Post-operative adhesions can occur following abdominal or pelvic surgery and are a significant cause of morbidity and mortality. Adhesions are an important cause of abdominal complaints, especially chronic abdominal pain, infertility, and bowel obstruction; responsible for an estimated $2.3 billion annual burden in the United States alone. There is a paucity of literature describing CT findings indicative of the presence of adhesions, however, findings suggestive of this entity may be discovered by a discerning eye. Early recognition and diagnosis may be beneficial for reducing consumption of health-care resources related to abdominal complaints. In addition, these findings may impact surgical planning and facilitate recognition of adhesion-related complications, said to be responsible for a significant number of malpractice claims. The following review discusses various CT findings which may indicate the presence of adhesions. These findings may initially be overlooked, but remain valuable as the clinical presentation evolves to become consistent with adhesive disease. Detection of bowel and peritoneal adhesions can be recognized on imaging modalities based on distortion of bowel loops. This pictorial review illustrates the spectrum of findings related to bowel adhesions encountered on computed tomography imaging.
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http://dx.doi.org/10.25259/JCIS_126_2020 | DOI Listing |
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Department of Medical Genetics, Medical Faculty, Aksaray University, Aksaray, Turkiye.
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School of Dentistry, Catholic University of Pelotas (UCPel), Campus da Saúde, Av. Fernando Osório, 1586-Pelotas, Pelotas, RS, Brazil.
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School of Cyber Science and Engineering, Sichuan University, Chengdu, China.
The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis.
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