Objective: Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data. Methods: We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC). Results: The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR. Conclusion The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.

Objective: Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data. Methods: We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC). Results: The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR. Conclusion The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.

Background/Aims: Three-dimensional (3D) flexible endoscopy, a new imaging modality that provides a stereoscopic view, can facilitate endoscopic hand suturing (EHS), a novel intraluminal suturing technique. This ex-vivo pilot study evaluated the usefulness of 3D endoscopy in EHS. Methods: Four endoscopists (two certified, two non-certified) performed EHS in six sessions on a soft resin pad. Each session involved five stitches, under alternating 3D and two-dimensional (2D) conditions. Suturing time (sec/session), changes in suturing time, and accuracy of suturing were compared between 2D and 3D conditions. Results: The mean suturing time was shorter in 3D than in 2D (9.8±3.4 min/session vs. 11.2±5.1 min/session) conditions and EHS was completed faster in 3D conditions, particularly by non-certified endoscopists. The suturing speed increased as the 3D sessions progressed. Error rates (failure to grasp the needle, failure to thread the needle, and puncture retrial) in the 3D condition were lower than those in the 2D condition, whereas there was no apparent difference in deviation distance. Conclusions 3D endoscopy may contribute to increasing the speed and accuracy of EHS in a short time period. Stereoscopic viewing during 3D endoscopy may help in efficient skill acquisition for EHS, particularly among novice endoscopists.