Search

Abstract Background C‐reactive protein to albumin ratio (CRP/Alb ratio, CAR) has been suggested as a potential prognostic biomarker in lung cancer. This updated systematic review and meta‐analysis aimed to assess the association between CAR and lung cancer prognosis in current literature. Methods A systematic search of databases was conducted to identify relevant studies published up to April 2023. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to assess the association between CAR and overall survival (OS) and progression‐free survival (PFS) and recurrence‐free survival (RF) in lung cancer patients. Results This meta‐analysis includes 16 studies with a total of 5337 patients, indicating a significant association between higher CAR and poorer OS, PFS, and RFS in lung cancer patients, with a pooled HR of 1.78 (95% CI = 1.60–1.99), 1.57 (95% CI = 1.36–1.80), and 1.97 (95% CI = 1.40–2.77), respectively. Conclusions This updated meta‐analysis provides evidence for the potential prognostic role of CAR in lung cancer, suggesting its utility as an effective and noninvasive biomarker for identifying high‐risk patients and informing treatment decisions in a cost‐effective manner. However, further large‐scale studies will be necessary to establish the optimal cut‐off value for CAR in lung cancer and confirm the present findings.

Abstract In this study, we explored the dynamic changes in blood sPD-L1 and its clinical value during anti-PD-1 immunotherapy in non-small cell lung cancer (NSCLC) patients. First, we established a sandwich ELISA for functional sPD-L1 that can bind to PD-1 and has biological functions. By monitoring functional sPD-L1 in 39 NSCLC patients treated with anti-PD-1 antibodies, we found a positive correlation between baseline sPD-L1 and tissue PD-L1 (P = 0.0376, r = 0.3581), with patients with lymph node metastasis having higher sPD-L1 levels (P = 0.0037) than those without lymph node metastasis. Although baseline functional sPD-L1 and PFS did not correlate significantly in this study, changes in sPD-L1 in patients with different clinical responses showed different trends. Blood sPD-L1 increased in 93% of patients after two cycles of anti-PD-1 treatment (P = 0.0054); sPD-L1 in nonresponsive patients continued to increase (P = 0.0181), but sPD-L1 started to decline in responsive patients. Blood IL-8 levels were associated with tumor load, and when combined with IL-8, the evaluation accuracy of sPD-L1 improved to 86.4%. This study preliminarily shows that the combination of sPD-L1 and IL-8 is a convenient and effective method for monitoring and evaluating the effectiveness of anti-PD-1 immunotherapy in NSCLC patients.

Background and objective Immunotherapy represented by immune checkpoint inhibitors (ICIs) has become the standard treatment for patients with non-oncogenic advanced non-small cell lung cancer (NSCLC). While lung cancer is most prevalent in elderly patients, these patients are rarely included in pivotal clinical trial studies. We aimed to describe the efficacy and safety of immunotherapy for elderly patients in the "real-world". Methods The data of older NSCLC patients and younger patients who received immunotherapy between July 2018 to October 2021 were retrospectively analyzed and the objective response rate (ORR) and progression-free survival (PFS) in different age groups (less than 60 years old was defined as the young group, 60 years-74 years old was the young old group, 75 years old and above was the old old group) were compared. And the impact of different clinical characteristics on treatment response and prognosis were analyzed in each age subgroup. Results A total of 21 young patients, 70 young old patients and 15 old old patients were included in this study, with ORR of 33.3%, 52.8% and 53.3%, respectively, without statistically significant difference (P=0.284). The median PFS was 9.1 mon, 7.6 mon and 10.9 mon, respectively, without statistically significant difference (P=0.654). Further analysis of the predictors of immunotherapy in each subgroup revealed that patients in the young old group and young group who received immunotherapy in the first line had a longer PFS. The difference of the incidence of adverse events was not statistically significant among the three groups (P>0.05). Conclusion The efficacy and safety of immunotherapy in elderly patients were similar to those in younger patients, and PFS was superior in the first-line immunotherapy. Further prospective studies are still needed to explore predictors of immunotherapy in elderly NSCLC patients.

ObjectiveThe purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.MethodsA total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient).ResultsThe accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients’ first examination and consultationConclusionThe INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

Lung cancer is one of the most severe forms of malignancy and a leading cause of cancer-related death worldwide, of which non-small cell lung cancer (NSCLC) is the most primary type observed in the clinic. NSCLC is mainly treated with surgery, radiotherapy, and chemotherapy. Additionally, targeted therapy and immunotherapy have also shown promising results. Several immunotherapies, including immune checkpoint inhibitors, have been developed for clinical use and have benefited patients with NSCLC. However, immunotherapy faces several challenges like poor response and unknown effective population. It is essential to identify novel predictive markers to further advance precision immunotherapy for NSCLC. Extracellular vesicles (EVs) present an important research direction. In this review, we focus on the role of EVs as a biomarker in NSCLC immunotherapy considering various perspectives, including the definition and properties of EVs, their role as biomarkers in current NSCLC immunotherapy, and different EV components as biomarkers in NSCLC immunotherapy research. We describe the cross-talk between the role of EVs as biomarkers and novel technical approaches or research concepts in NSCLC immunotherapy, such as neoadjuvants, multi-omics analysis, and the tumour microenvironment. This review will provide a reference for future research to improve the benefits of immunotherapy for patients with NSCLC.

BackgroundRemarkably, the anti-cancer efficacy of immunotherapy in lung adenocarcinoma (LUAD) has been demonstrated. However, predicting the beneficiaries of this expensive treatment is still a challenge.Materials and methodsA group of patients (N = 250) diagnosed with LUAD and receiving immunotherapy were retrospectively studied. They were randomly divided into a training dataset (80%) and a test dataset (20%). The training dataset was utilized to train neural network models to predict patients’ objective response rate (ORR), disease control rate (DCR), responders (progression-free survival time > 6 months), and overall survival (OS) possibility, which were validated by both the training and test datasets and packaged into a tool later.ResultsIn the training dataset, the tool scored 0.9016 area under the receiver operating characteristic (AUC) curve on ORR judgment, 0.8570 on DCR, and 0.8395 on responder prediction. In the test dataset, the tool scored 0.8173 AUC on ORR, 0.8244 on DCR, and 0.8214 on responder determination. As for OS prediction, the tool scored 0.6627 AUC in the training dataset and 0.6357 in the test dataset.ConclusionsThis immunotherapy efficacy predictive tool for LUAD patients based on neural networks could predict their ORR, DCR, and responder well.

BackgroundAt present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy.MethodsIn our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).ResultsA total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088–0.9879) in internal validation and 0.9491 (95%CI, 0.8704–1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437–0.8545) in internal validation and 0.7040 (95%CI, 0.5457–0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024–0.8975) in internal validation and 0.7602 (95%CI, 0.6236–0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995–0.8017) in internal validation and 0.7382 (95%CI, 0.7366–0.7398) in external validation.ConclusionsThe neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.

Objective: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. Methods: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model >(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient). Results: The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation. Conclusion: The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volumedoubling time to accurately treat patients with pulmonary nodules. [ABSTRACT FROM AUTHOR]