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OBJECTIVE: We aimed to predict the risk of postoperative adjuvant therapy using preoperative variables in young patients with early stage cervical cancer. The predicted risk can guide whether ovarian transposition should be performed during surgery. METHODS: In total, 886 patients with stage IB1-IIA cervical cancer aged 20-45 years who underwent modified radical or radical hysterectomy between January 2000 and December 2008 were included. Preoperative variables, preoperative laboratory findings, International Federation of Gynaecology and Obstetrics stage, tumor size, and pathological variables were collected. Patients with high risk factors or those who met the Sedlis criteria were considered adjuvant therapy risk (+); others were considered adjuvant therapy risk (-). A decision-tree model using preoperative variables was constructed to predict the risk of adjuvant therapy. RESULTS: Of 886 patients, 362 were adjuvant therapy risk (+) (40.9%). The decision-tree model with four distinct adjuvant therapy risks using tumor size and age were generated. Specifically, patients with tumor size ≤2.45 cm had low risk (49/367; 13.4%), those with tumor size ≤3.85 cm and >2.45 cm had moderate risk (136/314; 43.3%), those with tumor size >3.85 cm and age ≤39.5 years had high risk (92/109; 84.4%), and those with tumor size >3.85 cm and age >39.5 years had the highest risk (85/96; 88.5%). CONCLUSION: The risk of postoperative adjuvant therapy in young patients with early stage cervical cancer can be predicted using preoperative variables. We can decide whether ovarian transposition should be performed using the predicted risk.
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OBJECTIVES: Fertility-sparing treatment (FST) might be considered an option for reproductive patients with low-risk endometrial cancer (EC). On the other hand, the matching rates between preoperative assessment and postoperative pathology in low-risk EC patients are not high enough. We aimed to predict the postoperative pathology depending on preoperative myometrial invasion (MI) and grade in low-risk EC patients to help extend the current criteria for FST. METHODS/MATERIALS: This ancillary study (KGOG 2015S) of Korean Gynecologic Oncology Group 2015, a prospective, multicenter study included patients with no MI or MI <1/2 on preoperative MRI and endometrioid adenocarcinoma and grade 1 or 2 on endometrial biopsy. Among the eligible patients, Groups 1-4 were defined with no MI and grade 1, no MI and grade 2, MI <1/2 and grade 1, and MI <1/2 and grade 2, respectively. New prediction models using machine learning were developed. RESULTS: Among 251 eligible patients, Groups 1-4 included 106, 41, 74, and 30 patients, respectively. The new prediction models showed superior prediction values to those from conventional analysis. In the new prediction models, the best NPV, sensitivity, and AUC of preoperative each group to predict postoperative each group were as follows: 87.2%, 71.6%, and 0.732 (Group 1); 97.6%, 78.6%, and 0.656 (Group 2); 71.3%, 78.6% and 0.588 (Group 3); 91.8%, 64.9%, and 0.676% (Group 4). CONCLUSIONS: In low-risk EC patients, the prediction of postoperative pathology was ineffective, but the new prediction models provided a better prediction.