Evaluation of the Radiomics Method for the Prediction of Atypical Adenomatous Hyperplasia in Patients With Subcentimeter Pulmonary Ground-Glass Nodules.

OBJECTIVES: We aimed to develop a prediction model to distinguish atypical adenomatous hyperplasia (AAH) from early lung adenocarcinomas in patients with subcentimeter pulmonary ground-glass nodules (GGNs), which may help avoid aggressive surgical resection for patients with AAH. METHODS: Surgically confirmed cases of AAH and lung adenocarcinomas manifesting as GGNs of less than 1 cm were retrospectively collected. A prediction model based on radiomics and clinical features identified from a training set of cases was built to differentiate AAH from lung adenocarcinomas and tested on a validation set. RESULTS: Four hundred and eighty-five eligible cases were included and randomly assigned to the training (n = 339) or the validation sets (n = 146). The developed radiomics prediction model showed good discrimination performance to distinguish AAH from adenocarcinomas in both the training and the validation sets, with, respectively, 84.1% and 82.2% of accuracy, and AUCs of 0.899 (95% CI: 0.867-0.931) and 0.881 (95% CI: 0.827-0.936). CONCLUSION: The prediction model based on radiomics and clinical features can help differentiate AAH from adenocarcinomas manifesting as subcentimeter GGNs and may prevent aggressive resection for AAH patients, while reserving this treatment for adenocarcinomas.

Clinical Value and Pathologic Basis of Cystic Airspace Within Subsolid Nodules Confirmed as Lung Adenocarcinomas by Surgery.

PURPOSE: To investigate the clinical value and pathologic basis of cystic airspace within lung adenocarcinomas manifesting as subsolid nodules. PATIENTS AND METHODS: A retrospective study was conducted on a total of 541 surgically confirmed lung adenocarcinomas manifesting as subsolid nodules in computed tomography images, including 87 cases with cystic airspace and 454 cases without cystic airspace. The pathologic characteristics of the cases with and without cystic airspace were compared. The investigation of the pathologic structure of cystic airspace was attempted on the postoperative paraffin sections. RESULTS: There was a significant difference in the containing of cystic airspace between preinvasive and invasive adenocarcinomas (10.5 vs 26.6%; P < .001). Multivariate analysis indicated that cystic airspace is an independent predictor of invasive adenocarcinomas (odds ratio, 3.220; 95% confidence interval, 1.822-5.687). Nodules containing multiple cystic airspaces are more likely to be invasive adenocarcinomas than nodules with a single cystic airspace (47.1 vs 72.2%; P < .05). On paraffin sections, the walls of the cystic airspace seemed to be mainly composed of atypical hyperplasia and/or tumor cells on the surface and the remaining smooth muscle cells and stroma below, which is similar to the structure of bronchi. CONCLUSIONS: Cystic airspace may be a reliable predictor of invasive adenocarcinomas, the classification method based on the number of cystic airspaces might be suitable for the computed tomography-based typing of cystic airspace within subsolid nodules. Cystic airspace may derive from the destroyed and enlarged bronchi owing to the growth or infiltration of atypical hyperplasia and/or tumor cells.

Joint use of the radiomics method and frozen sections should be considered in the prediction of the final classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules.

OBJECTIVES: To evaluate the diagnostic accuracy of radiomics method and frozen sections (FS) for the pathological classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). MATERIALS AND METHODS: A dataset of 831 peripheral lung adenocarcinoma manifesting as GGNs in CT were divided into two cohorts: training cohort (n = 581) and validation cohort (n = 250). Combined with clinical features, the radiomics classifier was trained and validated to distinguish the pathological classification of these nodules. FS diagnoses in the validation cohort were collected. Diagnostic performance of the FS and radiomics methods was compared in the validation cohort. The predictive factors for the misdiagnosis of FS were determined via univariate and multivariate analyses. RESULTS: The accuracy of radiomics method in the training and validation cohorts was 72.5 % and 68.8 % respectively. The overall accuracy of FS in the validation cohort was 70.0 %. The concordance rate between FS and final pathology when FS had a different diagnosis from radiomics classifier was significantly lower than when FS had the same diagnosis as radiomics classifier (46 vs. 87 %, P < 0.001). Univariate and Multivariate analyses showed that different diagnosis between FS and radiomics classifier was the independent predictive factor for the misdiagnosis of FS (OR: 7.46; 95%CI: 4.00-13.91; P < 0.001). CONCLUSIONS: Radiomics classifier predictions may be a reliable reference for the classification of peripheral lung adenocarcinoma manifesting as GGNs when FS cannot provide a timely diagnosis. Intraoperative diagnoses of the cases where FS had a different diagnosis from radiomics method should be considered cautiously due to the higher misdiagnosis rate.

Improved image quality and diagnostic potential using ultra-high-resolution computed tomography of the lung with small scan FOV: A prospective study.

The aim of this study was to assess whether CT imaging using an ultra-high-resolution CT (UHRCT) scan with a small scan field of view (FOV) provides higher image quality and helps to reduce the follow-up period compared with a conventional high-resolution CT (CHRCT) scan. We identified patients with at least one pulmonary nodule at our hospital from July 2015 to November 2015. CHRCT and UHRCT scans were conducted in all enrolled patients. Three experienced radiologists evaluated the image quality using a 5-point score and made diagnoses. The paired images were displayed side by side in a random manner and annotations of scan information were removed. The following parameters including image quality, diagnostic confidence of radiologists, follow-up recommendations and diagnostic accuracy were assessed. A total of 52 patients (62 nodules) were included in this study. UHRCT scan provides a better image quality regarding the margin of nodules and solid internal component compared to that of CHRCT (P < 0.05). Readers have higher diagnostic confidence based on the UHRCT images than of CHRCT images (P<0.05). The follow-up recommendations were significantly different between UHRCT and CHRCT images (P<0.05). Compared with the surgical pathological findings, UHRCT had a relative higher diagnostic accuracy than CHRCT (P > 0.05). These findings suggest that the UHRCT prototype scanner provides a better image quality of subsolid nodules compared to CHRCT and contributes significantly to reduce the patients' follow-up period.