Citations

Abstract Aims Rheumatic heart disease (RHD) remains the most prevalent acquired cardiovascular disease among young adults in developing countries, and there is a critical need for an efficient, affordable, and available screening strategy to detect RHD in these resource-limited areas. The 12-lead electrocardiogram (ECG) is a ubiquitous, inexpensive diagnostic tool, and application of artificial intelligence to the ECG (AI-ECG) has allowed for screening of various cardiovascular pathologies. Methods and results Patients aged >18 years with severe RHD with at least one transthoracic echocardiogram and one standard 12-lead ECG performed at Groote Schuur Hospital, Cape Town, within 180 days of each other were eligible. A total of 920 RHD patients were identified, with 2199 corresponding controls from the same medical centre. Patients were allocated to derivation (80%), validation (10%), and testing (10%) groups, respectively. The AI-ECG to detect RHD from a standard 12-lead ECG was built using the Keras framework under Tensorflow backend. In the test group, the AI-ECG for RHD exhibited strong algorithm performance with an AUC of 0.890 (95% Confidence Interval 0.848–0.940) and an accuracy of 83.8%. The algorithm remained robust between RHD presentation: mitral valve alone (AUC 0.913) and mixed mitral and aortic valve disease (AUC 0.858). There were minor performance differences in the subgroup analysis of patient sex, age, and ECG characteristics. Conclusion In this study, an AI-ECG algorithm was successfully developed to screen for advanced RHD from a standard 12-lead ECG, and the AI-ECG performance remained acceptable across most subgroups, including RHD manifestation, patient sex, age <70 years, and ECG characteristics.

ABSTRACT Objective To (1) validate GAD65‐ELISA detection and quantification for type 1 diabetes mellitus and autoimmune neurological diagnoses, (2) correlate ELISA results (reference range < 5 IU/mL) with established radioimmunoprecipitation assay (RIA; ≤ 0.02 nmol/L), and (3) define ELISA clinical utility and pitfalls. Methods Serum performance for diabetes (FDA‐cleared, undiluted) was verified, and neurological laboratory‐developed serum and CSF dilution protocols were validated to extend the reportable range beyond > 250 IU/mL. ELISA and RIA values were correlated, including established neurological RIA cut‐offs (serum ≥ 20 nmol/L; CSF any positive). Results ELISA met analytical criteria (precision, accuracy, sensitivity, specificity, reference range) in serum and CSF and was clinically equivalent to RIA for autoimmune diabetes. Neurological ELISA cut‐offs were established at 10,000 IU/mL (serum) and 100 IU/mL (CSF). Precision was better below 10,000 IU/mL (CVs < 20%) than above (CVs 30.8% serum; 26.5% CSF). ELISA–RIA correlation and agreement was stronger below the neurological cut‐off (R 2 = 0.89) than above (R 2 = 0.36). Positive agreement for RIA‐defined neurological disease was 100% in serum and CSF; all serums were > 10,000 IU/mL. Clinical specificity was 97.5% in serum and 100% in CSF, exceeding reported RIA specificity. Screen results > 250 IU/mL spanned a wide range of dilution values; many were below the neurological cut‐off. Most patients with paired serum/CSF positivity showed elevated GAD65 IgG indices. Interpretation GAD65 ELISA and RIA have equivalent sensitivity for autoimmune diabetes and neurological testing, with higher specificity for ELISA. A serum cut‐off of 10,000 IU/mL is informative but requires clinical context, and dilution of screen > 250 IU/mL samples is essential for neurological interpretation.

Abstract Purpose: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded Early Detection Research Network–defined phase II biomarker bakeoff study of blood-based biomarker panels. The aims were to evaluate panel performance, to compare the panels’ performance with that of cancer antigen 19-9 (CA19-9) alone, and to evaluate the performance of new combinations of the individual biomarkers. Experimental Design: Ten biomarkers representing eight biomarker panels and CA19-9 were evaluated using plasma, serum, and germline DNA from 140 stage I to IV pancreatic ductal adenocarcinoma (PDAC) cases and 140 controls from three tertiary care institutions, with controls frequency matched to cases on age and sex. LASSO regression was employed to explore new biomarker combinations. The primary metric was area under the receiver operating characteristic curve (AUC). Results: The study population was 51% female, with median age 67.3 (minimum: 45, maximum: 90) years. Biomarker panel AUCs ranged from 89.9 to 96.3; the AUC for serum CA19-9 alone was 91.7 [95% confidence interval (CI), 87.8–95.6]. Two panels had significantly higher AUCs than serum CA19-9 alone, the CA19-9/FUT2/3 panel (AUC = 96.3, P = 0.002), and the tissue factor pathway inhibitor/tenascin C (TFPI/TNC-FNIII-C) panel (AUC = 95, P = 0.01). Exploratory models to recombine biomarkers retained all but two biomarkers [optimism-corrected AUC = 96.4 (94–98.9)]. Conclusions: The CA19-9/FUT2/3 panel was the best performing panel in this biomarker bakeoff. Its evaluation in larger studies is warranted. Biomarker bakeoffs are an effective strategy for comparing the performance of promising biomarkers for pancreatic cancer early detection, and the PCDC is well poised to conduct such studies. Recommendations for performing such studies are provided.

Abstract Background S0221 investigated weekly vs every 2 weeks dosing of doxorubicin (A) and cyclophosphamide (C) followed by paclitaxel in patients with high-risk early breast cancer. After an interim analysis, random assignment to the 2 AC arms was stopped for futility, and the trial was modified to study only the paclitaxel schedules. Methods Between December 2003 and November 2010, a total of 2716 patients were randomly assigned in a 2 × 2 factorial design to 15 weeks of weekly A and daily C vs 6 cycles of every 2 weeks AC; and weekly paclitaxel for 12 weeks vs 6 cycles of every 2 weeks paclitaxel. Between January 2011 and January 2012, an additional 578 patients were assigned to 4 cycles of every 2 weeks AC and randomly assigned to weekly vs every 2 weeks paclitaxel. Updated survival was assessed using log-rank tests and Cox regression models. We compared outcomes by breast cancer subtype as well. Results At a median follow-up of 12.1 years, there were no statistically significant differences among the 4 treatment arms in disease-free survival (DFS) (P = .91) or overall survival (P = .34) in the original protocol. Among the 578 patients assigned AC for 4 cycles and randomly assigned to paclitaxel weekly vs every 2 weeks paclitaxel, there were no overall differences in DFS (P = .32) or overall survival (P = .42). Conclusion As there were no statistically significant outcome differences in DFS or overall survival between the studied schedules of AC and paclitaxel with extended follow-up in the original or revised protocol, either paclitaxel schedule may be recommended, with selection based on toxicity, cost, or patient preference.

ABSTRACT Introduction Sinonasal sarcomas are exceedingly rare entities, constituting less than 7% of head and neck sarcomas. Their complex histology needs specialized treatment, which is often based on multimodal approaches including surgery, radiation therapy, and/or chemotherapy. This manuscript aims to gather expert opinions to establish common management principles for sinonasal sarcomas. Methods This international consensus followed a modified Delphi method in seven steps, including statements definition by the core group, expert panel recruitment, and a two‐round survey. Sixty‐two statements on sinonasal sarcoma management were developed. Experts from multiple continents participated, and results were anonymized and analyzed between March and May 2025. Results A total of 44 invited experts were recruited, 43.2% otorhinolaryngologists/head and neck surgeons, 31.8% medical oncologists, and 25% radiation oncologists. Participants varied in age and experience, representing Europe (70.5%), North America (18.2%), South America (6.8%), and Asia (4.5%). Among all histologies, biphenotypic sarcoma, chondrosarcoma, leiomyosarcoma, and myofibrosarcoma are principally treated with an upfront surgical management, differently from Ewing sarcoma and rhabdomyosarcoma in which chemotherapy, eventually associated with radiotherapy, is often chosen. In the remaining histologies (angiosarcoma, liposarcoma, malignant peripheral nerve sheath tumor [MPNST], osteosarcoma, and synovial sarcoma), a precise multimodal treatment is less standardized and needs to be discussed on a case‐by‐case basis. Conclusion Sinonasal sarcomas require a histology‐driven approach to determine upfront treatment, whether surgical, medical, or multimodal. Despite this structured strategy, prognosis remains highly variable across subtypes. Multidisciplinary evaluation and individualized management in referral centers are crucial to address the biological diversity and anatomical complexity of these rare malignancies.

Abstract Objective: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded EDRN defined phase 2 Biomarker Bakeoff study of eight promising biomarker panels using pancreatic cancer ductal adenocarcinoma (PDAC) case and general population healthy control samples from three institutions. The scientific questions were to determine whether panels could discriminate PDAC cases from controls with Receiver Operating Characteristic Area Under the Curve (AUC) &gt; 0.8, compare panel performance head-to-head via 95% confidence intervals (CI), and determine whether the panel improved upon serum CA19-9 alone. A secondary goal was to perform modeling to discover a new panel consisting of individual biomarkers. Methods: Eligible biomarkers were required to have had a prior EDRN defined phase 2 validation study published and be ready for validation in a sample set from multiple institutions. Three institutions submitted a total of 140 cases and 140 controls frequency matched to cases on age and sex. Four consortium member institutions submitted for validation eight panels consisting of ten plasma, serum and DNA genotyping biomarkers in addition to CA19-9. Locked panel formulas were submitted prior to specimen distribution. The primary metric was AUC with bootstrapped 95% CIs. LASSO penalized regression was used for exploratory panel building. Nested cross validation and internal-external validation were performed, and optimism-corrected performance estimates were computed. Results: The overall sample (N=280) was 51% female, median age of 67 years, and 94% were of non-Hispanic white race and ethnicity. Controls had median BMI 27.3, 56% never smokers, and 12% type II diabetes mellitus (DM2); cases (staged I to IV) had median BMI 26.6, 42% never smokers, and 26% DM2. Overall panel AUC estimates ranged from 89.9 to 96.3 and all were significantly larger than the null hypothesis value of 0.8 (p&lt;0.05). The apparent serum CA19-9 AUC was 91.7 (87.8, 95.6). Two panels were significantly different from serum CA19-9, a panel with FUT2/3 genotype with serum CA19-9 (p=0.002) and a panel containing tissue factor pathway inhibitor (TFPI), tenascin C (TNC), and plasma CA19-9 (p=0.01). In subset analyses by PDAC stage, no panel improved upon the null hypothesis value of 0.8 in stage I (p&gt;0.05), all panels improved upon the null hypothesis value of 0.8 in stage II and III (p&lt;0.05), and only serum CA19-9 did not improve upon the null hypothesis value of 0.8 in stage IV. All biomarkers but two microRNAs were retained in a LASSO model that had optimism corrected AUC=96.4 (95% CI: 94.0, 98.9). Conclusions: All submitted panels performed well overall in this blinded study, with the panel containing FUT2/3 genotype performing the best. Panel discovery improved upon serum CA19-9, though optimism corrected AUC 95% CIs overlap with those of apparent CA19-9 performance. Further work is needed to prioritize promising biomarkers and to improve detection of early stage PDAC. Citation Format: Ann L. Oberg, William R. Bamlet, Grant Izmirlian, Seetharaman Balasenthil, Surinder K. Batra, Masataka Hayashi, Daniel Herman, Michael A. Hollingsworth, Maneesh Jain, Ann M. Killary, Brianna M. Krusen, Suyu Liu, Gopalakrishnan Natarajan, Subrata Sen, Lynette M. Smith, Sudhir Srivastava, Brian M. Wolpin, Kenneth S. Zaret, Michael G. Goggins. Pancreatic Cancer Detection Consortium biomarker bake-off: A consortium Phase 2 blinded biomarker validation and panel discovery study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B070.

ABSTRACT Objective The optimal treatment for neurosarcoidosis myelitis is uncertain. We characterize incident neurosarcoidosis myelitis and assess treatment response by MRI and clinical scales. Methods Incident probable or definite neurosarcoidosis myelitis in adults was retrospectively identified from 13 academic medical centers. Cases were analyzed by initial treatment. The primary outcome was T1 post‐contrast gadolinium enhancement resolution at 6 months post‐treatment. Secondary outcomes were changes in modified Rankin scale (mRS) and Expanded Disability Status Scale (EDSS) from nadir to final follow‐up. Results Two hundred two patients were identified (median diagnosis age: 47 years (IQR 39–55); male: female 1.3:1). Median nadir mRS and EDSS were 2 (IQR 2–3) and 4 (IQR 2.5–6). At initial treatment, 129 (63.9%) received prolonged corticosteroids ≥ 4 weeks (group A 1 ), 36 (17.8%) received corticosteroids < 4 weeks (B 1 ), 21 (10.4%) received corticosteroids plus sarcoidosis‐directed immunosuppressant (E), and 16 (7.9%) received corticosteroids plus non‐sarcoidosis‐directed agents (F). In 167 cases with sufficient imaging, there were no significant differences in contrast enhancement resolution at 6 months (A 1 27/106 (25.5%), B 1 9/28 (32.1%), E 5/19 (26.3%), F 5/14 (35.7%); Fisher's exact p = 0.76). There were no significant differences in changes in mRS or EDSS among treatment groups (Kruskal–Wallis p = 0.69 and 0.63, respectively) after median follow‐up of 46.5 months (IQR 18–91.3). Interpretation Different initial immunosuppression strategies did not correlate with MRI contrast enhancement resolution at 6 months or clinical scales (mRS, EDSS). However, conclusions are limited by retrospective design, imbalanced cohorts, and insensitivity of binary MRI outcomes and available clinical scales for treatment response in neurosarcoidosis.

Artificial intelligence (AI) is reshaping neurosurgery, offering unprecedented opportunities to enhance diagnostics, personalize treatment, and predict outcomes. At the heart of this transformation is the ability to effectively harness big data (BD) within the electronic medical record. Understanding these data structures is essential for making sense of the vast volumes of information generated in modern neurosurgical practice. Equally important are the machine learning (ML) models driving these advancements. From supervised learning and convolutional neural networks to generative AI, these tools are already making a mark in areas such as brain tumor segmentation and spine surgery outcome predictions. Their versatility highlights the potential of ML to complement clinical expertise and streamline decision-making in neurosurgery. However, adopting BD and ML also brings ethical challenges that cannot be ignored. Bias in algorithms threatens to reinforce health disparities, whereas concerns about data privacy demand vigilance in handling sensitive patient information. In addition, the question of liability looms large as ML increasingly influences clinical decisions. The aim of the study was to provide a roadmap for neurosurgeons navigating the evolving intersection of BD, ML, and ethical responsibility in the AI era.