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Identification of women with high grade histopathology results after conisation by artificial neural networks

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Cooper DB, McCathran CE. Cervical dysplasia. In: StatPearls. [Internet]. Treasure Island (FL): StatPearls Publishing; 2021. [cited 2022 Jan 10]. Available at: http://www.ncbi.nlm.nih.gov/books/NBK430859/ Cooper DB McCathran CE Cervical dysplasia. In: StatPearls. [Internet] Treasure Island (FL) StatPearls Publishing; 2021 [cited 2022 Jan 10]. Available at http://www.ncbi.nlm.nih.gov/books/NBK430859/Search in Google Scholar

I nstitute of Oncology Ljubljana. [ZORA National programme for early detection of precancerous lesions]. [Slovenian]. [cited 2022 Jan 10]. Available at: https://zora.onko-i.si/za-zenske/rak-maternicnega-vratu I nstitute of Oncology Ljubljana [ZORA National programme for early detection of precancerous lesions]. [Slovenian] [cited 2022 Jan 10]. Available at https://zora.onko-i.si/za-zenske/rak-maternicnega-vratuSearch in Google Scholar

Momenimovahed Z, Salehiniya H. Incidence, mortality and risk factors of cervical cancer in the world. Biomed Res Ther 2017; 4: 1795-811. doi. org/10.15419/bmrat.v4i12.386 Momenimovahed Z Salehiniya H Incidence, mortality and risk factors of cervical cancer in the world Biomed Res Ther 2017 4 1795 811 org/10.15419/bmrat.v4i12.38610.15419/bmrat.v4i12.386Search in Google Scholar

Reich O. [Is early first intercourse a risk factor for cervical cancer?]. [German]. Gynakol Geburtshilfliche Rundsch 2005; 45: 251-6. doi. org/10.1159/000087143 Reich O [Is early first intercourse a risk factor for cervical cancer?] [German]. Gynakol Geburtshilfliche Rundsch 2005 45 251 6 org/10.1159/000087143Open DOISearch in Google Scholar

Lehtinen M, Ault KA, Lyytikainen E, Dillner J, Garland SM, Ferris DG et all. FUTURE I and II Study Group. Chlamydia trachomatis infection and risk of cervical intraepithelial neoplasia. Sex Transm Infect 2011; 87: 372-6. doi. org/10.1136/sti.2010.044354 Lehtinen M Ault KA Lyytikainen E Dillner J Garland SM Ferris DG et all FUTURE I and II Study Group Chlamydia trachomatis infection and risk of cervical intraepithelial neoplasia. Sex Transm Infect 2011 87 372 6 org/10.1136/sti.2010.044354Open DOISearch in Google Scholar

Bosch FX, Castellsagué X, Muñoz N, de Sanjosé S, Ghaffari AM, González LC, et al. Male sexual behavior and human papillomavirus DNA: key risk factors for cervical cancer in Spain. J Natl Cancer Inst 1996; 88: 1060-7. doi. org/10.1093/jnci/88.15.1060 Bosch FX Castellsagué X Muñoz N de Sanjosé S Ghaffari AM González LC et al Male sexual behavior and human papillomavirus DNA: key risk factors for cervical cancer in Spain J Natl Cancer Inst 1996 88 1060 7 org/10.1093/jnci/88.15.1060Open DOISearch in Google Scholar

Machida H, Eckhardt SE, Castaneda AV, Blake EA, Pham HQ, Roman LD, et al. Single marital status and infectious mortality in women with cervical cancer in the United States. Int J Gynecol Cancer 2017; 27: 1737-46. doi. org/10.1097/IGC.0000000000001068 Machida H Eckhardt SE Castaneda AV Blake EA Pham HQ Roman LD et al Single marital status and infectious mortality in women with cervical cancer in the United States Int J Gynecol Cancer 2017 27 1737 46 org/10.1097/IGC.0000000000001068Open DOISearch in Google Scholar

Fonseca-Moutinho JA. Smoking and cervical cancer. ISRN Obstet Gynecol 2011; 2011: 847684. doi.org/10.5402/2011/847684 Fonseca-Moutinho JA. Smoking and cervical cancer ISRN Obstet Gynecol 2011 2011 847684 doi.org/10.5402/2011/84768410.5402/2011/847684314005021785734Search in Google Scholar

Roura E, Castellsagué X, Pawlita M, Travier N, Waterboer T, Margall N, et al. Smoking as a major risk factor for cervical cancer and pre-cancer: results from the EPIC cohort: smoking and cervical cancer in EPIC. Int J Cancer 2014; 135: 453-66. doi.org/10.1002/ijc.28666 Roura E Castellsagué X Pawlita M Travier N Waterboer T Margall N et al Smoking as a major risk factor for cervical cancer and pre-cancer: results from the EPIC cohort: smoking and cervical cancer in EPIC Int J Cancer 2014 135 453 66 doi.org/10.1002/ijc.28666Open DOISearch in Google Scholar

Smith JS, Green J, de Gonzalez AB, Appleby P, Peto J, Plummer M, et al. Cervical cancer and use of hormonal contraceptives: a systematic review. The Lancet 2003; 361: 1159-67. doi.org/10.1016/s0140-6736(03)12949-2 Smith JS Green J de Gonzalez AB Appleby P Peto J Plummer M et al Cervical cancer and use of hormonal contraceptives: a systematic review The Lancet 2003 361 1159 67 doi.org/10.1016/s0140-6736(03)12949-210.1016/S0140-6736(03)12949-212686037Search in Google Scholar

Jensen K, Schmiedel S, Norrild B, Frederiksen K, Iftner T, Kjaer S. Parity as a cofactor for high-grade cervical disease among women with persistent human papillomavirus infection: a 13-year follow-up. Br J Cancer 2013 108: 234-9. doi.org/10.1038/bjc.2012.513 Jensen K Schmiedel S Norrild B Frederiksen K Iftner T Kjaer S Parity as a cofactor for high-grade cervical disease among women with persistent human papillomavirus infection: a 13-year follow-up Br J Cancer 2013 108 234 9 doi.org/10.1038/bjc.2012.513Open DOISearch in Google Scholar

Poorolajal J, Jenabi E. The association between BMI and cervical cancer risk: a meta-analysis. Eur J Cancer Prev 2016; 25: 232-8. doi.org/10.1097/CEJ.0000000000000164 Poorolajal J Jenabi E The association between BMI and cervical cancer risk: a meta-analysis Eur J Cancer Prev 2016 25 232 8 doi.org/10.1097/CEJ.0000000000000164Open DOISearch in Google Scholar

Saraiya M, Cheung LC, Soman A, Mix J, Kenney K, Chen X, et al. Risk of cervical precancer and cancer among uninsured and underserved women from 2009 to 2017. Am J Obstet Gynecol 2021; 224: 366.e1-e32. doi. org/10.1016/j.ajog.2020.10.001 Saraiya M Cheung LC Soman A Mix J Kenney K Chen X et al Risk of cervical precancer and cancer among uninsured and underserved women from 2009 to 2017 Am J Obstet Gynecol 2021 224 366 .e1-e32 org/10.1016/j.ajog.2020.10.00110.1016/j.ajog.2020.10.001800981133035473Search in Google Scholar

Zur Hausen H. Papillomaviruses and cancer: from basic studies to clinical application. Nat Rev Cancer 2002; 2: 342-50. doi.org/10.1038/nrc798 Zur Hausen H Papillomaviruses and cancer: from basic studies to clinical application Nat Rev Cancer 2002 2 342 50 doi.org/10.1038/nrc79810.1038/nrc79812044010Search in Google Scholar

Araldi RP, Sant’Ana TA, Módolo DG, de Melo TC, Spadacci-Morena DD, de Cassia Stocco R, et al. The human papillomavirus (HPV)-related cancer biology: an overview. Biomed Pharmacother 2018; 106: 1537-56. doi. org/10.1016/j.biopha.2018.06.149 Araldi RP Sant’Ana TA Módolo DG de Melo TC Spadacci-Morena DD de Cassia Stocco R et al The human papillomavirus (HPV)-related cancer biology: an overview Biomed Pharmacother 2018 106 1537 56 org/10.1016/j.biopha.2018.06.149Open DOISearch in Google Scholar

Bosch FX, Burchell AN, Schiffman M, Giuliano AR, de Sanjose S, Bruni L, et al. Epidemiology and natural history of human papillomavirus infections and type-specific implications in cervical neoplasia. Vaccine 2008; 26(Suppl 10): K1-16. doi.org/10.1016/j.vaccine.2008.05.064 Bosch FX Burchell AN Schiffman M Giuliano AR de Sanjose S Bruni L et al Epidemiology and natural history of human papillomavirus infections and type-specific implications in cervical neoplasia Vaccine 2008 26Suppl 10 K1 16 doi.org/10.1016/j.vaccine.2008.05.064Open DOISearch in Google Scholar

Melnikow J, Henderson JT, Burda BU, Senger CA, Durbin S, Weyrich MS. Screening for cervical cancer with high-risk human papillomavirus testing: updated evidence report and systematic review for the US preventive services task force. JAMA 2018; 320: 687-705. doi.org/10.1001/jama.2018.10400 Melnikow J Henderson JT Burda BU Senger CA Durbin S Weyrich MS Screening for cervical cancer with high-risk human papillomavirus testing: updated evidence report and systematic review for the US preventive services task force JAMA 2018 320 687 705 doi.org/10.1001/jama.2018.10400Open DOISearch in Google Scholar

Kononenko I. Machine learning. 2nd revised edition. Ljubljana: Založba FE in FRI; 2005. Kononenko I Machine learning. 2nd revised edition Ljubljana Založba FE in FRI; 2005Search in Google Scholar

Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001; 23: 89-109. doi: 10.1016/s0933-3657(01)00077-x Kononenko I Machine learning for medical diagnosis: history, state of the art and perspective Artif Intell Med 2001 23 89 109 10.1016/s0933-3657(01)00077-x11470218Open DOISearch in Google Scholar

Lavrač N, Kononenko I, Keravnou E, Kukar M, Zupan B. Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Comm 1998; 11: 191-218. Lavrač N Kononenko I Keravnou E Kukar M Zupan B Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction AI Comm 1998 11 191 218Search in Google Scholar

Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, et al. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 2018; 22: 1218-26. doi.org/10.1109/JBHI.2017.2731873 Yap MH Pons G Marti J Ganau S Sentis M Zwiggelaar R et al Automated breast ultrasound lesions detection using convolutional neural networks IEEE J Biomed Health Inform 2018 22 1218 26 doi.org/10.1109/JBHI.2017.2731873Open DOISearch in Google Scholar

Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 2019; 292: 60-6. doi.org/10.1148/radiol.2019182716 Yala A Lehman C Schuster T Portnoi T Barzilay R A deep learning mammography-based model for improved breast cancer risk prediction Radiology 2019 292 60 6 doi.org/10.1148/radiol.2019182716Open DOISearch in Google Scholar

Vogrin M, Trojner T, Kelc R. Artificial intelligence in musculoskeletal oncological radiology. Radiol Oncol 2020; 55: 1-6. doi.org/10.2478/raon-2020-0068 Vogrin M Trojner T Kelc R Artificial intelligence in musculoskeletal oncological radiology Radiol Oncol 2020 55 1 6 doi.org/10.2478/raon-2020-006810.2478/raon-2020-0068787726033885240Search in Google Scholar

Ha R, Chin C, Karcich J, Liu MZ, Chang P, Mutasa S, et al. Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset. J Digit Imaging 2019; 32: 693-701. doi.org/10.1007/s10278-018-0144-1 Ha R Chin C Karcich J Liu MZ Chang P Mutasa S et al Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset J Digit Imaging 2019 32 693 701 doi.org/10.1007/s10278-018-0144-110.1007/s10278-018-0144-1673712530361936Search in Google Scholar

Artificial intelligence-based triage for patients with acute abdominal pain in emergency department; a diagnostic accuracy study. [Internet]. [cited 2021 Dec 14]. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6548088/ Artificial intelligence-based triage for patients with acute abdominal pain in emergency department; a diagnostic accuracy study [Internet]. [cited 2021 Dec 14]. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6548088/Search in Google Scholar

Ivanuš U, Jerman T, Gašper Oblak U, Meglič L, Florjančič M, Strojan Fležar M, et al. The impact of the COVID-19 pandemic on organised cervical cancer screening: The first results of the Slovenian cervical screening programme and registry. Lancet Reg Health Eur 2021; 5: 100101. doi.org/10.1016/j.lanepe.2021.100101 Ivanuš U Jerman T Gašper Oblak U Meglič L Florjančič M Strojan Fležar M et al The impact of the COVID-19 pandemic on organised cervical cancer screening: The first results of the Slovenian cervical screening programme and registry Lancet Reg Health Eur 2021 5 100101 doi.org/10.1016/j.lanepe.2021.100101Open DOISearch in Google Scholar

Takač I, Arko D, Dovnik A. [Modern treatment and follow-up of cervical precancerous lesions]. [Slovenian]. In: Smrkolj Š, editor. Proceedings of the colposcopy refresher course. Ljubljana: Association for Gynaecological Oncology, Colposcopy and Cervical Pathology; Institute of Oncology Ljubljana; 2019: 142-61. Takač I Arko D Dovnik A [Modern treatment and follow-up of cervical precancerous lesions]. [Slovenian] In Smrkolj Š editor Proceedings of the colposcopy refresher course. Ljubljana: Association for Gynaecological Oncology, Colposcopy and Cervical Pathology Institute of Oncology Ljubljana 2019 14261Search in Google Scholar

Lasič A, Ivanuš U, Jerman T, Smrkolj Š, Cvjetičanin B, Lukanovič D, et al. [Analysis of conizations in Slovenia 2009-2018: diagnosis, treatment and outcomes of cervical precancerous lesions in Slovenia]. [Slovenian]. In: Proceedings of lectures. [Internet]. Ljubljana: Institute of Oncology; 2019. pp. 45-55. [cited 2021 Nov 10]. Available at: http://dirros.openscience.si/IzpisGradiva.php?lang=slv&id=11590 Lasič A Ivanuš U Jerman T Smrkolj Š Cvjetičanin B Lukanovič D et al [Analysis of conizations in Slovenia 2009-2018: diagnosis, treatment and outcomes of cervical precancerous lesions in Slovenia]. [Slovenian] In Proceedings of lectures. [Internet] Ljubljana Institute of Oncology; 2019 pp 45 55 [cited 2021 Nov 10]. Available at http://dirros.openscience.si/IzpisGradiva.php?lang=slv&id=11590Search in Google Scholar

Guy-Evans O. Neuron function, parts, structure, and types. [Internet]. SimplyPsychology 2021. [cited 2021 Dec 26]. Available at: https://www.simplypsychology.org/neuron.html Guy-Evans O. Neuron function, parts, structure, and types. [Internet] SimplyPsychology 2021 [cited 2021 Dec 26]. Available at https://www.simplypsychology.org/neuron.htmlSearch in Google Scholar

Goodfellow I, Bengio Y, Courville A. Deep learning. [Internet]. MIT Press 2016. [cited 2021 Dec 26]. Available at: https://www.deeplearningbook.org/ Goodfellow I Bengio Y Courville A Deep learning. [Internet] MIT Press 2016 [cited 2021 Dec 26]. Available at https://www.deeplearningbook.org/Search in Google Scholar

Florkowski CM. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin Biochem Rev 2008; 29(Suppl 1): S83-7. PMID: 18852864 Florkowski CM Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests Clin Biochem Rev 2008 29Suppl 1 S83 7 PMID: 18852864Search in Google Scholar

Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 2020; 21: 6. doi.org/10.1186/s12864-019-6413-7 Chicco D Jurman G The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation BMC Genomics 2020 21 6 doi.org/10.1186/s12864-019-6413-710.1186/s12864-019-6413-7694131231898477Search in Google Scholar

Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 2015; 10: e0118432. doi.org//10.1371/journal.pone.0118432 Saito T Rehmsmeier M The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets PLoS One 2015 10 e0118432 doi.org//10.1371/journal.pone.011843210.1371/journal.pone.0118432434980025738806Search in Google Scholar

Mohammed R, Rawashdeh J, Abdullah M. Machine learning with oversampling and undersampling techniques: overview study and experimental results. In: 2020 11th International Conference on Information and Communication Systems (ICICS); 2020. pp 243-8. doi.org/10.1109/ICICS49469.2020.239556 Mohammed R Rawashdeh J Abdullah M Machine learning with oversampling and undersampling techniques: overview study and experimental results In 2020 11th International Conference on Information and Communication Systems (ICICS); 2020 pp 243 8 doi.org/10.1109/ICICS49469.2020.239556Open DOISearch in Google Scholar

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling. Technique 2002; 16: 321-57. doi.org/10.1613/jair.953 Chawla NV Bowyer KW Hall LO Kegelmeyer WP SMOTE: synthetic minority over-sampling Technique 2002 16 321 57 doi.org/10.1613/jair.953Open DOISearch in Google Scholar

Witten IH, Frank E, Hall MA. Data mining: practical machine learning tools and techniques. 3rd edition. Burlington, MA: Morgan Kaufmann; 2011. Witten IH Frank E Hall MA Data mining: practical machine learning tools and techniques. 3rd edition Burlington, MA Morgan Kaufmann; 201110.1016/B978-0-12-374856-0.00001-8Search in Google Scholar

Airola A, Pahikkala T, Waegeman W, De Baets B, Salakoski T. An experimental comparison of cross-validation techniques for estimating the area under the ROC curve. Comput Stat Data Anal 2011; 55: 1828-44. doi.org/10.1016/j.csda.2010.11.018 Airola A Pahikkala T Waegeman W De Baets B Salakoski T An experimental comparison of cross-validation techniques for estimating the area under the ROC curve Comput Stat Data Anal 2011 55 1828 44 doi.org/10.1016/j.csda.2010.11.018Open DOISearch in Google Scholar

Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw 2008; 21: 42736. doi.org/10.1016/j.neunet.2007.12.031 Mazurowski MA Habas PA Zurada JM Lo JY Baker JA Tourassi GD Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance Neural Netw 2008 21 42736 doi.org/10.1016/j.neunet.2007.12.031Open DOISearch in Google Scholar

Mango LJ. Computer-assisted cervical cancer screening using neural networks. Cancer Lett 1994; 77: 155-62. doi: 10.1016/0304-3835(94)90098-1 Mango LJ Computer-assisted cervical cancer screening using neural networks Cancer Lett 1994 77 155 62 10.1016/0304-3835(94)90098-18168062Open DOISearch in Google Scholar

Sompawong N, Mopan J, Pooprasert P, Himakhun W, Suwannarurk K, Ngamvirojcharoen J, et al. Automated PAP smear cervical cancer screening using deep learning. Annu Int Conf IEEE Eng Med Biol Soc 2019; 2019: 70448. doi.org/10.1109/EMBC.2019.8856369 Sompawong N Mopan J Pooprasert P Himakhun W Suwannarurk K Ngamvirojcharoen J et al Automated PAP smear cervical cancer screening using deep learning Annu Int Conf IEEE Eng Med Biol Soc 2019 2019 70448 doi.org/10.1109/EMBC.2019.8856369Open DOISearch in Google Scholar

Holmström O, Linder N, Kaingu H, Mbuuko N, Mbete J, Kinyua F, et al. Point-of-care digital cytology with artificial intelligence for cervical cancer screening in a resource-limited setting. JAMA 2021; 4: e211740. doi.org/10.1001/jamanetworkopen.2021.1740 Holmström O Linder N Kaingu H Mbuuko N Mbete J Kinyua F et al Point-of-care digital cytology with artificial intelligence for cervical cancer screening in a resource-limited setting JAMA 2021 4 e211740 doi.org/10.1001/jamanetworkopen.2021.1740Open DOISearch in Google Scholar

Bao H, Sun X, Zhang Y, Pang B, Li H, Zhou L, et al. The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: a population-based cohort study of 0.7 million women. Cancer Med 2020; 9: 6896-906. doi.org/10.1002/cam4.3296 Bao H Sun X Zhang Y Pang B Li H Zhou L et al The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: a population-based cohort study of 0.7 million women Cancer Med 2020 9 6896 906 doi.org/10.1002/cam4.3296Open DOISearch in Google Scholar

Turic B, Sun X, Wang J, Pang B. The role of AI in cervical cancer screening. [Internet]. Cervical cancer - A global public health treatise. In: Rakumar R, editor. IntechOpen; 2021. [cited 2022 Jan 12]. Available at: https://www.intechopen.com/chapters/76947 doi: 10.5772/intechopen.98348 Turic B Sun X Wang J Pang B The role of AI in cervical cancer screening. [Internet]. Cervical cancer - A global public health treatise In Rakumar R editor IntechOpen 2021 [cited 2022 Jan 12]. Available at https://www.intechopen.com/chapters/76947 10.5772/intechopen.98348Open DOISearch in Google Scholar

Barut MU, Kale A, Kuyumcuoğlu U, Bozkurt M, Ağaçayak E, Özekinci S, et al. Analysis of sensitivity, specificity, and positive and negative predictive values of smear and colposcopy in diagnosis of premalignant and malignant cervical lesions. Med Sci Monit 2015; 21: 3860-7. doi.org/10.12659/MSM.895227 Barut MU Kale A Kuyumcuoğlu U Bozkurt M Ağaçayak E Özekinci S et al Analysis of sensitivity, specificity, and positive and negative predictive values of smear and colposcopy in diagnosis of premalignant and malignant cervical lesions Med Sci Monit 2015 21 3860 7 doi.org/10.12659/MSM.895227Open DOISearch in Google Scholar

Chandran V, Sumithra MG, Karthick A, George T, Deivakani M, Elakkiya B, et al. Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images. Biomed Res Int 2021; 2021: 5584004. doi. org/10.1155/2021/5584004 Chandran V Sumithra MG Karthick A George T Deivakani M Elakkiya B et al Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images Biomed Res Int 2021 2021 5584004 org/10.1155/2021/558400410.1155/2021/5584004811290933997017Search in Google Scholar

Arbyn M, Kyrgiou M, Simoens C, Raifu AO, Koliopoulos G, Martin-Hirsch P, et al. Perinatal mortality and other severe adverse pregnancy outcomes associated with treatment of cervical intraepithelial neoplasia: meta-analysis. BMJ 2008; 337: 1284. doi.org/10.1136/bmj.a1284 Arbyn M Kyrgiou M Simoens C Raifu AO Koliopoulos G Martin-Hirsch P et al Perinatal mortality and other severe adverse pregnancy outcomes associated with treatment of cervical intraepithelial neoplasia: meta-analysis BMJ 2008 337 1284 doi.org/10.1136/bmj.a1284Open DOISearch in Google Scholar

Karakitsos P, Chrelias C, Pouliakis A, Koliopoulos G, Spathis A, Kyrgiou M, et al. Identification of women for referral to colposcopy by neural networks: a preliminary study based on LBC and molecular biomarkers. J Biomed Biotechnol 2012; 2012: e303192. doi.org/10.1155/2012/303192 Karakitsos P Chrelias C Pouliakis A Koliopoulos G Spathis A Kyrgiou M et al Identification of women for referral to colposcopy by neural networks: a preliminary study based on LBC and molecular biomarkers J Biomed Biotechnol 2012 2012 e303192 doi.org/10.1155/2012/30319210.1155/2012/303192347088923093840Search in Google Scholar

Pouliakis A, Karakitsou E, Chrelias C, Pappas A, Panayiotides I, Valasoulis G, et al. The application of classification and regression trees for the triage of women for referral to colposcopy and the estimation of risk for cervical intraepithelial neoplasia: a study based on 1625 cases with incomplete data from molecular tests. BioMed Res Int 2015; 2015: e914740. doi. org/10.1155/2015/914740 Pouliakis A Karakitsou E Chrelias C Pappas A Panayiotides I Valasoulis G et al The application of classification and regression trees for the triage of women for referral to colposcopy and the estimation of risk for cervical intraepithelial neoplasia: a study based on 1625 cases with incomplete data from molecular tests BioMed Res Int 2015 2015 e914740 org/10.1155/2015/91474010.1155/2015/914740453892226339651Search in Google Scholar

Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med 2011; 32: 605-44. doi.org/10.1016/j.ccm.2011.09.001 Dela Cruz CS Tanoue LT Matthay RA Lung cancer: epidemiology, etiology, and prevention Clin Chest Med 2011 32 605 44 doi.org/10.1016/j.ccm.2011.09.001Open DOISearch in Google Scholar

Mittelstadt BD, Floridi L. The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics 2016; 22: 303-41. doi. org/10.1007/s11948-015-9652-2 Mittelstadt BD Floridi L The ethics of big data: current and foreseeable issues in biomedical contexts Sci Eng Ethics 2016 22 303 41 org/10.1007/s11948-015-9652-2Open DOISearch in Google Scholar

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