Obstet Gynecol Sci.  2021 May;64(3):266-273. 10.5468/ogs.20248.

The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study

• Akazawa M
• Hashimoto K
• Noda K
• Yoshida K

• KMID: 2515978
• DOI: http://doi.org/10.5468/ogs.20248

Abstract

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.

Keyword

Machine learning; Recurrence; Endometrial cancer; Probability learning

Figure

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  Fig. 1 The receiver operating characteristic (ROC) curve of the 5 algorithms. The highest area under the curve (AUC) was 0.53 for logistic regression, followed by 0.52 for boosted trees, 0.48 for decision tree, and 0.47 for random forest. FPR, false positive rate; TPR, true positive rate.

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  Fig. 2 In the analysis of the importance of factors on the prediction of the recurrence, the random forest (RF) classifier showed that “age”, “stage”, and “carcinoembryonic antigen (CEA)” were the most valuable factors. CA125, carbohydrate antigen 125; BMI, body mass index; N, nodes; M, metastasis; T, tumor; CA19-9, carbohydrate antigen 19-9; PALA, para-aortic lymphadenectomy; OMT, omentectomy; PLA, pelvic lymphadenectomy; TAH, total abdominal hysterectomy.

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  Fig. 3 In the analysis of the importance of factors on the prediction of the recurrence, the decision tree (DT) classifier showed that “age”, “stage”, and “carbohydrate antigen 125 (CA125)” were the most valuable factors. CEA, carcinoembryonic antigen; BMI, body mass index; N, nodes; M, metastasis; T, tumor; CA19-9, carbohydrate antigen 19-9; PALA, para-aortic lymphadenectomy; OMT, omentectomy; PLA, pelvic lymphadenectomy; TAH, total abdominal hysterectomy.

• 

  Fig. 4 In the analysis of the importance of factors on the prediction of the recurrence, the boosted tree classifier showed that “age”, “stage”, and “carbohydrate antigen 125 (CA125)” were the most valuable factors. CEA, carcinoembryonic antigen; BMI, body mass index; N, nodes; M, metastasis; T, tumor; CA19-9, carbohydrate antigen 19-9; PALA, para-aortic lymphadenectomy; OMT, omentectomy; PLA, pelvic lymphadenectomy; TAH, total abdominal hysterectomy.

Reference

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