Citations

Abstract Coronary artery disease (CAD) is a leading health challenge worldwide. Exercise stress testing is a foundational non-invasive diagnostic tool. Nonetheless, its variable accuracy prompts the exploration of more reliable methods. Recent advancements in machine learning (ML), including deep learning and natural language processing, have shown potential in refining the interpretation of stress testing data. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of ML applications in stress electrocardiogram (ECG) and stress echocardiography for CAD prognosis. Medical Literature Analysis and Retrieval System Online, Web of Science, and the Cochrane Library were used as databases. We analysed the ML models, outcomes, and performance metrics. Overall, seven relevant studies were identified. Machine-learning applications in stress ECGs resulted in sensitivity and specificity improvements. Some models achieved rates of above 96% in both metrics and reduced false positives by up to 21%. In stress echocardiography, ML models demonstrated an increase in diagnostic precision. Some models achieved specificity and sensitivity rates of up to 92.7 and 84.4%, respectively. Natural language processing applications enabled the categorization of stress echocardiography reports, with accuracy rates nearing 98%. Limitations include a small, retrospective study pool and the exclusion of nuclear stress testing, due to its well-documented status. This review indicates the potential of artificial intelligence applications in refining CAD stress testing assessment. Further development for real-world use is warranted.

Objectives Though the concomitant occurrence of non-severe aortic stenosis (AS) and mitral regurgitation (MR) is highly prevalent, there are limited data to guide clinical decision-making in this condition. Here, we attempt to determine an aortic valve area (AVA) cut-off value associated with worse clinical outcomes in patients with combined non-severe AS and MR. Methods Single-centre, retrospective analysis of consecutive patients who underwent echocardiography examination between 2010 and 2021 with evidence of combined non-severe AS and MR. We excluded patients with ≥moderate aortic valve regurgitation or mitral stenosis, as well as patients who underwent any aortic or mitral intervention either prior or following our assessment (n=372). Results The final cohort consisted of 2933 patients with non-severe AS, 506 of them with >mild MR. Patients with both pathologies had lower cardiac output and worse diastolic function. Patients with an AVA ≤1.35 cm² in the presence of >mild MR had the highest rates of heart failure (HF) hospitalisations (HR 3.1, IQR 2.4–4, p<0.001) or mortality (HR 2, IQR 1.8–2.4, p<0.001), which remained significant after adjusting for clinical and echocardiographic parameters. Conclusion Patients with combined non-severe AS and MR have a higher rate of HF hospitalisations and mortality. An AVA≤1.35 cm² in the presence of >mild MR is associated with worse clinical outcomes.

Abstract Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes for patients with metastatic non-small cell lung cancer (NSCLC). However, clinical management is challenging. The PD-L1 biomarker does not optimally guide the choice between ICI monotherapy and combination ICI-chemotherapy, and there are currently no biomarkers to predict immune-related adverse events (irAEs). Here, we describe a versatile platform based on plasma proteomics and machine learning for generating predictive tools to support treatment decisions. Methods: Pre-treatment plasma samples were collected from 616 NSCLC patients receiving ICI-based therapy (NCT04056247). Clinical benefit (CB) data and irAEs were recorded. Proteomic profiling of plasma samples was performed using the SomaScan® assay, covering ~7000 proteins per sample. Proteins associated with Clinical Benefit (progression-free survival at 12 months) were classified as resistance-associated proteins (RAPs). Proteins associated with grade ≥3 irAEs occurring within the first 100 days of treatment were classified as toxicity-associated proteins (TAPs). RAPs and TAPs were integrated into two separate machine learning-based models, yielding CB and irAE probabilities, respectively. The CB model used a CB probability threshold to assign patients a PROphet-positive or PROphet-negative result. Bioinformatic analysis was performed on RAPs and TAPs. Results: CB and irAE models displayed strong predictive performance with a high correlation between predicted probability and observed CB or irAE rate (CB model: R2=0.98; p-value&lt;0.0001; irAE model: R2= 0.92; p-value &lt;0.0001). In the CB model, patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 vs. 10.8 months (hazard ratio, HR = 0.51; p-value&lt;0.001). Furthermore, patients with PD-L1 ≥50% tumors and a PROphet-negative result had significantly longer OS with ICI-chemotherapy in comparison to ICI monotherapy (HR=0.23, p-value = 0.0003), suggesting that combination therapy is preferable for such patients despite high PD-L1 levels. Bioinformatic analysis showed that RAPs related to immune regulation, angiogenesis, chemo-resistance, and other potential resistance mechanisms displayed higher expression levels in patients who did not benefit from treatment. Neutrophil- and inflammation-related proteins were significantly enriched in patients who experienced irAEs. Conclusions: We describe two machine-learning based predictive tools that may be used separately or in combination to inform treatment decisions based on the patient’s likelihood to benefit from ICI therapy and to develop serious irAEs. Our findings also provide biological insights related to treatment resistance and immune-related toxicity. Citation Format: Michal Harel, Petros Christopoulos, Igor Puzanov, Jair Bar, Iris Kamer, Niels Reinmuth, Ina Koch, Mor Moskovitz, Adva Levy-Barda, Alona Zer, Michal Lotem, David Farrugia, Rivka Katzenelson, Gillian Price, Abed Agbarya, Helen Cheley, Adam Hassani, Mahmoud Abu-Amna, Tom Geldart, Anirban Chatterjee, Andreas Polychronis, Maya Gottfried, Yanyan Lou, Tatiana Harkovsky, Alison Brewster, Ido Wolf, Ella Tepper, Ben Yellin, Itamar Sela, Coren Lahav, Yehonatan Elon, Raya Leibowitz, Adam P. Dicker, Young Kwang Chae, Ryan J. Sullivan, David Carbone, David Gandara, Jarushka Naidoo. Plasma proteomics-based models for predicting therapeutic benefit and immune-related adverse events in non-small cell lung cancer patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1208.

PURPOSE Current guidelines for the management of metastatic non–small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. PATIENTS AND METHODS We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247 ) of patients undergoing PD-1/PD-L1 inhibitor–based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. RESULTS Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor–based treatments, evidenced by high concordance between predicted and observed CB ( R 2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). CONCLUSION Plasma proteome–based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor–based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.

There is extensive coverage in the existing literature on implant-associated lymphomas like anaplastic large-cell lymphoma, but breast implant-associated squamous cell carcinoma (BIA-SCC) has received limited scholarly attention since its first case in 1992. Thus, this study aims to conduct a qualitative synthesis focused on the underexplored association between breast implants and BIA-SCC. A systematic review was conducted utilizing the PubMed, Web of Science, and Cochrane databases to identify all currently reported cases of BIA-SCC. Additionally, a literature review was performed to identify potential biochemical mechanisms that could lead to BIA-SCC. Studies were vetted for quality using the NIH quality assessment tool. From an initial pool of 246 papers, 11 met the quality criteria for inclusion, examining a total of 14 patients aged between 40 and 81 years. BIA-SCC was found in a diverse range of implants, including those with smooth and textured surfaces, as well as those filled with saline and silicone. The condition notably manifested a proclivity for aggressive clinical progression, as evidenced by a mortality rate approximating 21.4% within a post-diagnostic interval of six months. Our literature review reveals that chronic inflammation, driven by various external factors such as pathogens and implants, can initiate carcinogenesis through epigenetic modifications and immune system alterations. This includes effects from exosomes and macrophage polarization, showcasing potential pathways for the pathogenesis of BIA-SCC. The study highlights the pressing need for further investigation into BIA-SCC, a subject hitherto inadequately addressed in the academic sphere. This necessitates the urgency for early screening and intervention to improve postoperative outcomes. While the review is confined by its reliance on case reports and series, it serves as a valuable reference for future research endeavors.

Chronic myeloid leukemia (CML) is defined by the presence of Philadelphia chromosome resulting from a reciprocal translocation between chromosomes 9 and 22 [t9;22] that gives rise to a BCR::ABL1 fusion gene. CML occurs in 3 different phases (chronic, accelerated, and blast phase) and is usually diagnosed in the chronic phase in developed countries. Tyrosine kinase inhibitor (TKI) therapy is a highly effective treatment option for patients with chronic phase–CML. The primary goal of TKI therapy in patients with chronic phase–CML is to prevent disease progression to accelerated phase–CML or blast phase–CML. Discontinuation of TKI therapy with careful monitoring is feasible in selected patients. This manuscript discusses the recommendations outlined in the NCCN Guidelines for the diagnosis and management of patients with chronic phase–CML.

Objective Fluctuating sensorineural hearing loss (SNHL) has multiple etiologies, most commonly Ménière's disease (MD), recurrent sudden SNHL, and autoimmune inner ear disorders. Fluctuating SNHL has rarely been described as a symptom of spontaneous intracranial hypotension (SIH). Patient A 39-year-old previously healthy female presented with “Ménière's like” symptoms responsive to steroid treatment, which worsened during the day and improved in the supine position. Conservative treatment for MD consisting of low salt and caffeine diet and betahistine medication yielded no improvement. Secondary revision of brain imaging scans showed signs indicative of SIH, and a spinal cerebrospinal fluid leak was ultimately found and treated by a novel technique of transvenous fistula embolization by means of Onyx® glue, leading to gradual clinical improvement and near-complete resolution of symptoms. Conclusion SIH should be considered as part of the differential diagnosis of fluctuating SNHL. Clinical and radiological features should be known and sought. We suspect that early diagnosis and treatment can lead to cure and prevent permanent auditory damage.

AbstractPurpose has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging–Dynamic Contrast‐Enhanced (OSIPI‐DCE) challenge was designed to benchmark methods to better help the efforts to standardize measurement.MethodsA framework was created to evaluate values produced by DCE‐MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components.ResultsAcross the 10 received submissions, the score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0–1 = lowest–highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility.ConclusionsThis study reports results from the OSIPI‐DCE challenge and highlights the high inter‐software variability within estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real‐world clinical setting, many of these tools may perform differently with different benchmarking methodology.