RESUMO
BACKGROUND: It remains unclear whether laparoscopic multisegmental resection and anastomosis (LMRA) is safe and advantageous over traditional open multisegmental resection and anastomosis (OMRA) for treating synchronous colorectal cancer (SCRC) located in separate segments. AIM: To compare the short-term efficacy and long-term prognosis of OMRA as well as LMRA for SCRC located in separate segments. METHODS: Patients with SCRC who underwent surgery between January 2010 and December 2021 at the Cancer Hospital, Chinese Academy of Medical Sciences and the Peking University First Hospital were retrospectively recruited. In accordance with the inclusion and exclusion criteria, 109 patients who received right hemicolectomy together with anterior resection of the rectum or right hemicolectomy and sigmoid colectomy were finally included in the study. Patients were divided into the LMRA and OMRA groups (n = 68 and 41, respectively) according to the surgical method used. The groups were compared regarding the surgical procedure's short-term efficacy and its effect on long-term patient survival. RESULTS: LMRA patients showed markedly less intraoperative blood loss than OMRA patients (100 vs 200 mL, P = 0.006). Compared to OMRA patients, LMRA patients exhibited markedly shorter postoperative first exhaust time (2 vs 3 d, P = 0.001), postoperative first fluid intake time (3 vs 4 d, P = 0.012), and postoperative hospital stay (9 vs 12 d, P = 0.002). The incidence of total postoperative complications (Clavien-Dindo grade: ≥ II) was 2.9% and 17.1% (P = 0.025) in the LMRA and OMRA groups, respectively, while the incidence of anastomotic leakage was 2.9% and 7.3% (P = 0.558) in the LMRA and OMRA groups, respectively. Furthermore, the LMRA group had a higher mean number of lymph nodes dissected than the OMRA group (45.2 vs 37.3, P = 0.020). The 5-year overall survival (OS) and disease-free survival (DFS) rates in OMRA patients were 82.9% and 78.3%, respectively, while these rates in LMRA patients were 78.2% and 72.8%, respectively. Multivariate prognostic analysis revealed that N stage [OS: HR hazard ratio (HR) = 10.161, P = 0.026; DFS: HR = 13.017, P = 0.013], but not the surgical method (LMRA/OMRA) (OS: HR = 0.834, P = 0.749; DFS: HR = 0.812, P = 0.712), was the independent influencing factor in the OS and DFS of patients with SCRC. CONCLUSION: LMRA is safe and feasible for patients with SCRC located in separate segments. Compared to OMRA, the LMRA approach has more advantages related to short-term efficacy.
RESUMO
OBJECTIVES: To investigate the reliability and accuracy of deep learning technology in automatic sex estimation using the 3D reconstructed images of the computed tomography (CT) from the Chinese Han population. METHODS: The pelvic CT images of 700 individuals (350 males and 350 females) of the Chinese Han population aged 20 to 85 years were collected and reconstructed into 3D virtual skeletal models. The feature region images of the medial aspect of the ischiopubic ramus (MIPR) were intercepted. The Inception v4 was adopted as the image recognition model, and two methods of initial learning and transfer learning were used for training. Eighty percent of the individuals' images were randomly selected as the training and validation dataset, and the remaining were used as the test dataset. The left and right sides of the MIPR images were trained separately and combinedly. Subsequently, the models' performance was evaluated by overall accuracy, female accuracy, male accuracy, etc. RESULTS: When both sides of the MIPR images were trained separately with initial learning, the overall accuracy of the right model was 95.7%, the female accuracy and male accuracy were both 95.7%; the overall accuracy of the left model was 92.1%, the female accuracy was 88.6% and the male accuracy was 95.7%. When the left and right MIPR images were combined to train with initial learning, the overall accuracy of the model was 94.6%, the female accuracy was 92.1% and the male accuracy was 97.1%. When the left and right MIPR images were combined to train with transfer learning, the model achieved an overall accuracy of 95.7%, and the female and male accuracies were both 95.7%. CONCLUSIONS: The use of deep learning model of Inception v4 and transfer learning algorithm to construct a sex estimation model for pelvic MIPR images of Chinese Han population has high accuracy and well generalizability in human remains, which can effectively estimate the sex in adults.
