ABSTRACT
Background@#Inactive or old, healed tuberculosis (TB) on chest radiograph (CR) is often found in high TB incidence countries, and to avoid unnecessary evaluation and medication, differentiation from active TB is important. This study develops a deep learning (DL) model to estimate activity in a single chest radiographic analysis. @*Methods@#A total of 3,824 active TB CRs from 511 individuals and 2,277 inactive TB CRs from 558 individuals were retrospectively collected. A pretrained convolutional neural network was fine-tuned to classify active and inactive TB. The model was pretrained with 8,964 pneumonia and 8,525 normal cases from the National Institute of Health (NIH) dataset. During the pretraining phase, the DL model learns the following tasks: pneumonia vs. normal, pneumonia vs. active TB, and active TB vs. normal. The performance of the DL model was validated using three external datasets. Receiver operating characteristic analyses were performed to evaluate the diagnostic performance to determine active TB by DL model and radiologists. Sensitivities and specificities for determining active TB were evaluated for both the DL model and radiologists. @*Results@#The performance of the DL model showed area under the curve (AUC) values of 0.980 in internal validation, and 0.815 and 0.887 in external validation. The AUC values for the DL model, thoracic radiologist, and general radiologist, evaluated using one of the external validation datasets, were 0.815, 0.871, and 0.811, respectively. @*Conclusion@#This DL-based algorithm showed potential as an effective diagnostic tool to identify TB activity, and could be useful for the follow-up of patients with inactive TB in high TB burden countries.
ABSTRACT
Purpose@#To investigate the incidence of tuberculosis (TB) in healthcare workers (HCWs) with positive interferon-gamma release assay (IGRA) results based on chest X-ray (CXR) and CT findings and determine the role of imaging in the diagnosis of TB. @*Materials and Methods@#Among 1976 hospital personnel screened for TB using IGRA, IGRApositive subjects were retrospectively investigated. Clustered nodular and/or linear streaky opacities in the upper lung zone were considered positive on CXR. The CT findings were classified as active, indeterminate, inactive, or normal. The active or indeterminate class was considered CT-positive. @*Results@#IGRA was positive in 255 subjects (12.9%). CXR and CT were performed in 249 (99.2%) and 113 subjects (45.0%), respectively. CXR- and CT-positive findings were found in 7 of 249(2.8%) and 9 of 113 (8.0%) patients, respectively. Among the nine CT-positive subjects, active and indeterminate TB findings were found in 6 (5.3%) and 3 (2.7%) patients, respectively. Microbiological tests, including acid-fast bacilli staining, culture, and polymerase chain reaction for TB, were negative in all nine CT-positive subjects. Empirical anti-TB medications were administered to 9 CT-positive subjects, and 3 of these nine subjects were CXR-negative for pulmonary TB. @*Conclusion@#CT helped diagnose asymptomatic TB in IGRA-positive HCWs.
ABSTRACT
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.