MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma.

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma.

Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic triggers leading to the reprogramming of tumor behavior and resistance are an area increasingly studied in relation to the tumor molecular features associated with outcome. There are currently no metabolomic biomarkers for GBM. Studying the metabolomic alterations in GBM patients undergoing CRT could uncover the biochemical pathways involved in tumor response and resistance, leading to the identification of novel biomarkers and the optimization of the treatment response. The feature selection process identifies key factors to improve the model's accuracy and interpretability. This study utilizes a combined feature selection approach, incorporating both Least Absolute Shrinkage and Selection Operator (LASSO) and Minimum Redundancy-Maximum Relevance (mRMR), alongside a rank-based weighting method (i.e., MetaWise) to link metabolomic biomarkers to CRT and the 12-month and 20-month overall survival (OS) status in patients with GBM. Our method shows promising results, reducing feature dimensionality when employed on serum-based large-scale metabolomic datasets (University of Florida) for all our analyses. The proposed method successfully identified a set of eleven serum biomarkers shared among three datasets. The computational results show that the utilized method achieves 96.711%, 92.093%, and 86.910% accuracy rates with 48, 46, and 33 selected features for the CRT, 12-month, and 20-month OS-based metabolomic datasets, respectively. This discovery has implications for developing personalized treatment plans and improving patient outcomes.

Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics.

Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models for glioma grading with clinical and molecular predictors. To identify the best scheme for the given soft-voting-based ensemble learning model selections, we utilized publicly available TCGA and CGGA datasets and employed four dimensionality reduction methods to carry out a voting-based ensemble feature selection and five supervised models, with a total of sixteen combination sets. We also compared our proposed feature selection method with the LASSO feature selection method in isolation. The computational results indicate that the proposed method achieves 87.606% and 79.668% accuracy rates on TCGA and CGGA datasets, respectively, outperforming the LASSO feature selection method.

Extracorporeal membrane oxygenation as a bridge to lung transplantation in a Turkish lung transplantation program: our initial experience.

Lung transplantation is a life-saving treatment for patients with end-stage lung disease. Although the number of lung transplants has increased over the years, the number of available donor lungs has not increased at the same rate, leading to the death of transplant candidates on waiting lists. In this paper, we presented our initial experience with the use of extracorporeal membrane oxygenation (ECMO) as a bridge to lung transplantation. Between December 2016 and August 2018, we retrospectively reviewed the use of ECMO as a bridge to lung transplantation. Thirteen patients underwent preparative ECMO for bridging to lung transplantation, and seven patients successfully underwent bridging to lung transplantation. The average age of the patients was 45.7 years (range, 19-62 years). The ECMO support period lasted 3-55 days (mean, 18.7 days; median, 13 days). In seven patients, bridging to lung transplantation was performed successfully. The mean age of patients was 49.8 years (range 42-62). Bridging time was 3-55 days (mean, 19 days; median, 13 days). Two patients died in the early postoperative period. Five patients survived until discharge from the hospital. One-year survival was achieved in four patients. ECMO can be used safely for a long time to meet the physiological needs of critically ill patients. The use of ECMO as a bridge to lung transplantation is an acceptable treatment option to reduce the number of deaths on the waiting list. Despite the successful results achieved, this approach still involves risks and complications.

Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs.

Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature.

Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma.

Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.

Correction: Tasci et al. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers 2023, 15, 2672.

In the original publication [...].

Corrigendum: Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.

[This corrects the article DOI: 10.3389/fonc.2023.1127645.].

Serum CD133-Associated Proteins Identified by Machine Learning Are Connected to Neural Development, Cancer Pathways, and 12-Month Survival in Glioblastoma.

Glioma is the most prevalent type of primary central nervous system cancer, while glioblastoma (GBM) is its most aggressive variant, with a median survival of only 15 months when treated with maximal surgical resection followed by chemoradiation therapy (CRT). CD133 is a potentially significant GBM biomarker. However, current clinical biomarker studies rely on invasive tissue samples. These make prolonged data acquisition impossible, resulting in increased interest in the use of liquid biopsies. Our study, analyzed 7289 serum proteins from 109 patients with pathology-proven GBM obtained prior to CRT using the aptamer-based SOMAScan® proteomic assay technology. We developed a novel methodology that identified 24 proteins linked to both serum CD133 and 12-month overall survival (OS) through a multi-step machine learning (ML) analysis. These identified proteins were subsequently subjected to survival and clustering evaluations, categorizing patients into five risk groups that accurately predicted 12-month OS based on their protein profiles. Most of these proteins are involved in brain function, neural development, and/or cancer biology signaling, highlighting their significance and potential predictive value. Identifying these proteins provides a valuable foundation for future serum investigations as validation of clinically applicable GBM biomarkers can unlock immense potential for diagnostics and treatment monitoring.

Evaluation of donor-derived bacterial infections in lung transplant recipients.

Background: This study aims to evaluate the etiology and outcomes of donor-derived bacterial infections in patients undergoing lung transplantation. Methods: Between January 2013 and December 2017, a total of 71 lung transplant recipients (56 males, 15 females; median age: 43.3 years) were retrospectively analyzed. The diagnosis of donor-derived bacterial infection was defined as the isolation of the same bacteria with the same antibiotic susceptibility patterns in a lung sample of donor and in one sample obtained from patients after transplantation and the presence of clinical evidence of infection. Results: Ten (14%) patients were found to have donor-derived bacterial infection. Acinetobacter baumannii was found in three, Pseudomonas aeruginosa in three, Klebsiella pneumoniae in one, Enterobacter cloacae in one, Staphylococcus aureus in one, and both Klebsiella pneumoniae and Acinetobacter baumannii in one patient. Twenty-four of lung-transplant recipients and four patients with donor-derived infection died. Conclusion: Lung transplants are usually performed in hospitalized patients or in those admitted to the intensive care unit. These patients commonly experience infection and colonization with resistant microorganisms.

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics.

Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.

An Overview of CD133 as a Functional Unit of Prognosis and Treatment Resistance in Glioblastoma.

Biomarkers for resistance in Glioblastoma multiforme (GBM) are lacking, and progress in the clinic has been slow to arrive. CD133 (prominin-1) is a membrane-bound glycoprotein on the surface of cancer stem cells (CSCs) that has been associated with poor prognosis, therapy resistance, and tumor recurrence in GBM. Due to its connection to CSCs, to which tumor resistance and recurrence have been partially attributed in GBM, there is a growing field of research revolving around the potential role of CD133 in each of these processes. However, despite encouraging results in vitro and in vivo, the biological interplay of CD133 with these components is still unclear, causing a lack of clinical application. In parallel, omic data from biospecimens that include CD133 are beginning to emerge, increasing the importance of understanding CD133 for the effective use of these highly dimensional data sets. Given the significant mechanistic overlap, prioritization of the most robust findings is necessary to optimize the transition of CD133 to clinical applications using patient-derived biospecimens. As a result, this review aims to compile and analyze the current research regarding CD133 as a functional unit in GBM, exploring its connections to prognosis, the tumor microenvironment, tumor resistance, and tumor recurrence.

RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma.

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with a performance value of 93.921%, comparing favorably with techniques that do not employ feature selection.

Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.

Background: Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response. Purpose: We analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters. Materials and methods: 82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally. Results: 3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα via NF-κB, Myc target V1 signaling, UV response, unfolded protein response, peroxisome, and interferon response) were distinct between clinical survival groups and supported by WGCNA. 23 proteins were statistically signficant for OS with 5 (NETO2, CST7, SEMA6D, CBLN4, NPS) supported by KM. Conclusion: Distinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.

Revisiting Concurrent Radiation Therapy, Temozolomide, and the Histone Deacetylase Inhibitor Valproic Acid for Patients with Glioblastoma-Proteomic Alteration and Comparison Analysis with the Standard-of-Care Chemoirradiation.

BACKGROUND: Glioblastoma (GBM) is the most common brain tumor with an overall survival (OS) of less than 30% at two years. Valproic acid (VPA) demonstrated survival benefits documented in retrospective and prospective trials, when used in combination with chemo-radiotherapy (CRT). PURPOSE: The primary goal of this study was to examine if the differential alteration in proteomic expression pre vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA as compared to standard-of-care CRT. The second goal was to explore the associations between the proteomic alterations in response to VPA/RT/TMZ correlated to patient outcomes. The third goal was to use the proteomic profile to determine the mechanism of action of VPA in this setting. MATERIALS AND METHODS: Serum obtained pre- and post-CRT was analyzed using an aptamer-based SOMAScan® proteomic assay. Twenty-nine patients received CRT plus VPA, and 53 patients received CRT alone. Clinical data were obtained via a database and chart review. Tests for differences in protein expression changes between radiation therapy (RT) with or without VPA were conducted for individual proteins using two-sided t-tests, considering p-values of <0.05 as significant. Adjustment for age, sex, and other clinical covariates and hierarchical clustering of significant differentially expressed proteins was carried out, and Gene Set Enrichment analyses were performed using the Hallmark gene sets. Univariate Cox proportional hazards models were used to test the individual protein expression changes for an association with survival. The lasso Cox regression method and 10-fold cross-validation were employed to test the combinations of expression changes of proteins that could predict survival. Predictiveness curves were plotted for significant proteins for VPA response (p-value < 0.005) to show the survival probability vs. the protein expression percentiles. RESULTS: A total of 124 proteins were identified pre- vs. post-CRT that were differentially expressed between the cohorts who received CRT plus VPA and those who received CRT alone. Clinical factors did not confound the results, and distinct proteomic clustering in the VPA-treated population was identified. Time-dependent ROC curves for OS and PFS for landmark times of 20 months and 6 months, respectively, revealed AUC of 0.531, 0.756, 0.774 for OS and 0.535, 0.723, 0.806 for PFS for protein expression, clinical factors, and the combination of protein expression and clinical factors, respectively, indicating that the proteome can provide additional survival risk discrimination to that already provided by the standard clinical factors with a greater impact on PFS. Several proteins of interest were identified. Alterations in GALNT14 (increased) and CCL17 (decreased) (p = 0.003 and 0.003, respectively, FDR 0.198 for both) were associated with an improvement in both OS and PFS. The pre-CRT protein expression revealed 480 proteins predictive for OS and 212 for PFS (p < 0.05), of which 112 overlapped between OS and PFS. However, FDR-adjusted p values were high, with OS (the smallest p value of 0.586) and PFS (the smallest p value of 0.998). The protein PLCD3 had the lowest p-value (p = 0.002 and 0.0004 for OS and PFS, respectively), and its elevation prior to CRT predicted superior OS and PFS with VPA administration. Cancer hallmark genesets associated with proteomic alteration observed with the administration of VPA aligned with known signal transduction pathways of this agent in malignancy and non-malignancy settings, and GBM signaling, and included epithelial-mesenchymal transition, hedgehog signaling, Il6/JAK/STAT3, coagulation, NOTCH, apical junction, xenobiotic metabolism, and complement signaling. CONCLUSIONS: Differential alteration in proteomic expression pre- vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA. Using pre- vs. post-data, prognostic proteins emerged in the analysis. Using pre-CRT data, potentially predictive proteins were identified. The protein signals and hallmark gene sets associated with the alteration in the proteome identified between patients who received VPA and those who did not, align with known biological mechanisms of action of VPA and may allow for the identification of novel biomarkers associated with outcomes that can help advance the study of VPA in future prospective trials.

Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.

Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.

Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future.

Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma. While precision medicine can target specific molecular features in glioma, targeted therapies are often not feasible due to the lack of actionable markers and the high cost of molecular testing. This review summarizes the clinically relevant molecular features in glioma and the current cost of care for glioma patients, focusing on the molecular markers and meaningful clinical features that are linked to clinical outcomes and have a realistic possibility of being measured, which is a promising direction for precision medicine using artificial intelligence approaches.

The Next Frontier in Health Disparities-A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back.

Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits. We set out to analyze the literature on women's data disparities in glioma by exploring the origins of data in this area to understand the representation of women in study samples and omics analyses. Given the current emphasis on inclusive study design and research, we wanted to explore if sex bias continues to exist in present-day data sets and how sex differences in data may impact conclusions derived from large-scale data sets, omics, biospecimen analysis, novel interventions, and standard of care management.

Lung transplantation for non-cystic fibrosis bronchiectasis in Turkey: An initial institutional experience.

BACKGROUND/OBJECTIVE: Lung transplantation is a well-established treatment in patients who have bronchiectasis with diffuse involvement, and with a progressive decline in respiratory function despite maximal medical therapy. We have aimed to present pre-transplantation factors and our results of lung transplantation for non-cystic fibrosis bronchiectasis. METHODS: Patients who underwent lung transplantation for non-cystic fibrosis bronchiectasis between the dates of December 2016 and July 2019 were included. The patients' clinical parameters, pulmonary function tests, microbiological results, cardiac parameters, intraoperative data, and lung transplant outcomes were assessed retrospectively. RESULTS: Bilateral lung transplantation for bronchiectasis were performed in eleven patients. The mean age was 36.5 years (range 22-57 years). There were 4 (36.4%) female patients and 7 (63.6%) male patients. All patients had a high score as per the bronchiectasis severity index (BSI). The FACED score was moderate in six patients and severe in five patients. Preoperative colonization with Pseudomonas aeruginosa was observed in five patients. Hospital mortality was 18.2% (2/11). The 1-year mortality was 27.2% (3/11). Eight patients were alive. The mean follow-up period of patients with survival was 28.2 months (range 13-42 months). One patient was diagnosed with chronic lung allograft dysfunction (CLAD). The 3-year survival rates were 73%. CONCLUSION: Lung transplantation for bronchiectasis with end-stage lung disease can improve the quality of life and increase survival in selected patients. Further studies are needed to identify the optimal time for lung transplantation referral due to the availability of limited data.

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection.

Tuberculosis (TB) is known as a potentially dangerous and infectious disease that affects mostly lungs worldwide. The detection and treatment of TB at an early stage are critical for preventing the disease and decreasing the risk of mortality and transmission of it to others. Nowadays, as the most common medical imaging technique, chest radiography (CXR) is useful for determining thoracic diseases. Computer-aided detection (CADe) systems are also crucial mechanisms to provide more reliable, efficient, and systematic approaches with accelerating the decision-making process of clinicians. In this study, we propose voting and preprocessing variations-based ensemble CNN model for TB detection. We utilize 40 different variations in fine-tuned CNN models based on InceptionV3 and Xception by also using CLAHE (contrast-limited adaptive histogram equalization) preprocessing technique and 10 different image transformations for data augmentation types. After analyzing all these combination schemes, three or five best classifier models are selected as base learners for voting operations. We apply the Bayesian optimization-based weighted voting and the average of probabilities as a combination rule in soft voting methods on two TB CXR image datasets to get better results in various numbers of models. The computational results indicate that the proposed method achieves 97.500% and 97.699% accuracy rates on Montgomery and Shenzhen datasets, respectively. Furthermore, our method outperforms state-of-the-art results for the two TB detection datasets in terms of accuracy rate.