ArticleBMC cancer2017
Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.
Article in BMC cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cervical cancer survival prediction by machine learning algorithms: a systematic review.BMC cancer · 2023Pooled it
- Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy by Machine Learning.Current oncology (Toronto, Ont.) · 2022Trial
- Effectiveness of Artificial Intelligence Models in Predicting Lung Cancer Recurrence: A Gene Biomarker-Driven Review.Cancers · 2025Review
- Review
- A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.BMC cancer · 2025Article
- Circ_0051428 targeting miR-885-3p/MMP2 axis enhances the malignancy of cervical cancer.Open medicine (Warsaw, Poland) · 2024Article
- Effect of annualized surgeon volume on major surgical complications for abdominal and laparoscopic radical hysterectomy for cervical cancer in China, 2004-2016: a retrospective cohort study.BMC women's health · 2023Article
- Article
- Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.Cancers · 2022Review
- An 8-gene DNA methylation signature predicts the recurrence risk of cervical cancer.The Journal of international medical research · 2021Article
- Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study.Translational oncology · 2021Article
- External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival.Cancer research and treatment · 2021Article
- Early lung cancer diagnostic biomarker discovery by machine learning methods.Translational oncology · 2021Article
- Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning.Frontiers in artificial intelligence · 2021Review
- Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system.World journal of urology · 2020Article
- Calculating the overall survival probability in patients with cervical cancer: a nomogram and decision curve analysis-based study.BMC cancer · 2020Article
- Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice.The EPMA journal · 2020Article
- DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network.Scientific reports · 2020Article
- Progress of Artificial Intelligence in Gynecological Malignant Tumors.Cancer management and research · 2020Review
- An 8‑gene signature predicts the prognosis of cervical cancer following radiotherapy.Molecular medicine reports · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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