OBJECTIVE. The purpose of this study was to validate the usefulness of MDCT for diagnosis of a sharp or pointed esophageal foreign body according to esophageal level. MATERIALS AND METHODS. Forty-two patients with a history of sharp or pointed foreign body ingestion were reviewed retrospectively. Two observers interpreted the CT and the conventional radiography datasets separately. If a foreign body was directly identified, it was regarded as a positive finding. Even if no high-density foreign body was found, detection of a secondary finding was considered to be a positive finding. Diagnostic performance of MDCT and conventional radiography were compared according to esophageal level. Final diagnosis was made by esophagoscopy or surgery in addition to the clinicoradiologic result. RESULTS. MDCT was statistically superior to conventional radiography for diagnosis of a thoracic esophageal foreign body for both observers (p < 0.001 for each). No significant difference in sensitivity between CT and convention...

Hwang HJ, et al. Korean J Radiol. 2023 Aug;24(8):807-820. https://doi.org/10.3348/kjr.2023.0088

To compare the diagnostic performance of computed tomography (CT) and the Alvarado score and to determine whether patient age and sex influence the use of CT and the Alvarado score for diagnosing acute appendicitis. We retrospectively reviewed the CT scans and medical records of 372 patients who presented with right lower quadrant pain and underwent CT to rule out acute appendicitis.The population was classified by age and sex to determine its influence on the use of the Alvarado score and CT. Receiver operating characteristic analysis was used to calculate and compare the diagnostic performance of both modalities. The overall area under the receiver operating characteristic curves for CT and the Alvarado score was 0.965 (highly accurate) and 0.732 (moderately accurate), respectively. There was little effect of patient age and sex on diagnostic performance of CT. However, the diagnostic performance of the Alvarado score was variable according to the patient age and sex (lowest in older women). Furthermore, the overall sensitivity of the Alvarado score was too low (72.8%) for determining immediate surgical intervention. Clinical assessment using the Alvarado score should be supplemented with CT examination for accurate diagnosis of acute appendicitis in all patients who are suspected of acute appendicitis.

Background: The deep learning-based nodule detection (DLD) system improves nodule detection performance of observers on chest radiographs (CXRs). However, its performance in different pulmonary nodule (PN) locations remains unknown. Methods: We divided the CXR intrathoracic region into non-danger zone (NDZ) and danger zone (DZ). The DZ included the lung apices, paramediastinal areas, and retrodiaphragmatic areas, where nodules could be missed. We used a dataset of 300 CXRs (100 normal and 200 abnormal images with 216 PNs [107 NDZ and 109 DZ nodules]). Eight observers (two thoracic radiologists [TRs], two non-thoracic radiologists [NTRs], and four radiology residents [RRs]) interpreted each radiograph with and without the DLD system. The metric of lesion localization fraction (LLF; the number of correctly localized lesions divided by the total number of true lesions) was used to evaluate the diagnostic performance according to the nodule location. Results: The DLD system demonstrated a lower LLF for the detection of DZ nodules (64.2) than that of NDZ nodules (83.2, p = 0.008). For DZ nodule detection, the LLF of the DLD system (64.2) was lower than that of TRs (81.7, p < 0.001), which was comparable to that of NTRs (56.4, p = 0.531) and RRs (56.7, p = 0.459). Nonetheless, the LLF of RRs significantly improved from 56.7 to 65.6 using the DLD system (p = 0.021) for DZ nodule detection. Conclusion: The performance of the DLD system was lower in the detection of DZ nodules compared to that of NDZ nodules. Nonetheless, RR performance in detecting DZ nodules improved upon using the DLD system. Critical relevance statement: Despite the deep learning-based nodule detection system’s limitations in detecting danger zone nodules, it proves beneficial for less-experienced observers by providing valuable assistance in identifying these nodules, thereby advancing nodule detection in clinical practice. Key points: • The deep learning-based nodule detection (DLD) system can improve the diagnostic performance of observers in nodule detection. • The DLD system shows poor diagnostic performance in detecting danger zone nodules. • For less-experienced observers, the DLD system is helpful in detecting danger zone nodules. Graphical

OBJECTIVE. The purpose of this article was to evaluate the usefulness of ultralow-dose chest CT as an initial imaging study for evaluation of sharp fish bone esophageal foreign body (FB). MATERIALS AND METHODS. A total of 57 subjects who underwent ultralow-dose chest CT were included in this retrospective study. All subjects had a history of ingestion and symptoms of esophageal FB. All ultralow-dose chest CT data were reconstructed twice, once with filtered back projection (FBP) and once with iterative reconstruction, and three observers reviewed the images independently. ROC analysis was used to evaluate diagnostic performance of ultralow-dose chest CT. Intraclass correlation coefficient (ICC) was calculated for analysis of interobserver agreement. RESULTS. Among 57 patients, 42 were confirmed as having esophageal FB. Significant objective noise reduction of mediastinum was achieved using an iterative reconstruction technique. Subjective image noise of iterative reconstruction was significantly better th...