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.

OBJECTIVE: There is a lack of knowledge regarding the use of prognostic features in stage I lung adenocarcinoma (LUAD). Thus, we investigated clinicopathologic features associated with recurrence after complete resection for stage I LUAD.
METHODS: We performed a retrospective analysis of patients with pathologic stage I LUAD who underwent R0 resection from 2010 to 2020. Exclusion criteria included history of lung cancer, induction or adjuvant therapy, noninvasive or mucinous LUAD, and death within 90 days of surgery. Fine and Gray competing-risk regression assessed associations between clinicopathologic features and disease recurrence.
RESULTS: In total, 1912 patients met inclusion criteria. Most patients (1565 [82%]) had stage IA LUAD, and 250 developed recurrence: 141 (56%) distant and 109 (44%) locoregional only. The 5-year cumulative incidence of recurrence was 12% (95% CI, 11%-14%). Higher maximum standardized uptake value of the primary tumor (hazard ratio [HR], 1.04), sublobar resection (HR, 2.04), higher International Association for the Study of Lung Cancer grade (HR, 5.32 [grade 2]; HR, 7.93 [grade 3]), lymphovascular invasion (HR, 1.70), visceral pleural invasion (HR, 1.54), and tumor size (HR, 1.30) were independently associated with a hazard of recurrence. Tumors with 3 to 4 high-risk features had a higher cumulative incidence of recurrence at 5 years than tumors without these features (30% vs 4%; P < .001).
CONCLUSIONS: Recurrence after resection for stage I LUAD remains an issue for select patients. Commonly reported clinicopathologic features can be used to define patients at high risk of recurrence and should be considered when assessing the prognosis of patients with stage I disease.

OBJECTIVE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.
METHODS: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.
RESULTS: Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.
CONCLUSIONS: AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.

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.

UNASSIGNED: Early non-small cell lung cancer (NSCLC) that abuts adjacent structures requires careful evaluation due to its potential impact on postoperative outcomes and prognosis. We examined stage I NSCLC with invasion into adjacent structures, focusing on the prognostic implications after curative surgical resection.
UNASSIGNED: We retrospectively analyzed the records of 796 patients who underwent curative surgical resection for pathologic stage IA/IB NSCLC (i.e., visceral pleural invasion only) at a single center from 2008 to 2017. Patients were classified based on tumor abutment and then reclassified by the presence of visceral pleural invasion. Clinical characteristics, pathological features, and survival rates were compared.
UNASSIGNED: The study included 181 patients with abutting NSCLC (22.7% of all participants) and 615 with non-abutting tumors (77.3%). Those with tumor abutment exhibited higher rates of non-adenocarcinoma (26.5% vs. 9.9%, p<0.01) and visceral/lymphatic/vascular invasion (30.4%/33.1%/12.7% vs. 8.5%/22.4%/5.7%, respectively; p<0.01) compared to those without abutment. Multivariable analysis identified lymphatic invasion and male sex as risk factors for overall survival (OS) and disease-free survival (DFS) in stage I NSCLC measuring 3 cm or smaller. Age, smoking history, vascular invasion, and recurrence emerged as risk factors for OS, whereas the presence of non-pure ground-glass opacity was a risk factor for DFS.
UNASSIGNED: NSCLC lesions 3 cm or smaller that abut adjacent structures present higher rates of various risk factors than non-abutting lesions, necessitating evaluation of tumor invasion into adjacent structures and lymph node metastasis. In isolation, however, the presence of tumor abutment without visceral pleural invasion does not constitute a risk factor.

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.

UNASSIGNED: Visceral pleural invasion (VPI) is a poor prognostic factor that contributes to the upstaging of early lung cancers. However, the preoperative assessment of VPI presents challenges. This study was conducted to examine intraoperative pleural carcinoembryonic antigen (pCEA) level and maximum standardized uptake value (SUVmax) as predictive markers of VPI in patients with clinical T1N0M0 lung adenocarcinoma.
UNASSIGNED: A retrospective review was conducted of the medical records of 613 patients who underwent intraoperative pCEA sampling and lung resection for non-small cell lung cancer. Of these, 390 individuals with clinical stage I adenocarcinoma and tumors ≤30 mm were included. Based on computed tomography findings, these patients were divided into pleural contact (n=186) and non-pleural contact (n=204) groups. A receiver operating characteristic (ROC) curve was constructed to analyze the association between pCEA and SUVmax in relation to VPI. Additionally, logistic regression analysis was performed to evaluate risk factors for VPI in each group.
UNASSIGNED: ROC curve analysis revealed that pCEA level greater than 2.565 ng/mL (area under the curve [AUC]=0.751) and SUVmax above 4.25 (AUC=0.801) were highly predictive of VPI in patients exhibiting pleural contact. Based on multivariable analysis, pCEA (odds ratio [OR], 3.00; 95% confidence interval [CI], 1.14-7.87; p=0.026) and SUVmax (OR, 5.25; 95% CI, 1.90-14.50; p=0.001) were significant risk factors for VPI in the pleural contact group.
UNASSIGNED: In patients with clinical stage I lung adenocarcinoma exhibiting pleural contact, pCEA and SUVmax are potential predictive indicators of VPI. These markers may be helpful in planning for lung cancer surgery.

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: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.
METHODS: Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients.
RESULTS: The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models\' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons.
CONCLUSIONS: The deep learning model systems can be utilized in clinical applications via data expansion.