Objective: To compare survival outcomes, posttreatment complications, and quality of life (QoL) of early-stage cervical cancer patients with intermediate-risk factors between those who received adjuvant pelvic radiation and those without adjuvant treatment. Methods: Stages IB–IIA cervical cancer patients classified as having intermediate-risk following primary radical surgery were included. After propensity score weighted adjustment, all baseline demographic and pathological characteristics of 108 women who received adjuvant radiation and 111 women who had no adjuvant treatment were compared. The primary outcomes were progression-free survival (PFS) and overall survival (OS). The secondary outcomes included treatment-related complications and QoL. Results: Median follow-up time was 76.1 months in the adjuvant radiation group and 95.4 months in the observation group. The 5-year PFS (91.6% in the adjuvant radiation group and 88.4% in the observation group, p=0.42) and OS (90.1% in the adjuvant radiation group and 93.5% in the observation group, p=0.36) were not significantly different between the groups. There was no significant association between adjuvant treatment and overall recurrence/death in the Cox proportional hazard model. However, a substantial reduction in pelvic recurrence was observed in participants with adjuvant radiation (hazard ratio=0.15; 95% confidence interval=0.03–0.71). Grade 3/4 treatment-related morbidities and QoL scores were not significantly different between the groups. Conclusion Adjuvant radiation was associated with a lower risk of pelvic recurrence. However, its significant benefit in reducing overall recurrence and improving survival in early-stage cervical cancer patients with intermediate-risk factors could not be demonstrated.

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.