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Abstract Background and Aims Truncating variants in the TTN gene (TTNtv) are the most common genetic cause of dilated cardiomyopathy (DCM) but also occur as incidental findings in the general population. This study investigated factors associated with the clinical manifestation of TTNtv. Methods An international multicentre retrospective observational study was performed in families with TTNtv-related DCM. Shared frailty models were used to estimate associations of variant characteristics with lifetime risk of DCM, and logistic regression to estimate odds ratios (ORs) for individual-level clinical risk factor profiles (cardiac conditions, cardiovascular comorbidities, lifestyle) and DCM. Results A total of 3158 subjects in 1043 families with TTNtv-related DCM were studied. TTNtv-positive subjects were 21-fold more likely to develop DCM [OR, 21.21; 95% confidence interval (CI), 14.80–30.39]. Disease onset was earlier in males, but was similar for TTNtv of different types and locations. The presence of clinical risk factors was associated with earlier DCM onset (OR, 3.41; 95% CI, 2.06–5.64), with a prior history of atrial fibrillation having a two-fold increased odds of DCM (OR, 2.05; 95% CI, 1.27–3.32). The prevalence of clinical risk factors increased with age; however, the strength of the DCM association was greatest for young-onset (<30 years) disease (OR, 4.75; 95% CI, 2.35–9.60). Administration of beta-adrenergic receptor or renin-angiotensin system-blocking drugs prior to overt DCM was associated with 87% reduced odds of DCM (OR, .13; 95% CI, .08–.23). Conclusions Disease onset in TTNtv-associated familial DCM is dependent on individual patient context and is potentially modifiable by risk factor management and prophylactic therapeutic intervention.

Importance Pathologic complete response (pCR) is increasingly used as a surrogate end point for survival in randomized clinical trials (RCTs) in the field of gastrointestinal oncology. Although this approach has been endorsed by the US Food and Drug Administration, it is still novel and incompletely verified. Objective To evaluate the trial-level association between pCR and survival in rectal cancer RCTs examining the effectiveness of neoadjuvant therapy. Data Sources A meta-analysis was conducted to identify eligible RCTs in PubMed, EMBASE, and Cochrane databases published between database inception to January 3, 2024. Study Selection RCTs evaluating neoadjuvant therapies in patients with rectal cancer who underwent subsequent surgical resection and reported pCR, overall survival (OS), and disease-free survival (DFS) were included. Data Extraction and Synthesis Two investigators extracted the data, which were verified by a third author. The risk of bias and the certainty of the evidence were evaluated using the Cochrane Risk of Bias Tool, version 2, and the Grading of Recommendations Assessment, Development, and Evaluation tool, respectively. Adjusted odds and hazard ratios with their 95% CIs were extracted. Weighted linear regression was used to assess the correlation between pCR and both OS and DFS. Main Outcomes and Measures Trial-level correlation between pCR and both OS and DFS. Results Twenty-five RCTs, including 11 882 patients, met the inclusion criteria. On meta-regression analysis, pCR was not correlated with OS (β = 0.37; 95% CI, −0.98 to 1.71; P = .57). Similarly, pCR was not correlated with DFS (β = −0.84; 95% CI, −2.55 to 0.87; P = .32). Two studies (8%) used in the meta-analysis had high risk of bias. On sensitivity analysis excluding these studies, pCR was still not associated with OS or DFS. Subgroup analysis of RCTs using total neoadjuvant therapies was not possible due to limited sample size. Conclusions and Relevance In this systematic review and meta-analysis of RCTs comparing neoadjuvant therapies in rectal cancer, there was no trial-level association between pCR and survival. These results suggest that the use of pCR as a surrogate end point for survival should be reexamined and used more cautiously.

Key Points In individuals undergoing bariatric surgery, the magnitude of decline in GFR postbariatric surgery is directly associated with presurgery GFR. Reductions in GFR postsurgery were only weakly correlated with weight loss. In obese individuals, GFR-estimating equations performed best when deindexed and when applied in people with reduced kidney function. Background Important questions remain about how bariatric ( i.e ., weight loss) surgery affects measured GFR (mGFR) and eGFR as well as what factors influence change in mGFR postsurgery. Methods Data were pooled from all seven available studies (dates: 2004–2018) measuring GFR prebariatric and postbariatric surgery using gold standard methods. Change in postsurgery mGFR, factors that could influence change in mGFR, and effects on five GFR-estimating equations were analyzed using standard statistical methods. Results The cohort included 105 individuals from the United States and Europe. Sixty-eight percent were female, 97% were White, the mean age was 50 years (range, 24–70), and the mean body mass index was 46±8 kg/m 2 . The mean presurgery mGFR of 107 ml/min (range, 31–215) fell to 92 ml/min (−14%; 95% confidence interval, −21 to −10) postsurgery, with a strong linear relationship existing between presurgery mGFR and % change in GFR ( r =−0.51; −0.64 to −0.35). Individuals with presurgery mGFR ≥90, 60 to <90, and<60 ml/min had −25 (−32 to −19), 1 (−11 to 13), and 7 (0 to 14) ml/min mean postoperative changes in mGFR. Change in weight was significantly correlated with change in mGFR ( r =0.22; 0.03 to 0.40). After adjusting for sex, changes in mGFR postsurgery were associated with higher presurgical age (−0.6 ml/min per year; −1.1 to −0.2), mGFR (−0.5 ml/min per 1 ml/min; −0.6 to −0.4), and change in systolic BP (−0.3 ml/min per 1 mm Hg; −0.6 to 0.0). All GFR-estimating equations significantly underestimated postsurgical GFR reductions in people with preserved kidney function and had improved bias, precision, and accuracy when deindexed and applied to individuals with mGFR <90 ml/min. Conclusions In individuals with obesity undergoing bariatric surgery, the magnitude of postsurgical decline in mGFR was directly associated with presurgery GFR and weakly correlated with weight loss. In addition, GFR-estimating equations' performance improved when deindexed and used in people with reduced kidney function, with the combined creatinine/cystatin C equations having the best overall performance.

Abstract Background To utilize high spatial resolution reconstructions for cardiac imaging at energy‐integrating detector CT (EID)‐CT with comparable noise to similar reconstructions at photon‐counting detector (PCD)‐CT, methods to control EID‐CT image noise are needed. Supervised convolutional neural networks (CNN) have shown promise for denoising, but a challenge remains to efficiently create high‐quality and unbiased estimates of noise without access to dedicated software or proprietary information, such that natural noise texture is retained in CNN‐denoised CT images. Purpose This study aims to develop and test image‐based noise estimation methods that can be used to train a CNN model, and to evaluate denoising performance and noise texture preservation for EID‐CT coronary CT angiography (cCTA) images reconstructed with high‐resolution kernels. Methods U‐net CNN models were trained for denoising. To supervise training, noise‐only images were estimated directly from high‐resolution kernel (Bv59) reconstructed EID‐CT (HR EID‐CT) patient images using two different methods: subtraction of low‐ and high‐strength iterative reconstruction (IR); subtraction of adjacent image slices with the same IR strength. The noise estimates from these methods contain differing noise texture and anatomical information. Networks were trained and validated separately for three data sets: the training data from each of the two noise‐estimation methods, and a 50%–50% partition of training data between the two methods. The trained models were applied to two sets of testing data: CT images of a uniform water phantom to measure noise power spectra (NPS), and an independent cohort of seven patient cCTA HR EID‐CT exams. The denoised patient images were compared to standard resolution EID‐CT reconstructions (Bv40). As a low‐noise reference, patient images acquired on the same day with a PCD‐CT and reconstructed using a similar kernel as HR EID‐CT were used for comparison. Results Models trained with each noise‐image estimation method denoised the HR EID‐CT images by 74%–79% to achieve a comparable noise magnitude to the HR PCD‐CT images. The peak, average, and 10% peak frequencies of the NPS of the input images (6.08, 6.24, and 12.0 cm −1 ) were better approximated by the model trained on adjacent slice subtraction (6.56, 5.87, and 11.5 cm −1 ) than by the model trained on subtraction of low‐ and high‐IR images (4.64, 5.44, and 11.3 cm −1 ). In cCTA images, the IR subtraction model images retained anatomic structures from input images but resulted in undesirable salt‐and‐pepper noise texture and CT number bias. The model trained on adjacent slice subtraction images had more natural texture and no significant bias, but the model sometimes removed small anatomic structures. The model trained on the mixed training dataset preserved both noise texture and anatomy from the model inputs and enabled visualization of small structures seen in PCD‐CT images that were previously unresolved by EID‐CT. Conclusions The noise texture and anatomical accuracy in CT images denoised with an image‐based supervised CNN are greatly influenced by the characteristics and partitioning of training data. With higher‐resolution reconstructions and noise texture‐preserving deep learning denoising, the quality of cCTA images from EID‐CT can be enhanced to enable resolvability of subtle anatomy similar to PCD‐CT.

ABSTRACTIntroductionProton beam therapy (PT) offers a targeted approach to treating pediatric tumors by minimizing radiation exposure to surrounding healthy tissue. However, the specific toxicity profile associated with it remains underexplored. This study aims to investigate the incidence and risk factors of acute and late treatment‐related toxicities following PT in pediatric patients with pelvic tumors.MethodsUsing data from the Pediatric Proton Consortium Registry (PPCR), we analyzed 126 pediatric patients who underwent PT for pelvic tumors. We assessed the incidence of gastrointestinal (GI), genitourinary (GU), and musculoskeletal (MSK) toxicities at baseline, during treatment, and across follow‐up intervals.ResultsAcute toxicities were observed in 61.9% of patients posttreatment, with new GI, GU, and MSK toxicities occurring in 54.3%, 9.7%, and 7.0% of baseline toxicity‐free patients, respectively. Follow‐up indicated that 20.6%, 22.2%, and 13.5% of patients developed late GI, GU, and MSK toxicities, respectively. Use of passive scattering proton therapy and radiation to the abdomen were significant predictors of GI toxicity development, while higher radiation dose was associated with MSK toxicity development.ConclusionOur findings demonstrate the safety profile of PT among pediatric patients while identifying specific risk factors, such as passive scattering proton therapy and abdominal irradiation, that contribute to increased GI toxicity. While proton therapy appears to be a well‐tolerated treatment option, the observed toxicity rates highlight the need for continued monitoring and long‐term follow‐up.

Abstract Introduction: Radiotherapy (RT)-induced acute esophagitis (AE) is a common side effect in lung cancer patients receiving RT, which can significantly impact their quality of life. This highlights the need for a predictive model that can estimate AE risk in advance using pretreatment imaging data. However, collecting sufficient data for model development is often resource-intensive and costly. Additionally, acquiring a homogeneous training dataset (e.g., from a single center or modality) is not always feasible. This study aimed to develop an automated artificial intelligence-based 3D vision foundation model (VFM) combining standard-of-care planning computed tomography (pCT) and planned radiation dose maps to predict grade II or higher AE. Materials and methods: This study included 246 patients with non-small cell lung cancer who underwent either image-guided radiation treatment (IMRT) or proton-beam radiation therapy. The endpoint was grade two or higher AE (33% positive and 67% negative). The IMRT group consisted of 182 patients from a single center, whereas the proton therapy group included 64 patients from the Proton Collaborative Group trial from 11 different institutions. For each patient, pCT, dose maps, and radiotherapy segmentations were available. The VFM was created using a transformer pretrained using a large number of unlabeled volumetric 3D CTs from patients with varied diseases through a self-supervised learning approach, which extracts useful features directly from images. Next, the pretrained encoder was combined with fully connected classification layers and fine-tuned using stratified 5-fold cross-validation with 20 patients set aside for testing. We developed two models: a CT only and CT + dose model. Model performance was assessed using the area under the curve (AUC), specificity, and sensitivity. Results: In cross-validation, adding dose information to CT (CT+Dose) improved model performance, increasing AUC from 0.72 ± 0.08 to 0.76 ± 0.10 and specificity from 0.82 ± 0.09 to 0.87 ± 0.04, while sensitivity remained the same. On the independent test set, CT+Dose showed a marked improvement over CT only (AUC: 0.82 vs. 0.59; sensitivity: 0.57 vs. 0.14), with a slight decrease in specificity (0.77 vs. 1.00). Conclusion: A VFM model combining CT and radiation dose showed the capability to predict AE more accurately with higher specificity. Further studies on larger cohorts of testing patients are planned to assess model generalization. Citation Format: Chloe Min Seo Choi, Jue Jiang, Nikhil Mankuzhy, John Chang, Jing Zeng, Carlos Vargas, James Urbanic, Isabella Choi, Mark Mcdonald, James Gray, Joseph Deasy, Maria Thor, Charles Simone, Harini Veeraraghavan. Prediction of radiotherapy-Induced esophagitis in non-small cell lung cancer using a 3D vision foundation model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B022.

ABSTRACT Objective Total laryngectomy (TL) represents a major operation often performed on medically complex patients. The impact of blood transfusion in TL patients is not well defined. Here, we utilize a national database to characterize these patients and further investigate a potential dose‐dependent relationship between transfusion and postoperative outcomes. Methods The National Surgical Quality Program (NSQIP) was queried for patients who underwent TL with/without neck dissection (CPT 31360, 31365) between 2014 and 2022. Total units of transfused blood were only available for patients between 2020 and 2023. Statistical analysis involved hypothesis testing, multivariate logistic regression, and receiver operating characteristic (ROC) analysis. Results Among 2311 patients, 413 (17.9%) received transfusions. Non‐White race, ASA Class ≥ 3, hypertension, cancer with distant metastases, bleeding disorders, and elevated BUN, WBC, and INR were associated with transfusion ( p < 0.05). Multivariate analysis identified hypertension (OR 1.81), bleeding disorders (OR 2.64), ASA ≥ 3 (OR 2.40), concurrent free flap (OR 3.75) and lower hematocrit (OR 0.85) as predictors. Transfused patients had longer hospital stays (12 vs. 9 days), increased complications (e.g., stroke, DVT), higher reoperation (23.2% vs. 13.9%) and mortality (2.7% vs. 0.8%) ( p < 0.05). ROC analysis suggested transfusion > 2.5 units was associated with adverse outcomes. Conclusion TL remains a major surgical endeavor that can be associated with lengthy and complicated hospital courses. We show that perioperative transfusion may be associated with increased hospital stay, reoperation, and mortality in TL patients, with a potential dose‐dependent effect. Our study reinforces the risks associated with blood transfusion and further supports the thoughtful utilization of blood products in TL patients. Level of Evidence 3.

ImportanceEtiologic heterogeneity in breast carcinogenesis needs to be well characterized for targeted prevention. Associations between menopausal hormonal therapy (MHT) and oral contraceptive (OC) use and breast cancer intrinsic-like subtypes are not well understood.ObjectiveTo examine whether exogenous hormone use is differentially associated with breast cancer subtypes and to evaluate heterogeneity by intrinsic-like subtypes.Design, Setting, and ParticipantsThis study pooled data from 31 nested and population-based case-control studies involved in the Breast Cancer Association Consortium. The study population included individuals with breast cancer and control participants from 13 case-control studies nested in prospective cohorts (recruited between 1982 and 2011) and 18 population-based case-control studies (recruited between 1990 and 2013). Data analysis was performed in June 2024.ExposureMHT use (estrogen-progestin therapy [EPT] or estrogen-only therapy [ET]) in postmenopausal women and OC use in premenopausal women (never, past use, or current use).Main Outcomes and MeasuresBreast cancer intrinsic-like subtypes (luminal A–like, luminal B–like, luminal B–ERBB2 [formerly HER2 or HER2/neu]-like, ERBB2 enriched–like, or triple-negative) were determined by immunohistochemistry of tumor sections. Polytomous logistic regression was performed to estimate the association between exogenous hormones and risk of breast cancer by intrinsic-like subtypes. Analyses by subtypes were stratified by body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]; healthy weight, 18.5-&amp;lt;25; overweight, 25-&amp;lt;30; or obesity, ≥30).ResultsThis study included 42 269 individuals with breast cancer (11 901 [28.2%] premenopausal and 30 368 [71.8%] postmenopausal; 23 353 [55.2%] had a known intrinsic-like subtype) and 71 072 control participants. The mean (SD) age of all participants was 57.9 (10.9) years. In postmenopausal women, associations between current MHT use (EPT or ET) and breast cancer differed by subtype. Current EPT users with healthy weight were more likely to be diagnosed with luminal A–like (odds ratio [OR], 2.51 [95% CI, 2.26-2.80]) or luminal B–ERBB2-like (OR, 1.95 [95% CI, 1.61-2.37]) subtypes. These associations were attenuated but remained for individuals with overweight (OR, 1.40 [95% CI, 1.02-1.92]) or obesity (OR, 1.68 [95% CI, 1.01-2.78]). EPT use increased the odds of being diagnosed with luminal B–like tumors solely in women with healthy weight (OR, 1.47 [95% CI, 1.17-1.86]). Current ET use was positively associated with luminal A–like disease in women with healthy weight only (OR, 1.16 [95% CI, 1.01-1.32]), showing inverse associations with higher BMI (obesity: OR, 0.65 [95% CI, 0.50-0.85]). In premenopausal women, recent OC use was associated with luminal B–ERBB2-like (OR, 1.50 [95% CI, 1.09-2.08]), ERBB2 enriched–like (OR, 2.33 [95% CI, 1.55-3.51]), and triple-negative (OR, 1.75 [95% CI, 1.33-2.29]; P &amp;lt; .04 for heterogeneity) tumors.Conclusions and RelevanceIn this study, clear differences were observed in associations between current EPT use and luminal-like breast cancer subtypes and other subtypes. EPT users with healthy weight were more likely to be diagnosed with luminal-like breast cancer compared with nonusers. Subtype heterogeneity was less apparent in associations of OC and ET use. Future studies on contemporary formulations, patterns of use, and routes of administration of exogenous hormone usage are warranted.

Abstract Aims To assess the performance of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm in identifying patients with moderate to severe aortic stenosis (AS) in an Asian cohort from a tertiary care centre. Methods and results We identified a randomly selected patients ≥60 years old who underwent echocardiography and ECG within in 31 days between 2012 and 2021 at the Samsung Medical Center in Korea. Patients with previous cardiac surgery, prosthetic valves, or pacemakers were excluded. The AI-ECG model, originally developed and validated by Mayo Clinic in the USA, was applied without fine-tuning. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were calculated to compare AI-ECG predictions with TTE-confirmed AS status. Among 5425 patients, 1095 had moderate to severe AS, and 4330 age- and sex-matched patients without AS were included as controls. The AI-ECG model achieved an AUC of 0.85 (95% CI: 0.84–0.87) in detecting moderate to severe AS. Sensitivity, specificity, PPV, NPV, and accuracy were 0.83, 0.65, 0.37, 0.94, and 68.29%, respectively. The model's performance was consistent across various age and sex subgroups, with sensitivity increasing in older patients. Conclusion The AI-ECG model developed in the USA demonstrated comparable performance in detecting moderate to severe AS in an Asian cohort compared with its original validation population. These findings highlight the potential utility of AI-ECG as a non-invasive screening tool for AS across diverse patient populations.

SLC26A9 is a Cl - channel and electrogenic Cl - -HCO 3 ‑ exchanger found in several human epithelia, including the lung, intestine / GI tract, and kidney. SLC26 proteins are defined by a C-terminal, cytoplasmic sulfate transporter / anti-σ-domain (STAS). In mammals, several STAS domains have protein-protein interactions with the regulatory-region of CFTR (R-CFTR), the Cl - channel associated with cystic fibrosis. However, the functional consequences, i.e., regulatory effects (up/downregulation), of STAS-to-R-CFTR binding seem to differ by SLC26 member. Our prior work showed that the SLC26A9:CFTR interaction decreases SLC26A9 channel and exchange activity. If SLC26A9-STAS was replaced with SLC26A6-STAS, no inhibition of SLC26A9 conductance occurred when interacting with R-CFTR (PMID 19643730). Complete deletion of SLC26A9-STAS removed Cl - conductance. Thus, we hypothesized that a disordered loop within STAS is the likely interaction area, and therefore deleted this STAS-disordered loop (ΔLoop) to test this region’s functionality. We used a voltage clamp to measure Cl - currents (IV; conductance G calculated) in Xenopus laevis oocytes (n=10 / construct) injected with cRNA of ΔLoop and subcomponent areas of SLC26A9. Each construct contained a 3xFLAG tag such that stained oocytes from these constructs allowed determination of membrane expression. ΔLoop has three subregions: K/R rich, Middle, S/T rich. Both ΔLoop and ΔLoop(K/R) increase Cl - currents (and conductance) by 5x greater comparted to wild-type SLC26A9. Five amino acid extension of ΔLoop (N- or C-terminal) produces Cl - currents equivalent to ΔLoop deletion. However, 10 amino acid extension of ΔLoop (N- or C- direction) produces currents equivalent to WT-SLC26A9. Membrane localization indicates that ΔLoop and regional deletion constructs have similar membrane abundance to WT, while boundary deletions inhibit channel trafficking to the membrane. As ΔLoop(S/T) slightly increased conductance, the ordered S/T-rich motif [T 620 SVSYITFS 628 ] was examined for possible phosphorylation effects by serial alanine substitution. Each alanine substitution in the S/T-rich motif decrease SLC26A9 Cl - conductance vs WT-SLC26A9. However, for S623A the conductance decrease was due to minimal plasma membrane expression rather than apparent change of channel activity. Together these data suggest that the disordered loop is multifaceted and can maintain low conductance (autoinhibition) or by potential phosphorylation. As cystic fibrosis (CF) results from loss-of-function, trafficking or degradation mutations, the robust Cl - conductance from elimination of SLC26A9 autoinhibitory, disordered loop, presents an opportunity to supplement or replace Cl - conductance in CF tissues and potentially patients. R25-DK101405 This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.