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There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I–IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23–89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.

Prevention of muscle atrophy contributes to improved quality of life and life expectancy. In this study, we investigated the effects of laurel, selected from 34 spices and herbs, on dexamethasone (DEX)-induced skeletal muscle atrophy and deciphered the underlying mechanisms. Co-treatment of C2C12 myotubes with laurel for 12 h inhibited the DEX-induced expression of intracellular ubiquitin ligases—muscle atrophy F-box (atrogin-1/MAFbx) and muscle RING finger 1 (MuRF1)—and reduction in myotube diameter. Male Wistar rats were supplemented with 2% laurel for 17 days, with DEX-induced skeletal muscle atrophy occurring in the last 3 days. Laurel supplementation inhibited the mRNA expression of MuRF1, regulated DNA damage and development 1 (Redd1), and forkhead box class O 1 (Foxo1) in the muscles of rats. Mechanistically, we evaluated the effects of laurel on the cellular proteolysis machinery—namely, the ubiquitin/proteasome system and autophagy—and the mTOR signaling pathway, which regulates protein synthesis. These data indicated that the amelioration of DEX-induced skeletal muscle atrophy induced by laurel, is mainly mediated by the transcriptional inhibition of downstream factors of the ubiquitin-proteasome system. Thus, laurel may be a potential food ingredient that prevents muscle atrophy.

Albumin-bilirubin (ALBI) grade is an indicator of liver dysfunction and is useful for predicting postoperative prognosis of hepatocellular carcinomas. However, the significance of ALBI grade in non-small cell lung carcinoma (NSCLC) has not been elucidated. We analyzed 947 patients with pStage IA-IIIA NSCLC. We divided patients into ALBI grade 1 and grade 2/3 groups. We then analyzed the association of ABLI grade with clinicopathological characteristics and prognosis in NSCLC by using propensity-score matching. ALBI grade 2/3 was significantly associated with older age, male sex, advanced pT status, and histological type. Even after propensity-score matching, ALBI grade 2/3 patients had significantly worse cancer-specific survival (CSS) than ALBI grade 1 patients (5-year CSS: 87.3% versus 92.8%; p=0.0247). In multivariate analysis, ALBI grade 2/3 was an independent predictor of CSS (HR=1.9; 95%CI=1.11-3.11; p=0.0177). ALBI grade was an independent prognostic factor in surgically resected NSCLC.

IntroductionImmunotherapy is the fourth leading therapy for lung cancer following surgery, chemotherapy and radiotherapy. Recently, several studies have reported about the potential association between the gut microbiome and therapeutic response to immunotherapy. Nevertheless, the specific composition of the gut microbiome or combination of gut microbes that truly predict the efficacy of immunotherapy is not definitive.Methods and analysisThe present multicentre, prospective, observational study aims to discover the specific composition of the gut microbiome or combination of gut microbes predicting the therapeutic response to immunotherapy in lung cancer using artificial intelligence. The main inclusion criteria are as follows: (1) pathologically or cytologically confirmed metastatic or postoperative recurrent lung cancer including non-small cell lung cancer and small cell lung cancer; (2) age≥20 years at the time of informed consent; (3) planned treatment with immunotherapy including combination therapy and monotherapy, as the first-line immunotherapy; and (4) ability to provide faecal samples. In total, 400 patients will be enrolled prospectively. Enrolment will begin in 2021, and the final analyses will be completed by 2024.Ethics and disseminationThe study protocol was approved by the institutional review board of each participating centre in 2021 (Kyushu Cancer Center, IRB approved No. 2021-13, 8 June 2021 and Kyushu Medical Center, IRB approved No. 21-076, 31 August 2021). Study results will be disseminated through peer-reviewed journals and national and international conferences.Trial registration numberUMIN000046428.

Preoperative nutritional status is an important host-related prognostic factor for non-small cell lung carcinoma (NSCLC); however, the significance of postoperative changes in nutritional status remains unclear. This study aimed to elucidate the significance of postoperative decreases in serum albumin ([DELTA]Alb) on the outcomes of early-stage NSCLC. We analyzed 443 training cohort (TC) and 642 validation cohort (VC) patients with pStage IA NSCLC who underwent surgery and did not recur within 1 year. We measured preoperative serum albumin levels (preAlb) and postoperative levels 1 year after surgery (postAlb), and calculated [DELTA]Alb as (preAlb - postAlb)/preAlb x 100%. A cutoff value of 11% for [DELTA]Alb was defined on the basis of the receiver operating characteristic curve for the TC. We demonstrated a negative impact of postoperative decrease of the serum albumin on the prognosis of patients with early-stage NSCLC. Postoperative changes in nutritional status might be important in NSCLC outcomes.

Background While Personal Health Records (PHRs) are increasingly adopted globally, understanding public attitudes toward health information sharing remains crucial for successful implementation. This study investigated patterns in willingness to share personal health information among Japanese adults and identified factors influencing their sharing decisions. Methods A nationwide cross-sectional web-based survey was conducted among 20,000 Japanese adults in December 2023. Participants were recruited through quota sampling based on age, gender, and prefecture population ratios from the 2020 National Census. The survey examined willingness to share personal health information with nine types of recipients (healthcare providers, ambulance crew, application providers, family members, local authorities, employers, pharmaceutical companies, government agencies, and research institutions), trust levels in these recipients, and 17 factors influencing sharing decisions across health benefits, convenience, economic incentives, social significance, information details, transparency, and privacy considerations. Clustering analysis using Uniform Manifold Approximation and Projection (UMAP) and Ordering Points to Identify the Clustering Structure (OPTICS) algorithms was performed to identify distinct patterns in sharing preferences. Results Despite low PHR familiarity (88.4% unfamiliar), participants showed willingness to share health information with healthcare providers (65.0%) and family members (65.6%), but expressed lower willingness toward government agencies (28.6%) and research institutions (28.8%). Five distinct clusters were identified: family-only sharers (3.9%), mixed preference sharers (47.9%), comprehensive sharers (12.9%), non-sharers (22.1%), and healthcare-selective sharers (13.2%). Trust levels were highest for family members (85.6%) and healthcare professionals (78.8%), while significantly lower for government agencies (44.2%). Higher education, income, and PHR familiarity were associated with greater willingness to share, while privacy and security concerns were universal across all clusters. Conclusions The heterogeneous patterns in health information sharing preferences suggest the need for tailored PHR implementation strategies that address varying privacy concerns and trust levels across different population segments. Success in PHR adoption requires balanced approaches to trust-building, robust data protection, and targeted communication strategies that acknowledge diverse user needs while promoting the benefits of health data sharing.

Prolonged hospital stays can lead to inefficiencies in health care delivery and unnecessary consumption of medical resources. This study aimed to identify key clinical variances associated with prolonged length of stay (PLOS) in clinical pathways using a machine learning model trained on real-world data from the ePath system. We analyzed data from 480 patients with lung cancer (age: mean 68.3, SD 11.2 years; n=263, 54.8% men) who underwent video-assisted thoracoscopic surgery at a university hospital between 2019 and 2023. PLOS was defined as a hospital stay exceeding 9 days after video-assisted thoracoscopic surgery. The variables collected between admission and 4 days after surgery were examined, and those that showed a significant association with PLOS in univariate analyses (P<.01) were selected as predictors. Predictive models were developed using sparse linear regression methods (Lasso, ridge, and elastic net) and decision tree ensembles (random forest and extreme gradient boosting). The data were divided into derivation (earlier study period) and testing (later period) cohorts for temporal validation. The model performance was assessed using the area under the receiver operating characteristic curve, Brier score, and calibration plots. Counterfactual analysis was used to identify key clinical factors influencing PLOS. A 3D heatmap illustrated the temporal relationships between clinical factors and PLOS based on patient demographics, comorbidities, functional status, surgical details, care processes, medications, and variances recorded from admission to 4 days after surgery. Among the 5 algorithms evaluated, the ridge regression model demonstrated the best performance in terms of both discrimination and calibration. Specifically, it achieved area under the receiver operating characteristic curve values of 0.84 and 0.82 and Brier scores of 0.16 and 0.17 in the derivation and test cohorts, respectively. In the final model, a range of variables, including blood tests, care, patient background, procedures, and clinical variances, were associated with PLOS. Among these, particular emphasis was placed on clinical variances. Counterfactual analysis using the ridge regression model identified 6 key variables strongly linked to PLOS. In order of impact, these were abnormal respiratory sounds, postoperative fever, arrhythmia, impaired ambulation, complications after drain removal, and pulmonary air leaks. A machine learning-based model using ePath data effectively identified critical variances in the clinical pathways associated with PLOS. This automated tool may enhance clinical decision-making and improve patient management.

Background Immunotherapy has become a standard‐of‐care for patients with non‐small‐cell lung cancer (NSCLC). Although several biomarkers, such as programmed cell death‐1, have been shown to be useful in selecting patients likely to benefit from immune checkpoint inhibitors (ICIs), more useful and reliable ones should be investigated. The prognostic nutritional index (PNI) is a marker of the immune and nutritional status of the host, and is derived from serum albumin level and peripheral lymphocyte count. Although several groups reported its prognostic role in patients with NSCLC receiving a single ICI, there exist no reports which have demonstrated its role in the first‐line ICI combined with or without chemotherapy. Materials and Methods Two‐hundred and eighteen patients with NSCLC were included in the current study and received pembrolizumab alone or chemoimmunotherapy as the first‐line therapy. Cutoff value of the pretreatment PNI was set as 42.17. Results Among 218 patients, 123 (56.4%) had a high PNI (≥42.17), while 95 (43.6%) had a low PNI (<42.17). A significant association was observed between the PNI and both the progression‐free survival (PFS; hazard ratio [HR] =  0.67, 95% confidence interval [CI]: 0.51–0.88, p =  0.0021) and overall survival (OS; HR = 0.46, 95% CI: 0.32–0.67, p < 0.0001) in the entire population, respectively. The multivariate analysis identified the pretreatment PNI as an independent prognosticator for the PFS (p =  0.0011) and OS (p  < 0.0001), and in patients receiving either pembrolizumab alone or chemoimmunotherapy, the pretreatment PNI remained an independent prognostic factor for the OS (p = 0.0270 and 0.0006, respectively). Conclusion The PNI might help clinicians appropriately identifying patients with better treatment outcomes when receiving first‐line ICI therapy.

Introduction The reliability of data‐driven predictions in real‐world scenarios remains uncertain. This study aimed to develop and validate a machine‐learning‐based model for predicting clinical outcomes using real‐world data from an electronic clinical pathway (ePath) system. Methods All available data were collected from patients with lung cancer who underwent video‐assisted thoracoscopic surgery at two independent hospitals utilizing the ePath system. The primary clinical outcome of interest was prolonged air leak (PAL), defined as drainage removal more than 2 days post‐surgery. Data‐driven prediction models were developed in a cohort of 314 patients from a university hospital applying sparse linear regression models (least absolute shrinkage and selection operator, ridge, and elastic net) and decision tree ensemble models (random forest and extreme gradient boosting). Model performance was then validated in a cohort of 154 patients from a tertiary hospital using the area under the receiver operating characteristic curve (AUROC) and calibration plots. Results To mitigate bias, variables with missing data related to PAL or those with high rates of missing data were excluded from the dataset. Fivefold cross‐validation indicated improved AUROCs when utilizing key variables, even post‐imputation of missing data. Dichotomizing continuous variables enhanced performance, particularly when fewer variables were employed in the decision tree ensemble models. Consequently, regression models incorporating seven key variables in complete case analysis demonstrated superior discriminatory ability for both internal (AUROCs: 0.77–0.84) and external cohorts (AUROCs: 0.75–0.84). These models exhibited satisfactory calibration in both cohorts. Conclusions The data‐driven prediction model implementing the ePath system exhibited adequate performance in predicting PAL post‐video‐assisted thoracoscopic surgery, optimizing variables and considering population characteristics in a real‐world setting.