This study analyzes the effect, long-term survival rate, and complications about preserving the left colonic artery (LCAP) in rectal cancer surgery. Relevant articles were systematically retrieved from multiple electronic databases, for example, EMBASE, BioMed Central, PubMed, Web of Science, and Cochrane. The time for retrieving was from the establishment of the database to December 31, 2018. Evaluated endpoints were effect of LCAP on the curative effect of rectal neoplasms, such as operation time, the amount of bleeding during the operation, root lymph nodes positive number, and the related complications (anastomotic leakage, etc.), postoperative urinary retention, 5-year survival rate, and recurrence differences in rates. Totally 12 studies were included in this review. The meta-analysis showed that LCAP has less operation time and lower anastomotic leakage incidence. Intraoperative bleeding, root lymph nodes, and other complications did not show any significant difference. LCAP in radical rectal cancer surgery ensures both the radical resection of the tumor and the safety of the operation. So it can provide a new approach to the management of blood vessels and lymph nodes.
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http://dx.doi.org/10.1089/lap.2019.0406 | DOI Listing |
Sci Rep
December 2024
Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Republic of Korea.
Texture analysis generates image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters correlate with tumor biology and clinical attributes, their types and implications can be complex. To overcome this limitation, pseudotime analysis was applied to texture parameters to estimate changes in individual sample characteristics, and the prognostic significance of the estimated pseudotime of primary tumors was evaluated.
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December 2024
Department of Radiology, the Affiliated Taian City Central Hospital of Qingdao University, Tai'an, 271099, China.
This study aimed to investigate the correlation between baseline MRI features and baseline carcinoembryonic antigen (CEA) expression status in rectal cancer patients. A training cohort of 168 rectal cancer patients from Center 1 and an external validation cohort of 75 rectal cancer patients from Center 2 were collected. A nomogram was constructed based on the training cohort and validated using the external validation cohort to predict high baseline CEA expression in rectal cancer patients.
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December 2024
Department of General Surgery, Chifeng Municipal Hospital, Inner Mongolia Medical University, Inner Mongolia, 024000, People's Republic of China.
Rectal cancer is a prevalent global malignancy. Recurrence and metastasis significantly impact patient survival over the long term. This study aims to identify independent risk factors associated with distant metastases in rectal cancer (RC) patients and develop a prognostic columnar-line diagram.
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December 2024
Department of Pathology, Kitasato University School of Medicine, 1-15-1 Kitasato, Minami-ku, Sagamihara, 252-0374, Kanagawa, Japan.
To investigate the functional role of S100A4 in advanced colorectal carcinoma (Ad-CRC) and locally advanced rectal carcinoma (LAd-RC) receiving neoadjuvant chemoradiotherapy (NCRT). We analyzed histopathological and immunohistochemical sections from 150 patients with Ad-CRC and 177 LAd-RC patients treated with NCRT. S100A4 knockout (KO) HCT116 cells were also used.
View Article and Find Full Text PDFInt J Med Inform
December 2024
Chongqing Cancer Multiomics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China. Electronic address:
Background: With advancements in healthcare, traditional VTE risk assessment tools are increasingly insufficient to meet the demands of high-quality care, underscoring the need for innovative and specialized assessment methods.
Objective: Owing to the remarkable success of machine learning in supervised learning and disease prediction, our objective is to develop a reliable and efficient model for assessing VTE risk by leveraging the fundamental data and clinical characteristics of colorectal cancer patients within our medical facility.
Methods: Six commonly used machine learning algorithms were utilized in our study to predict the occurrence of VTE in patients with rectal cancer.
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