Tumor spread through air spaces (STAS) is a unique mechanism of lung cancer metastasis; however, its clinical value for stage I lung adenocarcinoma (ADC) remains unclear at present. We investigated the (1) prognosis of patients after sublobar resection compared with lobectomy for stage I lung adenocarcinoma with STAS; and (2) potential benefits of adjuvant chemotherapy (ACT) for patients with stage I ADC and STAS. A total of 3328 consecutive patients with stage I ADC were retrospectively identified between 2014 and 2018 at our institution; among them, 600 were diagnosed with STAS. Kaplan-Meier analysis and Cox proportional hazard regression models were used to evaluate the impact of STAS on overall survival (OS) and recurrence-free survival (RFS). Among stage IA patients with STAS, there was no significant difference between those who underwent sublobar resection and lobectomy in OS (P = 0.919) and RFS (P = 0.066). Multivariate analysis confirmed this result (sublobar resection versus lobectomy, OS: HR = 0.523, 95 % CI, 0.056-18.458, P = 0.714; RFS, HR = 0.360, 95 % CI, 0.115-1.565, P = 0.897). ACT did not improve the prognosis of stage IA patients but did improve the RFS of stage IB patients with high-risk recurrence factors, including poorly differentiated tumors, lymphovascular invasion and visceral pleural invasion (P = 0.046). Sublobar and lobectomy resection provided a comparable prognosis for stage IA ADC patients with STAS. When STAS was confirmed postoperatively, ACT should be considered for patients with stage IB with high-risk recurrence factors but not for those with stage IA disease.

Background: To investigate predictors of pathological invasiveness and prognosis of lung adenocarcinoma in patients with pure ground-glass nodules (pGGNs). Methods: Clinical and computed tomography (CT) features of invasive adenocarcinomas (IACs) and pre-IACs were retrospectively compared in 641 consecutive patients with pGGNs and confirmed lung adenocarcinomas who had undergone postoperative CT follow-up. Potential predictors of prognosis were investigated in these patients. Results: Of 659 pGGNs in 641 patients, 258 (39.1%) were adenocarcinomas in situ, 265 (40.2%) were minimally invasive adenocarcinomas, and 136 (20.6%) were IACs. Respective optimal cutoffs for age, serum carcinoembryonic antigen concentration, maximal diameter, mean diameter, and CT density for distinguishing pre-IACs from IACs were 53 years, 2.19 ng/mL, 10.78 mm, 10.09 mm, and − 582.28 Hounsfield units (HU). Univariable analysis indicated that sex, age, maximal diameter, mean diameter, CT density, and spiculation were significant predictors of lung IAC. In multivariable analysis age, maximal diameter, and CT density were significant predictors of lung IAC. During a median follow-up of 41 months no pGGN IACs recurred. Conclusions: pGGNs may be lung IACs, especially in patients aged > 55 years with lesions that are > 1 cm in diameter and exhibit CT density > − 600 HU. pGGN IACs of < 3 cm in diameter have good post-resection prognoses.

Background: Different pathological subtypes of lung adenocarcinoma lead to different treatment decisions and prognoses, and it is clinically important to distinguish invasive lung adenocarcinoma from preinvasive adenocarcinoma (adenocarcinoma in situ and minimally invasive adenocarcinoma). This study aims to investigate the performance of the deep learning approach based on high-resolution computed tomography (HRCT) images in the classification of tumor invasiveness and compare it with the performances of currently available approaches.Methods: In this study, we used a deep learning approach based on 3D conventional networks to automatically predict the invasiveness of pulmonary nodules. A total of 901 early-stage non-small cell lung cancer patients who underwent surgical treatment at Shanghai Chest Hospital between November 2015 and March 2017 were retrospectively included and randomly assigned to a training set (n=814) or testing set 1 (n=87). We subsequently included 116 patients who underwent surgical treatment and intraoperative frozen section between April 2019 and January 2020 to form testing set 2. We compared the performance of our deep learning approach in predicting tumor invasiveness with that of intraoperative frozen section analysis and human experts (radiologists and surgeons).Results: The deep learning approach yielded an area under the receiver operating characteristic curve (AUC) of 0.946 for distinguishing preinvasive adenocarcinoma from invasive lung adenocarcinoma in the testing set 1, which is significantly higher than the AUCs of human experts (P0.05) and junior thoracic surgeons (0.768, P>0.05).Conclusions: We developed a deep learning model that achieved comparable performance to intraoperative frozen section analysis in determining tumor invasiveness. The proposed method may contribute to clinical decisions related to the extent of surgical resection.

The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002–1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304–1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value − 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481–0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748–413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004–1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and − 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.

Computed tomography (CT) imaging can help to predict the pathological invasiveness of early-stage lung adenocarcinoma and guide surgical resection. This retrospective study investigated whether CT imaging could distinguish pre-invasive lung adenocarcinoma from IAC. It also compared final pathology prediction accuracy between CT imaging and intraoperative frozen section analysis. This study included 2093 patients with early-stage peripheral lung adenocarcinoma who underwent CT imaging and intraoperative frozen section analysis between March 2013 and November 2014. Nodules were classified as ground-glass (GGNs), part-solid (PSNs), and solid nodules according to CT findings; they were classified as pre-IAC and IAC according to final pathology. Univariate, multivariate, and receiver operating characteristic (ROC) curve analyses were performed to evaluate whether CT imaging could distinguish pre-IAC from IAC. The concordance rates of CT imaging and intraoperative frozen section analyses with final pathology were also compared to determine their accuracies. Multivariate analysis identified tumor size as an independent distinguishing factor. ROC curve analyses showed that the optimal cut-off sizes for distinguishing pre-IAC from IAC for GGNs, PSNs, and solid nodules were 10.79, 11.48, and 11.45 mm, respectively. The concordance rate of CT imaging with final pathology was significantly greater than the concordance rate of intraoperative frozen section analysis with final pathology (P = 0.041). CT imaging could distinguish pre-IAC from IAC in patients with early-stage lung adenocarcinoma. Because of its accuracy in predicting final pathology, CT imaging could contribute to decisions associated with surgical extent. Multicenter standardized trials are needed to confirm the findings in this study.

Unplanned reoperation is a potential risk factor for worse prognoses and reflects the quality of surgical treatment. This study compared the short-term outcomes between patients with and without reoperation and identified clinical factors predicting reoperation within 90 days following pulmonary surgery. Consecutive patients undergoing pulmonary resection from January 2012 to August 2021 at our institution were retrospectively reviewed. Clinical and operation-related data were collected and analyzed. Kaplan‒Meier, Cox hazard proportional regression, and propensity score matching were adopted for prognostic evaluation. A total of 90263 patients were included: 247 (0.27%) patients required reoperation within 90 days. Patients undergoing unplanned reoperation had higher mortality and more postoperative complications than the nonreoperation group. Reoperation within 24 h was associated with reduced odds of mortality relative to reoperation beyond 24 h. Independent risk factors for unplanned reoperation were male sex, benign lung disease, specific surgical locations, lobectomy, and pneumonectomy. A history of smoking, pulmonary tuberculosis, intraoperative pleural adhesion, and postoperative complications were also identified as predisposing factors. The most common complication was hemorrhage in 75.7% (187 of 247). Our study found that unplanned reoperation was a rare but serious event that increased the risk of postoperative complications and mortality. We identified several risk factors that could be used to stratify patients according to their reoperation risk and suggest that high-risk patients should receive more intensive monitoring and preventive measures. Moreover, our study indicated that reoperating within 24 h could improve the outcomes for patients who needed reoperation.