Background: In resectable rectal cancer trials, pathological parameters are early preoperative treatment efficacy measures. Their validation as surrogate end points for long-term clinical outcomes would allow to reduce trial duration. The aim was to evaluate potential surrogates for overall survival (OS) and local control (LC) in preoperative T3/T4 rectal cancer trials. Candidate variables included ypT and ypN stages, T downstaging, tumor regression grade (TRG), and circumferential resection margin (CRM) status.
Patients And Methods: In the Fédération Francophone de Cancérologie Digestive (FFCD) 9203 trial, 742 eligible patients were randomly assigned to receive preoperative radiotherapy with or without concurrent chemotherapy. Surrogacy was evaluated using Prentice criteria and the proportion of treatment effect (PTE) explained by each potential surrogate.
Results: None of the candidate surrogates fulfilled all Prentice criteria. Data analyses did not provide interpretable PTE measures for OS. Regarding LC, the highest PTE was reached by TRG, which explained 12% of the effect on local recurrence. This proportion may not exceed 41% [95% confidence interval (CI) -1% to 41%]. PTE explained by the CRM status was associated with a wide uncertainty (95% CI -81% to 105%), which does not exclude a potentially high degree of surrogacy.
Conclusion: In the FFCD 9203 trial, pathological parameters were not surrogate for OS or LC.
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http://dx.doi.org/10.1093/annonc/mdp340 | 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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