Balkan Medical JournalPub Date : 2026-09-01Epub Date: 2026-07-02DOI: 10.4274/balkanmedj.galenos.2026.2026-4-269
Jibo Sun, Xirui Chen, Xiangpeng Wang, Xing He, Lu Guo, Dajun Li, Lian Wang, Guanping Liu, Xiang Tong, Hong Fan
{"title":"Admission-Based Machine-Learning Models for Predicting Mechanical Ventilation and Mortality in Fibrotic Interstitial Lung Disease: A Multicenter Cohort Study with External Validation.","authors":"Jibo Sun, Xirui Chen, Xiangpeng Wang, Xing He, Lu Guo, Dajun Li, Lian Wang, Guanping Liu, Xiang Tong, Hong Fan","doi":"10.4274/balkanmedj.galenos.2026.2026-4-269","DOIUrl":"10.4274/balkanmedj.galenos.2026.2026-4-269","url":null,"abstract":"<p><strong>Background: </strong>Admission-based risk stratification tools are limited for hospitalized patients with fibrotic interstitial lung disease (F-ILD).</p><p><strong>Aims: </strong>To develop and externally validate admission-based machine-learning models for predicting mechanical ventilation (MV), 30-day and 3-month mortality, and long-term all-cause and cause-specific mortality in hospitalized patients with F-ILD.</p><p><strong>Study design: </strong>Multicenter retrospective cohort study.</p><p><strong>Methods: </strong>This study included hospitalized adults with F-ILD from two tertiary hospitals in China. Clinical characteristics and laboratory test results obtained within 24 hours of admission were used as candidate predictors. Machine-learning models were developed to predict MV, 30-day and 3-month mortality, and long-term all-cause and cause-specific mortality, with internal testing and independent external validation. Model performance, clinical utility, and interpretability were evaluated.</p><p><strong>Results: </strong>A total of 1,272 patients were included (derivation cohort, n = 1,006; external validation cohort, n = 266). In the external validation cohort, the models demonstrated robust discrimination for MV [area under the curve (AUC), 0.913], 30-day mortality (AUC, 0.926), and 3-month mortality (AUC, 0.813). For long-term all-cause mortality, the final model achieved a C-index of 0.768, with time-dependent AUCs of 0.811 at 12 months and 0.768 at 24 months. The corresponding values for cause-specific mortality were 0.768, 0.815, and 0.761, respectively. Lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index (PNI) emerged as key predictors of MV and short-term mortality, whereas long-term mortality risk was additionally associated with older age, male sex, smoking history, an idiopathic pulmonary fibrosis phenotype, and lower albumin-to-globulin ratio and platelet-to-white blood cell ratio.</p><p><strong>Conclusion: </strong>Admission-based models demonstrated strong discriminatory performance for predicting MV and mortality in hospitalized patients with F-ILD. Elevated LDH and NLR levels, together with lower PNI, characterized a high-risk profile for acute deterioration and death. Local recalibration may be necessary before implementation in new clinical settings.</p>","PeriodicalId":8690,"journal":{"name":"Balkan Medical Journal","volume":" ","pages":"513-523"},"PeriodicalIF":4.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13530708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148366894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Balkan Medical JournalPub Date : 2026-07-31Epub Date: 2026-06-29DOI: 10.4274/balkanmedj.galenos.2026.2026-5-57
Thinh Le Phuoc, Ngoc Huu Duc Tran, Pham Nguyen Hung Vo, Vuong Hoang Trung Le
{"title":"Airway-Dominant Relapsing Polychondritis Mimicking a Subglottic Tumor in an Adolescent: A Diagnostic Pitfall.","authors":"Thinh Le Phuoc, Ngoc Huu Duc Tran, Pham Nguyen Hung Vo, Vuong Hoang Trung Le","doi":"10.4274/balkanmedj.galenos.2026.2026-5-57","DOIUrl":"10.4274/balkanmedj.galenos.2026.2026-5-57","url":null,"abstract":"","PeriodicalId":8690,"journal":{"name":"Balkan Medical Journal","volume":" ","pages":"483-485"},"PeriodicalIF":4.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148343773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Balkan Medical JournalPub Date : 2026-07-31Epub Date: 2026-07-17DOI: 10.4274/balkanmedj.galenos.2026.2026-6-3
Burak Kocak, Andrea Ponsiglione, Vien Ngoc Dang, Matthias Dietzel, Karim Lekadir, Renato Cuocolo
{"title":"Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.","authors":"Burak Kocak, Andrea Ponsiglione, Vien Ngoc Dang, Matthias Dietzel, Karim Lekadir, Renato Cuocolo","doi":"10.4274/balkanmedj.galenos.2026.2026-6-3","DOIUrl":"10.4274/balkanmedj.galenos.2026.2026-6-3","url":null,"abstract":"<p><p>Artificial intelligence (AI) is increasingly being investigated and, in selected clinical settings, implemented to support diagnosis, triage, and workflow optimization. Although these systems have the potential to improve access, consistency, and efficiency, they may also reproduce or amplify health inequities when bias is introduced during development, evaluation, implementation, or postdeployment use. This clinician-oriented narrative review adopts a practical lifecycle approach to explain how algorithmic unfairness becomes clinically relevant, how clinicians can recognize it, and how institutions can mitigate its impact. We first outline the ethical, clinical, and mathematical dimensions of fairness. We then examine fairness risks and sources of bias across six stages of the healthcare AI lifecycle: problem formulation, data generation, model development, evaluation, implementation, and postdeployment monitoring and governance. Key mechanisms include biased proxy outcomes, unrepresentative or error-prone data and labels, model shortcut learning, hidden stratification, distribution shift, and human-AI interaction effects (e.g., automation bias and alert fatigue), all of which can create feedback loops and contribute to fairness drift over time. For each stage, we identify clinician-facing red flags and practical mitigation strategies, including defining clinically meaningful outcomes, using representative and well-documented datasets, conducting subgroup-stratified evaluations, performing external and prospective validation, justifying decision thresholds, implementing safeguards for human-AI interactions, and maintaining continuous postdeployment monitoring, including postmarket surveillance for regulated medical devices. Fairness cannot be ensured through a single metric, publication, regulatory clearance, or one-time validation. Instead, equitable healthcare AI requires transparent design, rigorous evaluation, local governance, and ongoing monitoring across diverse populations, clinical sites, devices, workflows, and time. Fairness should therefore be regarded as a continuous clinical and institutional responsibility rather than a downstream technical consideration.</p>","PeriodicalId":8690,"journal":{"name":"Balkan Medical Journal","volume":" ","pages":"418-433"},"PeriodicalIF":4.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Balkan Medical JournalPub Date : 2026-07-31Epub Date: 2026-06-23DOI: 10.4274/balkanmedj.galenos.2026.2026.090626
Nihat Demirhan Demirkıran, Wolf Petersen
{"title":"Artificial Intelligence in Orthopaedics: The Future Through the Lens of Knee Surgery.","authors":"Nihat Demirhan Demirkıran, Wolf Petersen","doi":"10.4274/balkanmedj.galenos.2026.2026.090626","DOIUrl":"10.4274/balkanmedj.galenos.2026.2026.090626","url":null,"abstract":"","PeriodicalId":8690,"journal":{"name":"Balkan Medical Journal","volume":" ","pages":"414-417"},"PeriodicalIF":4.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"<i>SLC16A3</i>-Induced Lactate Remodeling Drives Immune Evasion in Clear Cell Renal Cell Carcinoma via an Autocrine GPR81-ERK-c-MYC Feedback Loop.","authors":"Shidong Zuo, Jinkai Dong, Yuhao Dong, Chengwei Fu, Xuechao Li, Baobo Zhao, Lu Wang, Yongjie Zhang, Lingsheng Kong, Tao Yang, Lijun Chen","doi":"10.4274/balkanmedj.galenos.2026.2026-1-247","DOIUrl":"10.4274/balkanmedj.galenos.2026.2026-1-247","url":null,"abstract":"<p><strong>Background: </strong>Extensive studies have implicated glycolytic reprogramming and lactate accumulation in immune evasion. However, clustered regularly interspaced short palindromic repeats (CRISPR)-based <i>in vivo</i> screens systematically interrogating metabolic drivers of immune checkpoint blockade resistance in clear cell renal cell carcinoma (ccRCC) remain scarce. Consequently, direct <i>in vivo</i> evidence delineating how the lactate exporter solute carrier family 16 member 3 (<i>SLC16A3</i>; monocarboxylate transporter 4, MCT4) rewires tumor lactate metabolism to promote immune escape in ccRCC is still lacking.</p><p><strong>Aims: </strong>To identify key metabolic genes governing immune checkpoint blockade resistance in ccRCC via <i>in vivo</i> CRISPR screening, elucidate the molecular mechanisms of <i>SLC16A3</i>-mediated lactate reprogramming in regulating glycolysis and immune suppression, and validate the therapeutic potential of MCT4 inhibition combined with anti-programmed cell death protein-1 (anti-PD-1) therapy for ccRCC.</p><p><strong>Study design: </strong>Integrated in vivo CRISPR screening, functional assays, and clinical validation.</p><p><strong>Methods: </strong>A metabolic CRISPR library-based screen was conducted in an <i>in vivo</i> immunocompetent ccRCC model treated with an anti-PD-1 antibody. Single-guide RNAs (sgRNAs) targeting glycolysis-associated genes were differentially enriched. Analyses of The Cancer Genome Atlas dataset and the Tumor Immune Estimation Resource database were performed, and an institutional tissue microarray analysis assessed survival outcomes. <i>SLC16A3</i>-knockout and <i>SLC16A3</i>-overexpressing RENCA cells were generated to evaluate tumor growth in immunodeficient and immunocompetent mice, alongside immune cell infiltration profiling. Seahorse metabolic assays, flow cytometry, lactate-treated macrophage assays, and G-protein-coupled receptor 81 (GPR81) antagonism or genetic suppression were used to dissect the lactate export/receptor axis. Additionally, <i>SLC16A3</i>-knockdown and <i>SLC16A3</i>-overexpressing 786-O and SN12C ccRCC cells were established. RNA sequencing, protein stability, ubiquitination, and phosphorylation analyses were conducted to elucidate the underlying molecular mechanisms.</p><p><strong>Results: </strong>Anti-PD-1 therapy selectively reduced sgRNA counts for glycolysis-lactate genes, most notably Ldha and <i>SLC16A3</i>. While <i>SLC16A3</i> depletion modestly restricted intrinsic ccRCC proliferation in immunodeficient models, its predominant effect was remodeling the tumor immune microenvironment. <i>SLC16A3</i>-mediated lactate export activated macrophage extracellular signal-regulated kinase (ERK) signaling in a GPR81-dependent manner, promoted M2 macrophage polarization, and suppressed CD8<sup>+</sup> T-cell function. Concurrently, exported lactate engaged tumor GPR81 to activate autocrine ERK signaling and phosphorylate c-MYC at Ser62, preven","PeriodicalId":8690,"journal":{"name":"Balkan Medical Journal","volume":" ","pages":"434-448"},"PeriodicalIF":4.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424917/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147760827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Integrating the Interaction Between Extrathyroidal Extension and Tumor Size to Optimize T Stage in Differentiated Thyroid Cancer.","authors":"Hui Ouyang, Xiaolin Dou, Botao Sun, Jinlong Chen, Hongyan Zai, Xinying Li, Fada Xia","doi":"10.4274/balkanmedj.galenos.2026.2026-1-269","DOIUrl":"10.4274/balkanmedj.galenos.2026.2026-1-269","url":null,"abstract":"<p><strong>Background: </strong>The American Joint Committee on Cancer (AJCC) T stage for differentiated thyroid cancer (DTC) has limited prognostic stratification capability, and the interaction between extrathyroidal extension (ETE) and tumor size remains inadequately characterized.</p><p><strong>Aims: </strong>To investigate the interaction between ETE and tumor size and to develop a modified T stage with improved prognostic stratification for DTC.</p><p><strong>Study design: </strong>Retrospective, population-based cohort study.</p><p><strong>Methods: </strong>We analyzed 163,616 patients with DTC from the Surveillance, Epidemiology, and End Results database, divided into development (n = 106,516; 2004-2015) and validation (n = 57,100; 2016-2021) cohorts. Disease-specific survival was the primary outcome. We evaluated the interaction between ETE and tumor size on additive and multiplicative scales and developed a modified T stage using recursive partitioning analysis. Performance assessment included discrimination (C-index), calibration (Brier score), and five standard cancer staging criteria.</p><p><strong>Results: </strong>Significant positive additive interactions were observed between ETE and tumor size, with relative excess risk due to interaction (RERI) increasing from minimal ETE (MinETE) [RERI = 1.90; 95% confidence interval (CI): 1.02-2.78] to Gross4b ETE (RERI = 6.55; 95% CI: 2.74-10.35). In tumors > 2 cm, MinETE was associated with a 4.58-fold increased risk (95% CI: 3.77-5.55), compared with a 2.34-fold increase (95% CI: 2.05-2.66) in smaller tumors, indicating size-dependent prognostic effects. The modified T stage incorporating these interactions demonstrated improved discrimination (C-index: 0.775; 95% CI: 0.770-0.779) compared with the AJCC 8<sup>th</sup> edition T stage (C-index: 0.766; 95% CI: 0.761-0.770). This represents a statistically significant but numerically modest improvement, with consistent enhancement across all performance metrics. The modified system also improved prediction of lymph node and distant metastases and provided clearer tumor-node-metastasis stage separation.</p><p><strong>Conclusion: </strong>The modified T stage incorporating size-dependent effects of ETE was associated with improved prognostic stratification compared with the AJCC 8<sup>th</sup> edition. These findings support consideration of tumor size-ETE interaction in future staging refinements to enable more precise risk stratification and personalized treatment strategies for patients with DTC. Prospective, multicenter validation is warranted to confirm these findings and evaluate their impact on clinical decision-making.</p>","PeriodicalId":8690,"journal":{"name":"Balkan Medical Journal","volume":" ","pages":"449-459"},"PeriodicalIF":4.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147979799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Balkan Medical JournalPub Date : 2026-07-31Epub Date: 2026-05-14DOI: 10.4274/balkanmedj.galenos.2026.2026-3-200
Mert Doğan, Ahmet Hakan Ateş, Hayrettin Hakan Aykan, Uğur Canpolat, Kudret Aytemir
{"title":"LBBAP Unmasks Severe Aortic Stenosis and Percutaneous Closure of Post-AVR Iatrogenic VSD.","authors":"Mert Doğan, Ahmet Hakan Ateş, Hayrettin Hakan Aykan, Uğur Canpolat, Kudret Aytemir","doi":"10.4274/balkanmedj.galenos.2026.2026-3-200","DOIUrl":"10.4274/balkanmedj.galenos.2026.2026-3-200","url":null,"abstract":"","PeriodicalId":8690,"journal":{"name":"Balkan Medical Journal","volume":" ","pages":"471-473"},"PeriodicalIF":4.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147939970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}