Citations

Performing 3D ultrasound imaging at a real-time volume rate (e.g., >20 Hz) is a challenging task. While 2D array transducers remain the most practical approach for real-time 3D imaging, the large number of transducer elements (e.g., several thousand) that are necessary to cover an effective 3D field-of-view impose a fundamental constraint on imaging speed. Although solutions such as multiplexing and specialized transducers, including sparse arrays and row-column-addressing arrays, have been developed to address this limitation, they inevitably compromise imaging quality (e.g., SNR, resolution) in favor of speed. Coupled with the high equipment cost of 2D arrays, these compromises hinder the widespread adoption of 3D ultrasound imaging technologies in clinical settings. In this presentation, we introduce an innovative transducer clip-on device comprising a water-immersible, fast-tilting electromechanical acoustic reflector and a redirecting reflector to enable real-time 3D ultrasound imaging using common 1D array transducers. We will first introduce the principles underlying our novel technique, followed by validation studies incorporating simulation and experimental data. We will also demonstrate the feasibility of using the clip-on device to achieve a high 3D imaging volume rate that is suitable for advanced imaging modes such as shear wave elastography, blood flow imaging, and super-resolution ultrasound localization microscopy.

Abstract Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org .

AbstractTargeting NLRP3 inflammasome has been recognized as a promising therapeutic strategy for the treatment of numerous common diseases. UK5099, a long‐established inhibitor of mitochondrial pyruvate carrier (MPC), is previously found to inhibit macrophage inflammatory responses independent of MPC expression. However, the mechanisms by which UK5099 inhibit inflammatory responses remain unclear. Here, it is shown that UK5099 is a potent inhibitor of the NLRP3 inflammasome in both mouse and human primary macrophages. UK5099 selectively suppresses the activation of the NLRP3 but not the NLRC4 or AIM2 inflammasomes. Of note, UK5099 retains activities on NLRP3 in macrophages devoid of MPC expression, indicating this inhibitory effect is MPC‐independent. Mechanistically, UK5099 abrogates mitochondria‐NLRP3 interaction and in turn inhibits the assembly of the NLRP3 inflammasome. Further, a single dose of UK5099 persistently reduces IL‐1β production in an endotoxemia mouse model. Importantly, structure modification reveals that the inhibitory activities of UK5099 on NLRP3 are unrelated to the existence of the activated double bond within the UK5099 molecule. Thus, this study uncovers a previously unknown molecular target for UK5099, which not only offers a new candidate for the treatment of NLRP3‐driven diseases but also confounds its use as an MPC inhibitor in immunometabolism studies.

8555 Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of non-small cell lung cancer (NSCLC), with historically poor outcome. Excellent response of metastatic PSC to immunotherapy was previously reported, but its outcome compared with other NSCLCs is unknown in the immunotherapy era. Methods: NCDB was queried for stage IV NSCLC diagnosed from 2016 to 2020. Patients who were treated with chemotherapy only or immunotherapy only in the first course of treatment were included. Log-rank was used for survival analysis and adjusted by cox regression for age, gender, race, academic/integrated center, insurance, and comorbidity. Results: A total of 37,617 patients were identified, including 201 PSCs and 37,416 other NSCLCs. Pleomorphic (54%) was the most common PSC subtype. There was significant interaction between the survival effect of treatment and histology (interaction p = 0.024). When treated with immunotherapy alone, no significant difference was found between PSC and other NSCLCs (median survival 11.4 vs 12.4 months, logrank p = 0.56, HR 1.02, 95% CI 0.81 – 1.30). However, when treated with chemotherapy alone, PSC remained significantly associated with worse survival (median survival 4.5 vs. 8.5 months, logrank p <0.001, HR 0.67, 95% CI 0.54 – 0.83). Among PSC subtypes, spindle/giant cell PSCs were more likely to benefit from immunotherapy alone than pleomorphic PSCs (interaction p = 0.005), when compared with chemotherapy (Spindle/giant cell subtype: HR 0.24, 95% CI 0.14 – 0.42; Pleomorphic subtype: HR 0.60, 95% CI 0.37 – 0.99). Conclusions: Compared with other NSCLCs, PSC may not be associated with worse survival when treated with immunotherapy alone as it was when treated with chemotherapy. Distinct PSC subtypes may respond differently to immunotherapy and warrants further research. [Table: see text]

Abstract The significance of biomarkers in second-line treatment for acute graft-versus-host disease (GVHD) has not been well characterized. We analyzed clinical data and serum samples at the initiation of second-line systemic treatment of acute GVHD from 167 patients from 17 centers of the Mount Sinai Acute GVHD International Consortium (MAGIC) between 2016 and 2021. Sixty-two patients received ruxolitinib-based therapy, whereas 102 received other systemic agents. In agreement with prospective trials, ruxolitinib resulted in a higher day 28 (D28) overall response Frate than nonruxolitinib therapies (55% vs 31%, P = .003) and patients who received ruxolitinib had significantly lower nonrelapse mortality (NRM) than those who received nonruxolitinib therapies (point estimates at 2-year: 35% vs 61%, P = .002). Biomarker analyses demonstrated that the benefit from ruxolitinib was observed only in patients with low MAGIC algorithm probabilities (MAPs) at the start of second-line treatment. Among patients with a low MAP, those who received ruxolitinib experienced significantly lower NRM than those who received nonruxolitinib therapies (point estimates at 2-year: 12% vs 41%, P = .016). However, patients with high MAP experienced high NRM regardless of treatment with ruxolitinib or nonruxolitinib therapies (point estimates at 2-year: 67% vs 80%, P = .65). A landmark analysis demonstrated that the relationship between the D28 response and NRM largely depends on the MAP level at the initiation of second-line therapy. In conclusion, MAP measured at second-line systemic treatment for acute GVHD predicts treatment response and NRM. The outcomes of patients with high MAP are poor regardless of treatment choice, and ruxolitinib appears to primarily benefit patients with low MAP.

The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen deprivation therapy to an era of advanced imaging, precision diagnostics, genomics, and targeted treatment options. Concurrently, the emergence of large language models (LLMs) has dramatically transformed the paradigm for artificial intelligence (AI). This convergence of advancements in prostate cancer management and AI provides a compelling rationale to comprehensively review the current state of AI applications in prostate cancer care. Here, we review the advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer from early interception to survivorship care. We subsequently discuss the role of AI in prostate cancer drug discovery, clinical trials, and clinical practice guidelines. In the localized disease setting, deep learning models demonstrated impressive performance in detecting and grading prostate cancer using imaging and pathology data. For biochemically recurrent diseases, machine learning approaches are being tested for improved risk stratification and treatment decisions. In advanced prostate cancer, deep learning can potentially improve prognostication and assist in clinical decision making. Furthermore, LLMs are poised to revolutionize information summarization and extraction, clinical trial design and operations, drug development, evidence synthesis, and clinical practice guidelines. Synergistic integration of multimodal data integration and human-AI integration are emerging as a key strategy to unlock the full potential of AI in prostate cancer care.

ImportanceThe optimal radiotherapy technique for unresectable locally advanced non–small cell lung cancer (NSCLC) is controversial, so evaluating long-term prospective outcomes of intensity-modulated radiotherapy (IMRT) is important.ObjectiveTo compare long-term prospective outcomes of patients receiving IMRT and 3-dimensional conformal radiotherapy (3D-CRT) with concurrent carboplatin/paclitaxel for locally advanced NSCLC.Design, Setting, and ParticipantsA secondary analysis of a prospective phase 3 randomized clinical trial NRG Oncology–RTOG 0617 assessed 483 patients receiving chemoradiotherapy (3D-CRT vs IMRT) for locally advanced NSCLC based on stratification.Main Outcomes and MeasuresLong-term outcomes were analyzed, including overall survival (OS), progression-free survival (PFS), time to local failure, development of second cancers, and severe grade 3 or higher adverse events (AEs) per Common Terminology Criteria for Adverse Events, version 3. The percentage of an organ volume (V) receiving a specified amount of radiation in units of Gy is reported as V(radiation dose).ResultsOf 483 patients (median [IQR] age, 64 [57-70] years; 194 [40.2%] female), 228 (47.2%) received IMRT, and 255 (52.8%) received 3D-CRT (median [IQR] follow-up, 5.2 [4.8-6.0] years). IMRT was associated with a 2-fold reduction in grade 3 or higher pneumonitis AEs compared with 3D-CRT (8 [3.5%] vs 21 [8.2%]; P = .03). On univariate analysis, heart V20, V40, and V60 were associated with worse OS (hazard ratios, 1.06 [95% CI, 1.04-1.09]; 1.09 [95% CI, 1.05-1.13]; 1.16 [95% CI, 1.09-1.24], respectively; all P &amp;lt; .001). IMRT significantly reduced heart V40 compared to 3D-CRT (16.5% vs 20.5%; P &amp;lt; .001). Heart V40 (&amp;lt;20%) had better OS than V40 (≥20%) (median [IQR], 2.5 [2.1-3.1] years vs 1.7 [1.5-2.0] years; P &amp;lt; .001). On multivariable analysis, heart V40 (≥20%), was associated with worse OS (hazard ratio, 1.34 [95% CI, 1.06-1.70]; P = .01), whereas lung V5 and age had no association with OS. Patients receiving IMRT and 3D-CRT had similar rates of developing secondary cancers (15 [6.6%] vs 14 [5.5%]) with long-term follow-up.Conclusions and RelevanceThese findings support the standard use of IMRT for locally advanced NSCLC. IMRT should aim to minimize lung V20 and heart V20 to V60, rather than constraining low-dose radiation bath. Lung V5 and age were not associated with survival and should not be considered a contraindication for chemoradiotherapy.Trial RegistrationClinicalTrials.gov Identifier: NCT00533949

Abstract We evaluate the impact of donor types on outcomes of hematopoietic cell transplantation (HCT) in myelofibrosis, using the Center for International Blood and Marrow Transplant Research registry data for HCTs done between 2013 and 2019. In all 1597 patients, the use of haploidentical donors increased from 3% in 2013 to 19% in 2019. In study-eligible 1032 patients who received peripheral blood grafts for chronic-phase myelofibrosis, 38% of recipients of haploidentical HCT were non-White/Caucasian. Matched sibling donor (MSD)–HCTs were associated with superior overall survival (OS) in the first 3 months (haploidentical hazard ratio [HR], 5.80 [95% confidence interval (CI), 2.52-13.35]; matched unrelated (MUD) HR, 4.50 [95% CI, 2.24-9.03]; mismatched unrelated HR, 5.13 [95% CI, 1.44-18.31]; P &lt; .001). This difference in OS aligns with lower graft failure with MSD (haploidentical HR, 6.11 [95% CI, 2.98-12.54]; matched unrelated HR, 2.33 [95% CI, 1.20-4.51]; mismatched unrelated HR, 1.82 [95% CI, 0.58-5.72]). There was no significant difference in OS among haploidentical, MUD, and mismatched unrelated donor HCTs in the first 3 months. Donor type was not associated with differences in OS beyond 3 months after HCT, relapse, disease-free survival, or OS among patients who underwent HCT within 24 months of diagnosis. Patients who experienced graft failure had more advanced disease and commonly used nonmyeloablative conditioning. Although MSD-HCTs were superior, there is no significant difference in HCT outcomes from haploidentical and MUDs. These results establish haploidentical HCT with posttransplantation cyclophosphamide as a viable option in myelofibrosis, especially for ethnic minorities underrepresented in the donor registries.