Is direct radiologist supervision of abdominal computed tomography (CT) scans necessary?

2005 ◽  
Vol 60 (7) ◽  
pp. 756-757 ◽  
Author(s):  
E.J. Adam
2013 ◽  
Vol 2013 ◽  
pp. 1-2
Author(s):  
Suat Keskin ◽  
Cihan Şimşek ◽  
Zeynep Keskin

Amyand’s hernia, named for the first person to describe an inguinal hernia containing the vermiform appendix, is an uncommon variant of an inguinal hernia. Amyand’s hernia is an extremely rare condition and is often misdiagnosed. Traditionally, these hernias have been diagnosed at surgery but are increasingly diagnosed by abdominal computed tomography (CT) scans. CT of the abdomen may help in guiding the diagnosis.


2005 ◽  
Vol 60 (7) ◽  
pp. 758-761 ◽  
Author(s):  
V. Goh ◽  
S. Halligan ◽  
J.M. Anderson ◽  
J. Hugill ◽  
A. Leonard

2019 ◽  
Vol 65 (4) ◽  
pp. 590-595
Author(s):  
Arkadiy Naumenko ◽  
Kseniya Sapova ◽  
Oleg Konoplev ◽  
Svetlana Astashchenko ◽  
Igor Chernushevich

Precise localization and excision of the originating site of a sinonasal inverted papilloma is essential for decreasing tumor recurrence. In this study we evaluated the use of preoperative computed tomography (CT) to pinpoint the attachment/origi-nating sites of the tumor.


2017 ◽  
Vol 35 (1) ◽  
pp. 21-25 ◽  
Author(s):  
Hyung Soo Kim ◽  
Chang Hee Lee ◽  
Seong Hyun Kim ◽  
Jeong Woo Kim ◽  
Cheol Min Park ◽  
...  

2019 ◽  
Vol 12 (S 01) ◽  
pp. S39-S44
Author(s):  
Michael Okoli ◽  
Kevin Lutsky ◽  
Michael Rivlin ◽  
Brian Katt ◽  
Pedro Beredjiklian

Abstract Introduction The purpose of this study is to determine the radiographic dimensions of the finger metacarpals and to compare these measurements with headless compression screws commonly used for fracture fixation. Materials and Methods We analyzed computed tomography (CT) scans of the index, long, ring, and small metacarpal bones and measured the metacarpal length, distance from the isthmus to the metacarpal head, and intramedullary diameter of the isthmus. Metacarpals with previous fractures or hardware were excluded. We compared these dimensions with the size of several commercially available headless screws used for intramedullary fixation. Results A total of 223 metacarpals from 57 patients were analyzed. The index metacarpal was the longest, averaging 67.6 mm in length. The mean distance from the most distal aspect of the metacarpal head to the isthmus was 40.3, 39.5, 34.4, and 31 mm for the index, long, ring, and small metacarpals, respectively. The narrowest diameter of the isthmus was a mean of 2.6, 2.7, 2.3, and 3 mm for the index, long, ring, and small metacarpals, respectively. Of 33 commercially available screws, only 27% percent reached the isthmus of the index metacarpal followed by 42, 48, and 58% in the long, ring, and small metacarpals, respectively. Conclusion The index and long metacarpals are at a particular risk of screw mismatch given their relatively long lengths and narrow isthmus diameters.


2021 ◽  
Vol 10 (11) ◽  
pp. 2456
Author(s):  
Raminta Luksaite-Lukste ◽  
Ruta Kliokyte ◽  
Arturas Samuilis ◽  
Eugenijus Jasiunas ◽  
Martynas Luksta ◽  
...  

(1) Background: Diagnosis of acute appendicitis (AA) remains challenging; either computed tomography (CT) is universally used or negative appendectomy rates of up to 30% are reported. Transabdominal ultrasound (TUS) as the first-choice imaging modality might be useful in adult patients to reduce the need for CT scans while maintaining low negative appendectomy (NA) rates. The aim of this study was to report the results of the conditional CT strategy for the diagnosis of acute appendicitis. (2) Methods: All patients suspected of acute appendicitis were prospectively registered from 1 January 2016 to 31 December 2018. Data on their clinical, radiological and surgical outcomes are presented. (3) Results: A total of 1855 patients were enrolled in our study: 1206 (65.0%) were women, 649 (35.0%) were men, and the median age was 34 years (IQR, 24.5–51). TUS was performed in 1851 (99.8%) patients, and CT in 463 (25.0%) patients. Appendices were not visualized on TUS in 1320 patients (71.3%). Furthermore, 172 (37.1%) of 463 CTs were diagnosed with AA, 42 (9.1%) CTs revealed alternative emergency diagnosis and 249 (53.8%) CTs were normal. Overall, 519 (28.0%) patients were diagnosed with AA: 464 appendectomies and 27 diagnostic laparoscopies were performed. The NA rate was 4.2%. The sensitivity and specificity for TUS and CT are as follows: 71.4% and 96.2%; 93.8% and 93.6%. (4) Conclusion: A conditional CT strategy is effective in reducing NA rates and avoids unnecessary CT in a large proportion of patients. Observation and repeated TUS might be useful in unclear cases.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.


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