Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

Obrzut, Bogdan; Kusy, Maciej; Semczuk, Andrzej; Obrzut, Marzanna; Kluska, Jacek

doi:10.1186/s12885-017-3806-3

Cited by 46 publications

(50 citation statements)

References 76 publications

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“…Previous survival analysis studies using machine learning involved the use of support vector machine-based prediction models (sensitivity = 0.89; specificity = 0.73) or decision support systems (C-index = 0.84 with an accuracy of 86%) to predict breast cancer recurrence 9,10 , and probabilistic neural networks to predict cervical cancer recurrence (sensitivity = 0.975; accuracy = 0.892) 11 . In their research on lung cancer, Lynch et al compared various supervised machine learning classification techniques using the Surveillance, Epidemiology, and End Results (SEER) database and showed that the models in which the gradient boosting machine was utilized with the root-mean-squared error (RMSE) were the most accurate 12 .…”

Section: Discussionmentioning

confidence: 99%

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

Lee

Chun

Hong

et al. 2020

Sci Rep

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Accurate prediction of non-small cell lung cancer (nScLc) prognosis after surgery remains challenging. the cox proportional hazard (pH) model is widely used, however, there are some limitations associated with it. in this study, we developed novel neural network models called binned time survival analysis (DeepBTS) models using 30 clinico-pathological features of surgically resected NSCLC patients (training cohort, n = 1,022; external validation cohort, n = 298). We employed the root-mean-square error (in the supervised learning model, s-DeepBtS) or negative log-likelihood (in the semi-unsupervised learning model, su-DeepBtS) as the loss function. the su-DeepBtS algorithm achieved better performance (c-index = 0.7306; AUC = 0.7677) than the other models (Cox PH: C-index = 0.7048 and AUC = 0.7390; s-DeepBtS: c-index = 0.7126 and AUC = 0.7420). The top 14 features were selected using su-DeepBTS model as a selector and could distinguish the low-and high-risk groups in the training cohort (p = 1.86 × 10 −11) and validation cohort (p = 1.04 × 10 −10). When trained with the optimal feature set for each model, the su-DeepBtS model could predict the prognoses of nScLc better than the traditional model, especially in stage i patients. follow-up studies using combined radiological, pathological imaging, and genomic data to enhance the performance of our model are ongoing.

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Section: Discussionmentioning

confidence: 99%

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

Lee

Chun

Hong

et al. 2020

Sci Rep

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“…Another consequent limitation of the retrospective de-sign is the lack of adequate power to calculate a sample size at which the outcomes could be statistically reliable. Finally, we applied a Cox proportional hazards regression to investigate the impact of patient/tumor characteristics on survival, while a novel model, based on deep-learning neural network models, has proven to be more effective in predicting patients' survival; 42,43 the use of such a model in our setting may be recommended to improve treatment decision-making and outcomes by providing more accurate predictions.…”

Section: Predictormentioning

confidence: 99%

Indicators of survival and prognostic factors in women treated for cervical cancer at a tertiary care center in Saudi Arabia

Anfinan

Sait

2020

Ann Saudi Med

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BACKGROUND: Investigating survival in cervical cancer at the local level is crucial to determine the effectiveness of overall management, as it reflects the level of care provided and awareness among the population about screening and early diagnosis. OBJECTIVES: Analyze overall survival (OS) and disease-free survival (DFS) among patients treated for cervical cancer and to investigate clinical, management-and outcome-related independent factors associated with survival. DESIGN: A retrospective medical record review. SETTING: Gynecology oncology unit in a tertiary care center. PATIENTS AND METHODS: All women with cervical cancer who were treated and followed up between January 1999 and December 2017. Baseline demographic and clinical data, tumor characteristics, treatment options and outcomes including recurrence were collected and analyzed as factors and predictors of survival. MAIN OUTCOME MEASURES: OS and DFS among patients treated for cervical cancer. SAMPLE SIZE: 190 patients. RESULTS: The 190 patients had a mean (SD) age of 54.2 (13.1) years (median 52.0, interquartile range, 46-62), and median (IQR) follow-up time was 37.0 (12.0-69.0) months. Tumor characteristics showed FIGO stage (I [19.0%], II [48.9%], III [18.4%], IV [13.6%]), grade (I [15.8%], II [46.8%], III [35.8%]) and the most frequent histological type was squamous cell carcinoma (77.4%). Patients received initial radiotherapy with concurrent chemotherapy (53.2%), initial radical hysterectomy (24.7%), systemic chemotherapy (6.3%) and palliative care (4.7%). Mean OS and DFS were 97.1 (82.2, 111.9) and 85.2 (70.4, 100.0) months, respectively. Recurrence and mortality rates were 25.8% and 46.8%, occurring after a median (IQR) time=13.0 (6.0-28.0) and 20.0 (9.0-45.0) months, respectively. Survival was independently associated with grade II (hazard ratio [HR]=3.6, 95%CI: 1.3-9.7, P=.012), grade III (HR=4.5, 95%CI:1.6-12.6, P=.004), number of regional organs involved (1-3 organs: HR=7.8, 95%CI: 1.2, 49.1, P=.030), and recurrence (HR=2.23, P=.001). CONCLUSION: Survival was about 8 years in our institution, which is predicted by the tumor grade, regional organs involved and recurrence. Remarkably, this study found a high percentage of patients diagnosed at an advanced stage, which probably impacts survival and stresses the need for improving early detection. LIMITATIONS: Retrospective design, resulting in recall bias and missing data. CONFLICT OF INTEREST: None.

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“…The AUROC curve is a summary measure of performance that indicates whether on average a true positive is ranked higher than a false positive rate or not. AUROC curve was also used for evaluation of different techniques [18,27] in biomedical data mining. There are 50% of cervical cancer identification in females age (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54) and around 20% diagnosed more than 65 years old as well as around 15% of between the age of (20 -30).…”

Section: Resultsmentioning

confidence: 99%

“…Positive results were obtained in most experiments as compared to other methods. Bogdan Obrzut et al [27] utilized computational intelligence methods for prediction for cervical cancer patients. The probabilistic neural network (PNN) was a very efficient method for predicting overall survival in cervical cancer patients treated with radical hysterectomy.…”

Section: Introductionmentioning

confidence: 99%

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Alam¹,

Milhan²,

Atif³

et al. 2019

ijacsa

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Cervical cancer remains an important reason of deaths worldwide because effective access to cervical screening methods is a big challenge. Data mining techniques including decision tree algorithms are used in biomedical research for predictive analysis. The imbalanced dataset was obtained from the dataset archive belongs to the University of California, Irvine. Synthetic Minority Oversampling Technique (SMOTE) has been used to balance the dataset in which the number of instances has increased. The dataset consists of patient age, number of pregnancies, contraceptives usage, smoking patterns and chronological records of sexually transmitted diseases (STDs). Microsoft azure machine learning tool was used for simulation of results. This paper mainly focuses on cervical cancer prediction through different screening methods using data mining techniques like Boosted decision tree, decision forest and decision jungle algorithms as well performance evaluation has done on the basis of AUROC (Area under Receiver operating characteristic) curve, accuracy, specificity and sensitivity. 10-fold cross-validation method was utilized to authenticate the results and Boosted decision tree has given the best results. Boosted decision tree provided very high prediction with 0.978 on AUROC curve while Hinslemann screening method has used. The results obtained by other classifiers were significantly worse than boosted decision tree.

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Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

Cited by 46 publications

References 76 publications

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

Indicators of survival and prognostic factors in women treated for cervical cancer at a tertiary care center in Saudi Arabia

Cervical Cancer Prediction through Different Screening Methods using Data Mining

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