Predicting complication risks after sleeve lobectomy for non-small cell lung cancer.

Background: Sleeve lobectomy is a challenging procedure with a high risk of postoperative complications. To facilitate surgical decision-making and optimize perioperative treatment, we developed risk stratification models to quantify the probability of postoperative complications after sleeve lobectomy. Methods: We retrospectively analyzed the clinical features of 691 non-small cell lung cancer (NSCLC) patients who underwent sleeve lobectomy between July 2016 and December 2019. Logistic regression models were trained and validated in the cohort to predict overall complications, major complications, and specific minor complications. The impact of specific complications in prognostic stratification was explored via the Kaplan-Meier method. Results: Of 691 included patients, 232 (33.5%) developed complications, including 35 (5.1%) and 197 (28.5%) patients with major and minor complications, respectively. The models showed robust discrimination, yielding an area under the receiver operating characteristic (ROC) curve (AUC) of 0.853 [95% confidence interval (CI): 0.705-0.885] for predicting overall postoperative complication risk and 0.751 (95% CI: 0.727-0.762) specifically for major complication risks. Models predicting minor complications also achieved good performance, with AUCs ranging from 0.78 to 0.89. Survival analyses revealed a significant association between postoperative complications and poor prognosis. Conclusions: Risk stratification models could accurately predict the probability and severity of complications in NSCLC patients following sleeve lobectomy, which may inform clinical decision-making for future patients.

Predicting therapeutic response to neoadjuvant immunotherapy based on an integration model in resectable stage IIIA (N2) non-small cell lung cancer.

OBJECTIVE: Accurately predicting response during neoadjuvant chemoimmunotherapy for resectable non-small cell lung cancer remains clinically challenging. In this study, we investigated the effectiveness of blood-based tumor mutational burden (bTMB) and a deep learning (DL) model in predicting major pathologic response (MPR) and survival from a phase 2 trial. METHODS: Blood samples were prospectively collected from 45 patients with stage IIIA (N2) non-small cell lung cancer undergoing neoadjuvant chemoimmunotherapy. An integrated model, combining the computed tomography-based DL score, bTMB, and clinical factors, was developed to predict tumor response to neoadjuvant chemoimmunotherapy. RESULTS: At baseline, bTMB were detected in 77.8% (35 of 45) of patients. Baseline bTMB ≥11 mutations/megabase was associated with significantly greater MPR rates (77.8% vs 38.5%, P = .042), and longer disease-free survival (P = .043), but not overall survival (P = .131), compared with bTMB <11 mutations/megabase in 35 patients with bTMB available. The developed DL model achieved an area under the curve of 0.703 in all patients. Importantly, the predictive performance of the integrated model improved to an area under the curve of 0.820 when combining the DL score with bTMB and clinical factors. Baseline circulating tumor DNA (ctDNA) status was not associated with pathologic response and survival. Compared with ctDNA residual, ctDNA clearance before surgery was associated with significantly greater MPR rates (88.2% vs 11.1%, P < .001) and improved disease-free survival (P = .010). CONCLUSIONS: The integrated model shows promise as a predictor of tumor response to neoadjuvant chemoimmunotherapy. Serial ctDNA dynamics provide a reliable tool for monitoring tumor response.

Spread Through Air Spaces in Residual Tumor Classification for Clinical IA Lung Adenocarcinoma.

BACKGROUND: We aimed to validate the prognostic implication of uncertain resection, R(un), proposed by International Association for the Study of Lung Cancer (IASLC) and evaluate the prognostic value of spread through air spaces (STAS) in reclassifying the R classification among patients with lung adenocarcinoma after segmentectomy. METHODS: We enrolled 1007 patients who underwent segmentectomy for c-stage IA lung adenocarcinoma between 2014 and 2017. Recurrence-free survival (RFS) and overall survival (OS) were compared to evaluate the prognostic value of IASLC-R(un) and STAS. Whether STAS would skip into complementary lobectomy was evaluated in a prospective cohort. RESULTS: The current IASLC-R(un) failed to significantly stratify the RFS (P = .078) in segmentectomy, and STAS was a stronger risk factor of poor prognosis for both RFS and OS (P < .001). Moreover, the presence of STAS was associated with increased locoregional recurrence in patients undergoing segmentectomy (P < .001) but not in those treated with lobectomy (P = .187), indicating that only STAS-positive segmentectomy was consistent with the concept of R(un) in relapse pattern. After reclassifying STAS-positive segmentectomy into the R(un) category, the proposed R(un) showed an improvement in prognosis stratification. In addition, 2 of 30 patients (6.2%) in the prospective cohort who underwent initial segmentectomy and complementary lobectomy had STAS clusters in the complementary lobectomy specimens. CONCLUSIONS: Unfavorable prognosis, relapse patterns consistent with R(un), and pathologic verification that saltatory spread of STAS observed in complementary lobectomy specimens supported reclassifying STAS-positive segmentectomy as R(un). STAS is a critical concern for the surgical completeness evaluation after segmentectomy.

Neoadjuvant chemoimmunotherapy confers survival advantage for patients undergoing sleeve lobectomy.

OBJECTIVES: It has been demonstrated that neoadjuvant immune checkpoint inhibitor (ICI) plus chemotherapy was safe and feasible referred to neoadjuvant chemotherapy for patients with non-small cell lung cancer undergoing sleeve lobectomy. Nevertheless, no survival data were reported in the previous researches. Therefore, we conducted this study to compare neoadjuvant ICI plus chemotherapy versus neoadjuvant chemotherapy followed by sleeve lobectomy for long-term survival outcomes. METHODS: Patients who underwent bronchial sleeve lobectomy following neoadjuvant ICI plus chemotherapy or neoadjuvant chemotherapy were retrospectively identified. Treatment response, perioperative outcomes, event-free survival and overall survival were compared between groups in the overall and the inverse probability of treatment weighting-adjusted cohort. RESULTS: A total of 139 patients with 39 lung cancer recurrence and 21 death were included. Among them, 83 (59.7%) and 56 (40.3%) patients received neoadjuvant chemotherapy and neoadjuvant ICI plus chemotherapy, respectively. After inverse probability of treatment weighting, more patients achieved complete pathological response in the neoadjuvant ICI plus chemotherapy group (6.0% vs 26.3%, P < 0.001). There was no significant difference regarding overall postoperative complication (23.8% vs 20.2%, P = 0.624) and specific complications (all P > 0.05). Patients receiving neoadjuvant ICI plus chemotherapy had favourable event-free survival (hazard ratio 0.37, 95% confidence interval 0.16-0.85, P = 0.020) and overall survival (hazard ratio 0.23, 95% confidence interval 0.06-0.80, P = 0.021). Multivariable analysis revealed that neoadjuvant ICI plus chemotherapy was an independent predictor for favourable event-free survival (hazard ratio 0.37, 95% confidence interval 0.15-0.86, P = 0.020, adjusted for clinical TNM stage). CONCLUSIONS: Neoadjuvant ICI plus chemotherapy was correlated with favourable long-term survival in patients with non-small cell lung cancer undergoing sleeve lobectomy.

Incorporation of the lepidic component as an additional pathological T descriptor for non-small cell lung cancer: Data from 3335 cases of lung adenocarcinoma.

OBJECTIVES: The Lepidic Component (LP) identifies a subgroup with an excellent prognosis for lung adenocarcinoma (LUAD). Our research aimed to propose an improved pathological T (pT) stage for LUAD based on LP. MATERIALS AND METHODS: Totally, 3335 surgical patients with pathological stage I LUAD were incorporated. Factors affecting survival were investigated by analyzing recurrence-free survival (RFS) and overall survival (OS) using the Kaplan-Meier method and Cox regression analyses. Subgroup analysis based on Lepidic Ratio (LR) was further evaluated. The net benefit from the modified pT category (pTm) was assessed using the Area Under the time-dependent Receiver Operating Curve (AUC), Harrell's Concordance Index (C-index), Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). RESULTS: The presence of LP (LP+) was identified in 1425 (42.7 %) patients, indicating a significantly better RFS (P < 0.001) and OS (P < 0.001) than those without LP, and similar results were reproduced in pT1a-pT2a subcategory (P < 0.050 for all). Multivariable Cox analysis revealed LP+ as an independent prognostic factor for both RFS (HR, 0.622; P < 0.001) and OS (HR, 0.710; P = 0.019). However, lepidic ratio (LR) was not independently associated with both RFS and OS for LP+ patients. The 5-year RFS and OS rates between T1a (LP-) and T1b (LP+), T1b (LP-) and T1c (LP+), and T1b (LP-) and T2a (LP+) were comparable (P > 0.050 for all). After modification, compared with current 8th edition pT stage system (pT8), pTm independently predicted RFS and OS, and AUCs, c-index, NRI, and IDI analysis all demonstrated pTm holds better discrimination performances than pT8 for LUAD prognosis. CONCLUSION: LP can be an additional down-staged T descriptor for pathological stage I LUAD and improve the survival predictive performance of reclassification.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction. METHODS: A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups. RESULTS: The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS). CONCLUSION: The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

Prognostic outcomes and recurrence patterns in resected stage I lung adenocarcinoma harbouring atypical epidermal growth factor receptor mutations.

OBJECTIVES: Limited data exist on the characteristics of atypical epidermal growth factor receptor (EGFR) mutations in early-stage lung cancer. Our goal was to elucidate the associations with outcomes and recurrence patterns in resected stage I lung adenocarcinoma harbouring atypical EGFR mutations. METHODS: Eligible patients between 2014 and 2019 were retrospectively identified and grouped into exon20 insertion mutations and major atypical mutations, which included G719X, L861Q and S768I. Disease-free survival (DFS) was evaluated in the entire cohort and stratified by radiologic characteristics. Recurrence patterns were investigated and compared between groups. A competing risk model was used to estimate the cumulative incidence of recurrence. RESULTS: A total of 710 patients were finally included. Among them, 289 (40.7%) patients had exon 20 insertion mutations and 421 (59.3%) patients had major atypical mutations. There was no significant difference regarding DFS (P = 0.142) between groups in the entire cohort. The interaction between mutation subtype and the presence of ground-glass opacities was significant (hazard ratio 2.00, 95% confidence interval 1.59-2.51, P < 0.001), indicating DFS between exon 20 insertion mutations and major atypical mutations may be different among subsolid and solid tumours. Survival analysis consistently revealed no significant difference in subsolid tumours (P = 0.680), but favourable DFS of exon 20 insertion mutations in solid tumours (P = 0.037). Furthermore, patients with exon 20 insertion mutations had a lower risk of developing bone metastases did those with radiologic solid tumours (Gray's test, P = 0.012). CONCLUSIONS: Exon 20 insertion mutations were correlated with favourable DFS and lower incidence of bone metastases in radiologic solid lung adenocarcinomas harbouring atypical EGFR mutations.

A Highly Sensitive and Specific Non-Invasive Test through Genome-Wide 5-Hydroxymethylation Mapping for Early Detection of Lung Cancer.

Low-dose computed tomography screening can increase the detection for non-small-cell lung cancer (NSCLC). To improve the diagnostic accuracy of early-stage NSCLC detection, ultrasensitive methods are used to detect cell-free DNA (cfDNA) 5-hydroxymethylcytosine (5hmC) in plasma. Genome-wide 5hmC is profiled in 1990 cfDNA samples collected from patients with non-small cell lung cancer (NSCLC, n = 727), healthy controls (HEA, n = 1,092), as well as patients with small cell lung cancer (SCLC, n = 41), followed by sample randomization, differential analysis, feature selection, and modeling using a machine learning approach. Differentially modified features reflecting tissue origin. A weighted diagnostic model comprised of 105 features is developed to compute a detection score for each individual, which showed an area under the curve (AUC) range of 86.4%-93.1% in the internal and external validation sets for distinguishing lung cancer from HEA controls, significantly outperforming serum biomarkers (p < 0.001). The 5hmC-based model detected high-risk pulmonary nodules (AUC: 82%)and lung cancer of different subtypes with high accuracy as well. A highly sensitive and specific blood-based test is developed for detecting lung cancer. The 5hmC biomarkers in cfDNA offer a promising blood-based test for lung cancer.

Validation of the Proposed International Association for the Study of Lung Cancer Residual Tumor Classification to Upgrade Extracapsular Extension of Tumor in Nodes From R0 to Incomplete Resection.

INTRODUCTION: The International Association for the Study of Lung Cancer (IASLC) proposed a revised R classification to upstage extracapsular extension (ECE) of tumor in nodes from R0 to R1. Nevertheless, evidence to confirm this proposal is insufficient. METHODS: The study included 4061 surgical patients with NSCLC. After reclassification by IASLC-R classification, overall survival (OS) was analyzed to compare patients with ECE with those with R0, R(un), and incomplete resection (R1 and R2). The recurrence pattern of ECE was evaluated to determine whether it correlated with incomplete resection. RESULTS: Among 1136 patients with N disease, those without ECE (n = 754, 67%) had a significantly better OS than those with ECE (n = 382, 33%) (p < 0.001). This negative prognostic significance was consistent across multiple subgroups. Multivariate analysis revealed that ECE was an independent prognostic risk factor (p < 0.001). When patients with ECE were separated from the IASLC-R1 group, their OS was significantly worse than that of IASLC-R(un) patients, but comparable to that of the remaining patients in the IASLC-R1 patients when analyzing all patients and patients with N disease. Moreover, patients with ECE had an increased risk of local recurrence in the mediastinum (p < 0.001), ipsilateral lung (p = 0.031), and malignant pleural effusion or nodes (p = 0.004) but not distant recurrence including contralateral or both lungs (p = 0.268), liver (p = 0.728), brain (p = 0.252), or bone (p = 0.322). CONCLUSIONS: The prognosis of ECE patients is comparable with that of R1 patients. Moreover, their higher risk of local recurrence strongly suggests the presence of occult residual tumor cells in the surgical hemithoracic cavity. Therefore, upgrading ECE into incomplete resection is reasonable.

PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer.

Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.

Prognostic Significance of the Proposed Residual Tumor Classification in Patients With NSCLC After Sleeve Lobectomy.

Introduction: To validate the residual tumor (R) classification proposed by the International Association for the Study of Lung Cancer (IASLC) in NSCLC after sleeve lobectomy. Methods: A total of 682 patients were analyzed. The R status, on the basis of the Union for International Cancer Control (UICC) criteria, was recategorized according to the IASLC descriptors. Recurrence-free survival (RFS) and overall survival (OS) among different R classifications were assessed for the entire cohort and pathologic node (pN) subgroups. Results: All in all, 631 (92.5%), 48 (7.1%), and three patients (0.4%) were classified as R0, R1, and R2, respectively, by the UICC criteria, whereas 489 (71.7%), 110 (16.1%), and 83 patients (12.2%), received R0, uncertain resection (R[un]), and R1/2 resection, respectively, according to the IASLC criteria. There were 96 patients (15.2%) with UICC R0 who were reclassified as R(un), mainly because of the positive highest mediastinal node station (82 of 96, 85.4%). A total of 46 patients (7.3%) were reassigned from UICC R0 to IASLC R1/2 owing to extracapsular extension. For the entire cohort, patients with R(un) and R1/2 exhibited worse RFS (R[un], adjusted p = 0.023; R1/2, adjusted p = 0.001) and OS (R[un], adjusted p = 0.040; R1/2, adjusted p = 0.051) compared with R0. No significant differences were observed between R(un) and R1/2 (RFS, adjusted p = 0.586; OS, adjusted p = 0.781). Furthermore, subgroup analysis revealed a distinct prognostic impact of the IASLC R status-with prognostic significances in the pN1 and pN2 subgroups, but not in the pN0 subgroup. Conclusions: The IASLC R descriptors helped to stratify the prognosis of NSCLC after sleeve lobectomy, with its prognostic impact varied among pN stages.

The number of metastatic lymph nodes is more predictive of prognosis than location-based N stage for nonsmall cell lung cancer: a retrospective cohort study.

BACKGROUND: The eighth edition of nodal classification is defined only by the anatomical location of metastatic lymph nodes and has limited prognostic discrimination power. The authors aimed to evaluate the prognostic significance and discriminatory capability of the number of metastatic lymph nodes (nN) in resected nonsmall cell lung cancer. MATERIALS AND METHODS: Patients with stage IA to IIIB resected nonsmall cell lung cancer between 1 January 2009 and 31 December 2013 were analyzed as a Chinese cohort. The optimal thresholds for the nN classification were determined by the X-tile. The receiver operating characteristic curve, net reclassification improvement and standardized net benefit calculated by decision curve analysis was estimated to quantify the nN classification's performance in prognostic stratification. External validation in the surveillance, epidemiology, and end results database was performed to test the robustness of the nN classification. RESULTS: Both cohorts showed a stepwise prognosis deterioration with increasing nN. One to three, four to six, and more than six were selected as optimal thresholds of nN classification in the Chinese cohort, which included 4432 patients, then validated in the SEER cohort ( n =28 022 patients). Multivariate Cox analysis showed the nN classification was an independent predictive factor for overall survival in both cohorts (Chinese cohort and SEER cohort: N 0 vs. N 1-3 , P <0.001; N 0 vs. N 3-6 , P <0.001; N 0 vs. N >6 , P <0.001). And prognostic discriminatory capability was improved in the nN classification compared with location-based N classification [5-year NRI score, 0.106 (95% CI: 0.049-0.132) and 5-year time-independent AUC, 0.593 (95% CI: 0.560-0.625) vs. 0.554 (95% CI: 0.520-0.588), P <0.001]. CONCLUSIONS: The nN classification tended to be a superior prognostic indicator than the location-based N classification. The number of metastatic lymph nodes should be considered in the future revision of the TNM system.

A bioengineered trachea-like structure improves survival in a rabbit tracheal defect model.

A practical strategy for engineering a trachea-like structure that could be used to repair or replace a damaged or injured trachea is an unmet need. Here, we fabricated bioengineered cartilage (BC) rings from three-dimensionally printed fibers of poly(ɛ-caprolactone) (PCL) and rabbit chondrocytes. The extracellular matrix (ECM) secreted by the chondrocytes combined with the PCL fibers formed a "concrete-rebar structure," with ECM deposited along the PCL fibers, forming a grid similar to that of native cartilage. PCL fiber-hydrogel rings were then fabricated and alternately stacked with BC rings on silicone tubes. This trachea-like structure underwent vascularization after heterotopic transplantation into rabbits for 4 weeks. The vascularized bioengineered trachea-like structure was then orthotopically transplanted by end-to-end anastomosis to native rabbit trachea after a segment of trachea had been resected. The bioengineered trachea-like structure displayed mechanical properties similar to native rabbit trachea and transmural angiogenesis between the rings. The 8-week survival rate in transplanted rabbits was 83.3%, and the respiratory rate of these animals was similar to preoperative levels. This bioengineered trachea-like structure may have potential for treating tracheal stenosis and other tracheal injuries.

Brief Report: Acetaminophen Reduces Neoadjuvant Chemoimmunotherapy Efficacy in Patients With NSCLC by Promoting Neutrophil Extracellular Trap Formation: Analysis From a Phase 2 Clinical Trial.

Introduction: Neoadjuvant chemoimmunotherapy has recently been the standard of care for resectable locally advanced NSCLC. Factors affecting the neoadjuvant immunotherapy efficacy, however, remain elusive. Metabolites have been found to modulate immunity and associate with immunotherapeutic efficacy in advanced tumors. Therefore, we aimed to investigate the impact of plasma metabolites on the pathologic response after neoadjuvant chemoimmunotherapy. Methods: Patients with stage IIIA (N2) NSCLC who underwent neoadjuvant chemoimmunotherapy in a prospective phase 2 clinical trial (NCT04422392) were enrolled. Metabolomic profiling of the plasma before treatment was performed using liquid chromatography-mass spectrometry. A Lewis lung carcinoma mouse model was further used to investigate the underlying mechanisms. Proteomics and multiplexed immunofluorescence of the mice tumor were performed. Results: A total of 39 patients who underwent three cycles of anti-programmed cell death-protein 1 (anti-PD-1) (sintilimab) and chemotherapy were included. The level of acetaminophen (APAP) was found to be significantly elevated in patients who did not achieve major pathologic response. The level of APAP remained an independent predictor for major pathologic response in multivariate logistic analysis. In the Lewis lung carcinoma mouse model, combination of APAP and anti-PD-1 treatment significantly reduced the treatment efficacy compared with anti-PD-1 treatment alone. Proteomics of the tumor revealed that myeloid leukocyte activation and neutrophil activation pathways were enriched after APAP treatment. Tumor microenvironment featuring analysis also revealed that the combination treatment group was characterized with more abundant neutrophil signature. Further multiplexed immunofluorescence confirmed that more neutrophil extracellular trap formation was observed in the combination treatment group. Conclusions: APAP could impair neoadjuvant chemoimmunotherapy efficacy in patients with NSCLC by promoting neutrophil activation and neutrophil extracellular trap formation.

Prognosis of early-stage lung adenocarcinoma in young patients.

Lung adenocarcinoma (LUAD) is a familiar lung cancer with a poor prognosis. This study was meant to determine whether there are differences in survival between younger and older patients with early-stage LUAD because of the rise in the incidence of LUAD in young individuals over the previous few decades. We analysed the clinical, therapeutic and prognostic features of a cohort (2012-2013) of 831 consecutive patients with stage I/II LUAD who underwent curative surgical resection at Shanghai Pulmonary Hospital. Propensity score matching (PSM) was performed for age, sex, tumour size, tumour stage and therapy in a 2:1 ratio between the two groups without taking gender, illness stage at operation or decisive treatment into account. Following PSM analysis to create a 2:1 match for comparison, the final survival study included 163 patients with early-stage LUAD <50 years and 326 patients ≥50 years. Surprisingly, younger patients were overwhelmingly female (65.6%) and never smokers (85.9%). There were no statistical differences between the two groups in terms of the overall survival rate (P = 0.067) or time to advancement (P = 0.76). In conclusion, no significant differences stood out between older and younger patients with stage I/II LUAD regarding overall and disease-free survival rates. Younger patients with early-stage LUAD were more likely to be female and never smokers, which suggests that risk factors other than active smoking may be responsible for lung carcinogenesis in these patients.

Rivaroxaban versus nadroparin for thromboprophylaxis following thoracic surgery for lung cancer: A randomized, noninferiority trial.

The benefit of rivaroxaban in thromboprophylaxis after oncologic lung surgery remains unknown. To evaluate the efficacy and safety of rivaroxaban, patients who underwent thoracic surgery for lung cancer were enrolled, and randomly assigned to rivaroxaban or nadroparin groups in a 1:1 ratio; anticoagulants were initiated 12-24 h after surgery and continued until discharge. Four hundred participants were required according to a noninferiority margin of 2%, assuming venous thromboembolism (VTE) occurrence rates of 6.0% and 12.6% for patients in the rivaroxaban and nadroparin groups, respectively. The primary efficacy outcome was any VTE during the treatment and 30-day follow-up periods. The safety outcome was any on-treatment bleeding event. Finally, 403 patients were randomized (intention-to-treat [ITT] population), with 381 included in per-protocol (PP) population. The primary efficacy outcomes occurred in 12.5% (25/200) of the rivaroxaban group and 17.7% (36/203) of the nadroparin group (absolute risk reduction, -5.2%; 95% confidence interval [CI], [-12.2-1.7]), indicating the noninferiority of rivaroxaban in ITT population. Sensitivity analysis was performed in the PP population and yielded similar results, confirming the noninferiority of rivaroxaban. In the safety analysis population, the incidence of any on-treatment bleeding events did not differ significantly between the groups (12.2% for rivaroxaban vs. 7.0% for nadroparin; relative risk [RR], 1.9; 95% CI, [0.9-3.7]; p = .08), including major bleeding (9.7% vs. 6.5%; RR, 1.6 [95% CI, 0.9-3.7]; p = .24), and nonmajor bleeding (2.6% vs. 0.5%; RR, 5.2 [95% CI, 0.6-45.2]; p = .13). Rivaroxaban for thromboprophylaxis after oncologic lung surgery was shown to be noninferior to nadroparin.

The development and validation of a circulating tumor cells-based integrated model for improving the indeterminate lung solid nodules diagnosis.

Background: There is a risk of over investigation and delayed treatment in the work up of solid nodules. Thus, the aim of our study was to develop and validate an integrated model that estimates the malignant risk for indeterminate pulmonary solid nodules (IPSNs). Methods: Patients included in this study with IPSNs who was diagnosed malignant or benign by histopathology. Univariate and multivariate logistic regression were used to build integrated model based on clinical, circulating tumor cells (CTCs) and radiomics features. The performance of the integrated model was estimated by applying receiver operating characteristic (ROC) analysis, and tested in different nodules size and intermediate risk IPSNs. Net reclassification index (NRI) was applied to quantify the additional benefit derived from the integrated model. Results: The integrated model yielded areas under the ROC curves (AUCs) of 0.83 and 0.76 in internal and external set, respectively, outperforming CTCs (0.70, P=0.001; 0.68, P=0.128), the Mayo clinical model (0.68, P<0.001; 0.55, P=0.007), the and radiomics model (0.72, P=0.002; 0.67, P=0.050) in both validation sets. Robust performance with high sensitivity up to 98% was also maintained in IPSNs with different solid size and intermediate risk probability. The performance of the integrated model was comparable with positron emission tomography/computed tomography (PET-CT) examination (P=0.308) among the participants with established PET-CT records. NRI demonstrated that the integrated model provided net reclassification of at least 10% on the external validation set compared with single CTCs test. Conclusions: The integrated model could complement conventional risk models to improve the diagnosis of IPSNs, which is not inferior to PET-CT and could help to guide clinician's decision-making on clinically specific population.