School of Computing, Queen's University, Kingston, Ontario, Canada.
Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada.
Sci Data. 2024 Feb 6;11(1):172. doi: 10.1038/s41597-024-02981-2.
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.
肝脏是结直肠癌转移的常见部位。结直肠癌肝转移(CRLM)患者的治疗选择困难;尽管肝切除术可治愈少数 CRLM 患者,但复发很常见。因此,可靠的术前复发预测可能是医生在治疗 CRLM 时选择肝切除术最佳候选者的有价值工具。人们假设,在切除前,可以通过对未来肝残留的术前 CT 图像进行定量图像分析来找到复发的证据。为了验证这一假设,我们从一个大型的单一机构系列患者(n=197)中收集了术前肝脏 CT 扫描、临床病理数据和复发/生存数据,这些患者均接受了 CRLM 的肝切除术。对于每个患者,我们还创建了肝脏、血管、肿瘤和未来肝残留的分割。这是同类中最大的数据集,可作为资源,有助于开发用于预测 CRLM 肝切除术后肝复发的定量成像生物标志物和机器学习模型。