Abstract
BackgroundComputational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5–year overall survival prediction in patients with cervical cancer treated by radical hysterectomy.MethodsThe data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model.ResultsThe best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse.ConclusionsThe PNN model is an effective tool for predicting 5–year overall survival in cervical cancer patients treated with radical hysterectomy.
Highlights
Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool
Among 117 patients that qualified for a radical Piver III hysterectomy and pelvic lymphadenectomy, 15 did not enter the trial: 3 were excluded because the histopathologic analysis of the operative specimen revealed an endometrial cancer with cervical extension, 4 continued postoperative treatment and follow-up at another institution, 3 refused further participation in the study protocol, and 5 were lost from follow-up
The best results in the prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy were obtained by probabilistic neural network (PNN)
Summary
Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. In Poland, the annual incidence of invasive cervical cancer is 8.9/100,000 woman, and in 2012, 2783 new cases of cervical cancer were diagnosed and 1669 women died [2]. The FIGO staging system serves as the main tool for estimating the general prognosis, it does not include all established prognostic factors, such as lymph node metastases, lymph-vascular space invasion, deep stromal infiltration, or histologic subtype. Another method for individual prediction of survival in cervical carcinoma is the recently developed nomograms based on selected demographic and clinical parameters [3, 4].
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