Visceral pleural invasion: predictable on CT?

CT-guided needle biopsy is not associated with increased ipsilateral pleural metastasis

INTRODUCTION

Histological confirmation of a lung tumor is the prerequisite for treatment planning. It has been suspected that CT-guided needle biopsy (CTGNB) exposes the patient to a higher risk of pleural recurrence. However, the distance between tumor and pleura has largely been neglected as a possible confounder when comparing CTGNB to bronchoscopy.

METHODS

All patients with lung cancer histologically confirmed by bronchoscopy or CTGNB between 2010 and 2020 were enrolled and studied. Patients' medical histories, radiologic and pathologic findings and surgical records were reviewed. Pleural recurrence was diagnosed by pleural biopsy, fluid cytology, or by CT chest imaging showing progressive pleural nodules.

RESULTS

In this retrospective unicenter analysis, 844 patients underwent curative resection for early-stage lung cancer between 2010 and 2020. Median follow-up was 47.5 months (3-137). 27 patients (3.2 %) with ipsilateral pleural recurrence (IPR) were identified. The distance of the tumor to the pleura was significantly smaller in patients who underwent CTGNB. A tendency of increased risk of IPR was observed in tumors located in the lower lobe (HR: 2.18 [±0.43], p = 0.068), but only microscopic pleural invasion was a significant independent predictive factor for increased risk of IPR (HR: 5.33 [± 0.51], p = 0.001) by multivariate cox analysis. Biopsy by CTGNB did not affect IPR (HR: 1.298 [± 0.39], p = 0.504).

CONCLUSION

CTGNB is safe and not associated with an increased incidence of IPR in our cohort of patients. This observation remains to be validated in a larger multicenter patient cohort.

Segmentectomy vs Lobectomy for Early Non-Small Cell Lung Cancer With Visceral Pleural Invasion

BACKGROUND

Recent prospective trials have demonstrated the noninferiority of segmentectomy to lobectomy in the surgical management of early non-small cell lung cancer (NSCLC). It remains unknown, however, whether segmentectomy is sufficient for treating small tumors with visceral pleural invasion (VPI), a known indicator of aggressive disease biology and poor prognosis in NSCLC.

METHODS

Patients in the National Cancer Database (2010-2020) with cT1a-bN0M0 NSCLC and VPI and additional high-risk features who underwent segmentectomy or lobectomy were identified for analysis. Only patients with no comorbidities were included in this analysis to reduce selection bias. Overall survival of patients who underwent segmentectomy vs lobectomy was evaluated using multivariable-adjusted Cox proportional hazards and propensity score- matched analyses. Short-term and pathologic outcomes were also evaluated.

RESULTS

Of the 2568 patients with cT1a-bN0M0 NSCLC and VPI included in our overall cohort, 178 (7%) underwent segmentectomy and 2390 (93%) underwent lobectomy. No significant differences were found in the 5-year overall survival between patients undergoing segmentectomy vs lobectomy in multivariable-adjusted and propensity score-matched analyses (adjusted hazard ratio, 0.91 [95% CI, 0.55-1.51], P = .72; 86% [95% CI, 75%-92%] vs 76% [95% CI, 65%-84%], P = .15, respectively). There were also no differences in surgical margin positivity, 30-day readmission, and 30- and 90-day mortality between patients undergoing either surgical approach.

CONCLUSIONS

In this national analysis, no differences were found in survival or in short-term outcomes between patients undergoing segmentectomy vs lobectomy for early-stage NSCLC with VPI. Our findings suggest that if VPI is detected after segmentectomy for cT1a-bN0M0 tumors, completion lobectomy is unlikely to confer an additional survival advantage.

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.

Using CT imaging features to predict visceral pleural invasion of non-small-cell lung cancer

AIM

To examine the diagnostic performance of different models based on computed tomography (CT) imaging features in differentiating the invasiveness of non-small-cell lung cancer (NSCLC) with multiple pleural contact types.

MATERIALS AND METHODS

A total of 1,573 patients with NSCLC (tumour size ≤3 cm) were included retrospectively. The clinical and pathological data and preoperative imaging features of these patients were investigated and their relationships with visceral pleural invasion (VPI) were compared statistically. Multivariate logistic regression was used to eliminate confounding factors and establish different predictive models.

RESULTS

By univariate analysis and multivariable adjustment, surgical history, tumour marker (TM), number of pleural tags, length of solid contact and obstructive inflammation were identified as independent risk predictors of pleural invasiveness (p=0.014, 0.003, <0.001, <0.001, and 0.017, respectively). In the training group, comparison of the diagnostic efficacy between the combined model including these five independent predictors and the image feature model involving the latter three imaging predictors were as follows: sensitivity of 88.9% versus 77% and specificity of 73.5% versus 84.1%, with AUC of 0.868 (95% CI: 0.848-0.886) versus 0.862 (95% CI: 0.842-0.880; p=0.377). In the validation group, the sensitivity and specificity of these two models were as follow: the combined model, 93.5% and 74.3%, the imaging feature model, 77.4% and 81.3%, and their areas under the curve (AUCs) were both 0.884 (95% CI: 0.842-0.919). The best cut-off value of length of solid contact was 7.5 mm (sensitivity 68.9%, specificity 75.5%).

CONCLUSIONS

The image feature model showed great potential in predicting pleural invasiveness, and had comparable diagnostic efficacy compared with the combined model containing clinical data.

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.

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

Background

Visceral pleural invasion (VPI) is a clinical manifestation associated with a poor prognosis, and diagnosing it preoperatively is highly imperative for successful sublobar resection of these peripheral tumors. We evaluated the roles of computed tomography (CT) features and circulating tumor cells (CTCs) for improving VPI detection in patients with clinical T1N0M0 invasive lung adenocarcinoma.

Methods

Three hundred and ninety-one patients were reviewed retrospectively in this study, of which 234 presented with a pleural tag or pleural contact on CT images. CTCs positive for the foliate receptors were enriched and analyzed prior to surgery. Logistic regression analyses were performed to assess the association of CT features and CTCs with VPI, and the receiver operating characteristic (ROC) curve was generated to compare the predictive power of these variables.

Results

Patients mostly underwent either segmentectomies (18.9%) or lobectomies (79.0%). Only 49 of the 234 patients with pleural involvement on CT showed pathologically confirmed VPI. Multivariate logistic regression analysis revealed that CTC level ≥10.42 FU/3 mL was a significant VPI risk factor for invasive adenocarcinoma cases ≤30 mm [adjusted odds ratio (OR) =4.62, 95% confidence interval (CI): 2.05–10.44, P<0.001]. Based on CT features, subgroup analyses showed that the solid portion size was a statistically significant independent predictor of VPI for these peripheral nodules with pleural tag, while the solid portion length of the interface was an independent predictor of pleural contact. The receiver operating curve analyses showed that the combination of CTC and CT features were highly predictive of VPI [area under the curve (AUC) =0.921 for pleural contact and 0.862 for the pleural tag, respectively].

Conclusions

CTC, combined with CT features of pleural tag or pleural contact, could significantly improve VPI detection in invasive lung adenocarcinomas at clinical T1N0M0 stage prior to the patient’s surgery.