Correlation between pleural tags on CT and visceral pleural invasion of peripheral lung cancer that does not appear touching the pleural surface

The nodule-pleura relationship affects pneumothorax in CT-guided percutaneous transthoracic needle biopsy: avoiding to cross pleural tail sign may reduce the incidence of pneumothorax

OBJECTIVES

To explore the role of nodule-pleural relationship, including nodule with pleural tail sign (PTS), nodule with pleural contact and nodule with pleural unrelated in CT-guided percutaneous transthoracic needle biopsy (PTNB)-induced pneumothorax, and whether employing different puncture routes has an impact on the incidence of pneumothorax in PTNB of nodules with PTS.

METHODS

Between April 1, 2019, to June 30, 2021, 775 consecutive PTNB procedures of pulmonary nodules in the Peking University Cancer Hospital were retrospectively reviewed. The univariate and multivariate regression analysis were used to identify the risk factors for pneumothorax in PTNB.

RESULTS

The nodule with pleural contact group has a lower incidence of pneumothorax than the nodule with PTS group (p = 0.001) and the nodule with pleural unrelated group (p = 0.002). It was observed that a higher incidence of pneumothorax caused by crossing PTS compared with no crossing PTS (p < 0.001). Independent risk factors for pneumothorax included crossing PTS (p < 0.001), perifocal emphysema (p < 0.001), biopsy side up (p < 0.001), longer puncture time (p < 0.001), deeper needle insertion depth (intrapulmonary) (p < 0.001) and nodules in the middle or lower lobe (p = 0.007).

CONCLUSION

Patients with crossing PTS, a nodule in the middle or lower lobe, longer puncture time, biopsy side up, deeper needle insertion depth (intrapulmonary), and perifocal emphysema were more likely to experience pneumothorax in PTNB. When performing the biopsy on a nodule with PTS, selecting a route that avoids crossing through the PTS may be advisable to reduce the risk of pneumothorax.

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.

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.

Diagnostic performance and prognostic value of CT-defined visceral pleural invasion in early-stage lung adenocarcinomas

OBJECTIVES

To analyze the diagnostic performance and prognostic value of CT-defined visceral pleural invasion (CT-VPI) in early-stage lung adenocarcinomas.

METHODS

Among patients with clinical stage I lung adenocarcinomas, half of patients were randomly selected for a diagnostic study, in which five thoracic radiologists determined the presence of CT-VPI. Probabilities for CT-VPI were obtained using deep learning (DL). Areas under the receiver operating characteristic curve (AUCs) and binary diagnostic measures were calculated and compared. Inter-rater agreement was assessed. For all patients, the prognostic value of CT-VPI by two radiologists and DL (using high-sensitivity and high-specificity cutoffs) was investigated using Cox regression.

RESULTS

In 681 patients (median age, 65 years [interquartile range, 58-71]; 382 women), pathologic VPI was positive in 130 patients. For the diagnostic study (n = 339), the pooled AUC of five radiologists was similar to that of DL (0.78 vs. 0.79; p = 0.76). The binary diagnostic performance of radiologists was variable (sensitivity, 45.3-71.9%; specificity, 71.6-88.7%). Inter-rater agreement was moderate (weighted Fleiss κ, 0.51; 95%CI: 0.43-0.55). For overall survival (n = 680), CT-VPI by radiologists (adjusted hazard ratio [HR], 1.27 and 0.99; 95%CI: 0.84-1.92 and 0.63-1.56; p = 0.26 and 0.97) or DL (HR, 1.44 and 1.06; 95%CI: 0.86-2.42 and 0.67-1.68; p = 0.17 and 0.80) was not prognostic. CT-VPI by an attending radiologist was prognostic only in radiologically solid tumors (HR, 1.82; 95%CI: 1.07-3.07; p = 0.03).

CONCLUSION

The diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas. This feature may be applied for radiologically solid tumors, but substantial reader variability should be overcome.

CLINICAL RELEVANCE STATEMENT

Although the diagnostic performance and prognostic value of CT-VPI are limited in clinical stage I lung adenocarcinomas, this parameter may be applied for radiologically solid tumors with appropriate caution regarding inter-reader variability.

KEY POINTS

• Use of CT-defined visceral pleural invasion in clinical staging should be cautious, because prognostic value of CT-defined visceral pleural invasion remains unexplored. • Diagnostic performance and prognostic value of CT-defined visceral pleural invasion varied among radiologists and deep learning. • Role of CT-defined visceral pleural invasion in clinical staging may be limited to radiologically solid tumors.

A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma

Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.

Diagnosis, treatment, and prognosis of stage IB non-small cell lung cancer with visceral pleural invasion

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.

CT Predictors of Visceral Pleural Invasion in Patients with Non-Small Cell Lung Cancers 30 mm or Smaller

Background CT-defined visceral pleural invasion (VPI) is an important indicator of prognosis for non-small cell lung cancer (NSCLC). However, there is a lack of studies focused on small subpleural NSCLCs (≤30 mm). Purpose To identify CT features predictive of VPI in patients with subpleural NSCLCs 30 mm or smaller. Materials and Methods This study is a retrospective review of patients enrolled in the Initiative for Early Lung Cancer Research on Treatment (IELCART) at Mount Sinai Hospital between July 2014 and February 2023. Subpleural nodules 30 mm or smaller were classified into two groups: a pleural-attached group and a pleural-tag group. Preoperative CT features suggestive of VPI were evaluated for each group separately. Multivariable logistic regression analysis adjusted for sex, age, nodule size, and smoking status was used to determine predictive factors for VPI. Model performance was analyzed with the area under the receiver operating characteristic curve (AUC), and models were compared using Akaike information criterion (AIC). Results Of 379 patients with NSCLC with subpleural nodules, 37 had subsolid nodules and 342 had solid nodules. Eighty-eight patients (22%) had documented VPI, all in solid nodules. Of the 342 solid nodules (46% in male patients, 54% in female patients; median age, 71 years; IQR: 66, 76), 226 were pleural-attached nodules and 116 were pleural-tag nodules. VPI was more frequent for pleural-attached nodules than for pleural-tag nodules (31% [69 of 226] vs 16% [19 of 116], P = .005). For pleural-attached nodules, jellyfish sign (odds ratio [OR], 21.60; P < .001), pleural thickening (OR, 6.57; P < .001), and contact surface area (OR, 1.05; P = .01) independently predicted VPI. The jellyfish sign led to a better VPI prediction (AUC, 0.84; 95% CI: 0.78, 0.90). For pleural-tag nodules, multiple tags to different pleura surfaces enabled independent prediction of VPI (OR, 9.30; P = .001). Conclusions For patients with solid NSCLC (≤30 mm), CT predictors of VPI were the jellyfish sign, pleural thickening, contact surface area (pleural-attached nodules), and multiple tags to different pleura surfaces (pleural-tag nodules). © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Nishino in this issue.

Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface

BACKGROUND

The preoperative determination of visceral pleural invasion (VPI) in patients with malignant solitary pulmonary nodules (SPNs) is essential for determining the surgical range and selecting adjuvant chemotherapy.

OBJECTIVES

This study aimed to systematically investigate risk factors of VPI in patients with SPN and construct a preoperative predictive model for such patients.

DESIGN

This is a retrospective study. The clinical, radiological, and pathological characteristics of study subjects were reviewed, and the groups with and without VPI were compared.

METHODS

Multivariate logistic analysis was utilized to identify independent risk factors for VPI. Moreover, a predictive nomogram was constructed to assess the likelihood of VPI occurrence.

RESULTS

Of the 364 enrolled cases, SPNs adjacent to the pleura with VPI were found in 110 (30.2%) patients. By incorporating four preoperative variables, including tumor diameter (>2 cm), maximum computed tomography value (>200 Hu), air bronchogram sign, and age, a preoperative predictive nomogram was constructed. The nomogram demonstrated good discriminative ability, with a C-index of 0.736 (95% CI (0.662-0.790)). Furthermore, our data indicated that the air bronchogram sign (odd ratio (OR) 1.81, 95% CI (0.99-3.89), p = 0.048), a maximum diameter >2 cm (OR 24.48, 95% CI (8.43-71.07), p < 0.001), pathological type (OR 5.01, 95% CI (2.61-9.64), p < 0.001), and Ki-67 >30% (OR 2.95, 95% CI (1.40-6.21), p = 0.004) were overall independent risk factors for VPI.

CONCLUSION

This study investigated the risk factors for VPI in malignant SPNs touching the pleural surface. Additionally, a nomogram was developed to predict the likelihood of VPI in such patients, facilitating informed decision-making regarding surgical approaches and treatment protocols.

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.

Multi-dimensional radiomics analysis to predict visceral pleural invasion in lung adenocarcinoma of ≤3 cm maximum diameter

AIM

To explore the value of radiomics analysis in preoperatively predicting visceral pleural invasion (VPI) of lung adenocarcinoma (LAC) with ≤3 cm maximum diameter and to compare the performance of two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics models.

MATERIALS AND METHODS

A total of 391 LAC patients were enrolled retrospectively, of whom 142 were VPI (+) and 249 were VPI (-). Radiomics features were extracted from 2D and 3D regions of interest (ROIs) of tumours in CT images. 2D and 3D radiomics models were developed combining the optimal radiomics features by using the logistic regression machine-learning method and radiomics scores (rad-scores) were calculated. Nomograms were constructed by integrating independent risk factors and rad-scores. The performance of each model was evaluated by using the receiver operator characteristic (ROC) curve, decision curve analysis (DCA), clinical impact curve (CIC), and calculating the area under the curve (AUC).

RESULTS

There was no difference in the VPI prediction between 2D and 3D radiomics models (training group: 2D AUC=0.835, 3D AUC=0.836, p=0.896; validation group: 2D AUC=0.803, 3D AUC=0.794, p=0.567). The 2D and 3D nomograms performed similarly regarding discrimination (training group: 2D AUC=0.867, 3D AUC=0.862, p=0.409, validation group: 2D AUC=0.835, 3D AUC=0.827, p=0.558), and outperformed their corresponding radiomics models and the clinical model. DCA and CIC revealed that the 2D nomogram had slightly better clinical utility.

CONCLUSION

The 2D radiomics model has a similar discrimination capability compared with the 3D radiomics model. The 2D nomogram performs slightly better for individual VPI prediction in LAC.

[Research Progress of Relationship between Pleural Deformation and Visceral Pleural Invasion in Lung Cancer Manifesting as Ground-glass Opacity]

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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Predictive Value of 18 F-FDG PET/MRI for Pleural Invasion in Solid and Subsolid Lung Adenocarcinomas Smaller Than 3 cm

BACKGROUND

Positron emission tomography (PET)/MRI combines the characteristics of metabolism imaging and high soft tissue resolution, and could provide high diagnostic efficacy for assessment of pleural invasion (PI) of lung cancer.

PURPOSE

To investigate the application of ¹⁸ F-fluorodeoxyglucose (FDG) PET/MRI for predicting PI of lung cancer with the maximum diameter ≤3 cm.

STUDY TYPE

Prospective.

POPULATION

A total of 44 patients with non-small cell lung cancer (NSCLC), age from 39 to 79 years old, including 19 (56.82%) females.

FIELD STRENGTH/SEQUENCE

A 3-T, hybrid PET/MRI including axial fast spin echo respiratory-triggered T2 fat-suppressed imaging (T2FS) and echo planar imaging diffusion-weighted imaging (DWI).

ASSESSMENT

The maximum standardized uptake value (SUVmax) of all lesions was measured on PET images. Localized effusion outside the contact between the nodules and the pleura on T2FS and signal at the contact between the nodules and the pleura on DWI were evaluated by experienced physicians through visual assessment of the MRI sequences.

STATISTICAL TESTS

Three models (models 1-3) were developed, incorporating CT, CT and PET, PET and MRI features, and Lasso regression was used in feature selection. The receiver operating characteristic (ROC) curve for PI diagnosis was visualized for each model, and the area under the curve (AUC) was calculated. The DeLong test was used to compare the different AUCs. A P value < 0.05 was considered statistically significant.

RESULTS

The AUC of models 1-3 was 0.762, 0.829, and 0.915, respectively. The DeLong test showed a statistically significant difference between the AUCs of model 1 vs. model 3, while the differences between the AUCs of model 1 vs. model 2 (P = 0.253) and model 2 vs. model 3 (P = 0.075) were not statistically significant.

DATA CONCLUSION

¹⁸ F-FDG PET/MRI might show high predictive value for lung adenocarcinoma smaller than 3 cm with PI.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

The prognosis of different types of pleural tags based on radiologic-pathologic comparison

Objectives

There are increasing numbers of studies of pleural tags (PTs). The purpose of this case series was to classify the PTs in patients with peripheral pulmonary adenocarcinoma based on radiologic-pathologic comparison and to study the prognosis.

Methods

The clinical, imaging, pathological and prognostic data of 161 patients with peripheral pulmonary adenocarcinoma in three hospitals were analyzed retrospectively. We classified PTs using computed tomography (CT) for pathologic comparison.

Results

According to the relationship between tumors and pleural on CT images, PTs were classified into four types: type 1, one or more linear pleural tag; type 2, one or more linear pleural tag with soft tissue component at the pleural end; type 3, one soft tissue cord-like pleural tag; type 4, directly abutting the visceral pleura, pulling or pushing the visceral pleura. In these PTs, the incidence of visceral pleural invasion (VPI) was high in type 2 (46.88%) and type 3 (56.41%) of PTs. Our prognostic analysis showed that micropapillary or solid histological subtype (HR = 5.766, 95% CI: 1.435–23.159, P = 0.014) and type 3 of PTs (HR = 11.058, 95% CI: 1.349–90.623, P = 0.025) were two independent risk factors for tumor progression.

Conclusions

PT is a risk factor for poor prognosis in patients with peripheral pulmonary adenocarcinoma, the presence of which on CT images can remind us to provide patients with a more reasonable treatment.

A predictive nomogram for lymph node metastasis in part-solid invasive lung adenocarcinoma: A complement to the IASLC novel grading system

Background

The International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system for invasive lung adenocarcinoma, but lymphatic invasion was not evaluated. Meanwhile, the scope of lymph node dissection in part-solid invasive lung adenocarcinoma (PSILA) is still controversial. Therefore, this study aims to explore preoperative risk factors for lymph node metastasis in PSILA, to provide reference for intraoperative dissection of lymph nodes.

Methods

From 2018 to 2020, clinical data of patients (stage cN0) consecutively diagnosed as PSILA were retrospectively analyzed and classified according to the novel grading system. Logistic regression was conducted to screen the clinicopathological factors of lymph node metastasis in PSILA.

Results

A large cohort of 960 patients with PSILA who underwent lobectomy or sub-lobectomy were enrolled. By logistic regression analyses, solid part size, bronchial cutoff sign, spiculation, and carbohydrate antigen 199 (CA199) were eventually identified as independent risk factors for lymph node metastasis, based on which a nomogram was built to preoperatively predict the risk of lymph node metastasis [area under the receiver operating characteristic curve (AUC)=0.858; concordance index = 0.857; best cutoff, 0.027]. This suggests that intraoperative systematic lymph node dissection is recommended when the predicted risk value exceeds 0.027. Reproducibility of the novel grading system was verified.

Conclusions

The novel IASLC grading system was applicative in real world. The nomogram for preoperative prediction of lymph node metastasis may provide reference for the lymph node dissection strategy during PSILA surgeries.