Rectal cancer is prone to local recurrence and systemic metastasis. However, owing to improvements in TNM staging and treatment, including a more widespread use of rectal MRI and increased radiologist awareness of the key rectal cancer TNM staging features, the mortality rate of rectal cancer has been declining over the past few decades in adults over 50 years of age. Currently, rectal MRI plays a key role in the pre- and posttreatment evaluation of rectal cancer, assisting the multidisciplinary team in tailoring the most appropriate treatment option. The benefits achieved with rectal MRI are strictly dependent on obtaining good-quality images, which is important for the characterization of the main anatomic structures and their relationship with the tumor. In primary staging, rectal MRI helps the radiologist (a) describe the tumor location and morphology, (b) provide its T and N categories, (c) detect the presence of extramural vascular invasion, and (d) identify its relationship with surrounding structures, including the sphincter complex and involvement of the mesorectal fascia. These features help diagnose locally advanced rectal tumors (categories T3c-d, T4, N1, and N2), for which neoadjuvant chemoradiotherapy (CRT) is indicated. In restaging after neoadjuvant CRT, in addition to reassessing the features noted during primary staging, rectal MRI can help in the assessment of treatment response, especially with the emergence of nonsurgical approaches such as "watch and wait." RSNA, 2019.
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http://dx.doi.org/10.1148/rg.2019180114 | 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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