically heterogeneous, but which risk factors differ in their associations across tumor subtypes remains unclear. We conducted a large, pooled analysis to evaluate independent, dose-response associations between breast cancer risk factors and quantitative tumor features. Methods Analyses of 15,731 invasive breast cancers from 24 studies evaluated associations (p-trend) between reproductive and hormonal factors, body mass index (BMI), alcohol, smoking, and family history in relation to quantitative immunohistochemistry measures on tissue microarrays (ER, PR, HER2, KI67, TP53) and tumor grade. Analyses in a subset of 10 population-based studies estimated subtype-specific odds ratios (ORs) comparing cases to controls. A Bayesian False Discovery Probability (BFDP) &lt;0.2 was used to identify associations with strong statistical evidence. Results Nulliparity and later age at menopause were associated with higher ER-positivity (p-trend=0.021 and 0.001, respectively), with corresponding OR[ER+] (95% CI) = 1.49 (1.16-1.90) for nulliparous vs. parous and 1.07 (1.03-1.11) per 5 years. Current combined menopausal hormone therapy (MHT) use was associated with lower grade (p-trend&lt;0.001), with OR [grade1] = 3.37 (2.69-4.21) for current vs. never users. Higher BMI was associated with lower ER-positivity and higher grade in premenopausal women (p-trend&lt;0.001 and &lt;0.001), with OR[ER+] = 0.80 (0.74-0.87) and OR[grade1] = 0.75 (0.63-0.88) per 5 units, and with higher PR-positivity and higher grade in postmenopausal women (p-trend&lt;0.001 and &lt;0.001), with OR[PR+] = 1.08 (1.03-1.14) and OR[grade 3] = 1.10 (1.04-1.17) per 5 units. Conclusion This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity. Associations with HER2, KI67 and TP53, or other risk factors did not meet our threshold for strong evidence.

BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imaging, despite the widespread availability of computed tomography (CT) attenuation correction scans. The aim of this study was to determine whether fully automated, opportunistically derived, artificial intelligence-based aortic measurements from myocardial perfusion imaging CT attenuation correction scans are associated with adverse outcomes in a large multicenter cohort. METHODS: CT attenuation correction scans from patients undergoing positron emission tomography/CT and single-photon emission CT/CT myocardial perfusion imaging across 10 centers were included. A deep learning model automatically segmented the thoracic aorta, and a postprocessing algorithm extracted maximum ascending and descending diameters. The aortic size index was calculated by indexing the diameter to body surface area. RESULTS: A total of 29 339 patients (56% men; median age, 66 years [interquartile range, 58–75 years]) were included. Over a median follow-up of 3.5 years (interquartile range, 1.9–5.0 years), 5083 (17.3%) patients died. Median ascending and descending aortic size index values were 1.8 cm/m 2 (interquartile range, 1.6–2.0) and 1.5 cm/m 2 (interquartile range, 1.4–1.6), respectively, with an increase with age and higher values in females. Elevated aortic size index thresholds (ascending >2.2 cm/m 2 ; descending >1.6 cm/m 2 ) were significantly associated with increased all-cause mortality (ascending: adjusted hazard ratio, 1.16 [95% CI, 1.07–1.26], P <0.001; descending: adjusted hazard ratio, 1.23 [95% CI, 1.14–1.31]; P <0.001). Notably, the prognostic value of an abnormal aortic size index persisted independent of age, sex, and perfusion abnormalities. CONCLUSIONS: Artificial intelligence can unlock previously unused information within routine myocardial perfusion imaging CT attenuation correction scans by rapidly and automatically quantifying aortic size at scale. Opportunistic aortic measurements derived from CT attenuation correction may serve as an adjunctive risk biomarker and could add prognostic value to standard myocardial perfusion imaging without additional imaging or radiation.

Abstract Background Patients with breast cancer undergoing chemotherapy experience multiple symptoms that cause decrements in quality of life. A deeper understanding of serum metabolites as biomarkers may guide in predicting symptom occurrence and severity. This study evaluated change in serum metabolite levels from before chemotherapy (T1) to after the initial first or second cycle of chemotherapy (T2), after the third cycle of chemotherapy (T3) and finally after a month of chemotherapy completion (T4). Change in metabolite levels were analyzed in association with symptom occurrence. Methods Patients with breast cancer (n = 10) completed study measures at four timepoints. Neuropsychological and gastrointestinal (GI) symptoms were evaluated using the Memorial Symptom Assessment Scale. Dietary intake was assessed using the NHANES Dietary Screener Questionnaire. Serum metabolites were analyzed using gas chromatography-mass spectrophotometry. Results Five patients experienced > 3 GI symptoms at T2 and T3. At all 4 timepoints, ≥ 6 patients experienced moderate to severe neuropsychological symptoms severity. Daily mean intake of fiber, whole grain, and calcium were less than the recommended guidelines. Compared to T1, long chain fatty acid and amino acid derivative levels decreased at T2 and T3 (p < 0.05). Increase in metabolite levels of glucose-6-phosphate (p = 0.02) and 6-phosphogluconate (p = 0.05) at T2 were associated with fatigue occurrence. Conclusions Patients experienced several gastrointestinal and neuropsychological symptoms during chemotherapy and early survivorship. Chemotherapy-induced changes in metabolites involved in cellular energy metabolism may explain patient symptom experience. Metabolite level changes overtime may reflect inadequate dietary intake. Larger sample size studies may guide in building metabolic prediction models for symptom burden.

Abstract Aims Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) reduce major adverse cardiovascular events in diabetics. This study aimed to investigate the cardiac, cancer, and overall mortality effects of GLP-1 RAs in diabetics treated with anthracyclines. Methods and results We retrospectively identified 1384 diabetics treated with anthracyclines from 2013 to 2023; 56 received GLP-1 RAs between their first and last dose of anthracycline. These patients were propensity score matched 4:1 to 224 controls without concurrent GLP-1 RA use. Because stage was not available for 10 of the 56 patients, a repeat analysis was conducted with additional matching for cancer stage in 46 cases and 92 controls. Major endpoints were rate of all-cause and cancer-related and cardiovascular disease (CVD)–related mortality, no evidence of disease (NED), and hospitalization for CVD including heart failure (HF), over 5 years. Survival analyses were conducted using cause-specific hazards Cox regression. Patients on GLP-1 RAs had a significantly lower 5 year overall mortality (HR 0.46, CI 0.24–0.87, P = 0.007) and cancer-related mortality (HR 0.42, CI 0.19–0.91, P = 0.03). This mortality risk reduction remained significant even when accounting for covariates (HR 0.39, CI 0.20–0.75, P = 0.002). The percentage of patients with NED was higher in the GLP-1 RA than control group (60.9% vs. 38.0%, P = 0.007). A decline in left ventricular ejection fraction was seen in controls but not in patients on GLP-1 RAs. There was no significant difference in CVD hospitalization; the rate of HF-related hospitalization was very low overall. Conclusion GLP-1 RAs may offer survival benefits and better cancer- and cardiac function-related outcomes in diabetics undergoing anthracycline chemotherapy.

ABSTRACT Background Alcohol intake is a modifiable risk factor for breast cancer, yet patterns of alcohol intake in women with breast cancer remain underexplored. This study evaluates demographic and clinical predictors of alcohol intake in patients with breast cancer. Methods Patients at Mayo Clinic Rochester diagnosed with breast cancer (Stage 0–3) between July 2014 to March 2022 were surveyed about their average alcohol intake over the 10 years prior to their diagnosis. Alcohol intake was categorized as < 1, 1–4, 5–14, and ≥ 15 drinks per week. Demographic, behavioral, and clinical data were analyzed using univariate Fisher exact tests and multivariate multinomial logistic regression. Results Of 2030 participants (mean age at diagnosis: 59 years; 99.3% female; 95.9% White), 29% consumed < 1 drink/week, 48% reported 1–4 drinks/week, 20% consumed 5–14 drinks/week, and 3% reported ≥ 15 drinks/week. Older age at diagnosis (≥ 60 years), non‐White race, financial difficulty, and never smoking were associated with less alcohol intake. Patients who reported higher levels of mild–moderate intensity exercise reported more alcohol intake. Tumor stage, tumor receptors, and BRCA mutation status were not associated with alcohol intake. PROMIS‐10 physical health scores correlated with alcohol intake in univariate but not multivariate models. Conclusion Alcohol intake is more common prior to a breast cancer diagnosis for patients who are younger, White, financially secure, exercising, and smoking. These findings may inform alcohol intake‐related breast cancer prevention efforts and highlight patient subgroups who could benefit from targeted counseling at the time of diagnosis.