Interstitial lung abnormalities (ILA) on routine chest CT: Comparison of radiologists' visual evaluation and automated quantification.

PURPOSE: We aimed to evaluate the performance of a fully automated quantitative software in detecting interstitial lung abnormalities (ILA) according to the Fleischner Society guidelines on routine chest CT compared with radiologists' visual analysis. METHOD: This retrospective single-centre study included participants with ILA findings and 1:2 matched controls who underwent routine chest CT using various CT protocols for health screening. Two thoracic radiologists independently reviewed the CT images using the Fleischner Society guidelines. We developed a fully automated quantitative tool for detecting ILA by modifying deep learning-based quantification of interstitial lung disease and evaluated its performance using the radiologists' consensus for ILA as a reference standard. RESULTS: A total of 336 participants (mean age, 70.5 ± 6.1 years; M:F = 282:54) were included. Inter-reader agreements were substantial for the presence of ILA (weighted κ, 0.74) and fair for its subtypes (weighted κ, 0.38). The quantification system for identifying ILA using a threshold of 5 % in at least one zone showed 67.6 % sensitivity, 93.3 % specificity, and 90.5 % accuracy. Eight of 20 (40 %) false positives identified by the system were underestimated by readers for ILA extent. Contrast-enhancement in a certain vendor and suboptimal inspiration caused a true false-positive on the system (all P < 0.05). The best cut-off value of abnormality extent detecting ILA on the system was 3.6 % (sensitivity, 84.8 %; specificity 92.4 %). CONCLUSIONS: Inter-reader agreement was substantial for ILA but only fair for its subtypes. Applying an automated quantification system in routine clinical practice may aid the objective identification of ILA.

A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography.

PURPOSE: We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. MATERIALS AND METHODS: The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU. RESULTS: The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity. CONCLUSIONS: Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification.

OBJECTIVE: To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists. METHODS: 283 participants who underwent a baseline ultra-LDCT scan in Moscow Lung Cancer Screening, between February 2017-2018, and had at least one solid lung nodule, were included. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (AVIEW LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Discrepancies were stratified into two groups: positive-misclassification (PM); nodule classified by the reader as a NELSON-plus /EUPS-indeterminate/positive nodule, which at the reference consensus read was < 100 mm3, and negative-misclassification (NM); nodule classified as a NELSON-plus /EUPS-negative nodule, which at consensus read was ≥ 100 mm3. RESULTS: 1149 nodules with a solid-component were detected, of which 878 were classified as solid nodules. For the largest solid nodule per participant (n = 283); 61 [21.6 %; 53 PM, 8 NM] discrepancies were reported for AI as a standalone reader, compared to 43 [15.1 %; 22 PM, 21 NM], 36 [12.7 %; 25 PM, 11 NM], 29 [10.2 %; 25 PM, 4 NM], 28 [9.9 %; 6 PM, 22 NM], and 50 [17.7 %; 15 PM, 35 NM] discrepancies for readers 1, 2, 3, 4, and 5 respectively. CONCLUSION: Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists' workload could be drastically diminished by up to 86.7%.

Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT.

Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and Methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss κ, 0.32 vs 0.47, respectively; P = .005). Conclusion The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping.

OBJECTIVE: Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. MATERIALS AND METHODS: A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. RESULTS: The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) (r = -0.659-0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV1 and FEV1/FVC (R² = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R² = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R² = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. CONCLUSION: The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

Optimum diameter threshold for lung nodules at baseline lung cancer screening with low-dose chest CT: exploration of results from the Korean Lung Cancer Screening Project.

OBJECTIVES: To explore the optimum diameter threshold for solid nodules to define positive results at baseline screening low-dose CT (LDCT) and to compare two-dimensional and volumetric measurement of lung nodules for the diagnosis of lung cancers. METHODS: We included consecutive participants from the Korean Lung Cancer Screening project between 2017 and 2018. The average transverse diameter and effective diameter (diameter of a sphere with the same volume) of lung nodules were measured by semi-automated segmentation. Diagnostic performances for lung cancers diagnosed within 1 year after LDCT were evaluated using area under receiver-operating characteristic curves (AUCs), sensitivities, and specificities, with diameter thresholds for solid nodules ranging from 6 to 10 mm. The reduction of unnecessary follow-up LDCTs and the diagnostic delay of lung cancers were estimated for each threshold. RESULTS: Fifty-two lung cancers were diagnosed among 10,424 (10,141 men; median age 62 years) participants within 1 year after LDCT. Average transverse (0.980) and effective diameters (0.981) showed similar AUCs (p = .739). Elevating the average transverse diameter threshold from 6 to 9 mm resulted in a significantly increased specificity (91.7 to 96.7%, p < .001), a modest reduction in sensitivity (96.2 to 94.2%, p = .317), a 60.2% estimated reduction of unnecessary follow-up LDCTs, and a diagnostic delay in 1.9% of lung cancers. Elevating the threshold to 10 mm led to a significant reduction in sensitivity (86.5%, p = .025). CONCLUSIONS: Elevating the diameter threshold for solid nodules from 6 to 9 mm may lead to a substantial reduction in unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. KEY POINTS: • Elevation of the diameter threshold for solid nodules from 6 to 9 mm can substantially reduce unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. • The average transverse and effective diameters of lung nodules showed similar performances for the prediction of a lung cancer diagnosis.

Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading.

OBJECTIVES: To evaluate the degree of variability in computer-assisted interpretation of low-dose chest CTs (LDCTs) among radiologists in a nationwide lung cancer screening (LCS) program, through comparison with a retrospective interpretation from a central laboratory. MATERIALS AND METHODS: Consecutive baseline LDCTs (n = 3353) from a nationwide LCS program were investigated. In the institutional reading, 20 radiologists in 14 institutions interpreted LDCTs using computer-aided detection and semi-automated segmentation systems for lung nodules. In the retrospective central review, a single radiologist re-interpreted all LDCTs using the same system, recording any non-calcified nodules ≥ 3 mm without arbitrary rejection of semi-automated segmentation to minimize the intervention of radiologist's discretion. Positive results (requiring additional follow-up LDCTs or diagnostic procedures) were initially classified by the lung CT screening reporting and data system (Lung-RADS) during the interpretation, while the classifications based on the volumetric criteria from the Dutch-Belgian lung cancer screening trial (NELSON) were retrospectively applied. Variabilities in positive rates were assessed with coefficients of variation (CVs). RESULTS: In the institutional reading, positive rates by the Lung-RADS ranged from 7.5 to 43.3%, and those by the NELSON ranged from 11.4 to 45.0% across radiologists. The central review exhibited higher positive rates by Lung-RADS (20.0% vs. 27.3%; p < .001) and the NELSON (23.1% vs. 37.0%; p < .001), and lower inter-institution variability (CV, 0.30 vs. 0.12, p = .003 by Lung-RADS; CV, 0.25 vs. 0.12, p = .014 by the NELSON) compared to the institutional reading. CONCLUSION: Considerable inter-institution variability in the interpretation of LCS results is caused by different usage of the computer-assisted system. KEY POINTS: • Considerable variability existed in the interpretation of screening LDCT among radiologists partly from the different usage of the computerized system. • A retrospective reading of low-dose chest CTs in the central laboratory resulted in reduced variability but an increased positive rate.

Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system.

OBJECTIVES: We aimed to compare the CT interpretation before and after the implementation of a computerized system for lung nodule detection and measurements in a nationwide lung cancer screening program. METHODS: Our screening program started in April 2017, with 14 participating institutions. Initially, all CTs were interpreted using interpretation systems in each institution and manual nodule measurement (conventional system). A cloud-based CT interpretation system, equipped with semi-automated measurement and CAD (computer-aided detection) for lung nodules (cloud-based system), was implemented during the project. Positive rates and performances for lung cancer diagnosis based on the Lung-RADS version 1.0 were compared between the conventional and cloud-based systems. RESULTS: A total of 1821 (M:F = 1782:39, mean age 62.7 years, 16 confirmed lung cancers) and 4666 participants (M:F = 4560:106, mean age 62.8 years, 31 confirmed lung cancers) were included in the conventional and cloud-based systems, respectively. Significantly more nodules were detected in the cloud-based system (0.76 vs. 1.07 nodule/participant, p < .001). Positive rate did not differ significantly between the two systems (9.9% vs. 11.0%, p = .211), while their variability across institutions was significantly lower in the cloud-based system (coefficients of variability, 0.519 vs. 0.311, p = .018). The Lung-RADS-based sensitivity (93.8% vs. 93.5%, p = .979) and specificity (90.9% vs. 89.6%, p = .132) did not differ significantly between the two systems. CONCLUSION: Implementation of CAD and semi-automated measurement for lung nodules in a nationwide lung cancer screening program resulted in increased number of detected nodules and reduced variability in positive rates across institutions. KEY POINTS: • Computer-aided CT reading detected more lung nodules than radiologists alone in lung cancer screening. • Positive rate in lung cancer screening did not change with computer-aided reading. • Computer-aided CT reading reduced inter-institutional variability in lung cancer screening.

Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net.

We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supervised manner. To enhance the accuracy of the segmented airway tree, we simultaneously took three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal and fine-tuned the parameters that influence the tree length and the number of leakage. The gold standard of airway segmentation was generated by a semi-automated method using AVIEW™. The 2.5D CNN was trained and evaluated on a subset of inspiratory thoracic CT scans taken from the Korean obstructive lung disease study, which includes normal subjects and chronic obstructive pulmonary disease patients. The reliability and further practicality of our proposed method was demonstrated in multiple datasets. In eight test datasets collected by the same imaging protocol, the percentage detected tree length, false positive rate, and Dice similarity coefficient of our method were 92.16%, 7.74%, and 0.8997 ±â€¯0.0892, respectively. In 20 test datasets of the EXACT'09 challenge, the percentage detected tree length was 60.1% and the false positive rate was 4.56%. Our fully automated (end-to-end) segmentation method could be applied in radiologic practice.

Knockdown of TWIST1 enhances arsenic trioxide- and ionizing radiation-induced cell death in lung cancer cells by promoting mitochondrial dysfunction.

TWIST1 is implicated in the process of epithelial mesenchymal transition, metastasis, stemness, and drug resistance in cancer cells, and therefore is a potential target for cancer therapy. In the present study, we found that knockdown of TWIST1 by small interfering RNA (siRNA) enhanced arsenic trioxide (ATO)- and ionizing radiation (IR)-induced cell death in non-small-cell lung cancer cells. Interestingly, intracellular reactive oxygen species levels were increased in cells treated with TWIST1 siRNA and further increased by co-treatment with ATO or IR. Pretreatment of lung cancer cells with the antioxidant N-acetyl-cysteine markedly suppressed the cell death induced by combined treatment with TWIST1 siRNA and ATO or IR. Moreover, treatment of cells with TWIST1 siRNA induced mitochondrial membrane depolarization and significantly increased mitochondrial fragmentation (fission) and upregulated the fission-related proteins FIS1 and DRP1. Collectively, our results demonstrate that siRNA-mediated TWIST1 knockdown induces mitochondrial dysfunction and enhances IR- and ATO-induced cell death in lung cancer cells.

An engineering view on megatrends in radiology: digitization to quantitative tools of medicine.

Within six months of the discovery of X-ray in 1895, the technology was used to scan the interior of the human body, paving the way for many innovations in the field of medicine, including an ultrasound device in 1950, a CT scanner in 1972, and MRI in 1980. More recent decades have witnessed developments such as digital imaging using a picture archiving and communication system, computer-aided detection/diagnosis, organ-specific workstations, and molecular, functional, and quantitative imaging. One of the latest technical breakthrough in the field of radiology has been imaging genomics and robotic interventions for biopsy and theragnosis. This review provides an engineering perspective on these developments and several other megatrends in radiology.

Redd1 inhibits the invasiveness of non-small cell lung cancer cells.

Redd1 acts as a negative regulator of mTOR in response to various stress conditions, but its specific physiological role is currently unclear. In the present study, we showed that Redd1 inhibits the invasive activity of non-small cell lung cancer (NSCLC) cells. Interestingly, expression of Redd1 was extremely low in H1299 cells displaying high invasiveness, compared with that in H460 cells with lower invasive activity. Overexpression of Redd1 inhibited the invasive activity of H1299 cells, while suppression with specific siRNAs enhanced the invasiveness of H460 cells. Knockdown of the mTOR downstream substrate, S6K, resulted in a decrease in the invasive property of H1299 cells. Our results provide preliminary evidence that Redd1 inhibits the invasive activity of NSCLC cells via suppression of the mTOR downstream pathway.