Dan Levy Faber

Non-small cell lung cancer (NSCLC) exhibits genomic heterogeneity that affects tumor immunogenicity and PD-L1 expression. Patient clustering based on shared mutational profiles using social network analysis (SNA) has been narrowly explored. The study aimed to identify subgroups of NSCLC patients with similar somatic mutation profiles using network-based modularity clustering, and to compare these groups with respect to PD-L1 expression, Tumor mutation burden (TMB), and clinical variables. Data of patients with stage 4 (metastatic) NSCLC, whose tumor tissue samples were collected between 2022 and 2024, were analyzed. This retrospective study included NSCLC patients harboring actionable driver mutations in genes such as EGFR, KRAS, ALK, BRAF, MET. A social network of 129 patients was constructed. Two distinct genomic clusters were identified. Cluster 2 (n = 55) showed a higher prevalence of KRAS, TP53, BRAF, STK11 and additional mutations, while cluster 1 (n = 74) displayed a limited number of driver mutations. Cluster 2 had significantly higher PD-L1 expression (29.8% vs. 13.7%, p = 0.001) and higher TMB (7.8 vs. 5.8, p = 0.021). In multivariate logistic regression, both PD-L1 and TMB were associated with cluster assignment (p < 0.05). Mutation-based SNA clustering delineated two biologically distinct subgroups of NSCLC patients. The highly mutated cluster displayed higher PD-L1 expression and TMB, a profile consistent with a more immunogenic phenotype. This method offers a novel integrative approach that requires prospective validation before clinical implementation.

Non–small cell lung cancer (NSCLC) exhibits genomic heterogeneity that affects tumor immunogenicity and PD-L1 expression. Patient clustering based on shared mutational profiles using social network analysis (SNA) has been narrowly explored. The study aimed to identify subgroups of NSCLC patients with similar somatic mutation profiles using network-based modularity clustering, and to compare these groups with respect to PD-L1 expression, Tumor mutation burden (TMB), and clinical variables. Data of NSCLC patients who underwent surgery between 2022 and 2024 was analyzed. This retrospective study included NSCLC patients harboring actionable driver mutations in genes such as EGFR, KRAS, ALK, BRAF, MET. A social network of 129 patients was constructed. Two distinct genomic clusters were identified. Cluster 2 (n=55) showed a higher prevalence of KRAS, TP53, BRAF, STK11 and additional mutations, while cluster 1 (n=74) displayed a limited number of driver mutations. Cluster 2 had significantly higher PD-L1 expression (29.8% vs. 13.7%, p=0.001) and higher TMB (7.8 vs. 5.8, p=0.021). In multivariate logistic regression, both PD-L1 and TMB independently predicted cluster assignment (p<0.05). Mutation-based SNA clustering successfully delineated biologically distinct subgroups of NSCLC patients. The highly mutated cluster displayed increased immunogenicity, reflected in elevated PD-L1 and TMB levels. This method offers a novel integrative approach that requires prospective validation before clinical implementation.

Background/Objectives: The aim of this study was to evaluate whether no suction management to chest drains after pulmonary resection shortens the duration of chest tube placement and improves postoperative outcomes compared to routine application of negative-pressure suction to chest drains. Methods: A single-center randomized controlled study was conducted in patients undergoing lung resection. Patients received a single 28Fr chest tube attached to a digital drainage system. Patients were randomized to Control (chest tube on continuous suction) or No Suction. Results: From December 2022 to April 2025, 309 patients were enrolled; 23 patients were excluded for protocol deviations. In total, 286 patients were analyzed (149 Control, 137 No Suction). Chest tube duration was shorter in the No Suction (mean 40.2 ± 43.8 h) than the Control group (53.6 ± 67.8 h, p = 0.002). Hospital length of stay and the incidence of prolonged air leak did not differ between No Suction and Control. In multivariable regression, suction was associated with a 13 h longer time to drain removal (95% confidence interval 0.03 to 27 h; p = 0.050), without a significant effect on length of stay or odds of prolonged air leak. Patients with underlying lung disease or undergoing anatomical resection had overall longer chest tube durations. Conclusions: Application of suction to chest tubes after lung resection prolongs the duration of chest drainage without improving clinical outcomes. Managing chest tubes with physiologic intrapleural pressure and no suction may allow for earlier removal of drains and should be considered as the approach in uncomplicated lung resections.

Background/Objectives: To evaluate whether no suction management to chest drains after pulmonary resection shortens the duration of chest tube placement and improves postoperative outcomes compared to routine application of negative-pressure suction to chest drains. Methods: A single-center randomized controlled study was conducted in patients undergoing lung resection. Patients received a single 28Fr chest tube attached to a digital drainage system. Patients were randomized to Control (chest tube on continuous suction) or No Suction. Results: From December 2022 to April 2025, 309 patients were enrolled; 23 patients were excluded for protocol deviations. 286 patients were analyzed (149 Control, 137 No Suction). Chest tube duration was shorter in the No Suction (mean 40.2 ± 43.8 hours) than the Control group (53.6 ± 67.8 hours, p=0.002). Hospital length of stay and the incidence of prolonged air leak did not differ between No Suction and Control. In multivariable regression, suction was associated with a 13-hour longer time to drain removal (95% confidence interval 0.03 to 27 hours; p=0.050), without a significant effect on length of stay or odds of prolonged air leak. Patients with underlying lung disease or undergoing anatomical resection had overall longer chest tube durations. Conclusions: Application of suction to chest tubes after lung resection prolongs the duration of chest drainage without improving clinical outcomes. Managing chest tubes with physiologic intrapleural pressure and no suction may allow for earlier removal of drains and should be considered as the approach in uncomplicated lung resections.

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers—e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1, 2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n = 38, n = 12, and n = 23, respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n = 31 and n = 25, respectively). Cluster comparisons demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and tumor mutational burden. No significant associations were found regarding ethnicity or microsatellite instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy.

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers— e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1,2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n=38, n=12 and n=23 respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n=31, n=25 respectively). Multivariate analysis demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and Tumor Mutational Burden. No significant associations were found regarding ethnicity or Microsatellite Instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy.

Background/Objectives: Ground-glass opacity (GGO) refers to areas of increased lung opacity on computed tomography (CT) scans. Distinguishing malignant from benign lesions using CT scans remains significantly challenging. This study aims to compare the performances of a linear multivariate statistical regression and an AI deep learning method in their abilities to predict GGO malignancy, given a set of pixel features extracted from CT scans. Methods: This retrospective study investigated patients from the Carmel Medical Center with findings of GGO nodules in their lung CT scans. Forty-seven consecutive patients were found to have either pure or part-solid GGO lesions, as defined by two independent radiologists. After manually segmenting the GGOs in the CT scans, pixel features were extracted using the MaZda software package, which analyzes six different image texture features. These textural variables were then compiled as input for the multivariate statistical regression. Additionally, an AI deep learning method, developed by our group and hosted on the cloud, was applied to the CT images containing the GGOs. Results: Among the 47 patients, 32 were diagnosed by pathology with malignant lesions and 15 with benign findings. Using the multivariate statistical regression, we identified 19 variables with statistically significant or near-significant differences through univariate analysis. In subsequent multivariate analyses, two independent variables that could distinguish between benign and malignant GGO lesions were identified: S(4,4)AngScMom (p = 0.012) and WavEnLH_s-2 (p = 0.008). The regression formula based on these two variables yielded a sensitivity of 91% and a specificity of 67% AUC: 0.8 (95% CI: [0.65, 0.94]). The AI deep learning model demonstrated a sensitivity of 100% and a specificity of 80% AUC: 0.96 (95% CI: [0.86, 1.00]). Conclusions: This proof-of-concept study demonstrates the superior performance of the AI deep learning model compared to the multivariate statistical regression, particularly in terms of sensitivity and specificity. However, given the small sample size, these results could potentially change with larger patient cohorts.

Background: A ground glass nodule (GGN) is a radiologically descriptive term for a lung parenchymal area with increased attenuation and preserved bronchial and vascular structures. GGNs are further divided into pure versus subsolid lesions. The differential diagnosis for GGNs is wide and contains a malignant possibility for a lung adenocarcinoma precursor or tumor. Clinical and pathological staging of GGNs is based on the lesions’ solid component and falls into a specific classification including T0 for TIS, T1mi for minimally invasive adenocarcinoma (MIA) and T1abc for lepidic predominant adenocarcinoma (LPA) according to the eighth edition of the TNM classification of lung cancer. Correlation between solid parts seen on a CT scan and the tumor pathological invasive component is not absolute. Methods: This retrospective study collected the data of 68 GGNs that were operated upon in Carmel Medical Center. A comparison between preoperative clinical staging and post-surgery pathological staging was conducted. Results: Over a third of the lesions, twenty-four (35.3%), were upstaged while only four (5.9%) lesions were downstaged. Another third of the lesions, twenty-three (33.8%), kept their stage. In three (4.4%) cases, premalignant lesion atypical adenomatous hyperplasia (AAH) was diagnosed. Ten (14.7%) cases were diagnosed as non-malignant on final pathology. These findings show an overall low agreement between the clinical and pathological stages of GGNs. Conclusions: The relatively high percentage of upstaging tumors detected in this study and the overall safe and short surgical procedure advocate for surgical resection even in the presence of a significant number of non-malignant lesions that retrospectively do not mandate intervention at all.

Non-small cell lung cancer (NSCLC) is the most common pulmonary malignancy, frequently diagnosed at an advanced stage (III/IV). Patients in the Locally Advanced Stage Subgroup (IIIA) are relatively few, yet compose heterogenic phenotypes, posing a diagnostic and treating challenge, leading to a lack of clinical guidelines regarding the optimal standard of care. Several approaches exist, with a general agreement that a combined oncological and surgical modality approach is required. In this current retrospective descriptive study, patients with operable stage IIIA NSCLC who underwent surgery between 2013 and 2020 were evaluated on several aspects, including the initial diagnosis, neoadjuvant regimens, outcomes of surgical intervention, and overall survival at 2 years and 5 years following treatment. A total of 35 patients had neoadjuvant oncological treatment (mostly chemoradiation therapy) prior to surgery, out of which 28 patients were diagnosed with stage IIIA NSCLC. In post-operative assessment of pathological staging, downstaging was reported in 19 patients, of which 25% of cases were defined as a complete pathological response. The 2-year overall survival rate was 65% and the 5-year overall survival rate was 62%. The main pattern of disease recurrence was distant metastasis.

Non-Small Cell Lung Cancer (NSCLC) is the most common pulmonary malignancy, most frequently diagnosed at an advanced stage (III/IV). Patients in the Locally-Advanced Stage Subgroup (IIIA) are relatively few, yet compose of heterogenic phenotypes posing a diagnostic and treating challenge, leading to lack of clinical guidelines regarding the optimal standard of care. Several approaches exist, with a general agreement that a combined oncological and surgical modality approach is required. In this current retrospective descriptive study, operable Stage IIIA NSCLC patients who underwent surgery between 2013-2020 were evaluated on several aspects, including initial diagnosis, neoadjuvant regimens, outcomes of surgical intervention, overall survival at 2-years and 5-years following treatment. A total of 35 patients had neoadjuvant oncological treatment (mostly chemoradiation therapy) prior to surgery, out of which 28 patients were diagnosed with Stage IIIA NSCLC. In post-operative assessment of pathological staging, downstaging was reported in 20 patients, of which 7 (25%) cases were defined as complete pathological response. The 2-years overall survival rate was 65% and the 5-years overall survival rate was 60%. The main pattern of disease recurrence was distant metastasis.