We report a case of a 50-year-old man who developed metastatic pancreatic cancer from a primary rectal cancer that had been curatively removed 3 years previously. The patient presented with a tumor that occupied the head of the pancreas, associated with obstructive jaundice, but the main pancreatic duct was not dilated. The patient was initially diagnosed as having primary pancreatic cancer. Cytological examination of the bile was conclusive for the presence of adenocarcinoma. The patient refused surgical treatment and chose to have gemcitabine therapy (1000 mg/body), which was given 27 times over 10 months. For 1 year, local disease progression was slow and no distant metastases developed; therefore, the initial diagnosis of pancreatic cancer was questioned. At that time, the patient asked for the tumor to be removed, and pancreaticoduodenectomy was performed. On histology, including immunohistochemical staining for cytokeratin 20 (positive) and cytokeratin 7 (negative), the tumor was shown to be a metastatic pancreatic carcinoma that had originated from the original rectal cancer.
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http://dx.doi.org/10.1007/s00534-007-1219-4 | 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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