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BackgroundFew real-world studies exist regarding the clinical value of local consolidative therapy (LCT) for oligo-residual disease (ORD) in NSCLC patients treated with immune checkpoint inhibitors. Therefore, we retrospectively evaluated whether LCT could improve the prognosis of NSCL patients with ORD after effective first-line PD-1/PD-L1 inhibitors treatment.MethodsA total of 132 patients with metastatic NSCLC who had received first-line PD-1/PD-L1inhibitors-based systemic treatment and developed ORD (defined as residual tumors limited to three organs and five lesions) were included. The LCT group consisted of 41 patients received LCTs for oligo-residual lesions before treatment failure, and the remaining 91 patients, who did not receive local therapies, constituted the non-LCT group. The progression-free survival (PFS) and overall survival (OS) of the two groups were analyzed.ResultsWith a median follow-up of 12.0 months, 86 patients developed progressive disease and 42 patients died. Compared with the non-LCT group, LCT group exhibited significant longer progression-free survival (PFS) (median 11.0 vs. 7.0 months, P=0.017) and overall survival (OS) (median 26.0 vs. 17.0 months, P=0.003). Multivariable analysis demonstrated that LCT was an independent predictor of prolonged PFS (HR=0.606, 95% CI=0.370–0.964, P=0.035) and OS (HR=0.467, 95% CI=0.229–0.949, P=0.035). Subgroup analysis revealed that the dominant population considerably benefited from LCT in terms of PFS and OS included patients with 1-2 residual tumor sites (mPFS: 11.0 vs. 7.0 months, P=0.013; mOS: 23.0 vs. 17.0 months, P=0.018) and those with high PD-L1 expression (mPFS: 13.0 vs. 7.0 months, P=0.018; mOS: 34.0 vs. 16.0 months, P=0.030). In addition, the All-LCT group had significantly longer PFS (mPFS 16.0 vs. 7.0, P=0.002) and OS (mOS 28.0 vs. 17.0, P= 0.002) than did the non-LCT group. However, patients who received LCT to only some of their lesions had not experienced improvements in PFS (P=0.546) or OS (P=0.198).ConclusionLCT may provide extra survival benefits among patients with oligo-residual NSCLC after effective first-line PD-1/PD-L1 inhibitors treatment, particularly in those patients with one or two residual lesions, high PD-L1 expression, or who had received LCT for all lesions. LCT may be a novel treatment option for this specific population.

Abstract Background The retrospective cohort study was conducted to estimate the opioid-sparing anesthesia and limited side-effects with ultrasound (US)-guided ESPB using programmed intermittent bolus (PIB) or continuous infusion (CI) and standard opioid-based anesthesia in patients undergoing video-assisted thoracoscopic lobectomy (VATS). Methods Patients underwent VATS were stratified into either control group or one of the two ESPB groups in a 1:2:2 ratio depending on whether PIB was implemented or not. The primary endpoint was intra- and post-operative opioids consumption over the first 48 h following surgery. Results A total of 180 cases were included in the analysis. Cumulative perioperative opioid administration was found to be significantly different between PIB, CI and control group (both p

As the amount of ground-penetrating radar (GPR) data increases significantly with the high demands of nondestructive detection methods under urban roads, a method suitable for time-lapse data dynamic monitoring should be developed to quickly identify targets on GPR profiles and compare time-lapse datasets. This study conducted a field experiment aiming to monitor one backfill pit using three-dimensional GPR (3D GPR), and the time-lapse data collected over four months were used to train U-Net, a fast neural network based on convolutional neural networks (CNNs). Consequently, a trained network model that could effectively segment the backfill pit from inline profiles was obtained, whose Intersection over Union (IoU) was 0.83 on the test dataset. Moreover, segmentation masks were compared, demonstrating that a change in the southwest side of the backfill pit may exist. The results demonstrate the potential of machine learning algorithms in time-lapse 3D GPR data segmentation and dynamic monitoring.