Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma.

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.

High-risk features associated with recurrence in stage I 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.

Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.

PURPOSE: 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. CONCLUSION: 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.

The International Association for the Study of Lung Cancer (IASLC) Staging Project for Lung Cancer: Recommendation to Introduce Spread Through Air Spaces as a Histologic Descriptor in the Ninth Edition of the TNM Classification of Lung Cancer. Analysis of 4061 Pathologic Stage I NSCLC.

INTRODUCTION: 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.

Impact of adjuvant chemotherapy on patients with stage IB non-small cell lung cancer with visceral pleural invasion.

Background: Adjuvant chemotherapy has reduced the risk of recurrence and death in stage IB non-small cell lung cancer (NSCLC) with high-risk factors; however, the impact of visceral pleural invasion (VPI) on outcomes in stage IB NSCLC treated with adjuvant chemotherapy remains controversial. The aim of this study was to explore the clinical and prognostic significance of adjuvant chemotherapy for stage IB (1-4 cm) NSCLC with VPI. Methods: This retrospective study included 251 patients admitted between January 2008 and May 2018 from four hospitals who underwent complete resection for Tumor-Node-Metastasis (TNM) 8th edition stage IB NSCLC with VPI. The relationship between adjuvant chemotherapy and overall survival (OS) or recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: Of 251 patients with stage IB NSCLC with VPI, 122 (48.6%) received adjuvant chemotherapy after surgical resection and 129 (51.4%) were placed under observation. Multivariable analysis showed that adjuvant chemotherapy was an independent predictor of RFS [adjusted hazard ratio (aHR), 0.57; 95% confidence interval (CI): 0.33-0.96; P=0.036]. A micropapillary pattern (aHR, 2.46; 95% CI: 1.33-4.55; P=0.004) and lymphovascular invasion (aHR, 2.86; 95% CI: 1.49-5.48; P=0.002) were associated with a higher risk of recurrence. Multivariable analysis also showed that adjuvant chemotherapy was an independent predictor of OS (aHR, 0.22; 95% CI: 0.09-0.58; P=0.002). In a subgroup analysis of patients with a tumor size of 1-3 cm, adjuvant chemotherapy was associated with improved RFS and OS, and this association was maintained even when patients with VPI had additional risk factors. Conclusions: Our study shows that adjuvant chemotherapy is appropriate for patients with stage IB (1-4 cm) NSCLC with VPI, and even those with smaller tumors (1-3 cm).

Prognostic Analysis of Stage I Non-Small Cell Lung Cancer Abutting Adjacent Structures on Preoperative Computed Tomography.

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Research progress in predicting visceral pleural invasion of lung cancer: a narrative review.

Background and Objective: In lung cancer, visceral pleural invasion (VPI) affects the selection of surgical methods, the scope of lymph node dissection and the need for adjuvant chemotherapy. Preoperative or intraoperative prediction and diagnosis of VPI of lung cancer is helpful for choosing the best treatment plan and improving the prognosis of patients. This review aims to summarize the research progress of the clinical significance of VPI assessment, the intraoperative diagnosis technology of VPI, and various imaging methods for preoperative prediction of VPI. The diagnostic efficacy, advantages and disadvantages of various methods were summarized. The challenges and prospects for future research will also be discussed. Methods: A comprehensive, non-systematic review of the latest literature was carried out in order to investigate the progress of predicting VPI. PubMed database was being examined and the last run was on 4 August 2022. Key Content and Findings: The pathological diagnosis and clinical significance of VPI of lung cancer were discussed in this review. The research progress of prediction and diagnosis of VPI in recent years was summarized. The results showed that preoperative imaging examination and intraoperative freezing pathology were of great value. Conclusions: VPI is one of the adverse prognostic factors in patients with lung cancer. Accurate prediction of VPI status before surgery can provide guidance and help for the selection of clinical operation and postoperative treatment. There are some advantages and limitations in predicting VPI based on traditional computed tomography (CT) signs, 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT and magnetic resonance imaging (MRI) techniques. As an emerging technology, radiomics and deep learning show great potential and represent the future research direction.

Pleural Carcinoembryonic Antigen and Maximum Standardized Uptake Value as Predictive Indicators of Visceral Pleural Invasion in Clinical T1N0M0 Lung Adenocarcinoma.

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

Predicting visceral pleural invasion in lung adenocarcinoma presenting as part-solid density utilizing a nomogram model combined with radiomics and clinical features.

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.

Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

PURPOSE: 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. CONCLUSION: The deep learning model systems can be utilized in clinical applications via data expansion.

Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology.

The aim of the present study was to develop a non-invasive method based on histological imaging and clinical features for predicting the preoperative status of visceral pleural invasion (VPI) in patients with lung adenocarcinoma (LUAD) located near the pleura. VPI is associated with a worse prognosis of LUAD; therefore, early and accurate detection is critical for effective treatment planning. A total of 112 patients with preoperative computed tomography presentation of adjacent pleura and postoperative pathological findings confirmed as invasive LUAD were retrospectively enrolled. Clinical and histological imaging features were combined to develop a preoperative VPI prediction model and validate the model's efficacy. Finally, a nomogram for predicting LUAD was established and validated using a logistic regression algorithm. Both the clinical signature and radiomics signature (Rad signature) exhibited a perfect fit in the training cohort. The clinical signature was overfitted in the testing cohort, whereas the Rad signature showed a good fit. To combine clinical and radiomics signatures for optimal performance, a nomogram was created using the logistic regression algorithm. The results indicated that this approach had the highest predictive performance, with an area under the curve of 0.957 for the clinical signature and 0.900 for the Rad signature. In conclusion, histological imaging and clinical features can be combined in columnar maps to predict the preoperative VPI status of patients with adjacent pleural infiltrative lung carcinoma.

Development and validation of 18F-FDG PET/CT radiomics-based nomogram to predict visceral pleural invasion in solid lung adenocarcinoma.

OBJECTIVES: 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.

Prognostic Impact of Visceral Pleural Invasion in Resected Solitary Lung Metastases from Gastric Cancer.

PURPOSE: Pulmonary resection of metastases from gastric cancer is extremely rare because gastric cancer metastasis to the lungs or thoracic cavity occurs as multiple pulmonary metastases, carcinomatous lymphangitis, or carcinomatous pleurisy. Therefore, the significance of surgery for pulmonary metastasis of gastric cancer remains unclear. This study aimed to investigate the surgical outcomes and prognostic factors for survival after the resection of pulmonary metastases from gastric cancer. METHODS: From 2007 to 2019, 13 patients with pulmonary metastasis of gastric cancer underwent metastasectomy. Surgical outcomes were analyzed to determine the prognostic factors for recurrence and overall survival (OS). RESULTS: All the patients underwent pulmonary resection for solitary metastases. At the median follow-up time of 45.6 months (range, 4.8-106.8 months), five patients experienced a recurrence of gastric cancer after metastasectomy. The 5-year recurrence-free survival rate was 44.4%, and the 5-year OS rate after pulmonary resection was 45.3%. Univariate analysis revealed that visceral pleural invasion (VPI) was an unfavorable prognostic factor for both recurrence-free and OS. CONCLUSION: Pulmonary resection of solitary metastases from gastric cancer may be an effective therapeutic option to improve survival. VPI in gastric cancer metastasis is a negative prognostic factor.

Upstaged from cT1a-c to pT2a lung cancer, related to visceral pleural invasion patients, after segmentectomy: is it an indication to complete resection to lobectomy?

OBJECTIVES: Segmentectomy may be indicated for T1a-cN0 non-small-cell lung cancer. However, several patients are upstaged pT2a at final pathological examination due to visceral pleural invasion (VPI). As resection is usually not completed to lobectomy, this may raise issue of potential worse prognosis. The aim of this study is to compare prognosis of VPI upstaged cT1N0 patients operated on by segmentectomy or lobectomy. METHODS: Data of patients from 3 centres were analysed. This was a retrospective study, of patients operated on from April 2007 to December 2019. Survival and recurrence were assessed by Kaplan-Meier method and cox regression analysis. RESULTS: Lobectomy and segmentectomy were performed in 191 (75.4%) and in 62 (24.5%) patients, respectively. No difference in 5-year disease-free survival rate between lobectomy (70%) and segmentectomy (64.7%) was observed. There was no difference in loco-regional recurrence, nor in ipsilateral pleural recurrence. The distant recurrence rate was higher (P = 0.027) in the segmentectomy group. Five-year overall survival rate was similar for both lobectomy (73%) and segmentectomy (75.8%) groups. After propensity score matching, there was no difference in 5-year disease-free survival rate (P = 0.27) between lobectomy (85%) and segmentectomy (66.9%), and in 5-year overall survival rate (P = 0.42) between the 2 groups (lobectomy 76.3% vs segmentectomy 80.1%). Segmentectomy was not impacting neither recurrence, nor survival. CONCLUSIONS: Detection of VPI (pT2a upstage) in patients who underwent segmentectomy for cT1a-c non-small-cell lung cancer does not seem to be an indication to extend resection to lobectomy.

Effect of pleural invasion on survival of patients with small cell lung cancer: Propensity score analysis and nomogram establishment based on the SEER database.

Objectives: Pleural invasion (PI) is identified as an adverse prognostic factor for non-small cell lung cancer (NSCLC), but its value in small cell lung cancer (SCLC) remains unclear. We aimed to evaluate the survival effect of PI on overall survival (OS) in SCLC, meanwhile, we established a predictive nomogram based on related risk factors for OS in SCLC patients with PI. Methods: We extracted the data of patients diagnosed with primary SCLC between 2010 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. The propensity score matching (PSM) method was used to minimize the baseline difference between the non-PI and PI groups. Kaplan-Meier curves and the log-rank test were used for survival analysis. Univariate and multivariate Cox regression analyses were applied to identify the independent prognostic factors. Randomly divided the patients with PI into training (70%) and validation (30%) cohorts. A prognostic nomogram was established based on the training cohort and was evaluated in the validation cohort. The C-index, receiver operating characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) were applied to assess the performance of the nomogram. Results: A total of 1,770 primary SCLC patients were enrolled, including1321patients with non-PI and 449 patients with PI. After PSM, the 387 patients in the PI group matched the 387 patients in the non-PI group. By Kaplan-Meier survival analysis, we observed the exact beneficial effect of non-PI on OS in both original and matched cohorts. Multivariate Cox analysis showed similar results to demonstrate a statistically significant benefit for patients with non-PI in both original and matched cohorts. Age, N stage, M stage, surgery, radiotherapy, and chemotherapy were independent prognostic factors for SCLC patients with PI. The C-index of the nomogram in the training and validation cohort was 0.714 and 0.746, respectively. The ROC curves, calibration curves, and DCA curves also demonstrated good predictive performance in the training and validation cohorts of the prognostic nomogram. Conclusion: Our study shows that PI is an independent poor prognostic factor for SCLC patients. The nomogram is a useful and reliable tool to predict the OS in SCLC patients with PI. The nomogram can provide strong references to clinicians to facilitate clinic decisions.

Multivariate analysis based on the maximum standard unit value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography and computed tomography features for preoperative predicting of visceral pleural invasion in patients with subpleural clinical stage IA peripheral lung adenocarcinoma

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

The location of visceral pleural invasion in stage IB patients with non-small cell lung cancer: Comparison and prognosis.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Estudio inicial sobre la comparación del rendimiento diagnóstico de la PET/RM con [18F]FDG y la PET/TC con [18F]FDG para la estadificación torácica del cáncer de pulmón de células no pequeñas: enfoque en la invasión pleural / An initial study on the comparison of diagnostic performance of 18F-FDG PET/MR and 18F-FDG PET/CT for thoracic staging of non-small cell lung cancer: Focus on pleural invasion

Objetivo Comparar el rendimiento diagnóstico de la PET/RM con [18F]FDG y la PET/TC de forma preliminar en relación con la estadificación torácica del cáncer de pulmón de células no pequeñas (CPCNP) con un enfoque especial en la evaluación de la invasión pleural. Métodos Se incluyeron 52 pacientes con CPCNP con confirmación histopatológica y sometidos a seguimiento durante un año más. Se realizó una PET/TC con [18F]FDG de cuerpo entero y a continuación una PET/RM torácica para la estadificación torácica inicial. Las imágenes de PET/RM torácica se adquirieron simultáneamente e incluyeron secuencias potenciadas en T2, con y sin saturación grasa, en T1 y de difusión. Dos radiólogos evaluaron de forma independiente la estadificación T, N torácica y la afectación pleural. Se utilizó la prueba de Chi-cuadrado de McNemar para comparar las diferencias entre PET/TC y PET/RM en los criterios de evaluación. Se realizó análisis ROC de eficacia diagnóstica con calculó del área bajo la curva (AUC) para el estudio de la invasión pleural. Resultados La PET/RM mostró una mayor sensibilidad y especificidad en la detección de invasión pleural respecto a la PET/TC; 82 vs. 64% (p=0,625), 98 vs. 95% (p=1.000). Los resultados del análisis ROC de la PET/TC vs. la PET/RM respecto a la invasión pleural fueron los siguientes: AUCPET/TC=0,79, AUCPET/RM=0,90, p=0,21. Los resultados de la estadificación T y N fueron casi idénticos en la PET/TC y la PET/RM. Las diferencias existentes entre la PET/TC y la PET/RM para la estadificación T y N y la precisión de la invasión pleural no fueron estadísticamente significativas (p>0,05 en cada una). Conclusión La PET/RM y la PET/TC demostraron un rendimiento equivalente en la evaluación de la estadificación torácica preoperatoria de los pacientes con CPCNP (AU)

Objective To compare the diagnostic performance of 18F-FDG PET/MR and PET/CT preliminarily for the thoracic staging of non-small cell lung cancer (NSCLC) with a special focus on pleural invasion evaluation. Methods Fifty-two patients with pathologically confirmed NSCLC were included and followed for another year. Whole-body 18F-FDG PET/CT and subsequent thoracic PET/MR were performed for initial thoracic staging. Thoracic (simultaneous) PET/MR acquired PET images and MRI sequences including T2 weighted imaging, with and without fat saturation, T1 weighted imaging, and diffusion weighted imaging. Two radiologists independently assessed the thoracic T, N staging and pleural involvement. The McNemar Chi-square test was used to compare the differences between PET/CT and PET/MR in the criteria. The area under the receiver-operating-characteristic curves (AUC) was calculated. Result Compared to PET/CT, PET/MR exhibited higher sensitivity, specificity in the detection of pleural invasion; 82% vs. 64% (P=.625), 98% vs. 95% (P=1.000), PET/MR to PET/CT, respectively. The receiver-operating-characteristic analysis results of PET/CT vs. PET/MR for the pleural invasion were as follow: AUCPET/CT=0.79, AUCPET/MR=0.90, P=.21. Both T staging results and N staging results were approximately identical in PET/CT and PET/MR. Differences between PET/CT and PET/MR in T staging, N staging as well as pleural invasion accuracy were not statistically significant (P>.05, each). Conclusion PET/MR and PET/CT demonstrated equivalent performance about the evaluation of preoperative thoracic staging of NSCLC patients. PET/MR may have greater potential in pleural invasion evaluation for NSCLC, especially for solid nodules, crucial to clinical decision-making, though our results did not demonstrate statistical significance (AU)

Non-small Cell Lung Cancer Surpassing the Elastic Layer Should Remain Classified as pT2a.

We aimed to evaluate the prognostic value of visceral pleural invasion on the survival of node-negative non-small cell lung cancer ≤3 cm using a large cohort. The Kaplan-Meier method was used to compare overall survival (OS); competing risk analysis with Fine-Gray's test was used to compare cancer- specific survival between groups. The least absolute shrinkage and selection operator penalized Cox regression model was used to identify prognostic factors. In total, 9725 eligible cases were included in this study, and they were separated into 3 groups: tumor invasion beneath the elastic layer (PL0), 8837 cases; tumor invasion surpassing the elastic layer (PL1), 505 cases; and tumor invasion to the visceral pleural surface (PL2), 383 cases. Visceral pleural invasion was more likely to occur in poorly differentiated and larger-sized tumors. Survival curves displayed that PL0 conferred better survival rates than PL1 and PL2, and PL1 achieved outcomes equivalent to those of PL2. Tumor size and histology subset analyses further corroborated this conclusion. Least absolute shrinkage and selection operator -penalized Cox regression analysis confirmed that PL status was an independent prognostic factor for both OS and cancer- specific survival. This study supported the notion that in node-negative non-small cell lung cancer ≤3 cm, PL1 patients should remain classified as pT2a, which could improve staging accuracy.

Postoperative tegafur-uracil for stage I lung adenocarcinoma: first real-world data with an exploratory subgroup analysis.

PURPOSE: The effect of postoperative tegafur-uracil on overall survival (OS) after resection of stage I adenocarcinoma has been shown in clinical trials. The purpose of this study was to investigate whether findings from randomized trials of adjuvant tegafur-uracil are reproducible in a real-world setting. METHODS: A retrospective cohort study was performed using a multi-institutional database that included all patients who underwent complete resection of pathological stage I adenocarcinoma between 2014 and 2016. Survival outcomes for patients managed with and without tegafur-uracil were analyzed using the Kaplan-Meier method and a Cox proportional hazards model for the whole patient cohort and in a selected cohort based on eligibility criteria of a previous randomized trial. Propensity score matching was used to adjust for confounding effects. RESULTS: After propensity score matching, the hazard ratios for OS were 0.57 (95% confidence interval (CI) 0.29-1.14, P = 0.11) in the whole cohort and 0.69 (95% CI 0.32-1.50, P = 0.35) in the selected cohort. CONCLUSIONS: The effects of tegafur-uracil in this retrospective study appear to be consistent with those found in randomized clinical trials. These effects may be maximized in patients aged from 45 to 75 years.