BACKGROUND: Although visceral pleural invasion, lymphovascular invasion, tumor spread through air spaces, and poor differentiation are pathological risk factors associated with unfavorable prognosis in patients with lung adenocarcinoma, the cumulative impact of these factors on prognosis remains unclear.
METHODS: We enrolled 1532 patients with stage I lung adenocarcinoma. Patients were divided according to the number of risk factors as follows: Group A (without risk factors), Group B (one risk factor), and Group C (multiple risk factors). Moreover, we stratified patients into two subgroups based on tumor size (≤ 3 cm, 3-4 cm). Kaplan-Meier analysis was used to evaluate 5-year disease-free survival (DFS) and overall survival (OS).
RESULTS: Overall, 949, 404, and 179 patients were included in Groups A, B, and C, respectively. Group C had a larger tumor size and more cases of extrathoracic recurrence than the other groups. The 5-year DFS and OS gradually decreased across Groups A to C (DFS: 94.3%, 80.6%, and 64.3%, respectively, p < 0.001; OS: 97.2%, 92.7%, and 77%, respectively, p < 0.001). A similar trend was observed for tumors ≤ 3 cm in size (DFS: 95.2%, 83.2%, and 68.5%, respectively, p < 0.001; OS: 97.6%, 94.1%, and 79.6%, respectively, p < 0.001), but a less pronounced trend was observed for tumors between 3 and 4 cm in size (DFS: 72.1, 60.8, and 43.3%, respectively, p = 0.054; OS: 85.7, 82.1, and 64.7%, respectively, p = 0.16).
CONCLUSIONS: Postoperative survival worsened with increasing pathological risk factors in patients with stage I lung adenocarcinoma, especially those with tumor size ≤ 3 cm.

BACKGROUND: Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma.
METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model.
RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets.
CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

BACKGROUND: Spread through air spaces (STAS) consists of lung cancer tumor cells that are identified beyond the edge of the main tumor in the surrounding alveolar parenchyma. It has been reported by meta-analyses to be an independent prognostic factor in the major histologic types of lung cancer, but its role in lung cancer staging is not established.
METHODS: To assess the clinical importance of STAS in lung cancer staging, we evaluated 4061 surgically resected pathologic stage I R0 NSCLC collected from around the world in the International Association for the Study of Lung Cancer database. We focused on whether STAS could be a useful additional histologic descriptor to supplement the existing ones of visceral pleural invasion (VPI) and lymphovascular invasion (LVI).
RESULTS: STAS was found in 930 of 4061 of the pathologic stage I NSCLC (22.9%). Patients with tumors exhibiting STAS had a significantly worse recurrence-free and overall survival in both univariate and multivariable analyses involving cohorts consisting of all NSCLC, specific histologic types (adenocarcinoma and other NSCLC), and extent of resection (lobar and sublobar). Interestingly, STAS was independent of VPI in all of these analyses.
CONCLUSIONS: These data support our recommendation to include STAS as a histologic descriptor for the Ninth Edition of the TNM Classification of Lung Cancer. Hopefully, gathering these data in the coming years will facilitate a thorough analysis to better understand the relative impact of STAS, LVI, and VPI on lung cancer staging for the Tenth Edition TNM Stage Classification.

With the increasing implementation of early lung cancer screening and the increasing emphasis on physical examinations, the early-stage lung cancer detection rate continues to rise. Visceral pleural invasion (VPI), which denotes the tumor\'s breach of the elastic layer or reaching the surface of the visceral pleura, stands as a pivotal factor that impacts the prognosis of patients with non-small cell lung cancer (NSCLC) and directly influences the pathological staging of early-stage cases. According to the latest 9th edition of the TNM staging system for NSCLC, even when the tumor diameter is less than 3 cm, the final T stage remains T2a if VPI is present. There is considerable controversy within the guidelines regarding treatment options for stage IB NSCLC, especially among patients exhibiting VPI. Moreover, the precise determination of VPI is important in guiding treatment selection and prognostic evaluation in individuals with NSCLC. This article aims to provide a comprehensive review of the current status and advancements in studies pertaining to stage IB NSCLC accompanied by VPI.

The preoperative assessment of visceral pleural invasion (VPI) in patients with early lung adenocarcinoma is vital for surgical treatment. This study aims to develop and validate a CT-based radiomics nomogram to predict VPI in peripheral T1-sized solid lung adenocarcinoma. A total of 203 patients were selected as subjects, and were divided into a training cohort (n=141; scanned with Brilliance iCT256, Brilliance 64, Somatom Force, and Optima CT660) and a test cohort (n=62; scanned with Somatom Definition AS+). Radiomics characteristics were extracted from CT images. Variance thresholding, SelectKBest, and least absolute shrinkage and selection operator (LASSO) method were applied to determine optimum characteristics to construct the radiomic signature (radscore). After multivariate logistic regression analysis, a nomogram was structured regarding clinical factors, conventional CT features, and radscore. The nomogram property was tested based on its area under the curve (AUC). The nomogram based on the radscore and two conventional CT features (tumor pleura relationship and lymph node enlargement) showed high discrimination with an AUC of 0.877 (95% CI: 0.820-0.935) and 0.837 (95% CI: 0.737-0.937) in the training and test cohorts, respectively. The calibration curve and decision curve analysis showed good consistency and high clinical value of the nomogram. In conclusion, The CT-based radiomics nomogram was helpful in predicting VPI in peripheral T1-sized solid lung adenocarcinoma.

BACKGROUND: To develop and validate a preoperative nomogram model combining the radiomics signature and clinical features for preoperative prediction of visceral pleural invasion (VPI) in lung nodules presenting as part-solid density.
METHODS: We retrospectively reviewed 156 patients with pathologically confirmed invasive lung adenocarcinomas after surgery from January 2016 to August 2019. The patients were split into training and validation sets by a ratio of 7:3. The radiomic features were extracted with the aid of FeAture Explorer Pro (FAE). A CT-based radiomics model was constructed to predict the presence of VPI and internally validated. Multivariable regression analysis was conducted to construct a nomogram model, and the performance of the models were evaluated with the area under the receiver operating characteristic curve (AUC) and compared with each other.
RESULTS: The enrolled patients were split into training (n = 109) and validation sets (n = 47). A total of 806 features were extracted and the selected 10 optimal features were used in the construction of the radiomics model among the 707 stable features. The AUC of the nomogram model was 0.888 (95% CI: 0.762-0.961), which was superior to the clinical model (0.787, 95% CI: 0.643-0.893; p = 0.049) and comparable to the radiomics model (0.879, 95% CI: 0.751-0.965; p > 0.05). The nomogram model achieved a sensitivity of 90.5% and a specificity of 76.9% in the validation dataset.
CONCLUSIONS: The nomogram model could be considered as a noninvasive method to predict VPI with either highly sensitive or highly specific diagnoses depending on clinical needs.

OBJECTIVE: This study aimed to establish a radiomics model based on 18F-FDG PET/CT images to predict visceral pleural invasion (VPI) of solid lung adenocarcinoma preoperatively.
METHODS: We retrospectively enrolled 165 solid lung adenocarcinoma patients confirmed by histopathology with 18F-FDG PET/CT images. Patients were divided into training and validation at a ratio of 0.7. To find significant VPI predictors, we collected clinicopathological information and metabolic parameters measured from PET/CT images. Three-dimensional (3D) radiomics features were extracted from each PET and CT volume of interest (VOI). Receiver operating characteristic (ROC) curve was performed to determine the performance of the model. Accuracy, sensitivity, specificity and area under curve (AUC) were calculated. Finally, their performance was evaluated by concordance index (C-index) and decision curve analysis (DCA) in training and validation cohorts.
RESULTS: 165 patients were divided into training cohort (n = 116) and validation cohort (n = 49). Multivariate analysis showed that histology grade, maximum standardized uptake value (SUVmax), distance from the lesion to the pleura (DLP) and the radiomics features had statistically significant differences between patients with and without VPI (P < 0.05). A nomogram was developed based on the logistic regression method. The accuracy of ROC curve analysis of this model was 75.86% in the training cohort (AUC: 0.867; C-index: 0.867; sensitivity: 0.694; specificity: 0.889) and the accuracy rate in validation cohort was 71.55% (AUC: 0.889; C-index: 0.819; sensitivity: 0.654; specificity: 0.739).
CONCLUSIONS: A PET/CT-based radiomics model was developed with SUVmax, histology grade, DLP, and radiomics features. It can be easily used for individualized VPI prediction.

Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.
A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set.
The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit.
The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

Recently, early-stage lung cancer has been drawing more attention, especially in screening and treatment. Visceral pleural invasion in stage IB cancer is considered as risk factor for poor prognosis. Herein, we aimed to study the distinction between the different locations of visceral pleural invasion.
In this retrospective cohort study, we summarized 58,242 patient cases that underwent surgery from 2015 to 2018 at Shanghai Chest Hospital. Of those patients, 389 met the inclusion criteria. Patients with PL3 pleural invasion were excluded. The patients were dichotomized into the interlobar pleural and peripheral pleural groups. The outcomes measured were overall survival (OS) and recurrence-free survival (RFS) rates.
According to the initial analysis, the baseline characteristics of the two groups were largely balanced. In multivariate Cox analyses, we found that the location of visceral pleural invasion was not a risk factor for prognosis in the overall population (RFS: P = 0.726, OS: P = 0.599). However, we discovered that relative to patients with peripheral pleura invasion, those with interlobar pleura invasion, PL1 invasion, lesions with greater than 3 cm solid components, and those who underwent segmentectomy had a compromised prognosis. Additionally, tumors larger than 3 cm in size with interlobar pleura invasion showed poor prognosis in patients who underwent postoperative chemotherapy.
In most cases, the location of tumor invasion did not worsen the postoperative prognosis of stage IB non-small cell lung cancer patients with visceral pleural invasion. However, interlobar pleural invasion still had some potential risks compared to that of peripheral pleural invasion.

Visceral pleural invasion (VPI) is one of the negative prognostic factors of non-small cell lung cancer (NSCLC). With the popularization of computed tomography (CT) screening for lung cancer, more and more ground-glass nodule (GGN) have been found. However, it remains unclear whether the relationship between the pleural deformation of lung cancer manifesting as ground-glass opacity (GGO) and VPI affects the effect of sub-lobectomy, which is reviewed in this paper.
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【中文题目：磨玻璃结节型肺癌胸膜改变与脏层胸膜侵犯的相关性研究进展】 【中文摘要：脏层胸膜侵犯（visceral pleural invasion, VPI）是非小细胞肺癌（non-small cell lung cancer, NSCLC）预后的不良影响因素之一。随着计算机断层扫描（computed tomography, CT）肺癌筛查的普及，肺磨玻璃结节（ground-glass nodule, GGN）的发现越来越多，但是磨玻璃影（ground-glass opacity, GGO）型肺癌的胸膜改变与 VPI之间的关系是否影响亚肺叶切除的疗效尚不明确，本文对此进行了梳理。
】 【中文关键词：肺肿瘤；磨玻璃结节；胸膜改变；脏层胸膜侵犯】.