scholarly journals Improved Classification Accuracy for Identification of Cervical Cancer

2021 ◽  
pp. 245-258
Author(s):  
D. Merlin ◽  
Dr. J. G. R. Sathiaseelan
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Early Stage ◽  
Cancer Classification ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Cancer Dataset ◽  
Major Purpose ◽  
Early Stage Cervical Cancer

The major purpose of this research is to forecast cervical cancer, compare which algorithms perform well, and then choose the best algorithm to predict cervical cancer at an early stage. Cervical cancer classification can be automated using a machine learning system. This study evaluates multiple machine learning techniques for cervical cancer classification. For this classification, algorithms such as Decision Tree, Naive Bayes, KNN, SVM, and MLP are proposed and evaluated. The cervical cancer Dataset, which was retrieved from the UCI machine learning data repository, was used to test these methods. With the help of Sciklit-learn, the algorithms' results were compared in terms of Accuracy, Sensitivity, and Specificity. Sciklit-learn is a Python-based machine learning package that is available for free. Finally, the best model for predicting cervical cancer is developed.

scholarly journals Prediction of lymphovascular space invasion using a combination of tenascin-C, cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoran Li ◽  
Chen Xu ◽  
Yang Yu ◽  
Yan Guo ◽  
Hongzan Sun
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Early Stage ◽  
Cervical Squamous Cell Carcinoma ◽  
Tenascin C ◽  
Machine Learning Model ◽  
Pet Ct ◽  
Early Stage Cervical Cancer ◽  
Cox 2 ◽  
Lymphovascular Space Invasion

Abstract Background Lymphovascular space invasion is an independent prognostic factor in early-stage cervical cancer. However, there is a lack of non-invasive methods to detect lymphovascular space invasion. Some researchers found that Tenascin-C and Cyclooxygenase-2 was correlated with lymphovascular space invasion. Radiomics has been studied as an emerging tool for distinguishing tumor pathology stage, evaluating treatment response, and predicting prognosis. This study aimed to establish a machine learning model that combines radiomics based on PET imaging with tenascin-C (TNC) and cyclooxygenase-2 (COX-2) for predicting lymphovascular space invasion (LVSI) in patients with early-stage cervical cancer. Methods One hundred and twelve patients with early-stage cervical squamous cell carcinoma who underwent PET/CT examination were retrospectively analyzed. Four hundred one radiomics features based on PET/CT images were extracted and integrated into radiomics score (Rad-score). Immunohistochemical analysis was performed to evaluate TNC and COX-2 expression. Mann-Whitney U test was used to distinguish differences in the Rad-score, TNC, and COX-2 between LVSI and non-LVSI groups. The correlations of characteristics were tested by Spearman analysis. Machine learning models including radiomics model, protein model and combined model were established by logistic regression algorithm and evaluated by ROC curve. Pairwise comparisons of ROC curves were tested by DeLong test. Results The Rad-score of patients with LVSI was significantly higher than those without. A significant correlation was shown between LVSI and Rad-score (r = 0.631, p < 0.001). TNC was correlated to both the Rad-score (r = 0.244, p = 0.024) and COX-2 (r = 0.227, p = 0.036). The radiomics model had the best predictive performance among all models in training and external dataset (AUCs: 0.914, 0.806, respectively, p < 0.001). However, in testing dataset, the combined model had better efficiency for predicting LVSI than other models (AUCs: 0.801 vs. 0.756 and 0.801 vs. 0.631, respectively). Conclusion The machine learning model of the combination of PET radiomics with COX-2 and TNC provides a new tool for detecting LVSI in patients with early-stage cervical cancer. In the future, multicentric studies on larger sample of patients will be used to test the model. Trial registration This is a retrospective study and there is no experimental intervention on human participants. The Ethics Committee has confirmed that retrospectively registered is not required.

scholarly journals Prediction of lymphovascular space invasion using a combination of tenascin-C, Cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

2020 ◽  
Author(s):  
Xiaoran Li ◽  
Chen Xu ◽  
Yang Yu ◽  
Yan Guo ◽  
Hongzan Sun
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Squamous Cell Carcinoma ◽  
Early Stage ◽  
Cervical Squamous Cell Carcinoma ◽  
Combined Model ◽  
Pet Ct ◽  
Early Stage Cervical Cancer ◽  
Cox 2 ◽  
Lymphovascular Space Invasion

Abstract Background Lymphovascular space invasion is an independent prognostic factor in early-stage cervical cancer. However, there is a lack of non-invasive methods to detect lymphovascular space invasion. Some researchers found that Tenascin-C and Cyclooxygenase-2 was correlated with lymphovascular space invasion. Radiomics has been studied as an emerging tool for distinguishing tumor pathology stage, evaluating treatment response, and predicting prognosis. Materials and methods Eighty-six patients with early-stage cervical squamous cell carcinoma who underwent PET/CT examination were retrospectively analyzed. 401 radiomics features based on PET/CT images were extracted and integrated into radiomics score (Rad-score). Immunohistochemical analysis was performed to evaluate TNC and COX-2 expression. Mann-Whitney U test was used to distinguish differences in the Rad-score, TNC, and COX-2 between LVSI and non-LVSI groups. The correlations of characteristics were tested by Spearman analysis. Machine learning models including radiomics model, protein model and combined model were established by logistic regression algorithm and evaluated by ROC curve. Pairwise comparisons of ROC curves were tested by DeLong test. Results The Rad-score of patients with LVSI was significantly higher than those without. A significant correlation was shown between LVSI and Rad-score (r = 0.631, p < 0.001). TNC was correlated to both the Rad-score (r = 0.244, p = 0.024) and COX-2 (r = 0.227, p = 0.036). The radiomics model had the best predictive performance among all models (AUC = 0.914, p < 0.001) in training dataset. However, in testing dataset, the combined model had better efficiency for predicting LVSI than other models (AUCs: 0.801 vs. 0.756 and 0.801 vs. 0.631, respectively). Conclusion The machine learning model of the combination of PET radiomics with COX-2 and TNC provides a new tool for detecting LVSI in patients with early-stage cervical cancer.

scholarly journals iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1454
Author(s):  
Phasit Charoenkwan ◽  
Watshara Shoombuatong ◽  
Chalaithorn Nantasupha ◽  
Tanarat Muangmool ◽  
Prapaporn Suprasert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Early Stage ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Efficient System ◽  
Extreme Gradient Boosting ◽  
Independent Test ◽  
Early Stage Cervical Cancer

Radical hysterectomy is a recommended treatment for early-stage cervical cancer. However, the procedure is associated with significant morbidities resulting from the removal of the parametrium. Parametrial cancer invasion (PMI) is found in a minority of patients but the efficient system used to predict it is lacking. In this study, we develop a novel machine learning (ML)-based predictive model based on a random forest model (called iPMI) for the practical identification of PMI in women. Data of 1112 stage IA-IIA cervical cancer patients who underwent primary surgery were collected and considered as the training dataset, while data from an independent cohort of 116 consecutive patients were used as the independent test dataset. Based on these datasets, iPMI-Econ was then developed by using basic clinicopathological data available prior to surgery, while iPMI-Power was also introduced by adding pelvic node metastasis and uterine corpus invasion to the iPMI-Econ. Both 10-fold cross-validations and independent test results showed that iPMI-Power outperformed other well-known ML classifiers (e.g., logistic regression, decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes, support vector machine, and extreme gradient boosting). Upon comparison, it was found that iPMI-Power was effective and had a superior performance to other well-known ML classifiers in predicting PMI. It is anticipated that the proposed iPMI may serve as a cost-effective and rapid approach to guide important clinical decision-making.

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