Purpose: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer.
Methods: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment.
We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA). This algorithm is based on deep learning but does not require manual outlines of the PE regions. Given a CTPA scan, both intra- and extra-pulmonary arteries were firstly segmented.
View Article and Find Full Text PDFThe accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed.
View Article and Find Full Text PDFBackground: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy.
Methods: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy.