Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns.

With the popularization of lung cancer screening, many persistent subsolid nodules (SSNs) have been identified clinically, especially in Asian non-smokers. However, many studies have found that SSNs exhibit heterogeneous growth trends during long-term follow ups. This article adopted a narrative approach to extensively review the available literature on the topic to explore the definitions, rationale, and clinical application of different interval growths of subsolid pulmonary nodule management and follow-up strategies. The development of SSN growth thresholds with different growth patterns could support clinical decision making with follow-up guidelines to reduce over- and delayed diagnoses. In conclusion, using different SSN growth thresholds could optimize the follow-up management and clinical decision making of SSNs in lung cancer screening programs. This could further reduce the lung cancer mortality rate and potential harm from overdiagnosis and over management.

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education.

Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient-doctor cooperation and shared decision making.

Pulmonary vascular volume is associated with DLCO and fibrotic score in idiopathic pulmonary fibrosis: an observational study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a disease that primarily occurs in elderly individuals. However, it is difficult to diagnose and has a complex disease course. High-resolution computed tomography (HRCT) and lung function testing are crucial for its diagnosis and follow-up. However, the correlation of HRCT findings with lung function test results has not been extensively investigated. METHODS: This study retrospectively analysed the medical records and images of patients with IPF. Patients with evident emphysema and lung cancer were excluded. The diagnosis of all the included cases was confirmed following a discussion among specialists from multiple disciplines. The correlation of HRCT findings, including fibrotic score, HRCT lung volume, pulmonary artery trunk (PA) diameter and pulmonary vascular volume (PVV), with lung function test parameters, such as forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), was analysed. RESULTS: A total of 32 patients were included. Higher fibrotic and PVV scores were significantly correlated with lower DLCO (r = - 0.59, p = 0.01; r = - 0.43, p = 0.03, respectively) but not with FVC. Higher PVV score significantly correlated with higher fibrotic score (r = 0.59, p < 0.01) and PA diameter (r = 0.47, p = 0.006). CONCLUSION: Our study demonstrated the structural and functional correlation of IPF. The extent of lung fibrosis (fibrotic score) and PVV score were associated with DLCO but not with FVC. The PA diameter, which reflects the pulmonary artery pressure, was found to be associated with the PVV score.

Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study.

Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.

Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images.

Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively enrolled subjects who suffered from IPF in this study. Clinical data including age, gender, smoking status, and pulmonary function were recorded. Non-contrast chest CT for fibrotic score calculation and determination of three dimensional measures of whole-lung texture and emphysema were performed using a promising deep learning imaging platform. The results revealed that among 116 subjects with IPF (90 non-cancer and 26 lung cancer cases), the radiomics features showed significant differences between non-cancer and cancer patients. In the training cohort, the diagnostic accuracy using selected radiomics features with AUC of 0.66-0.73 (sensitivity of 80.0-85.0% and specificity of 54.2-59.7%) was not inferior to that obtained using traditional risk factors, such as gender, smoking status, and emphysema (%). In the validation cohort, the combination of radiomics features and traditional risk factors produced a diagnostic accuracy of 0.87 AUC and an accuracy of 75.0%. In this study, we found that whole-lung CT texture analysis is a promising tool for LC risk stratification of IPF patients.

Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.

ABSTRACT: The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.

Automatic Identification of First-Order Veins and Corolla Contours in Three-Dimensional Floral Images.

Defining and quantifying corolla traits are essential for studying corolla shape variation. Three-dimensional (3D) images of corollas contain comprehensive information regarding corolla structures and are optimal for studying corolla shapes. Conventionally, corolla traits are identified and quantified manually from 3D images. Manual identification is time consuming and labor intensive. In this study, approaches are proposed to automatically identify first-order veins and corolla contours in 3D corolla images. The first-order veins of the corollas were identified using Hessian of Gaussian and Dijkstra's algorithm. The contours of the corollas were identified using vector harmony and node distance thresholding. A total of 130 3D images of 28 species in the subtribe Ligeriinae were collected and used to test the proposed approaches. The successful detection rate reached 86.54%. Two derived traits, contour-vein ratio and corolla angle, were defined and quantified using the first-order veins and corolla contour results to investigate the relationship between corolla shapes and pollination types of the subtribe Ligeriinae. Analyses revealed that the mean corolla contour, mean absolute corolla angle, and mean contour-vein ratio of the ornithophilic species were significantly smaller compared with the other species. The mean corolla contour, mean corolla angle, and mean contour-vein ratio of the melittophilic species were significantly larger compared with those of the ornithophilic species. The proposed method was also applied to certain Gesneriaceae species in the subtribes Gloxiniinae, Streptocarpinae, and Didymocarpinae. The results revealed that the method could be applied to most fresh sympetalous flowers for identifying first-order veins and corolla contours.

Modified Lung-RADS Improves Performance of Screening LDCT in a Population with High Prevalence of Non-smoking-related Lung Cancer.

OBJECTIVES: We proposed a modification of the ACR Lung Imaging Reporting and Data System (Lung-RADS) to clarify the characteristics of subsolid nodules with categories 1-11, and to compare the diagnostic accuracy with Lung-RADS and National Lung Screening Trial criteria in an Asian population with high prevalence of adenocarcinoma. METHODS: We analyzed a retrospective cohort of 1978 consecutive healthy subjects (72.8% nonsmoker) who underwent low-dose computed tomography from August 2013 to October 2014 (1084 men, 894 women). Lung-RADS categories 2 and 3 were modified to include subcategories of 2A/2B/2C and 3A/3B/3C, respectively. Clinical information and nodule characteristics were recorded. Receiver operating characteristic curves were used to compare diagnostic accuracy at different cutoffs. RESULTS: Thirty-two subjects (30 nonsmokers) had pathology-proven adenocarcinoma spectrum lesions in the follow-up period (1.6 ± 0.5 years). Modified Lung-RADS, using modified Lung-RADS category 2C as cutoff, had an area under the curve (AUC) of 0.973 in predicting adenocarcinoma spectrum lesions (sensitivity of 100%, specificity of 89.3%), which was significantly higher than that of Lung-RADS (AUC = 0.815, P < .001) and National Lung Screening Trial (AUC = 0.906, P < .001). Furthermore, modified Lung-RADS showed an AUC of 0.992 in predicting invasive adenocarcinoma (sensitivity of 95%, specificity of 97.8%) when category 3B was used as cutoff. CONCLUSIONS: Modified Lung-RADS may substantially improve sensitivity while maintaining specificity for detection of adenocarcinoma spectrum lesions in an Asian population. Compared to Lung-RADS, it has enhanced ability to differentiate invasive from indolent adenocarcinoma by more refined subclassification of subsolid nodules using two cutoff values of category 2C and 3B. The effect of using modified Lung-RADS in clinical practice must be carefully studied in prospective large cohort studies.

Association between Petal Form Variation and CYC2-like Genotype in a Hybrid Line of Sinningia speciosa.

This study used three-dimensional (3D) micro-computed tomography (µCT) imaging to examine petal form variation in a hybrid cross of Sinningia speciosa between a cultivar with actinomorphic flowers and a variety with zygomorphic flowers. The major objectives were to determine the genotype-phenotype associations between the petal form variation and CYCLOIDEA2-like alleles in S. speciosa (SsCYC) and to morphologically investigate the differences in petal types between actinomorphic and zygomorphic flowers. In this study, µCT was used to accurately acquire 3D floral images. Landmark-based geometric morphometrics (GM) was applied to evaluate the major form variations of the petals. Nine morphological traits of the petals were defined according to the form variations quantified through the GM analysis. The results indicated that the outward curvature of dorsal petals, the midrib asymmetry of lateral petals, and the dilation of ventral region of the tube were closely associated with the SsCYC genotype. Multiple analyses of form similarity between the petals suggested that the dorsal and ventral petals of actinomorphic plants resembled the ventral petals of zygomorphic plants. This observation indicated that the transition from zygomorphic to actinomorphic flowers in S. speciosa might be caused by the ventralization of the dorsal petals. We demonstrated that the 3D-GM approach can be used to determine genotype-phenotype associations and to provide morphological evidence for the transition of petal types between actinomorphic and zygomorphic flowers in S. speciosa.