Immunotherapy for non-small cell lung cancer (NSCLC) is incorporated increasingly in first line treatments protocols. Multiple phase 3 studies have tested different medications targeting programmed death receptor 1 (PD-1), programmed death-ligand 1 (PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), with or without chemotherapy. The inclusion criteria differ between the various clinical trials, including the cut-off levels of PD-L1 expression on tumor cells, and the tumor histology (squamous or non-squamous). Patients with tumor expression levels of PD-L1 ≥ 50% are candidates for treatment with single agent Pembrolizumab or Atezolizumab. Patients with PD-L1 < 50% are candidates for immunotherapy with pembrolizumab as a single agent if PL-1 > 1%; immunotherapy doublet, Nivolumab and Ipilimumab, or single agent immunotherapy combined with chemotherapy. Here we review phase 3 clinical trials utilizing immunotherapy in the first line for treatment of NSCLC, including Pembrolizumab in KEYNOTE-024, KEYNOTE-042, KEYNOTE-189 and KEYNOTE-407; Nivolumab and Ipilimumab in CHECKMATE-227 and CHECKMATE 9LA; and Atezolizumab in IMpower110, IMpower130 and IMpower150.

Peer reviewed article authored by (Gorenberg M, Schwartz K). Read article or submit your manuscript for publishing.

Ionizing radiation (IR) exposure results in oxidative damage causing cytotoxic and genotoxic effects. Double-strand breaks (DSBs) are considered the most significant DNA lesions induced by ionizing radiation. The present study evaluates the radio protective effect of a novel antioxidant cocktail through quantification of DSB in peripheral blood lymphocytes (PBL) in vivo. The study included 16 consecutive patients who were divided into 2 groups, 6 patients received the novel antioxidant cocktail and 10 control patients. Blood samples were drawn from the patients undergoing bone scan, before the injection of the ^99mTc MDP tracer and 2 h after the injection. Quantification of the IR damage was done by Immunofluorescence analysis of the phosphorylated histone, γ-H2AX, used to monitor DSB induction and repair in PBL. The radiation effect of the control group was measured by 2 variables, the average DBSs foci per nucleus and the percent of the DSB bearing cells in PBL. The findings showed a significant increase in the DSBs after isotope injection with an average increment of 0.29 ± 0.13 of foci/nucleus and 17.07% ± 7.68 more DSB bearing cells (p < 0.05). The cocktail treated group showed a lower difference average of − 2.79% ± 6.13 DSB bearing cells. A paired t-test revealed a significant difference between the groups (p < 0.005) confirming the cocktail’s protective effect. The novel anti-oxidant treatment decreases the oxidative stress-induced DNA damage and can be considered as a preventative treatment before radiation exposure.

Purpose. The current pilot study investigates the effectiveness of a novel device in the management of chronic low back pain (LBP). This device is able to automatically measure skin impedance in a selected body area and, immediately afterwards, to stimulate multiple points that are targeted according to differentiation in their electrical properties (peripheral nerve ends—milinated A δ fibers) with high-intensity electrical stimulation. Materials and Methods. Nineteen outpatients were included in the study, 15 females (79%) and 4 men (21%), mean age 52.1±10.8 years, all diagnosed with nonspecific chronic LBP. The protocol consisted of 6 treatment sessions, 2–4 days apart. Each session included a <1 minute automatic impedance screening, followed by a 20-minute treatment of lowest impedance points according to proprietary algorithms. Outcome Measures. The primary outcome measure consisted of changes in pain intensity as measured on a 100 mm pain visual analogue scale (VAS) obtained at enrollment, before and 2 hours after each treatment. Secondary outcome measures were the Oswestry Disability Index (ODI) and lumbar flexion range of motion (ROM) obtained at baseline and each week during treatment. Results. The mean±SD baseline VAS score for all participants was 61±14. There were no significant changes in VAS scores between enrollment and before the first treatment (55±16; P=.102). During treatment, VAS scores decreased significantly compared with baseline scores by 39±17 mm (P<.001). Notably, VAS scores of all the patients, except for one, decreased by more than 20 mm after the fourth treatment, thus showing marked improvement in 95% of enrolled patients. ODI decreased throughout the entire treatment period, with significant changes from baseline already at the first week (P=.001). Lumbar flexion ROM showed a mean increase of 2.1 cm during treatment, but was not statistically significant. Conclusion. The results of the current pilot study show that treatment with this novel device produced a clinically significant reduction in back pain in 95% of patients after four treatment sessions. The decrease both in pain and perceived disability, combined with the improvement in ROM, support further investigation of the use of this therapy in the treatment of LBP.

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.