Are textbook lungs really normal? A cadaveric study on the anatomical and clinical importance of variations in the major lung fissures, and the incomplete right horizontal fissure.

INTRODUCTION: The lungs have three main fissures: the right oblique fissure (ROF), right horizontal fissure (RHF), and left oblique fissure (LOF). These can be complete, incomplete or absent; quantifying the degree of completeness of these fissures is novel. Standard textbooks often refer to the fissures as complete, but awareness of variation is essential in thoracic surgery. MATERIALS AND METHODS: Fissures in 81 pairs of cadaveric lungs were classified. Oblique fissures were measured from lung hila posteriorly to the lung hila anteriorly; and the RHF measured from the ROF to the anteromedial lung edge. The degree of completeness of fissures was expressed as a percentage of the total projected length were they to be complete. The frequency and location of accessory fissures was noted. RESULTS: LOF were complete in 66/81 (81.5%), incomplete in 13/81 (16.0%) and absent in 2/81 (2.47%); ROF were complete in 52/81 (64.2%), incomplete in 29/81 (35.8%) and never absent; RHF were more variable, complete in 18/81 (22.2%), incomplete in 54/81 (66.7%) and absent in 9/81 (11.1%). LOF and ROF were on average 97.1% and 91.6% complete, respectively, being deficient posteriorly at the lung hila. The RHF on average 69.4% complete, being deficient anteromedially. There were accessory fissures in 10 left and 19 right lungs. CONCLUSIONS: This study provides a projection of the anatomy thoracic surgeons may encounter at operation, in particular the variable RHF. This knowledge is essential for optimal outcomes in both benign and oncological procedures influenced by the fissures.

Genomic and Immunophenotypic Landscape of Acquired Resistance to PD-(L)1 Blockade in Non-Small-Cell Lung Cancer.

PURPOSE: Although immune checkpoint inhibitors (ICI) have extended survival in patients with non-small-cell lung cancer (NSCLC), acquired resistance (AR) to ICI frequently develops after an initial benefit. However, the mechanisms of AR to ICI in NSCLC are largely unknown. METHODS: Comprehensive tumor genomic profiling, machine learning-based assessment of tumor-infiltrating lymphocytes, multiplexed immunofluorescence, and/or HLA-I immunohistochemistry (IHC) were performed on matched pre- and post-ICI tumor biopsies from patients with NSCLC treated with ICI at the Dana-Farber Cancer Institute who developed AR to ICI. Two additional cohorts of patients with intervening chemotherapy or targeted therapies between biopsies were included as controls. RESULTS: We performed comprehensive genomic profiling and immunophenotypic characterization on samples from 82 patients with NSCLC and matched pre- and post-ICI biopsies and compared findings with a control cohort of patients with non-ICI intervening therapies between biopsies (chemotherapy, N = 32; targeted therapies, N = 89; both, N = 17). Putative resistance mutations were identified in 27.8% of immunotherapy-treated cases and included acquired loss-of-function mutations in STK11, B2M, APC, MTOR, KEAP1, and JAK1/2; these acquired alterations were not observed in the control groups. Immunophenotyping of matched pre- and post-ICI samples demonstrated significant decreases in intratumoral lymphocytes, CD3e+ and CD8a+ T cells, and PD-L1-PD1 engagement, as well as increased distance between tumor cells and CD8+PD-1+ T cells. There was a significant decrease in HLA class I expression in the immunotherapy cohort at the time of AR compared with the chemotherapy (P = .005) and the targeted therapy (P = .01) cohorts. CONCLUSION: These findings highlight the genomic and immunophenotypic heterogeneity of ICI resistance in NSCLC, which will need to be considered when developing novel therapeutic strategies aimed at overcoming resistance.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories.

Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.

Federated benchmarking of medical artificial intelligence with MedPerf.

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

How to communicate with the public about chemical, biological, radiological, or nuclear terrorism: a systematic review of the literature.

A deliberate attack involving chemical, biological, radiological, or nuclear (CBRN) material has the potential to cause substantial fear among the public. This presents problems for communicators, who will need to provide information quickly after an attack while ensuring that their messages are easily understood and likely to be attended to by members of the public. Identifying in advance what people would want to know, where they would get information from, and how messages should be presented might allow communicators to ensure that their messages have the best chance of having their desired effect. In this review, we identified all peer-reviewed studies that have assessed communication strategies or information needs using hypothetical CBRN scenarios or in actual CBRN incidents. We identified 33 relevant studies. Their results support existing psychological models of why people engage in health protective behaviors, with information about the severity of the incident, the likelihood of being exposed, the efficacy and costs or risks of recommended behaviors, and the ability of individuals to perform recommended behaviors being sought by the public. Trust plays a crucial role in ensuring that people attend to messages. Finally, while a large variety of spokespeople and sources were identified as being turned to in the event of an incident, the use of multiple information sources was also common, affirming the importance of communicating a consistent message through multiple channels. Further research is required to extend these predominantly US-based findings to other countries and to confirm the findings of research using hypothetical scenarios.