Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort.

OBJECTIVE: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. MATERIALS AND METHODS: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). RESULTS: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. CONCLUSION: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case-control study.

ABSTRACT: Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 major thoracic abnormalities visible on chest radiographs (CR) and to compare the performance of physicians with and without the assistance of the algorithm. A subset of 244 subjects (60% abnormal CRs) was evaluated. Abnormal findings included mass/nodules (55%), consolidation (21%), and pneumothorax (24%). Observer performance tests were conducted to assess whether the performance of physicians could be enhanced with the algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) and the area under the jackknife alternative free-response ROC (JAFROC) were measured to evaluate the performance of the algorithm and physicians in image classification and lesion detection, respectively. The AUCs for nodule/mass, consolidation, and pneumothorax were 0.9883, 1.000, and 0.9997, respectively. For the image classification, the overall AUC of the pooled physicians was 0.8679 without DCAD and 0.9112 with DCAD. Regarding lesion detection, the pooled observers exhibited a weighted JAFROC figure of merit (FOM) of 0.8426 without DCAD and 0.9112 with DCAD. DCAD for CRs could enhance physicians' performance in the detection of 3 major thoracic abnormalities.

Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers.

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.

Cone-Beam Computed Tomography-Hepatic Arteriography as a Diagnostic Tool for Small Hypervascular Hepatocellular Carcinomas: Method and Clinical Implications.

OBJECTIVE: This study proposes a novel reference standard for hypervascular hepatocellular carcinomas (HCCs), established by cone-beam computed tomography-hepatic arteriography (CBCT-HA) and two-year imaging follow-up, and discusses its clinical implication on tumor staging and understanding the intrahepatic distant recurrence (IDR) in relation to dynamic computed tomography (CT). MATERIALS AND METHODS: In this retrospective study, 99 patients were enrolled, who underwent CBCT-HA during initial chemoembolization for HCC suspected on CT. All patients underwent chemoembolization and regular clinical and imaging follow-up for two years. If IDR appeared on follow-up imaging, initial CBCT-HA images were reviewed to determine if a hypervascular focus pre-existed at the site of recurrence. Pre-existing hypervascular foci on CBCT-HA were regarded as HCCs in initial presentation. Initial HCCs were classified into three groups according to their mode of detection (Group I, detected on CT and CBCT-HA; Group II, additionally detected on CBCT-HA; Group III, confirmed by interval growth). We assessed the influence of CBCT-HA and two-year follow-up on initial tumor stage and calculated the proportion of IDR that pre-existed in initial CBCT-HA. RESULTS: A total of 405 nodules were confirmed as HCCs, and 297 nodules initially pre-existed. Of the initial 297 HCCs, 149 (50.2%) lesions were in Group I, 74 (24.9%) lesions were in Group II, and the remaining 74 (24.9%) lesions were in Group III. After applying CBCT-HA findings, 11 patients upstaged in T stage, and 4 patients had a change in Milan criteria. Our reference standard for HCC indicated that 120 of 148 (81.1%) one-year IDR and 148 of 256 (57.8%) two-year IDR existed on initial CBCT-HA. CONCLUSION: The proposed method enabled the confirmation of many sub-centimeter-sized, faintly vascularized HCC nodules that pre-existed initially but clinically manifested as IDR. Our reference standard for HCC helped in understanding the nature of IDR and the early development of HCC as well as the clinical impact of tumor staging and treatment decision.

Growth of thymic epithelial tumors and thymic cysts: Differential radiological points.

BACKGROUND: The growth rate of thymic epithelial tumors (TETs) and thymic cysts was investigated to determine whether they can be differentiated and clinico-radiological predictors of interval growth was identified. METHODS: This retrospective study included 122 patients with pathologically proven thymic cysts (n = 56) or TETs (n = 66) who underwent two serial chest computed tomography scans at least eight weeks apart. Average diameters and attenuation were measured, volume-doubling times (VDTs) were calculated, and clinical characteristics were recorded. VDTs were compared using the log-rank test. Predictors of growth were analyzed using the log-rank test and Cox regression analysis. RESULTS: The frequency of growth did not differ significantly between TETs and thymic cysts (P = 0.279). The VDT of thymic cysts (median 324 days) was not significantly different from that of the TETs (median 475 days; P = 0.808). Water attenuation (≤ 20 Hounsfield units) predicted growth in thymic cysts (P = 0.016; hazard ratio 13.2, 95% confidence interval 1.6-107.3), while lesion size (> 17.2 mm) predicted growth in TETs (P = 0.008 for size, P = 0.029 for size*time). For the growing lesions, the positive and negative predictive values of water attenuation for thymic cysts were 93% and 80%, respectively. CONCLUSION: The frequencies of interval growth and VDTs were indistinguishable between TETs and thymic cysts. Water attenuation and lesion size predicted growth in thymic cysts and TETs, respectively. Among the growing lesions, water attenuation was a differential feature of thymic cysts.

Diagnostic accuracy of contrast-enhanced dynamic CT for small hypervascular hepatocellular carcinoma and assessment of dynamic enhancement patterns: Results of two-year follow-up using cone-beam CT hepatic arteriography.

OBJECTIVE: To evaluate the accuracy of CT for small, hypervascular hepatocellular carcinomas (HCCs) and assess the enhancement patterns on CT. MATERIALS AND METHODS: Ninety-nine patients who underwent cone-beam CT hepatic arteriography (CBCT-HA) during initial chemoembolization for HCC suspected on CT were enrolled in this study. A total of 297 hypervascular HCCs (142 ≥ 1 cm, 155 < 1 cm) were confirmed as HCCs based on two-year follow-up CT and CBCT-HA images. During the two-year follow-up, pre-existing hypervascular foci on CBCT-HA were regarded as HCCs at the initial presentation. Two radiologists categorized HCCs according to the following enhancement patterns on CT: type I, arterial enhancement and washout; type II, arterial enhancement without washout; and type III, no arterial enhancement. Two blinded reviewers rated the possibility of HCC. RESULTS: For the 297 HCCs, the enhancement patterns according to size were as follows: type I ≥1 cm in 114 HCCs; type I <1 cm in 40 HCCs; type II ≥1 cm in 16 HCCs; type II <1 cm in 37 HCCs; type III ≥1 cm in 12 HCCs; and type III <1 cm in 10 HCCs. The remaining 68 HCCs (22.9%) were not detected on CT. The detection rates of HCCs ≥ 1 cm were 83.1%, 76.8%, and 83.1% in the formal report for reviewer 1 and reviewer 2. In comparison, the detection rates of HCCs < 1 cm were 20.6%, 17.4%, and 17.4% in the formal report for reviewer 1 and reviewer 2. CONCLUSION: Many subcentimeter sized hypervascular HCCs were frequently missed or not evident on CT at the initial diagnostic workup. CT has limitations for diagnosing HCCs that are <1 cm in size or have atypical enhancement patterns.

Therapeutic response assessment using 3D ultrasound for hepatic metastasis from colorectal cancer: Application of a personalized, 3D-printed tumor model using CT images.

BACKGROUND & AIMS: To evaluate accuracy and reliability of three-dimensional ultrasound (3D US) for response evaluation of hepatic metastasis from colorectal cancer (CRC) using a personalized 3D-printed tumor model. METHODS: Twenty patients with liver metastasis from CRC who underwent baseline and after chemotherapy CT, were retrospectively included. Personalized 3D-printed tumor models using CT were fabricated. Two radiologists measured volume of each 3D printing model using 3D US. With CT as a reference, we compared difference between CT and US tumor volume. The response evaluation was based on Response Evaluation Criteria in Solid Tumors (RECIST) criteria. RESULTS: 3D US tumor volume showed no significant difference from CT volume (7.18 ± 5.44 mL, 8.31 ± 6.32 mL vs 7.42 ± 5.76 mL in CT, p>0.05). 3D US provided a high correlation coefficient with CT (r = 0.953, r = 0.97) as well as a high inter-observer intraclass correlation (0.978; 0.958-0.988). Regarding response, 3D US was in agreement with CT in 17 and 18 out of 20 patients for observer 1 and 2 with excellent agreement (κ = 0.961). CONCLUSIONS: 3D US tumor volume using a personalized 3D-printed model is an accurate and reliable method for the response evaluation in comparison with CT tumor volume.

Association between Image Characteristics on Chest CT and Severe Pleural Adhesion during Lung Cancer Surgery.

The aim of this study was to investigate the association between image characteristics on preoperative chest CT and severe pleural adhesion during surgery in lung cancer patients. We included consecutive 124 patients who underwent lung cancer surgeries. Preoperative chest CT was retrospectively reviewed to assess pleural thickening or calcification, pulmonary calcified nodules, active pulmonary inflammation, extent of emphysema, interstitial pneumonitis, and bronchiectasis in the operated thorax. The extent of pleural thickening or calcification was visually estimated and categorized into two groups: localized and diffuse. We measured total size of pulmonary calcified nodules. The extent of emphysema, interstitial pneumonitis, and bronchiectasis was also evaluated with a visual scoring system. The occurrence of severe pleural adhesion during lung cancer surgery was retrospectively investigated from the electrical medical records. We performed logistic regression analysis to determine the association of image characteristic on chest CT with severe pleural adhesion. Localized pleural thickening was found in 8 patients (6.5%), localized pleural calcification in 8 (6.5%), pulmonary calcified nodules in 28 (22.6%), and active pulmonary inflammation in 22 (17.7%). There was no patient with diffuse pleural thickening or calcification in this study. Trivial, mild, and moderate emphysema was found in 31 (25.0%), 21 (16.9%), and 12 (9.7%) patients, respectively. Severe pleural adhesion was found in 31 (25.0%) patients. The association of localized pleural thickening or calcification on CT with severe pleural adhesion was not found (P = 0.405 and 0.107, respectively). Size of pulmonary calcified nodules and extent of emphysema were significant variables in a univariate analysis (P = 0.045 and 0.005, respectively). In a multivariate analysis, moderate emphysema was significantly associated with severe pleural adhesion (odds ratio of 11.202, P = 0.001). In conclusion, severe pleural adhesion might be found during lung cancer surgery, provided that preoperative chest CT shows substantial pulmonary calcified nodules or emphysema.

Measurement Variability of Persistent Pulmonary Subsolid Nodules on Same-Day Repeat CT: What Is the Threshold to Determine True Nodule Growth during Follow-Up?

PURPOSE: To assess the measurement variability of subsolid nodules (SSNs) in follow-up situations and to compare the degree of variability between measurement metrics. METHODS: Two same-day repeat-CT scans of 69 patients (24 men and 45 women) with 69 SSNs were randomly assigned as initial or follow-up scans and were read by the same (situation 1) or different readers (situation 2). SSN size and solid portion size were measured in both situations. Measurement variability was calculated and coefficients of variation were used for comparisons. RESULTS: Measurement variability for the longest and average diameter of SSNs was ±1.3 mm (±13.0%) and ±1.3 mm (±14.4%) in situation 1, and ±2.2 mm (±21.0%) and ±2.1 mm (±21.3%) in situation 2, respectively. For solid portion, measurement variability on lung and mediastinal windows was ±1.2 mm (±27.1%) and ±0.8 mm (±24.0%) in situation 1, and ±3.7 mm (±61.0%) and ±1.5 mm (±47.3%) in situation 2, respectively. There were no significant differences in the degree of variability between the longest and average diameters and between the lung and mediastinal window settings (p>0.05). However, measurement variability significantly increased when the follow-up and initial CT readers were different (p<0.001). CONCLUSIONS: A cutoff of ±2.2 mm can be reliably used to determine true nodule growth on follow-up CT. Solid portion measurements were not reliable in evaluating SSNs' change when readers of initial and follow-up CT were different.

Differentiation of large (≥ 5 cm) gastrointestinal stromal tumors from benign subepithelial tumors in the stomach: radiologists' performance using CT.

PURPOSE: To identify significant CT findings for the differentiation of large (≥ 5 cm) gastric gastrointestinal stromal tumors (GIST) from benign subepithelial tumors and to assess whether radiologists' performance in differentiation is improved with knowledge of significant CT criteria. MATERIALS AND METHODS: One-hundred twenty patients with pathologically proven large (≥ 5 cm) GISTs (n=99), schwannomas (n=16), and leiomyomas (n=5) who underwent CT were enrolled. Two radiologists (A and B) retrospectively reviewed their CT images in consensus for the location, size, degree and pattern of enhancement, contour, growth pattern and the presence of calcification, necrosis, surface ulceration, or enlarged lymph nodes. CT findings considered significant for differentiation were determined using uni- and multivariate statistical analyses. Thereafter, two successive review sessions for the differentiation of GIST from non-GIST were independently performed by two other reviewers (C and D) with different expertise of 2 and 9 years using a 5-point confidence scale. At the first session, reviewers interpreted CT images without knowledge of significant CT findings. At the second session, the results of statistical analyses were provided to the reviewers. To assess improvement in radiologists' performance, a pairwise comparison of receiver operating curves (ROC) was performed. RESULTS: Heterogeneous enhancement, presence of necrosis, absence of lymph nodes, and mean size of ≥ 6 cm were found to be significant for differentiating GIST from schwannoma (P<0.05). Non-cardial location, heterogeneous enhancement, and presence of necrosis were differential CT features of GIST from leiomyoma (P<0.05). Multivariate analyses indicated that absence of enlarged LNs was the only statistically significant variable for GIST differentiating from schwannoma. The area under the curve of both reviewers obtained using ROC significantly increased from 0.682 and 0.613 to 0.903 and 0.904, respectively, with information of the significant CT findings differentiating GISTs from non-GISTs (P<0.001). CONCLUSION: Non-cardial location, heterogeneous enhancement, presence of necrosis, larger lesion size, and absence of lymphadenopathy are highly suggestive CT findings for large GISTs in differentiation from schwannomas or leiomyomas. Regardless of radiologists' expertise, diagnostic performance in differentiation can be significantly improved with knowledge of these CT findings.