Fully Automated Quantitative Coronary Angiography Versus Optical Coherence Tomography Guidance for Coronary Stent Implantation (FLASH): Study Protocol for a Randomized Controlled Non-Inferiority Trial.

BACKGROUND: Artificial intelligence-based quantitative coronary angiography (AI-QCA) has been developed to provide a more objective and reproducible data about the severity of coronary artery stenosis and the dimensions of the vessel for intervention in real-time, overcoming the limitations of significant inter- and intra-observer variability, and time-consuming nature of on-site QCA, without requiring extra time and effort. Compared with the subjective nature of visually estimated conventional CAG guidance, AI-QCA guidance provides a more practical and standardized angiography-based approach. Although the advantage of intravascular imaging-guided PCI is increasingly recognized, their broader adoption is limited by clinical and economic barriers in many catheterization laboratories. METHODS: The FLASH (Fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation) trial is a randomized, investigator-initiated, multicenter, open-label, non-inferiority trial comparing the AI-QCA-assisted PCI strategy with optical coherence tomography-guided PCI strategy in patients with significant coronary artery disease. All operators will utilize a novel, standardized AI-QCA software and PCI protocol in the AI-QCA-assisted group. A total of 400 patients will be randomized to either group at a 1:1 ratio. The primary endpoint is the minimal stent area (mm2), determined by the final OCT run after completion of PCI. Clinical follow-up and cost-effectiveness evaluations are planned at 1 month and 6 months for all patients enrolled in the study. RESULTS: Enrollment of a total of 400 patients from the 13 participating centers in South Korea will be completed in February 2024. Follow-up of the last enrolled patients will be completed in August 2024, and primary results will be available by late 2024. CONCLUSION: The FLASH is the first clinical trial to evaluate the feasibility of AI-QCA-assisted PCI, and will provide the clinical evidence on AI-QCA assistance in the field of coronary intervention.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. METHODS: AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). RESULTS: AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. CONCLUSION: AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Quantitative Coronary Angiography vs Intravascular Ultrasonography to Guide Drug-Eluting Stent Implantation: A Randomized Clinical Trial.

Importance: Although intravascular ultrasonography (IVUS) guidance promotes favorable outcomes after percutaneous coronary intervention (PCI), many catheterization laboratories worldwide lack access. Objective: To investigate whether systematic implementation of quantitative coronary angiography (QCA) to assist angiography-guided PCI could be an alternative strategy to IVUS guidance during stent implantation. Design, Setting, and Participants: This randomized, open-label, noninferiority clinical trial enrolled adults (aged ≥18 years) with chronic or acute coronary syndrome and angiographically confirmed native coronary artery stenosis requiring PCI. Patients were enrolled in 6 cardiac centers in Korea from February 23, 2017, to August 23, 2021, and follow-up occurred through August 25, 2022. All principal analyses were performed according to the intention-to-treat principle. Interventions: After successful guidewire crossing of the first target lesion, patients were randomized in a 1:1 ratio to receive either QCA- or IVUS-guided PCI. Main Outcomes and Measures: The primary outcome was target lesion failure at 12 months, defined as a composite of cardiac death, target vessel myocardial infarction, or ischemia-driven target lesion revascularization. The trial was designed assuming an event rate of 8%, with the upper limit of the 1-sided 97.5% CI of the absolute difference in 12-month target lesion failure (QCA-guided PCI minus IVUS-guided PCI) to be less than 3.5 percentage points for noninferiority. Results: The trial included 1528 patients who underwent PCI with QCA guidance (763; mean [SD] age, 64.1 [9.9] years; 574 males [75.2%]) or IVUS guidance (765; mean [SD] age, 64.6 [9.5] years; 622 males [81.3%]). The post-PCI mean (SD) minimum lumen diameter was similar between the QCA- and IVUS-guided PCI groups (2.57 [0.55] vs 2.60 [0.58] mm, P = .26). Target lesion failure at 12 months occurred in 29 of 763 patients (3.81%) in the QCA-guided PCI group and 29 of 765 patients (3.80%) in the IVUS-guided PCI group (absolute risk difference, 0.01 percentage points [95% CI, -1.91 to 1.93 percentage points]; hazard ratio, 1.00 [95% CI, 0.60-1.68]; P = .99). There was no difference in the rates of stent edge dissection (1.2% vs 0.7%, P = .25), coronary perforation (0.2% vs 0.4%, P = .41), or stent thrombosis (0.53% vs 0.66%, P = .74) between the QCA- and IVUS-guided PCI groups. The risk of the primary end point was consistent regardless of subgroup, with no significant interaction. Conclusions and Relevance: Findings of this randomized clinical trial indicate that QCA and IVUS guidance during PCI showed similar rates of target lesion failure at 12 months. However, due to the lower-than-expected rates of target lesion failure in this trial, the findings should be interpreted with caution. Trial Registration: ClinicalTrials.gov Identifier: NCT02978456.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

Association of metabolic health and obesity with coronary heart disease in adult cancer survivors.

BACKGROUND: The metabolically healthy obese (MHO) phenotype is associated with an increased risk of coronary heart disease (CHD) in the general population. However, association of metabolic health and obesity phenotypes with CHD risk in adult cancer survivors remains unclear. We aimed to investigate the associations between different metabolic health and obesity phenotypes with incident CHD in adult cancer survivors. METHODS: We used National Health Insurance Service (NHIS) to identify a cohort of 173,951 adult cancer survivors aged more than 20 years free of cardiovascular complications. Metabolically healthy nonobese (MHN), MHO, metabolically unhealthy nonobese (MUN), metabolically unhealthy obese (MUO) phenotypes were created using as at least three out of five metabolic health criteria along with obesity (body mass index ≥ 25.0 kg/m2). We used Cox proportional hazards model to assess CHD risk in each metabolic health and obesity phenotypes. RESULTS: During 1,376,050 person-years of follow-up, adult cancer survivors with MHO phenotype had a significantly higher risk of CHD (hazard ratio [HR] = 1.52; 95% confidence intervals [CI]: 1.41 to 1.65) as compared to those without obesity and metabolic abnormalities. MUN (HR = 1.81; 95% CI: 1.59 to 2.06) and MUO (HR = 1.92; 95% CI: 1.72 to 2.15) phenotypes were also associated with an increased risk of CHD among adult cancer survivors. CONCLUSIONS: Adult cancer survivors with MHO phenotype had a higher risk of CHD than those who are MHN. Metabolic health status and obesity were jointly associated with CHD risk in adult cancer survivors.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

Increased risk of ischemic stroke associated with elevated gamma-glutamyl transferase level in adult cancer survivors: a population-based cohort study.

Adult cancer survivors may have an increased risk of developing ischemic stroke, potentially influenced by cancer treatment-related factors and shared risk factors with stroke. However, the association between gamma-glutamyl transferase (GGT) levels and the risk of ischemic stroke in this population remains understudied. Therefore, our study aimed to examine the relationship between GGT levels and the risk of ischemic stroke using a population-based cohort of adult cancer survivors. A population-based cohort of adult cancer survivors was derived from the National Health Insurance Service-Health Screening Cohort between 2003 and 2005 who survived after diagnosis of primary cancer and participated in the biennial national health screening program between 2009 and 2010. Cox proportional hazards model adjusted for sociodemographic factors, health status and behavior, and clinical characteristics was used to investigate the association between GGT level and ischemic stroke in adult cancer survivors. Among 3095 adult cancer survivors, 80 (2.58%) incident cases of ischemic stroke occurred over a mean follow-up of 8.2 years. Compared to the lowest GGT quartile, the hazard ratios (HRs) for ischemic stroke were 1.56 (95% CI 0.75-3.26), 2.36 (95% CI 1.12-4.99), and 2.40 (95% CI 1.05-5.46) for the second, third, and fourth sex-specific quartiles, respectively (Ptrend = 0.013). No significant effect modification was observed by sex, insurance premium, and alcohol consumption. High GGT level is associated with an increased risk of ischemic stroke in adult cancer survivors independent of sex, insurance premium, and alcohol consumption.

GenHPF: General Healthcare Predictive Framework for Multi-task Multi-source Learning.

Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of medical records, tend to not generalize well to other tasks or databases in which the data fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting EHRs into a hierarchical textual representation while incorporating as many features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task learning experiments with single-source and multi-source settings, on three publicly available EHR datasets with different schemas for 12 clinically meaningful prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning, improving average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while also showing comparable results when trained on a single EHR dataset. Furthermore, we demonstrate that self-supervised pretraining using multi-source datasets is effective when combined with GenHPF, resulting in a 0.6 pretraining. By eliminating the need for preprocessing and feature engineering, we believe that this work offers a solid framework for multi-task and multi-source learning that can be leveraged to speed up the scaling and usage of predictive algorithms in healthcare.1.

A protocol of a single arm, prospective, open-label, multicenter, phase II study of ramucirumab and erlotinib in treatment-naïve non-small cell lung cancer patients with EGFR mutation and brain metastases (SPIRAL-BRAIN study).

Background: The combination of erlotinib, a first-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), and ramucirumab, an anti-vascular endothelial growth factor receptor (VEGFR) antibody, is one of the most effective treatments for patients with non-small cell lung cancer (NSCLC) and EGFR mutation. However, little is known about the safety and efficacy of this combination treatment for patients with brain metastases. Methods: This single arm, prospective, open-label, multicenter, phase II study will recruit 32 NSCLC patients with EGFR mutation (except for T790M mutation) and brain metastases (asymptomatic or mild symptoms). Patients will be treated with erlotinib at a dose of 150 mg/body once daily and ramucirumab at a dose of 10 mg/kg once every 2 weeks. The primary endpoint is intracranial overall response rate (iORR) and the secondary endpoints are intracranial disease control rate, intracranial progression-free survival (iPFS), extracranial ORR, extracranial PFS, ORR, overall PFS, overall survival (OS), and safety. The planned number of enrollments was calculated based on a one-sample binomial test (normal approximation) with a two-sided α level of 5% and 80% power, assuming that the expected iORR is 65% and the iORR threshold is 40%. Discussion: A prospective study to confirm the safety and efficacy of the combined erlotinib plus ramucirumab treatment for NSCLC patients with EGFR mutation and brain metastases is ongoing. Trial Registration: Japan Registry of Clinical Trials, jRCTs051220059.

A randomized phase III study of docetaxel alone versus docetaxel plus S-1 in patients with previously treated non-small cell lung cancer: JMTO LC09-01.

BACKGROUND: This study evaluated the efficacy and safety of the combination chemotherapy of docetaxel plus S-1 in patients with previously treated non-small cell lung cancer (NSCLC) compared to docetaxel alone. METHODS: Patients with previously treated NSCLC were randomly assigned to docetaxel alone (arm A) or a combination of docetaxel and S-1 (arm B) for a maximum of four cycles. The primary endpoint was overall survival (OS). RESULTS: The study was terminated early because of poor accrual. The number of patients evaluated were 74 and 77 in arm A and arm B, respectively. The median OS was 9.8 months (95% confidence interval [CI]: 6.8-15.2) and 12.3 months (95% CI: 9.2-14.5) in arms A and B, respectively. In arms A and B, the median progression-free survival was 3.5 months (95% CI: 2.7-4.0) and 4.1 months (95% CI: 3.2-4.7), respectively. No statistically significant difference was observed in OS (hazard ratio [HR]: 0.984, 95% CI: 0.682-1.419, p = 0.4569) or progression-free survival (HR: 0.823, 95% CI: 0.528-1.282, p = 0.0953). The major toxicity was myelosuppression. The incidence of grade 3 or more neutropenia was higher in arm A than in arm B (44.6% vs. 35.1%). However, the incidence of grade 3 or more febrile neutropenia and infection with neutropenia (12.2% vs. 22.1%) was more frequently observed in arm B. CONCLUSIONS: The prematurely terminated study did not show the benefit of two cytotoxic agents over single-agent therapy for previously treated NSCLC patients.

Efficacy and safety of standard dose triple combination of telmisartan 80 mg/amlodipine 5 mg/chlorthalidone 25 mg in primary hypertension: A randomized, double-blind, active-controlled, multicenter phase 3 trial.

The authors evaluated the efficacy, safety, and characteristics of patients who respond well to standard dose triple combination therapy including chlorthalidone 25 mg with telmisartan 80 mg plus amlodipine 5 mg in hypertensive patients. This is a multicenter, double-blind, active-controlled, phase 3, randomized trial. Patients are randomized to triple combination (telmisartan 40 mg/amlodipine 5 mg/chlorthalidone 12.5 mg, TEL/AML/CHTD group) or dual combination (telmisartan 40 mg/amlodipine 5 mg, TEL/AML group) treatment and then dose up titration to TEL 80/AML5/CHTD25mg and TEL80/AML5, respectively. The primary endpoint is the change of mean sitting systolic blood pressure (MSSBP) at week 8. A Target BP achievement rate, a response rate, and the safety endpoints are also evaluated. Total 374 patients (mean age = 60.9 ± 10.7 years, male = 78.3%) were randomized to the study. The baseline MSSBPs/diastolic BPs were 149.9 ± 12.2/88.5 ± 10.4 mm Hg. After 8 weeks treatment, the change of MSSBPs at week 8 are -19.1 ± 14.9 mm Hg (TEL/AML/CHTD) and -11.4 ± 14.7 mm Hg (TEL/AML) (p < .0001). The achievement rates of target BP (53.8% vs. 37.8%, p = .0017) and responder rate (54.8% vs. 35.6%, p = .0001) at week 8 were significantly higher in TEL/AML/CHTD. There are no serious adverse event and no one discontinued medication due to adverse event. Among the TEL 80/AML5/CHTD25mg treatment group, patients of female or age ≥ 65 years old showed higher rate of target BP achievement than relatively young male. (61.4 vs. 46.8%, p = .042) Our study showed standard dose triple combination of telmisartan 80 mg/amlodipine 5 mg/chlorthalidone 25 mg is efficacious and safe in treatment of primary hypertension. Target BP achievement with triple therapy would be facilitated in female or old age.

Analysis of Tumor Heterogeneity Through AXL Activation in Primary Resistance to EGFR Tyrosine Kinase Inhibitors.

Introduction: EGFR tyrosine kinase inhibitors are standard therapeutic agents for patients with advanced NSCLC harboring EGFR mutations. Nevertheless, some patients exhibit primary resistance to EGFR tyrosine kinase inhibitors in the first-line treatment setting. AXL, a member of the TYRO3, AXL, and MERTK family of receptor tyrosine kinases, is involved in primary resistance to EGFR tyrosine kinase inhibitors in EGFR-mutated NSCLC. Methods: We investigated spatial tumor heterogeneity using autopsy specimens and a patient-derived cell line from a patient with EGFR-mutated NSCLC having primary resistance to erlotinib plus ramucirumab. Results: Quantitative polymerase chain reaction analysis revealed that AXL mRNA expression differed at each metastatic site. In addition, AXL expression levels were likely to be negatively correlated with the effectiveness of erlotinib plus ramucirumab therapy. Analysis of a patient-derived cell line established from the left pleural effusion before initiation of treatment revealed that the combination of EGFR tyrosine kinase inhibitors and an AXL inhibitor remarkably inhibited cell viability and increased cell apoptosis in comparison with EGFR tyrosine kinase inhibitor monotherapy or combination therapy of these inhibitors with ramucirumab. Conclusions: Our observations suggest that AXL expression may play a critical role in the progression of spatial tumor heterogeneity and primary resistance to EGFR tyrosine kinase inhibitors in patients with EGFR-mutated NSCLC.

Rationale and design of phase II clinical trial of dual inhibition with ramucirumab and erlotinib in EGFR exon 19 deletion-positive treatment-naïve non-small cell lung cancer with high PD-L1 expression (SPIRAL-3D study).

Background: Osimertinib is a standard treatment option for epidermal growth factor receptor (EGFR) mutation-positive non-small cell lung cancer (NSCLC). However, osimertinib monotherapy yields poor clinical outcomes in some patients, necessitating the development of novel treatment strategies. In addition, several studies have suggested that high programmed cell death-ligand 1 (PD-L1) expression is associated with poor progression-free survival (PFS) for osimertinib monotherapy in patients with advanced NSCLC harboring EGFR mutations. Objective: To evaluate the clinical efficacy of erlotinib plus ramucirumab for EGFR exon 19 deletion-positive treatment-naïve NSCLC with high PD-L1 expression. Design: A single-arm, prospective, open-label, phase II study. Methods and Analysis: Patients with treatment-naïve EGFR exon 19 deletion-positive NSCLC with high PD-L1 expression and a performance status of 0-2 will receive combination therapy with erlotinib plus ramucirumab until evidence of disease progression or development of unacceptable toxicity. High PD-L1 expression is defined as a tumor proportion score of 50% or higher, as determined by PD-L1 immunohistochemistry 22C3 pharmDx testing. The Kaplan-Meier method and the Brookmeyer and Crowley method with the arcsine square-root transformation will be used with PFS as the primary endpoint. The secondary endpoints include overall response rate, disease control rate, overall survival, and safety. A total of 25 patients will be enrolled. Ethics: The study has been approved by the Clinical Research Review Board, Kyoto Prefectural University of Medicine, Kyoto, Japan, and written informed consent will be obtained from all patients. Discussion: To the best of our knowledge, this is the first clinical trial to focus on PD-L1 expression in EGFR mutation-positive NSCLC. If the primary end point is met, combination therapy with erlotinib and ramucirumab could become a potential treatment option for this clinical population. Trial Registration: This trial was registered with the Japan Registry for Clinical Trials on 12 January 2023 (jRCTs 051220149).

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography.

BACKGROUND: Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room. PURPOSE: This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA. METHODS: Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients. RESULTS: The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second. CONCLUSION: Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.

Prospective Observational Study Evaluating the Prognostic Value of the G8 Screening Tool for Extensive-Stage Small Cell Lung Cancer Patients Who Received Programmed Death-Ligand 1 Inhibitor plus Platinum-Etoposide Chemotherapy.

BACKGROUND: Programmed death-ligand 1 (PD-L1) inhibitor plus platinum-etoposide chemotherapy is used as a first-line treatment for extensive-stage small cell lung cancer (ES-SCLC), regardless of age. OBJECTIVE: We examined the role of the Geriatric 8 (G8) screening tool for evaluating treatment outcomes in patients with ES-SCLC treated with PD-L1 inhibitor plus platinum-etoposide chemotherapy as first-line therapy. PATIENTS AND METHODS: Between September 2019 and October 2021, we prospectively evaluated patients with ES-SCLC treated with immunochemotherapy at ten institutions in Japan. The G8 score was assessed before treatment initiation. RESULTS: We evaluated 44 patients with ES-SCLC. Patients with G8 score > 11 had longer overall survival (OS) than those with G8 score ≤ 11 (not reached versus 8.3 months; log-rank test, p = 0.005). In univariate and multivariate analyses, G8 score > 11 [hazard ratio (HR) 0.34; 95% confidence interval (CI) 0.15-0.75; p = 0.008 and HR 0.34; 95% CI 0.14-0.82; p = 0.02, respectively) and performance status (PS) of 2 (HR 5.42; 95% CI 2.08-14.2; p < 0.001 and HR 6.94; 95% CI 2.25-21.4; p < 0.001, respectively) were independent prognostic factors for OS. Among patients with good PS (0 or 1), the OS in patients with G8 score > 11 was significantly longer than that in patients with G8 score ≤ 11 (not reached versus 12.3 months; log-rank test, p = 0.02). CONCLUSIONS: G8 score evaluation before treatment initiation was useful as a prognostic factor for ES-SCLC patients who received PD-L1 inhibitors and platinum-etoposide chemotherapy, even with good PS.

Association of dietary sodium intake with impaired fasting glucose in adult cancer survivors: A population-based cross-sectional study.

BACKGROUND: Dietary sodium intake is a crucial lifestyle factor that should be assessed in adult cancer survivors due to their increased risk of adverse health outcomes compared to the general population. However, its with impaired fasting glucose (IFG) in adult cancer survivors remains unclear. This study aimed to investigate the association of dietary sodium intake categorized by the American Heart Association (AHA) recommendation with IFG in the community-dwelling adult cancer survivors. METHODS: A total of 1,052 adult cancer survivors without diabetes were identified from the sixth and seventh Korea National Health and Nutrition Examination Survey (KNHANES), 2013-2018. Data on dietary sodium intake was categorized as <1,500 mg/day, 1,500-2,999 mg/day, 2,300-3,999 mg/day, and ≥4,000 mg/day according to the AHA recommendation. A multiple logistic regression model adjusted for demographic, lifestyle, and health status was used to compute odds ratios (OR) and 95% confidence intervals (95% CI) for IFG according to dietary sodium intake categories. RESULTS: After adjusting for confounding variables identified in the KNHANES, the adjusted OR among the adult cancer survivors who consumed 1,500-2,999 mg/day, 2,300-3,999 mg/day, and ≥4,000 mg/day of dietary sodium were 1.16 (95% CI: 0.25-5.27), 1.93 (95% CI: 0.40-9.37), and 2.67 (95% CI: 0.59-12.18), respectively, as compared to those who consumed <1,500 mg/day (P value for trend = 0.036). CONCLUSION: Among community-dwelling adult cancer survivors, high dietary sodium intake was marginally associated with increased odds of IFG. Well-designed cohort studies or randomized clinical trials are needed to establish more epidemiologic evidence on this association in adult cancer survivors.