Journal of minimally invasive gynecology · 2026
Preoperative Prediction of Prolonged Operative Time in Laparoscopic Ovarian Cystectomy Using Convolutional Neural Network-Extracted Ultrasound Image Features.
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Kim Jisoo, Kim Yeoeun, Bak Hyemi, Jang Myung Eun, Park Chul Min, Cho Angela
Аннотация
OBJECTIVE: The aim of this study was to identify clinical and imaging factors associated with prolonged operative time and to explore whether convolutional neural network (CNN)-derived ultrasound features provide incremental predictive value beyond conventional clinical variables.
DESIGN: Retrospective cohort study.
SETTING: Single academic medical center.
PARTICIPANTS: A total of 247 patients who underwent laparoscopic ovarian cystectomy for presumed benign ovarian tumors were included in the study. Eligible participants were 18 years or older, had preoperative ultrasound images and complete operative records, and were initially planned for ovarian cystectomy. Patients were excluded if they were scheduled for oophorectomy or if other major procedures, such as myomectomy or hysterectomy, were planned concurrently.
INTERVENTIONS: We used two predictive models: a logistic regression model based on preoperative clinical variables (e.g., CA-125 levels, bilaterality of ovarian cysts, and robotic surgery) to predict prolonged operative time, defined as the upper quartile (25%) of operative duration in our cohort, and a second logistic regression model combining these variables with CNN-derived features from preoperative ultrasound images to enhance predictive accuracy.
RESULTS: Multivariable analysis revealed that robotic surgery, bilaterality of ovarian cysts, CA-125 levels, and two CNN-derived imaging features were independently associated with prolonged operative time. Incorporation of CNN-derived ultrasound features increased the area under the curve from 0.889 to 0.920, although the difference did not reach statistical significance.
CONCLUSION: The combined model, incorporating clinical and CNN-extracted imaging features, demonstrates the feasibility of predicting prolonged operative time in laparoscopic ovarian cystectomy. This approach may support preoperative risk stratification and serve as a foundation for future studies exploring its role in surgical scheduling and resource planning, pending external validation.
DESIGN: Retrospective cohort study.
SETTING: Single academic medical center.
PARTICIPANTS: A total of 247 patients who underwent laparoscopic ovarian cystectomy for presumed benign ovarian tumors were included in the study. Eligible participants were 18 years or older, had preoperative ultrasound images and complete operative records, and were initially planned for ovarian cystectomy. Patients were excluded if they were scheduled for oophorectomy or if other major procedures, such as myomectomy or hysterectomy, were planned concurrently.
INTERVENTIONS: We used two predictive models: a logistic regression model based on preoperative clinical variables (e.g., CA-125 levels, bilaterality of ovarian cysts, and robotic surgery) to predict prolonged operative time, defined as the upper quartile (25%) of operative duration in our cohort, and a second logistic regression model combining these variables with CNN-derived features from preoperative ultrasound images to enhance predictive accuracy.
RESULTS: Multivariable analysis revealed that robotic surgery, bilaterality of ovarian cysts, CA-125 levels, and two CNN-derived imaging features were independently associated with prolonged operative time. Incorporation of CNN-derived ultrasound features increased the area under the curve from 0.889 to 0.920, although the difference did not reach statistical significance.
CONCLUSION: The combined model, incorporating clinical and CNN-extracted imaging features, demonstrates the feasibility of predicting prolonged operative time in laparoscopic ovarian cystectomy. This approach may support preoperative risk stratification and serve as a foundation for future studies exploring its role in surgical scheduling and resource planning, pending external validation.