Convex border of peripheral non-small cell lung cancer on CT images as a potential indicator of pleural invasion

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.

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.

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.

Differentiation of pulmonary solid nodules attached to the pleura detected by thin-section CT

BACKGROUND

Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs.

RESULTS

From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients' age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037-3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235-8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161-4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567-17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847-12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526-16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001).

CONCLUSION

In patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant.

CRITICAL RELEVANCE STATEMENT

The benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection.

KEY POINTS

• The solid pleura-attached nodules (SPANs) are closely related to the pleura. • Relationship between nodule and pleura and pleural changes are important for differentiating SPANs. • Benign SPANs frequently have broad pleural thickening or embed in thickened pleura. • Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs. • Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.

CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study

AIM

To develop a nomogram based on computed tomography (CT) texture analysis for the preoperative prediction of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma.

MATERIALS AND METHODS

A dataset of chest CT containing lung nodules was collected from two institutions, and all surgically resected nodules were classified pathologically based on the presence of visceral pleural invasion. Each nodule on the CT image was segmented automatically by artificial-intelligence software and its CT texture features were extracted. The dataset was divided into training and external validation cohorts according to the institution, and a nomogram for predicting visceral pleural invasion was developed and validated.

RESULTS

Of a total of 313 patients enrolled from two independent institutions, 63 were diagnosed with visceral pleural invasion. Three-dimensional (3D) CT long diameter, skewness, and sphericity, and chronic obstructive pulmonary disease were identified as independent predictors for visceral pleural invasion by multivariable logistic regression. The nomogram based on multivariable logistic regression showed great discriminative ability, as indicated by a C-index of 0.890 (95% confidence interval [CI]: 0.867-0.914) and 0.864 (95% CI: 0.817-0.911) for the training and external validation cohorts, respectively. Additionally, calibration of the nomogram revealed good predictive ability, as indicated by the Brier score (0.108 and 0.100 for the training and external validation cohorts, respectively).

CONCLUSIONS

A nomogram was developed that could compute the probability of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma with good calibration and discrimination. The nomogram has potential as a reliable tool for clinical evaluation and decision-making.

Diagnosis of visceral pleural invasion using confocal laser endomicroscopy during lung cancer surgery

Background

Visceral pleural invasion (VPI) in lung cancer is a significant prognostic factor; however, it is difficult to diagnose preoperatively or intraoperatively. In this study, we examined the possibility of intraoperative diagnosis of VPI using confocal laser endomicroscopy (CLE).

Methods

Among patients with primary lung cancer who underwent surgery between April 2018 and August 2019, those in whom the tumor was in contact with the pleura on chest computed tomography and whose pleural changes were intraoperatively confirmed were enrolled in this study. In the 35 patients who underwent lung resection (6 cases with visceral pleural infiltration), the area where pleural change was noted was observed and a short video was recorded using CLE. Based on the video images, three evaluators determined the defect ratio (0%, 25%, 50%, 75%, and 100%) of the autofluorescence-positive structure. The area under the receiver operating characteristic curve was used to evaluate the diagnostic performance for VPI. In 15 cases (3 cases with VPI), a validation study was performed for intraoperative VPI according to the cutoff value of the defect ratio of the autofluorescence-positive structure.

Results

The areas under the receiver operating characteristic curve for the defect ratio of the autofluorescence-positive structure were 0.86–0.91 for the three readers. Using defect ratio of autofluorescence-positive structure cutoff of ≥50% as predictor of VPI, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 83.3–100.0%, 57.7–73.1%, 35.3–41.7%, 95.0–100.0%, and 75.0–78.1%, respectively, for the three readers. In the validation study, the sensitivity was 100%, the specificity was 83.3%, and the diagnostic accuracy rate was 86.7%.

Conclusions

The diagnosis of VPI through CLE is simple, non-invasive, and has high diagnostic accuracy rates. This method may be applicable for determining surgical procedures.

Solitary Pulmonary Inflammatory Nodule: CT Features and Pathological Findings

Purpose

Solitary pulmonary inflammatory nodules (SPINs) are frequently misdiagnosed as malignancy. We aimed to investigate CT features and pathological findings of SPINs for improving diagnosis strategies.

Patients and Methods

In this retrospective study, 225 and 310 consecutive patients with confirmed SPINs and lung cancerous nodules were enrolled from January 2013 to December 2020. Nodules were classified into different types based on the key CT features: I, homogeneous and well-defined nodules with smooth (Ia), coarse (Ib), or spiculated margins (Ic); II, nodules with blurred boundaries, peripheral patches, or both; III, nodules exhibiting heterogeneous density; and IV, polygonal nodules. The pathological findings of SPINs were simultaneously studied and summarized.

Results

Among the 225 SPINs, type I (Ia, Ib, and Ic), II, III, and IV were 137 (60.9%) (47 [20.9%], 33 [14.7%], and 57 [25.3%]), 62 (27.6%), 12 (5.3%) and 14 (6.2%), respectively. Correspondingly, those in 310 cancerous nodules were 275 (88.7%) (119 [38.4%], 70 [22.6%], and 86 [27.7%]), 20 (6.5%), 15 (4.8%), and 0, respectively. Compared with lung cancers, type I nodules were less common but type II and IV nodules were more common in SPINs (each P < 0.0001). Though the frequencies of subtype I (P = 0.095) and type III (P = 0.796) nodules were similar between two groups, their specific CT features were significantly different. The main pathological findings of each type of SPINs were most extensively identical (82.2 - 100%).

Conclusion

Between cancerous nodules and SPINs, differences in overall or specific CT features exist. The type II and IV nodules are highly indicative of SPINs, and each type of SPINs have almost similar pathological findings.

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

OBJECTIVES

To evaluate the association between a sign and visceral pleural invasion (VPI) of peripheral non-small-cell lung cancer (NSCLC) that does not appear touching the pleural surface.

METHODS

A total of 221 consecutive patients with NSCLC that did not appear touching the pleural surface, ≤ 3 cm in solid tumor diameter, and was surgically resected between January 2009 and December 2015 were included. We focused on the flat distortion of the tumor caused by an arch-shaped linear tag between the tumor and the pleura on CT and named it a bridge tag sign. We evaluated the associations between the clinicopathological features of the tumor, including the bridge tag sign, and VPI. We also evaluated the associations between histopathological findings and the bridge tag sign. The utility of the bridge tag sign in the diagnosis of VPI was statistically assessed.

RESULTS

The bridge tag sign was observed in 48 (20.8%) patients. VPI was positive in 9 (4.1%) patients; among these, the bridge tag sign was positive in 8 patients. In multivariate analysis, a bridge tag sign was significantly associated with VPI. The bridge tag sign was associated with longer contact length of the pleura with the tumor and trapezoid type pleural retraction. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the bridge tag sign in the diagnosis of VPI were 88.9%, 83.5%, 83.7%, 18.6%, and 99.4%, respectively.

CONCLUSIONS

A bridge tag sign on CT might improve the accuracy of the prediction of VPI.

KEY POINTS

• We present the bridge tag sign which is defined as a flat distortion of an NSCLC tumor by an arch-shaped linear tag between the tumor and chest wall or interlobar fissure. • The bridge tag sign was an independent predictive factor for visceral pleural invasion. • The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the bridge tag sign in the diagnosis of visceral pleural invasion were 88.9%, 83.5%, 83.7%, 18.6%, and 99.4%, respectively.

Imaging Assessment of Visceral Pleural Surface Invasion by Lung Cancer: Comparison of CT and Contrast-Enhanced Radial T1-Weighted Gradient Echo 3-Tesla MRI

Objective

To compare the diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3-tesla (3T) magnetic resonance imaging (MRI) and computed tomography (CT) for the detection of visceral pleural surface invasion (VPSI). Visceral pleural invasion by non-small-cell lung cancer (NSCLC) can be classified into two types: PL1 (without VPSI), invasion of the elastic layer of the visceral pleura without reaching the visceral pleural surface, and PL2 (with VPSI), full invasion of the visceral pleura.

Materials and Methods

Thirty-three patients with pathologically confirmed VPSI by NSCLC were retrospectively reviewed. Multidetector CT and contrast-enhanced 3T MRI with a free-breathing radial three-dimensional fat-suppressed volumetric interpolated breath-hold examination (VIBE) pulse sequence were compared in terms of the length of contact, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. Supplemental evaluation of the tumor-pleura interface (smooth versus irregular) could only be performed with MRI (not discernible on CT).

Results

At the tumor-pleura interface, radial VIBE MRI revealed a smooth margin in 20 of 21 patients without VPSI and an irregular margin in 10 of 12 patients with VPSI, yielding an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F-score for VPSI detection of 91%, 83%, 95%, 91%, 91%, and 87%, respectively. The McNemar test and receiver operating characteristics curve analysis revealed no significant differences between the diagnostic accuracies of CT and MRI for evaluating the contact length, angle of mass margin, or arch distance-to-maximum tumor diameter ratio as predictors of VPSI.

Conclusion

The diagnostic performance of contrast-enhanced radial T1-weighted gradient-echo 3T MRI and CT were equal in terms of the contact length, angle of mass margin, and arch distance-to-maximum tumor diameter ratio. The advantage of MRI is its clear depiction of the tumor-pleura interface margin, facilitating VPSI detection.

Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs

OBJECTIVES

To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer.

METHODS

In this retrospective study, dataset 1 (for training, tuning, and internal validation) included 676 patients with clinical stage IA lung adenocarcinomas resected between 2009 and 2015. Dataset 2 (for temporal validation) included 141 patients with clinical stage I adenocarcinomas resected between 2017 and 2018. A CT-based deep learning model was developed for the prediction of VPI and validated in terms of discrimination and calibration. An observer performance study and a multivariable regression analysis were performed.

RESULTS

The area under the receiver operating characteristic curve (AUC) of the model was 0.75 (95% CI, 0.67-0.84), which was comparable to those of board-certified radiologists (AUC, 0.73-0.79; all p > 0.05). The model had a higher standardized partial AUC for a specificity range of 90 to 100% than the radiologists (all p < 0.05). The high sensitivity cutoff (0.245) yielded a sensitivity of 93.8% and a specificity of 31.2%, and the high specificity cutoff (0.448) resulted in a sensitivity of 47.9% and a specificity of 86.0%. Two of the three radiologists provided highly sensitive (93.8% and 97.9%) but not specific (48.4% and 40.9%) diagnoses. The model showed good calibration (p > 0.05), and its output was an independent predictor for VPI (adjusted odds ratio, 1.07; 95% CI, 1.03-1.11; p < 0.001).

CONCLUSIONS

The deep learning model demonstrated a radiologist-level performance. The model could achieve either highly sensitive or highly specific diagnoses depending on clinical needs.

KEY POINTS

• The preoperative CT-based deep learning model demonstrated an expert-level diagnostic performance for the presence of visceral pleural invasion in early-stage lung cancer. • Radiologists had a tendency toward highly sensitive, but not specific diagnoses for the visceral pleural invasion.

Why do myofibroblasts preferentially accumulate on the convex surface of the remodeling lung after pneumonectomy?

Myofibroblasts preferentially accumulate on the convex and not on the concave surfaces of the murine cardiac lobe during lung remodeling after pneumonectomy. This clear difference in function due to the organ shape is most likely mediated by the various mechanical forces generated on the lung's surface. For breathing, the lobe cyclically change its configuration. The cyclic deformation requires energy, depending on the local configuration of the lobe (e.g., convex vs. concave). Considering mechanical contributions to the internal energy of the system and according to the second law of thermodynamics, the system seeks the lowest energy state for equilibrium. Although additional energy for remodeling is required, the system chooses such remodeling sites that minimize the total energy of the new equilibrium state. To test this idea, an idealized, concave-convex configuration of the lobe is assumed. The lobe is made of two homogeneous and isotropic materials of different mechanical properties, the bulk parenchyma and the pleura, a thin, mesothelial cell layer surrounding it. While the whole system cyclically changes shape during breathing, we calculated the amount of mechanical energy per unit volume at the parenchyma-pleural interface where, we believe, myofibroblasts preferentially accumulate. Comparison between convex and concave surfaces indicates that convex surfaces store a lower amount of mechanical energy than the concave ones. We also show that any additional energy for remodeling is preferably done at the convex surface where the lowest new energy equilibrium state is achieved.

Visceral Pleural Invasion in Pulmonary Adenocarcinoma: Differences in CT Patterns between Solid and Subsolid Cancers

PURPOSE

To analyze the incidence and CT patterns of visceral pleural invasion (VPI) in adenocarcinomas on the basis of their CT presentation as solid or subsolid nodules.

MATERIALS AND METHODS

A total of 286 adenocarcinomas in direct contact with a pleural surface, resected at an institution between 2005 and 2016, were included in this retrospective, institutional review board-approved study. CT size and longest contact length with a pleural surface were measured and their ratios computed. Pleural deviation, pleural thickening, spiculations, different pleural tag types, pleural effusion, and the CT appearance of transgression into an adjacent lobe or infiltration of surrounding tissue were evaluated. Fisher exact tests and simple and multiple logistic regression models were used.

RESULTS

Of the 286 nodules, 179 of 286 (62.6%) were solid and 107 of 286 (37.4%) were subsolid. VPI was present in 49 of 286 (17.1%) nodules and was significantly more frequent in solid (44 of 179; 24.6%) than in subsolid nodules (five of 107; 4.7%; P < .001). In solid nodules, multiple regression analysis showed an association of higher contact length-to-size ratio (adjusted odds ratio [OR], 1.02; P = .007) and the presence of multiple pleural tag types (adjusted OR, 5.88; P = .002) with VPI. In subsolid nodules, longer pleural contact length of the solid nodular component (adjusted OR, 1.27; P = .017) and the CT appearance of transgression or infiltration (adjusted OR, 10.75; P = .037) were associated with VPI.

CONCLUSION

During preoperative evaluation of adenocarcinomas for the likelihood of VPI, whether a tumor manifests as a solid or a subsolid nodule is important to consider because the incidence of VPI is significantly higher in solid than in subsolid nodules. In addition, this study showed that the CT patterns associated with VPI differ between solid and subsolid nodules.© RSNA, 2019Supplemental material is available for this article.See also the commentary by Elicker in this issue.

Efficacy of erlotinib and celecoxib for patients with advanced non-small cell lung cancer: A retrospective study

This study evaluated the efficacy and toxicity of erlotinib and celecoxib (EC) for treating Chinese patients with advanced non-small cell lung cancer (ANSCLC) and epidermal growth factor receptor (EGFR) wild type.Totally, 75 subjects with ANSCLC and EGFR wild type were included. They all underwent EC treatment. The outcome measurements consisted of progression-free survival (PFS), overall survival (OS), complete response (CR), partial response (PR), stable disease (SD), progress disease (PD), and disease control rate (DCR). Additionally, adverse events were also documented.Two-year CR, PR, SD, PD, and DCR were 4.0%, 6.7%, 42.6%, 46.7%, and 53.3% respectively. The median PFS was 3.4 months, the median OS was 10.0 months. Additionally, acceptable toxicities were recorded in this study.The results showed that EC may be efficacious for patients with ANSCLC and EGFR wild type only, and acceptable toxicity among the Chinese Han population.

Prediction of pleural invasion using different imaging tools in non-small cell lung cancer

Clinical staging of non-small cell lung cancer (NSCLC) is used for planning therapeutic strategies. In particular, pleural invasion is regarded as an indicator for upstaging to T2 or T3 in the current 8th TNM staging system; patients with pleural invasion should be indicated for lobectomy rather than sublobar resection. Therefore, accurate preoperative prediction of pleural invasion is important for surgical planning. In recent years, different radiological investigations for patients with NSCLC have been widely used, and methods for more precise detection have been developed in the current medical imaging studies. Therefore, several radiological investigation tools have been used for the prediction of pleural invasion. In this article, to identify the imaging modalities for accurate prediction of pleural invasion, we reviewed the different methods used for this purpose and discussed their advantages and limitations.