Sleeve Lobectomy for Non-Small Cell Lung Cancers: Predictive CT Features for Resectability and Outcome Analysis

The outcomes of margin status after sleeve lobectomy for patients of non-small cell lung cancer

Background

Sleeve lobectomy is recognized as an alternative surgical operation to pneumonectomy because it preserves the most pulmonary function and has a considerable prognosis. In this study, we aimed to investigate the implications of residual status for patients after sleeve lobectomy.

Methods

In this retrospective cohort study, we summarized 58 242 patients who underwent surgeries from 2015 to 2018 in Shanghai Chest Hospital and found 456 eligible patients meeting the criteria. The status of R2 was excluded. The outcomes were overall survival (OS) and recurrence‐free survival (RFS). We performed a subgroup analysis to further our investigation.

Results

After the propensity score match, the baseline characteristic was balanced between two groups. The survival analysis showed no significant difference of overall survival and recurrence‐free survival between R0 and R1 groups (OS: p = 0.053; RFS: p = 0.14). In the multivariate Cox analysis, we found that the margin status was not a dependent risk factor to RFS (p = 0.119) and OS (p = 0.093). In the patients of R1, N stage and age were closely related to OS, but we did not find any significant risk variable in RFS for R1 status. In the subgroup analysis, R1 status may have a worse prognosis on patients with more lymph nodes examination. On further investigation, we demonstrated no differences among the four histological types of margin status.

Conclusion

In our study, we confirmed that the margin status after sleeve lobectomies was not the risk factor to prognosis. However, patients with more lymph nodes resection should pay attention to the margin status.

Surgical or medical strategy for locally-advanced, stage IIIA/B-N2 non-small cell lung cancer: Reproducibility of decision-making at a multidisciplinary tumor board

BACKGROUND

Stage IIIA/B-N2 is a very heterogeneous group of patients and accounts for one third of NSCLC at diagnosis. The best treatment strategy is established at a Multidisciplinary Tumor Board (MTB): surgical resection with neoadjuvant or adjuvant therapy versus definitive chemoradiation with immune checkpoint inhibitors consolidation. Despite the crucial role of MTBs in this complex setting, limited data is available regarding its performances and the reproducibility of the decision-making.

METHODS

Using a large cohort of IIIA/B-N2 NSCLC patients, we described patient's characteristics and treatment strategies established at the initial MTB: with a "surgical strategy" group, for potentially resectable disease, and a "medical strategy" group for non-resectable patients. A third group consisted of patients who were not eligible for surgery after neoadjuvant treatment and switched from the surgical to the medical strategy. We randomly selected 30 cases (10 in each of the 3 groups) for a blinded re-discussion at a fictive MTB and analyzed the reproducibility and factors associated with treatment decision.

RESULTS

Ninety-seven IIIA/B-N2 NSCLC patients were enrolled between June 2017 and December 2019. The initial MTB opted for a medical or a surgical strategy in 44% and 56% of patients respectively. We identified histology, tumor size and localization, extent of lymph node involvement and the presence of bulky mediastinal nodes as key decision-making factors. Thirteen patients were not eligible for surgical resection after neoadjuvant therapy and switched for a medical strategy. Overall concordance between the initial decision and the re-discussion was 70%. The kappa correlation coefficient was 0.43. Concordance was higher for patients with limited mediastinal node invasion. Survival did not appear to be impacted by conflicting decisions.

CONCLUSIONS

Reproducibility of treatment decision-making for stage IIIA/B-N2 NSCLC patients at a MTB is moderate but does not impact survival.

Preoperative chest computed tomography evaluation for predicting intraoperative lung resection strongly depends on interpreters experience

OBJECTIVES

Preoperative planning of lung resection extent is decisive for preoperative functional work-up and selection for multimodal treatment. It is mainly based on preoperative chest CT. We aimed at evaluating chest CT adequacy to predict the extent of lung resection and hypothesized a relation with CT interpreters' level of experience.

MATERIALS AND METHODS

A pseudonymized CT library was built from patients who had curative intent lung resection for centrally located NSCLC. CT library was interpreted by 20 thoracic surgery residents or attendings. Interpreters were blinded to intraoperative findings and scored one point when lung resection was adequately planned. Points were summed up in a score from 0 to 20. Interpreters' experience was evaluated through nine variables: age, position (resident vs. attending), years of experience in evaluating chest CTs, number of anatomic resections and sleeve resections attended as first assistant or performed as surgeon in presence of a teaching assistant or as main surgeon/teaching assistant. Variables characterizing interpreters' experience were divided into equal sized groups. Independent sample T-test and one-way ANOVA/Tukey post hoc tests were used to compare scores between groups.

RESULTS

CT library included 20 patients. Lung resections were lobectomy (n = 7, 35 %), sleeve lobectomy (n = 10, 50 %), sleeve bilobectomy (n = 2, 10 %), pneumonectomy (n = 1, 5%). Twenty interpreters scored a median of 10 (4-14). Attending surgeons had significantly higher mean scores (11.2 ± 1.3) compared to residents (7.7 ± 2.3, p = 0.001). All scores were significantly different between groups related to interpreters' levels of experience, except for interpreters'age.

CONCLUSION

Preoperative CT evaluation for predicting intraoperative lung resection for centrally located NSCLC strongly depends on interpreters' experience.

Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease

In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.