Prognostic Analysis of Limited Resection Versus Lobectomy in Stage IA Small Cell Lung Cancer Patients Based on the Surveillance, Epidemiology, and End Results Registry Database

Objective: The prognostic analysis of limited resection vs. lobectomy in stage IA small cell lung cancer (SCLC) remains scarce.Methods: Using the Surveillance, Epidemiology, and End Results registry (SEER) database, we identified patients who were diagnosed with pathological stage IA (T1a/bN0M0) SCLC from 2004 to 2013. The overall survival (OS) and lung cancer-specific survival (LCSS) rates of patients with different treatment schemes were compared in stratification analyses. Univariable and multivariable analyses were also performed to identify the significant predictors of OS and LCSS.Results: In total, we extracted 491 pathological stage IA SCLC patients, 106 (21.6%) of whom received lobectomy, 70 (14.3%) received sublobar resection and 315 (64.1%) received non-surgical treatment, respectively. There were significant differences among the groups based on different treatment schemes in OS (log-rank p < 0.0001) and LCSS (log-rank p < 0.0001). Furthermore, in subgroup analyses, we did not identify any differences between sublober resection group and lobectomy group in OS (log-rank p = 0.14) or LCSS (log-rank p = 0.4565). Patients with four or more lymph node dissection had better prognosis. Multivariable analyses revealed age, laterality, tumor location, and N number were still significant predictors of OS, whereas age, tumor location, and N number were significant predictors of LCSS.Conclusion: Surgery is an important component of multidisciplinary treatment for stage IA SCLC patients and sublober resection is not inferior to lobectomy for the specific patients

Comparison of perioperative outcomes among non-small cell lung cancer patients with neoadjuvant immune checkpoint inhibitor plus chemotherapy, EGFR-TKI, and chemotherapy alone: A real-world evidence study

Background: The utilization of neoadjuvant immune checkpoint inhibitor (ICI) plus chemotherapy has increased significantly for resectable non-small cell lung cancer (NSCLC). It is still unclear whether such a treatment paradigm affects perioperative outcomes compared with other neoadjuvant treatment. We aimed to evaluate the perioperative outcomes of pulmonary resection after neoadjuvant ICI plus chemotherapy and to compare them with neoadjuvant epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI) and neoadjuvant chemotherapy alone for resectable NSCLC. Methods: A retrospective cohort including 194 stage IB-IIIB NSCLC underwent surgical resection after neoadjuvant treatment between 2018 and 2020 were reviewed. Perioperative complications were evaluated using the Common Terminology Criteria for Adverse Events, and were compared using one-way analysis of variance for continuous variables and Pearson chi-square test. Results: There were 42, 54, and 98 patients in the neoadjuvant ICI plus chemotherapy, EGFR-TKI, and chemotherapy alone groups, respectively. The tumor size before neoadjuvant treatment was well balanced among the three groups (P=0.122). A shorter median surgical time was observed in the EGFR-TKI group than ICI plus chemotherapy group and chemotherapy group alone (120 Conclusions: Surgical resection for NSCLC following neoadjuvant ICI plus chemotherapy was safe and feasible, the perioperative outcomes were similar with neoadjuvant EGFR-TKI and chemotherapy alone without unexpected perioperative complications. Additional prospective studies are necessary to validate our findings

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules

Objective: Synchronous multiple ground-glass nodules (SM-GGNs) are a distinct entity of lung cancer which has been emerging increasingly in recent years in China. The oncogenesis molecular mechanisms of SM-GGNs remain elusive.Methods: We investigated single nucleotide variations (SNV), insertions and deletions (INDEL), somatic copy number variations (CNV), and germline mutations of 69 SM-GGN samples collected from 31 patients, using target sequencing (TRS) and whole exome sequencing (WES).Results: In the entire cohort, many known driver mutations were found, including EGFR (21.7%), BRAF (14.5%), and KRAS (6%). However, only one out of the 31 patients had the same somatic missense or truncated events within SM-GGNs, indicating the independent origins for almost all of these SM-GGNs. Many germline mutations with a low frequency in the Chinese population, and genes harboring both germline and somatic variations, were discovered in these pre-stage GGNs. These GGNs also bore large segments of copy number gains and/or losses. The CNV segment number tended to be positively correlated with the germline mutations (r = 0.57). The CNV sizes were correlated with the somatic mutations (r = 0.55). A moderate correlation (r = 0.54) was also shown between the somatic and germline mutations.Conclusion: Our data suggests that the precancerous unstable CNVs with potentially predisposing genetic backgrounds may foster the onset of driver mutations and the development of independent SM-GGNs during the local stimulation of mutagens

Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker

Background Tumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC).Methods CT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)).Results TMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB.Conclusion By combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC

Application of the Novel Grading System of Invasive Pulmonary Adenocarcinoma in a Real Diagnostic Scenario: A Brief Report of 9353 Cases

Introduction: The International Association for the Study of Lung Cancer proposed a novel grading system of invasive pulmonary adenocarcinoma (IPA), but the application of this grading system and its genotypic characterization in the real diagnostic scenario has never been reported. Methods: We prospectively collected and analyzed the clinicopathological and genotypic features of a cohort of 9353 consecutive patients with resected IPA, including 7134 patients with detection of common driver mutation. Results: In the entire cohort, 3 (0.3%) of lepidic, 1207 (19.0%) of acinar, and 126 (23.6%) of papillary predominant IPAs were diagnosed as grade 3. In chronological order, an evident downtrend of the proportion of grade 2 was observed in chronological order. Conversely, the diagnostic ratio of grade 1 (8.0%–14.5%) and grade 3 (27.9%–32.3%) experienced a gradual rise. EGFR mutation was more frequently detected in grade 2 (77.5%) and grade 1 (69.7%) IPA than grade 3 (53.7%, p < 0.001), whereas the mutation rates of KRAS, BRAF, ALK, and ROS1 were higher in grade 3 IPA. More importantly, the rate of EGFR mutation gradually fell as the proportion of high-grade components increased, to 24.3% in IPA with more than 90% high-grade components. Conclusions: The grading system for IPA could be applied to stratify patients with different clinicopathological and genotypic features in a real diagnostic scenario

Accuracy of a 3-Dimensionally Printed Navigational Template for Localizing Small Pulmonary Nodules:A Noninferiority Randomized Clinical Trial

Importance: Localization of small lung nodules are challenging because of the difficulty of nodule recognition during video-assisted thoracoscopic surgery. Using 3-dimensional (3-D) printing technology, a navigational template was recently created to assist percutaneous lung nodule localization; however, the efficacy and safety of this template have not yet been evaluated. Objective: To assess the noninferiority of the efficacy and safety of a 3-D-printed navigational template guide for localizing small peripheral lung nodules. Design, Setting, and Participants: This noninferiority randomized clinical trial conducted between October 2016 and October 2017 at Shanghai Pulmonary Hospital, Shanghai, China, compared the safety and precision of lung nodule localization using a template-guided approach vs the conventional computed tomography (CT)-guided approach. In total, 213 surgical candidates with small peripheral lung nodules (<2 cm) were recruited to undergo either CT- or template-guided lung nodule localization. An intention-to-treat analysis was conducted. Interventions: Percutaneous lung nodule localization. Main Outcomes and Measures: The primary outcome was the accuracy of lung nodule localization (localizer deviation), and secondary outcomes were procedural duration, radiation dosage, and complication rate. Results: Of the 200 patients randomized at a ratio of 1:1 to the template- and CT-guided groups, most were women (147 vs 53), body mass index ranged from 15.4 to 37.3, the mean (SD) nodule size was 9.7 (2.9) mm, and the mean distance between the outer edge of target nodule and the pleura was 7.8 (range, 0.0-43.9) mm. In total, 190 patients underwent either CT- or template-guided lung nodule localization and subsequent surgery. Among these patients, localizer deviation did not significantly differ between the template- and CT-guided groups (mean [SD], 8.7 [6.9] vs 9.6 [5.8] mm; P =.36). The mean (SD) procedural durations were 7.4 (3.2) minutes for the template-guided group and 9.5 (3.6) minutes for the CT-guided group (P <.001). The mean (SD) radiation dose was 229 (65) mGy × cm in the template-guided group and 313 (84) mGy × cm in CT-guided group (P <.001). Conclusions and Relevance: The use of the 3-D-printed navigational template for localization of small peripheral lung nodules showed efficacy and safety that were not substantially worse than those for the CT-guided approach while significantly simplifying the localization procedure and decreasing patient radiation exposure. Trial Registration: ClinicalTrials.gov identifier: NCT02952261

Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules

BACKGROUND: Clinical management of subsolid nodules (SSNs) is defined by the suspicion of tumor invasiveness. We sought to develop an artificial intelligent (AI) algorithm for invasiveness assessment of lung adenocarcinoma manifesting as radiological SSNs. We investigated the performance of this algorithm in classification of SSNs related to invasiveness. METHODS: A retrospective chest computed tomography (CT) dataset of 1,589 SSNs was constructed to develop (85%) and internally test (15%) the proposed AI diagnostic tool, SSNet. Diagnostic performance was evaluated in the hold-out test set and was further tested in an external cohort of 102 SSNs. Three thoracic surgeons and three radiologists were required to evaluate the invasiveness of SSNs on both test datasets to investigate the clinical utility of the proposed SSNet. RESULTS: In the differentiation of invasive adenocarcinoma (IA), SSNet achieved a similar area under the curve [AUC; 0.914, 95% confidence interval (CI): 0.813–0.987] with that of the 6 doctors (0.900, 95% CI: 0.867–0.922). When interpreting with the assistance of SSNet, the sensitivity of junior doctors, specificity of senior doctor, and their accuracy were significantly improved. In the external test, SSNet (AUC: 0.949, 95% CI: 0.884–1.000) achieved a better AUC than doctors (AUC: 0.883, 95% CI: 0.826–0.939) whose AUC increased (AUC: 0.908, 95% CI: 0.847–0.982) with SSNet assistance. In the histological subtype classifications, SSNet achieved better performance than practicing doctors. The AUCs of doctors were significantly improved with the assistance of SSNet in both 4-category and 3-category classifications to 0.836 (95% CI: 0.811–0.862) and 0.852 (95% CI: 0.825–0.882), respectively. CONCLUSIONS: The AI diagnostic system achieved non-inferior performance to doctors, and will potentially improve diagnostic performance and efficiency in SSN evaluation