ArticleContrast media & molecular imaging2022
Diagnosis of Nonperitonealized Colorectal Cancer with Computerized Tomography Image Features under Deep Learning.
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Total neoadjuvant therapy followed by total mesorectal excision for rectal cancer in older patients real world data and proof of concept.Frontiers in surgery · 2024Article
- The role of deep learning in diagnosing colorectal cancer.Przeglad gastroenterologiczny · 2023Review
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to explore the value of abdominal computerized tomography (CT) three-dimensional reconstruction using the dense residual single-axis super-resolution algorithm in the diagnosis of nonperitonealized colorectal cancer (CC). 103 patients with nonperitonealized CC (the lesion was located in the ascending colon or descending colon) were taken as the research subjects. The imagological tumor (T) staging, the extramural depth (EMD) of the cancer tissues, and the extramural vascular invasion (EMVI) grading were analyzed. A dense residual single-axis super-resolution network model was also constructed for enhancing CT images. It was found that the CT images processed using the algorithm were clear, and the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were 33.828 dB and 0.856, respectively. In the imagological T staging of CC patients, there were 17 cases in the T3 stage and 68 cases in the T4 stage. With the EMD increasing, the preoperative carcinoembryonic antigen (CEA) highly increased, and the difference was statistically significant (
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