Is there any correlation between HPV and early radioresponse before brachytherapy in cervix uteri carcinoma?

Yılmaz, Binnur Dönmez; Uysal, Emre; GÜRDAL, Necla; Özkan, Adem

doi:10.1007/s11547-020-01187-x

Cited by 1 publication

(2 citation statements)

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“…The authors acknowledge that some of the patients’ data were used in another study investigating the correlation between radiotherapy response and HPV infection status. 18 …”

Section: Methodsmentioning

confidence: 99%

“…A total of 851 features from each of the CE-T1 and T2WI images were extracted. Features were grouped as follows: 14 (1.64%) shape, 18 (2.11%) first order, 14 (1.64%) gray-level dependence matrix, 24 (2.82%) gray-level co-occurrence matrix, 16 (1.88%) gray-level run-length matrix, 16 (1.88%) gray-level sizezone matrix, 5 (0.06%) neighboring gray-tone difference matrix, and 744 (87.97%) waveletderived texture features. A combined dataset was created by concatenating features from T2WI and CE-T1.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

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Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models

İnce¹,

Uysal²,

Durak³

et al. 2023

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This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODSPre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper-based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTSForty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSIONMachine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy. KEYWORDSArtificial intelligence, human papillomavirus DNA tests, machine learning, radiology, uterine cervical neoplasms C ervical cancer is the fourth most common female cancer and the second most common in women aged 15-44. 1 The etiological factor in more than 95% of cervical cancer cases is human papillomavirus (HPV). [2][3][4] Fifteen of more than 200 oncogene types are identified as high risk, and type-16 and -18 HPV infections are the most common in women with cervical cancer. 5 In addition, several studies in the literature report that HPV DNA status is From the Clinic of Radiology (O.İ. 

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“…The authors acknowledge that some of the patients’ data were used in another study investigating the correlation between radiotherapy response and HPV infection status. 18 …”

Section: Methodsmentioning

confidence: 99%

Section: Feature Extraction and Selectionmentioning

confidence: 99%