Bronchovascular bundle thickening on CT as a predictor of survival and brain metastasis in patients with stage IA peripheral small cell lung cancer

AIM

To determine if bronchovascular bundle (BVB) thickening on pretreatment computed tomography (CT) images helps predict survival in patients with peripheral small cell lung cancer (pSCLC) ≤3 cm.

MATERIALS AND METHODS

The pretreatment CT examinations of 79 histopathologically proven pSCLC ≤3 cm (TNM stage I, 21; II, 13; III, 22; IV, 23) were reviewed retrospectively. The CT characteristics of the nodule and associated findings, including BVB thickening, were evaluated. Progression-free survival (PFS), overall survival (OS), and brain metastasis-free survival were compared with the presence of BVB thickening using Kaplan-Meier and Cox regression analysis.

RESULTS

Among the 79 patients, 34 (43%) had BVB thickening. BVB thickening was prevalent in patients with mediastinal lymph node metastasis (50.9% versus 22.7%; p=0.024) and distant metastasis (60.9% versus 35.7%; p=0.049). Out of the 21 patients with TNM stage IA disease, the 16 patients (76.2%) without BVB thickening showed better PFS, OS, and brain metastasis-free survival (mean, 1,762 versus 483 days; p=0.019: 2,243 versus 1,328 days; p=0.038: 2,274 versus 1,287 days; p=0.038, respectively). Multivariate Cox regression analysis showed that the absence of BVB thickening (hazard ratio [HR], 7.806; 95% CI, 1.241-49.091; p=0.029) and surgery (HR, 0.075; 95% CI, 0.008-0.746; p=0.027) were independent and useful prognostic factors for PFS.

CONCLUSIONS

BVB thickening was found more frequently in patients with advanced-stage pSCLC ≤3 cm, and the PFS was more favourable in patients without BVB thickening, with a similar tendency to that of OS and brain metastasis-free survival, in stage IA pSCLC.

A Single Enhanced Dual-Energy CT Scan May Distinguish Lung Squamous Cell Carcinoma From Adenocarcinoma During the Venous phase

RATIONALE AND OBJECTIVES

To investigate whether iodine quantification extracted from enhanced dual energy-computed tomography (DE-CT) is useful for distinguishing lung squamous cell carcinoma from adenocarcinoma and to evaluate whether a single scan evaluated during the venous phase (VP) can be substituted for scans evaluated during other phases.

MATERIALS AND METHODS

Sixty-two patients with lung cancer (32 squamous cell carcinomas; 30 adenocarcinomas) underwent enhanced dual-phase DE-CT scans, including an arterial phase and VP. The iodine concentration (IC), normalized iodine concentration (NIC), and slope of the curve (K) in lesions were measured during two scanning phases in two different pathological types of lung cancers. The differences in parameters (IC, NIC, and K) between these two types of lung cancers were statistically analyzed. In addition, the receiver operating characteristic curves of these parameters were performed to discriminate squamous cell carcinoma from adenocarcinoma.

RESULTS

The mean IC, NIC, and K in adenocarcinomas were all higher than those in squamous cell carcinomas during the two scanning phases. However, the differences in these parameters between the two types of cancers were significant only during the VP, not during the arterial phase. Receiver operating characteristic analysis demonstrated that the optimal thresholds of the IC, NIC, and K for discriminating squamous cell carcinoma from adenocarcinoma were 1.550, 0.227, and 1.608, respectively. In addition, the sensitivity, specificity, and area under the curve were 81.2%, 83.3%, and 0.871 for the IC; 56.2%, 93.3%, and 0.800 for the NIC; and 65.6%, 80%, and 0.720 for the K; 81.3%, 83.3%, and 0.874 for the IC + NIC; 68.8%, 93.3%, and 0.891 for the IC + NIC + K, respectively. The "IC + NIC + K" had the highest diagnostic efficiency for discriminating two types of lung cancers, but with low sensitivity. Whereas, "IC"and "IC + NIC" had the similar lower diagnostic efficiency, but with high sensitivity and specificity.

CONCLUSION

The iodine quantification parameters derived from enhanced DE-CT during the VP may be useful for distinguishing lung squamous cell carcinoma from adenocarcinoma.

Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics

AIM

To evaluate the predictive role of radiomics based on computed tomography (CT) in discriminating focal organising pneumonia (FOP) from peripheral lung adenocarcinoma (LA).

MATERIALS AND METHODS

Institutional research board approval was obtained for this retrospective study. One hundred and seventeen patients with FOP and 109 patients with LA who underwent thin-section CT from January 2011 to August 2017 were reviewed systematically and analysed. The clinical and radiological features were established as model A and multi-feature-based radiomics as model B. The diagnostic performance of model A, model B, and model A+B were evaluated and compared via receiver operating characteristic (ROC) curve analysis and logistic regression analysis.

RESULTS

Sex, symptoms, necrosis, and the halo sign were identified as independent predictors of LA. The area under the ROC curve (Az value), accuracy, sensitivity, and specificity of model A were 0.839, 75.7%, 82.6%, and 69.2% respectively. Model B showed significantly higher accuracy than model A (83.6% versus 75.7%, p=0.032). The top four best-performing features, WavEnLH_s-3, WavEnHH_s-3, Teta3, and Volume, performed as independent factors for discriminating LA. Regression analysis indicated that model B had superior model fit than model A with Akaike information criterion (AIC) values of 73.6% versus 59.1%, respectively. Combining model A with model B is useful in achieving better diagnostic performance in discriminating FOP from LA: the Az value, accuracy, sensitivity, and specificity were 0.956, 87.6%, 85.3%, and 89.7% respectively.

CONCLUSIONS

Radiomics based on CT exhibited better diagnostic accuracy and model fit than clinical and radiological features in discriminating FOP from LA. Combination of both achieved better diagnostic performance.

Intertumoral Heterogeneity in SCLC Is Influenced by the Cell Type of Origin

The extent to which early events shape tumor evolution is largely uncharacterized, even though a better understanding of these early events may help identify key vulnerabilities in advanced tumors. Here, using genetically defined mouse models of small cell lung cancer (SCLC), we uncovered distinct metastatic programs attributable to the cell type of origin. In one model, tumors gain metastatic ability through amplification of the transcription factor NFIB and a widespread increase in chromatin accessibility, whereas in the other model, tumors become metastatic in the absence of NFIB-driven chromatin alterations. Gene-expression and chromatin accessibility analyses identify distinct mechanisms as well as markers predictive of metastatic progression in both groups. Underlying the difference between the two programs was the cell type of origin of the tumors, with NFIB-independent metastases arising from mature neuroendocrine cells. Our findings underscore the importance of the identity of cell type of origin in influencing tumor evolution and metastatic mechanisms.Significance: We show that SCLC can arise from different cell types of origin, which profoundly influences the eventual genetic and epigenetic changes that enable metastatic progression. Understanding intertumoral heterogeneity in SCLC, and across cancer types, may illuminate mechanisms of tumor progression and uncover how the cell type of origin affects tumor evolution. Cancer Discov; 8(10); 1316-31. ©2018 AACR. See related commentary by Pozo et al., p. 1216 This article is highlighted in the In This Issue feature, p. 1195.

Diagnostic accuracy of computed tomography imaging for the detection of differences between peripheral small cell lung cancer and peripheral non-small cell lung cancer

Background

To evaluate the computed tomography features of peripheral small cell lung cancer and non-small cell lung cancer and to establish a predictive model to conveniently distinguish between them.

Materials and methods

We retrospectively reviewed the computed tomography features of 51 patients with peripheral small cell lung cancer and 207 patients with peripheral non-small cell lung cancer after pathological diagnosis. Thirteen computed tomography morphologic findings were included and analyzed statistically. Meaningful features were analyzed by logistic regression for multivariate analysis. We then used β-coefficients as the basis to establish an image scoring prediction model.

Result

The meaningful morphologic features for distinguishing between peripheral small cell lung cancer and other tumor types are multinodular shape and lymphadenectasis, with scores of 12 and 11, respectively. The scores ranged from −51 to 23, and the most reasonable cut-off was −24. The available area under the curve was 0.834 (95% confidence interval [CI] 0.783–0.877). Sensitivity and specificity were 86.3% (95% CI 0.737–0.943) and 69.6% (95% CI 0.628–0.758), respectively.

Conclusion

The image scoring predictive model that we constructed provides a simple and economical noninvasive method for distinguishing between peripheral small cell lung cancer and peripheral non-small cell lung cancer.

CT findings of small cell lung carcinoma: Can recognizable features be found?

The purpose of this study was to clarify the recognizable computed tomography (CT) features of small cell lung carcinoma (SCLC).Contrast enhanced CT scans were reviewed retrospectively for mass location, mediastinal extension, and other concomitant findings in 142 patients with pathologically proven SCLC. SCLC was classified into hilar mass only (type I), hilar mass with ipsilateral mediastinal extension (type II), hilar mass with bilateral mediastinal extension (type III), and peripheral mass (type IV). When mediastinal lymphadenopathy (m-LAP) was indistinguishable from a hilar mass, we defined it as a mediastinal conglomerate mass (m-CM). Type IIa or IIIa had ipsilateral or bilateral m-LAP and type IIb, IIIb or IIIc had ipsilateral or bilateral m-CM.Type I (n = 8, 5.6%), type II (n = 58, 40.8%), type III (n = 55, 38.8%), and type IV (n = 21, 14.8%) were manifested. The combination of a hilar mass and m-CM was found in 68 patients (47.9%). Type IV masses showed lobulation in 11, microlobulation in 4, both lobulated and irregular margins in 4, and spiculation in 2. A total of 120 patients (84.5%) had a bronchial stenosis/obstruction; single (n = 52) and 2 or more (n = 68). Ninety-five patients (67.0%) had vascular invasion including main/lobar pulmonary artery and superior vena cava, and 55 (38.7%) had pleural effusion and/or pleural nodules. Concomitant parenchymal findings (n = 92, 64.8%) were noted: contiguous consolidation/nodule (n = 45), hematogeneous spread (n = 32), lymphangitic spread (n = 21), obstructive pneumonia (n = 22), and obstructive atelectasis (n = 14).In conclusion, the recognizable CT features of SCLC were a hilar mass with m-CM. Most of the hilar masses showed 2 or more bronchial stenoses/obstructions. Most cases of peripheral SCLC manifested as a lobulated mass rather than a spiculated mass. Vascular invasion and concomitant parenchymal findings were observed commonly.

Size of solitary pulmonary nodule was the risk factor of malignancy

OBJECTIVE

The purpose of this study was to analyze the role of the sizes of solitary pulmonary nodules (SPNs) in predicting their potential malignancies.

METHODS

A total of 379 patients with pathologically confirmed SPNs were enrolled in this study. They were divided into three groups based on the SPN sizes: ≤10, 11-20, and >20 mm. The computed tomography (CT) findings of these SPNs were analyzed in these three groups to identify the malignant and benign SPNs. The risk factors were analyzed using binary logistic regression analysis.

RESULTS

Of these 379 patients, 120 had benign SPNs and 259 had malignant SPNs. In the ≤10 mm SPN group, air cavity density was the risk factor for malignancy, with the sensitivity, specificity, and accuracy being 77.8%, 75.0%, and 76.3%. In the 11-20 mm SPN group, age, glitches and vascular aggregation were the risk factors for malignancy, with the sensitivity, specificity, and accuracy being 91.3%, 56.9%, and 81.5%. In the >20 mm SPN group, age, lobulation, and vascular aggregation were the risk factors for malignancy, with the sensitivity, specificity, and accuracy being 88.6%, 57.1%, and 79.1%.

CONCLUSIONS

According to CT findings of SPNs, age, glitches, lobulation, vascular aggregation, and air cavity density are the risk factors of malignancy, whereas calcification and satellite lesions are the protective factors. During the course of development from small to large nodules, air cavity density could be firstly detected in early stages, followed by glitches and vascular aggregation. Lobulation is associated with relatively large lesions.

Thin-section CT findings in peripheral lung cancer of 3 cm or smaller: are there any characteristic features for predicting tumor histology or do they depend only on tumor size?

BACKGROUND

Ground-glass opacity (GGO) is reported to be characteristic to lepidic growth of neoplasm in subsolid nodules. In solid nodules of lung cancer, however, there is no characteristic feature to be reported.

PURPOSE

To study if there are any thin-section CT findings characteristic to tumor histology or if they are only related to tumor size in solid nodules of the lung cancer.

MATERIAL AND METHODS

This study included 106 solid peripheral lung cancers of 3 cm or smaller (56 adenocarcinomas, 33 squamous cell carcinomas, and 17 small cell carcinomas) in which 16-slice CT with 1 mm collimation was performed before surgery. Six morphologic findings (presence or absence of lobulation, coarse spiculation, air bronchogram, cavity, pleural tag, and pleural-based lesion) and four measurements (ratio of the greatest transverse and vertical diameter to the shortest transverse diameter and density of lobulation and coarse spiculation) on thin-section CT images were evaluated. Density of lobulation (coarse spiculation) was defined as the ratio of lobulation (coarse spiculation) number to the greatest transverse diameter of a nodule.

RESULTS

Air bronchogram (P < 0.01) was the only significant factor for predicting lung adenocarcinoma. The prevalence of air bronchogram was significantly greater in adenocarcinoma than in squamous cell carcinoma (P < 0.01) or small cell carcinoma (P < 0.01). As the tumor size advanced, significantly positive linear trends were seen in the prevalence of lobulation (P < 0.01), coarse spiculation (P < 0.01), and pleural tag (P < 0.01), and the mean values of density of lobulation (P < 0.01) and coarse spiculation (P < 0.01), while the significant negative linear trend was seen in the ratio of vertical diameter to the shortest transverse (P = 0.02).

CONCLUSION

Air bronchogram on thin-section CT is characteristic feature of solid adenocarcinoma of the lung. However, other thin-section CT findings are irrelevant to tumor histology and related only to tumor size.