Predicting Age from White Matter Diffusivity with Residual Learning.

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites.

Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.

Metabolic reprogramming by mutant GNAS creates an actionable dependency in intraductal papillary mucinous neoplasms of the pancreas.

BACKGROUND: Oncogenic 'hotspot' mutations of KRAS and GNAS are two major driver alterations in intraductal papillary mucinous neoplasms (IPMNs), which are bona fide precursors to pancreatic ductal adenocarcinoma. We previously reported that pancreas-specific Kras G12D and Gnas R201C co-expression in p48Cre; KrasLSL-G12D; Rosa26LSL-rtTA; Tg (TetO-GnasR201C) mice ('Kras;Gnas' mice) caused development of cystic lesions recapitulating IPMNs. OBJECTIVE: We aim to unveil the consequences of mutant Gnas R201C expression on phenotype, transcriptomic profile and genomic dependencies. DESIGN: We performed multimodal transcriptional profiling (bulk RNA sequencing, single-cell RNA sequencing and spatial transcriptomics) in the 'Kras;Gnas' autochthonous model and tumour-derived cell lines (Kras;Gnas cells), where Gnas R201C expression is inducible. A genome-wide CRISPR/Cas9 screen was conducted to identify potential vulnerabilities in KrasG12D;GnasR201C co-expressing cells. RESULTS: Induction of Gnas R201C-and resulting G(s)alpha signalling-leads to the emergence of a gene signature of gastric (pyloric type) metaplasia in pancreatic neoplastic epithelial cells. CRISPR screening identified the synthetic essentiality of glycolysis-related genes Gpi1 and Slc2a1 in Kras G12D;Gnas R201C co-expressing cells. Real-time metabolic analyses in Kras;Gnas cells and autochthonous Kras;Gnas model confirmed enhanced glycolysis on Gnas R201C induction. Induction of Gnas R201C made Kras G12D expressing cells more dependent on glycolysis for their survival. Protein kinase A-dependent phosphorylation of the glycolytic intermediate enzyme 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) was a driver of increased glycolysis on Gnas R201C induction. CONCLUSION: Multiple orthogonal approaches demonstrate that Kras G12D and Gnas R201C co-expression results in a gene signature of gastric pyloric metaplasia and glycolytic dependency during IPMN pathogenesis. The observed metabolic reprogramming may provide a potential target for therapeutics and interception of IPMNs.

Total Venous Control and Vein-to-the-Right Superior Mesenteric Artery Approach in Robotic Pancreatoduodenectomy.

BACKGROUND: Robotic pancreatoduodenectomy is an increasingly accepted alternative for the treatment of pancreatic ductal adenocarcinoma (PDAC).1 However, the ability to perform a meticulous robotic-assisted superior mesenteric artery (SMA) dissection to obtain a margin-negative resection remains unknown.2 PDAC within the head of the pancreas (HOP) that involves the superior mesenteric vein (SMV) and portal vein (PV) requires total venous control (TVC) and a 'vein-to-the-right' (or anterior artery-first) approach to SMA dissection to minimize venous congestion and operative blood loss.3-5 Here, we demonstrate a robotic pancreatoduodenectomy with TVC and a 'vein-to-the-right' approach. METHODS: A 70-year-old woman with cT2N0M0 HOP PDAC with lateral SMV involvement and right gastroepiploic vein occlusion underwent robotic pancreatoduodenectomy after neoadjuvant chemotherapy. After transecting the pancreas, we achieved TVC by dividing the small venous tributaries and encircling the SMV, splenic vein, and PV. We then proceeded with a 'vein-to-the-right' approach. The inferior pancreatoduodenal arteries were divided to minimize HOP inflow and decrease specimen bleeding. Once the specimen was dissected off the periadventitial plane of the distal SMA, the SMV dissection was carefully performed using a partial side-wall vein resection using a vascular stapler. RESULTS: Total operative time was 7.5 h and estimated blood loss was 25 mL. The patient recovered well postoperatively and was discharged on postoperative day 3. Final pathology exhibited a 2.4 cm, moderately to poorly differentiated adenocarcinoma with negative margins (ypT2N1, 2/38 lymph nodes positive). CONCLUSION: For tumors with lateral vein involvement, robotic pancreatoduodenectomy can be safely performed via TVC and a 'vein-to-the-right' approach.

Are paediatric cardiology textbooks obsolete in the current digital era?

INTRODUCTION: With the rise of online references, podcasts, webinars, self-test tools, and social media, it is worthwhile to understand whether textbooks continue to provide value in medical education, and to assess the capacity they serve during fellowship training. METHODS: A prospective mixed-methods study based on surveys that were disseminated to seven paediatric cardiology fellowship programmes around the world. Participants were asked to read an assigned chapter of Anderson's Pediatric Cardiology 4th Edition textbook, followed by the completion of the survey. Open-ended questions included theming and grouping responses as appropriate. RESULTS: The survey was completed by 36 participants. When asked about the content, organisation, and utility of the chapter, responses were generally positive, at greater than 89%. The chapters, overall, were rated relatively easy to read, scoring at 6.91, with standard deviations plus or minus 1.72, on a scale from 1 to 10, with higher values meaning better results. When asked to rank their preferences in where they obtain educational content, textbooks were ranked the second highest, with in-person teaching ranking first. Several themes were identified including the limitations of the use of textbook use, their value, and ways to enhance learning from their reading. There was also a near-unanimous desire for more time to self-learn and read during fellowship. CONCLUSIONS: Textbooks are still highly valued by trainees. Many opportunities exist, nonetheless, to improve how they can be organised to deliver information optimally. Future efforts should look towards making them more accessible, and to include more resources for asynchronous learning.

Identification of functional white matter networks in BOLD fMRI.

White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well-defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.

Impact of Adjuvant Chemotherapy on Resected Intraductal Papillary Mucinous Neoplasm-Derived Pancreatic Cancer: Results From an International Multicenter Study.

PURPOSE: The benefit of adjuvant therapy for intraductal papillary mucinous neoplasm (IPMN)-derived pancreatic ductal adenocarcinoma (PDAC) remains unclear because of severely limited evidence. Although biologically distinct entities, adjuvant therapy practices for IPMN-derived PDAC are largely founded on pancreatic intraepithelial neoplasia-derived PDAC. We aimed to evaluate the role of adjuvant chemotherapy in IPMN-derived PDAC. METHODS: This international multicenter retrospective cohort study (2005-2018) was conceived at the Verona Evidence-Based Medicine meeting. Cox regressions were performed to identify risk-adjusted hazard ratios (HR) associated with overall survival (OS). Kaplan-Meier curves and log-rank tests were employed for survival analysis. Logistic regression was performed to identify factors motivating adjuvant chemotherapy administration. A decision tree was proposed and categorized patients into overtreated, undertreated, and optimally treated cohorts. RESULTS: In 1,031 patients from 16 centers, nodal disease (HR, 2.88, P < .001) and elevated (≥37 to <200 µ/mL, HR, 1.44, P = .006) or markedly elevated (≥200 µ/mL, HR, 2.53, P < .001) carbohydrate antigen 19-9 (CA19-9) were associated with worse OS. Node-positive patients with elevated CA19-9 had an associated 34.4-month improvement in median OS (P = .047) after adjuvant chemotherapy while those with positive nodes and markedly elevated CA19-9 had an associated 12.6-month survival benefit (P < .001). Node-negative patients, regardless of CA19-9, did not have an associated benefit from adjuvant chemotherapy (all P > .05). Based on this model, we observed undertreatment in 18.1% and overtreatment in 61.2% of patients. Factors associated with chemotherapy administration included younger age, R1-margin, poorer differentiation, and nodal disease. CONCLUSION: Almost half of patients with resected IPMN-derived PDAC may be overtreated or undertreated. In patients with node-negative disease or normal CA19-9, adjuvant chemotherapy is not associated with a survival benefit, whereas those with node-positive disease and elevated CA19-9 have an associated benefit from adjuvant chemotherapy. A decision tree was proposed. Randomized controlled trials are needed for validation.

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI.

Diffusion MRI (dMRI) streamline tractography, the gold-standard for in vivo estimation of white matter (WM) pathways in the brain, has long been considered as a product of WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn to propagate streamlines directly from T1 and anatomical context. Training for this network has previously relied on high resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical scans. We define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. We show that under this metric T1 tractography generated by CoRNN reproduces diffusion tractography with approximately three millimeters of error.

Characterizing Low-cost Registration for Photographic Images to Computed Tomography.

Mapping information from photographic images to volumetric medical imaging scans is essential for linking spaces with physical environments, such as in image-guided surgery. Current methods of accurate photographic image to computed tomography (CT) image mapping can be computationally intensive and/or require specialized hardware. For general purpose 3-D mapping of bulk specimens in histological processing, a cost-effective solution is necessary. Here, we compare the integration of a commercial 3-D camera and cell phone imaging with a surface registration pipeline. Using surgical implants and chuck-eye steak as phantom tests, we obtain 3-D CT reconstruction and sets of photographic images from two sources: Canfield Imaging's H1 camera and an iPhone 14 Pro. We perform surface reconstruction from the photographic images using commercial tools and open-source code for Neural Radiance Fields (NeRF) respectively. We complete surface registration of the reconstructed surfaces with the iterative closest point (ICP) method. Manually placed landmarks were identified at three locations on each of the surfaces. Registration of the Canfield surfaces for three objects yields landmark distance errors of 1.747, 3.932, and 1.692 mm, while registration of the respective iPhone camera surfaces yields errors of 1.222, 2.061, and 5.155 mm. Photographic imaging of an organ sample prior to tissue sectioning provides a low-cost alternative to establish correspondence between histological samples and 3-D anatomical samples.

Effect of Complete Transurethral Resection on Oncologic Outcomes After Radiation Therapy for Muscle-Invasive Bladder Cancer.

PURPOSE: To compare the oncologic outcomes of patients with nonmetastatic muscle-invasive bladder cancer (MIBC) undergoing complete versus incomplete transurethral tumor resection (TURBT) before radiation therapy. METHODS AND MATERIALS: Patients with nonmetastatic MIBC who underwent curative-intent radiation therapy between 2002 and 2018 at 10 Canadian institutions were retrospectively evaluated. Inverse probability of treatment weighting was performed using baseline characteristics. Differences in survival outcomes by complete and incomplete TURBT were analyzed. RESULTS: Of the 757 patients included, 66% (498) had documentation of a complete and 34% (259) an incomplete TURBT. Before adjustment, 121 (47%) and 45 (9%) patients who underwent incomplete and complete TURBT, respectively, were diagnosed with cT3-4 tumor (P <.001). After weight-adjustment, all baseline cohort characteristics were balanced (absolute standardized differences < 0.1). The adjusted median follow-up was 27 months. Adjusted survival analyses showed no significant difference in 5-year overall survival (48% vs 52%, 1.03 [0.82-1.29]; P = .8), cancer-specific survival (64% vs 61%, 0.93 [0.70-1.25]; P = .7), metastasis-free survival (43% vs 46%, 0.97 [0.79-1.19]; P = .8), and disease-free survival (32% vs 35%, 0.95 [0.79-1.15]; P = .7) between the 2 groups. CONCLUSIONS: Complete TURBT may be associated with clinical organ-confined disease. Extent of TURBT was not independently associated with oncologic outcomes in patients with MIBC treated with radiation therapy.

Characterizing patterns of diffusion tensor imaging variance in aging brains.

Purpose: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here, we characterize the role of physiology, subject compliance, and the interaction of the subject with the scanner in the understanding of DTI variability, as modeled in the spatial variance of derived metrics in homogeneous regions. Approach: We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging, with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess the variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results: Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related ( p ≪ 0.001 ) to FA variance in the cuneus and occipital gyrus, but negatively ( p ≪ 0.001 ) in the caudate nucleus. Males show significantly ( p ≪ 0.001 ) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated ( p < 0.05 ) with a decrease in FA variance. Head motion increases during the rescan of DTI ( Δ µ = 0.045 mm per volume). Conclusions: The effects of each covariate on DTI variance and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

Kinetics of Renin Concentrations in Infants Undergoing Congenital Cardiac Surgery.

BACKGROUND: Elevated renin has been shown to predict poor response to standard vasoactive therapies and is associated with poor outcomes in adults. Similarly, elevated renin was associated with mortality in children with septic shock. Renin concentration profiles after pediatric cardiac surgery are unknown. The purpose of this study was to characterize renin kinetics after pediatric cardiac surgery. METHODS: Single-center retrospective study of infants who underwent cardiac surgery with cardiopulmonary bypass (CPB) utilizing serum samples obtained in the perioperative period to measure plasma renin concentrations (pg/mL). Time points included pre-bypass and 1, 4, and 24 h after initiation of CPB. RESULTS: Fifty patients (65% male) with a median age 5 months (interquartile range (IQR) 3.5, 6.5) were included. Renin concentrations peaked 4 h after CPB. There was a significant difference in preoperative and 4 h post-CPB renin concentration (4 h post-CPB vs preoperative: mean difference 100.6, 95% confidence interval (CI) 48.9-152.4, P < .001). Median renin concentration at 24 h after CPB was lower than the preoperative baseline. CONCLUSIONS: We describe renin kinetics in infants after CPB. Future studies based on these data can now be performed to evaluate the associations of elevated renin concentrations with adverse outcomes.

Harmonized connectome resampling for variance in voxel sizes.

To date, there has been no comprehensive study characterizing the effect of diffusion-weighted magnetic resonance imaging voxel resolution on the resulting connectome for high resolution subject data. Similarity in results improved with higher resolution, even after initial down-sampling. To ensure robust tractography and connectomes, resample data to 1 mm isotropic resolution.

Field-of-view extension for brain diffusion MRI via deep generative models.

Purpose: In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV. Results: For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achieved PSNR b 0 = 22.397 , SSIM b 0 = 0.905 , PSNR b 1300 = 22.479 , and SSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved PSNR b 0 = 21.304 , SSIM b 0 = 0.892 , PSNR b 1300 = 21.599 , and SSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) on both the WRAP and NACC datasets. Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.

Mechanisms of resistance to oncogenic KRAS inhibition in pancreatic cancer.

KRAS inhibitors demonstrate clinical efficacy in pancreatic ductal adenocarcinoma (PDAC); however, resistance is common. Among patients with KRASG12C-mutant PDAC treated with adagrasib or sotorasib, mutations in PIK3CA and KRAS, and amplifications of KRASG12C, MYC, MET, EGFR, and CDK6 emerged at acquired resistance. In PDAC cell lines and organoid models treated with the KRASG12D inhibitor MRTX1133, epithelial-to-mesenchymal transition and PI3K-AKT-mTOR signaling associate with resistance to therapy. MRTX1133 treatment of the KrasLSL-G12D/+;Trp53LSL-R172H/+;p48-Cre (KPC) mouse model yielded deep tumor regressions, but drug resistance ultimately emerged, accompanied by amplifications of Kras, Yap1, Myc, and Cdk6/Abcb1a/b, and co-evolution of drug-resistant transcriptional programs. Moreover, in KPC and PDX models, mesenchymal and basal-like cell states displayed increased response to KRAS inhibition compared to the classical state. Combination treatment with KRASG12D inhibition and chemotherapy significantly improved tumor control in PDAC mouse models. Collectively, these data elucidate co-evolving resistance mechanisms to KRAS inhibition and support multiple combination therapy strategies.

Effect of surgical approach on early return to intended oncologic therapy after resection for pancreatic ductal adenocarcinoma.

BACKGROUND: Although robotic pancreatectomy may facilitate an earlier functional recovery, the impact of a robotic pancreatectomy program during its early experience on the timing of return to intended oncologic therapy (RIOT) after surgery is unknown. METHODS: In this retrospective cohort study, we used propensity score matching with a 1:2 ratio to compare patients who underwent robotic or open surgery (distal pancreatectomy or pancreatoduodenectomy) for pancreatic ductal adenocarcinoma (PDAC) during the first 3 years of our robotic pancreatectomy experience (January 2018-December 2021). Generalized estimating equations modeling was used to evaluate the effect of surgical approach on early RIOT, defined as adjuvant chemotherapy initiation within 8 weeks after surgery, and late RIOT, defined as initiation within 12 weeks after surgery. RESULTS: The matched cohort included 26 patients who underwent robotic pancreatectomy and 52 patients who underwent open pancreatectomy. Rates of receipt of adjuvant chemotherapy were 96.2% and 78.9%, respectively. Rate of early RIOT in the robotic group (73.1% was higher than that in the open group (44.2%; P = 0.018). In multivariable analysis, a robotic approach was associated with early RIOT (odds ratio, 3.54; 95% confidence interval 1.08-11.62; P = 0.038). Surgical approach did not impact late RIOT (odds ratio, 3.21; 95% confidence interval 0.71-14.38; P = 0.128). CONCLUSIONS: Compared with open pancreatectomy, robotic pancreatectomy did not delay RIOT. In fact, odds of early RIOT were increased, which supports the oncological safety of our robotic pancreatectomy program during its implementation.

The Impact of Histologic Subtypes on Clinical Outcomes After Radiation-Based Therapy for Muscle-Invasive Bladder Cancer.

PURPOSE: Outcomes of radiation-based therapy (RT) for muscle-invasive bladder cancer (MIBC) with histologic subtypes of urothelial cancer (HS-UC) are lacking. Our objective was to compare survival outcomes of pure urothelial carcinoma (PUC) to HS-UC after RT. MATERIALS AND METHODS: A multicenter retrospective study of 864 patients with MIBC who underwent curative-intent RT to the bladder for MIBC (clinical T2-T4aN0-2M0) between 2001 and 2018 was conducted. Regression models were used to test the association between HS-UC and complete response (CR) and survival outcomes after RT. RESULTS: In total, 122 patients (14%) had HS-UC. Seventy-five (61%) had HS-UC with squamous and/or glandular differentiation. A CR was confirmed in 69% of patients with PUC and 63% with HS-UC. There were 207 (28%) and 31 (25%) patients who died of metastatic bladder cancer in the PUC and HS-UC groups, respectively. There were 361 (49%) and 58 (48%) patients who died of any cause in the PUC and HS-UC groups, respectively. Survival outcomes were not statistically different between the groups. The HS-UC status was not associated with survival outcomes in multivariable Cox regression analyses. CONCLUSIONS: In our study, HS-UC responded to RT with no significant difference in CR and survival outcomes compared to PUC. The presence of HS-UC in MIBC does not seem to confer resistance to RT, and patients should not be withheld from bladder preservation therapy options. Due to low numbers, definitive conclusions cannot be drawn for particular histologic subtypes.

New-onset diabetes is a predictive risk factor for pancreatic lesions in high-risk individuals: An observational cohort study.

Background and Objectives: Pancreatic cancer (PC) is the third cause of cancer-related deaths. Early detection and interception of premalignant pancreatic lesions represent a promising strategy to improve outcomes. We evaluated risk factors of focal pancreatic lesions (FPLs) in asymptomatic individuals at hereditary high risk for PC. Methods: This is an observational single-institution cohort study conducted over a period of 5 years. Surveillance was performed through imaging studies (EUS or magnetic resonance imaging/magnetic resonance cholangiopancreatography) and serum biomarkers. We collected demographic characteristics and used univariate and multivariate logistic regression models to evaluate associations between potential risk factors and odd ratios (ORs) for FPL development. Results: A total of 205 patients completed baseline screening. Patients were followed up to 53 months. We detected FPL in 37 patients (18%) at baseline; 2 patients had lesions progression during follow-up period, 1 of them to PC. Furthermore, 13 patients developed new FPLs during the follow-up period. Univariate and multivariate analyses revealed that new-onset diabetes (NOD) is strongly associated with the presence of FPL (OR, 10.94 [95% confidence interval, 3.01-51.79; P < 0.001]; OR, 9.98 [95% confidence interval, 2.15-46.33; P = 0.003]). Follow-up data analysis revealed that NOD is also predictive of lesions progression or development of new lesions during screening (26.7% vs. 2.6%; P = 0.005). Conclusions: In a PC high-risk cohort, NOD is significantly associated with presence of FPL at baseline and predictive of lesions progression or new lesions during surveillance.

Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score.

BACKGROUND AND OBJECTIVE: To develop the COVid Veteran (COVet) score for clinical deterioration in Veterans hospitalized with COVID-19 and further validate this model in both Veteran and non-Veteran samples. No such score has been derived and validated while incorporating a Veteran sample. DERIVATION COHORT: Adults (age ≥ 18 yr) hospitalized outside the ICU with a diagnosis of COVID-19 for model development to the Veterans Health Administration (VHA) (n = 80 hospitals). VALIDATION COHORT: External validation occurred in a VHA cohort of 34 hospitals, as well as six non-Veteran health systems for further external validation (n = 21 hospitals) between 2020 and 2023. PREDICTION MODEL: eXtreme Gradient Boosting machine learning methods were used, and performance was assessed using the area under the receiver operating characteristic curve and compared with the National Early Warning Score (NEWS). The primary outcome was transfer to the ICU or death within 24 hours of each new variable observation. Model predictor variables included demographics, vital signs, structured flowsheet data, and laboratory values. RESULTS: A total of 96,908 admissions occurred during the study period, of which 59,897 were in the Veteran sample and 37,011 were in the non-Veteran sample. During external validation in the Veteran sample, the model demonstrated excellent discrimination, with an area under the receiver operating characteristic curve of 0.88. This was significantly higher than NEWS (0.79; p < 0.01). In the non-Veteran sample, the model also demonstrated excellent discrimination (0.86 vs. 0.79 for NEWS; p < 0.01). The top three variables of importance were eosinophil percentage, mean oxygen saturation in the prior 24-hour period, and worst mental status in the prior 24-hour period. CONCLUSIONS: We used machine learning methods to develop and validate a highly accurate early warning score in both Veterans and non-Veterans hospitalized with COVID-19. The model could lead to earlier identification and therapy, which may improve outcomes.