Objective: To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. Methods: Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. Results: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area under the receiver operating characteristics curve (86.9%–91.0%), sensitivity (77.4%–82.4%), specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictive value (93.0%–94.4%), appeared satisfactory and comparable among all the algorithms.After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%, the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNM prediction. Conclusion We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.

Objective: To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. Methods: Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. Results: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area under the receiver operating characteristics curve (86.9%–91.0%), sensitivity (77.4%–82.4%), specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictive value (93.0%–94.4%), appeared satisfactory and comparable among all the algorithms.After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%, the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNM prediction. Conclusion We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.

Objective: To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. Methods: Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. Results: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area under the receiver operating characteristics curve (86.9%–91.0%), sensitivity (77.4%–82.4%), specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictive value (93.0%–94.4%), appeared satisfactory and comparable among all the algorithms.After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%, the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNM prediction. Conclusion We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.