Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.

Autor: Simpson AL; School of Computing, Queen's University, Kingston, Ontario, Canada. amber.simpson@queensu.ca.; Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada. amber.simpson@queensu.ca., Peoples J; School of Computing, Queen's University, Kingston, Ontario, Canada., Creasy JM; The Oregon Clinic, Portland, OR, USA., Fichtinger G; School of Computing, Queen's University, Kingston, Ontario, Canada., Gangai N; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Keshavamurthy KN; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Lasso A; School of Computing, Queen's University, Kingston, Ontario, Canada., Shia J; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., D'Angelica MI; Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Do RKG; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2024 Feb 06; Vol. 11 (1), pp. 172. Date of Electronic Publication: 2024 Feb 06.
DOI: 10.1038/s41597-024-02981-2
Abstrakt: The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.
(© 2024. The Author(s).)
Databáze: MEDLINE
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