Acharya U, Mookiah M, Sree SV, Yanti R, Martis R, Saba L et al (2012) Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification. Ultraschall in der Medizin 35(03):237–245. https://doi.org/10.1055/s-0032-1330336
Adamo JE, Bienvenu RV, Fields FO, Ghosh S, Jones CM, Liebman M et al (2018) The integration of emerging omics approaches to advance precision medicine: how can regulatory science help? J Clin Transl Sci 2(5):295–300. https://doi.org/10.1017/cts.2018.330
Akazawa M, Hashimoto K (2021) Artificial intelligence in gynecologic cancers: current status and future challenges–a systematic review. Artif Intell Med 120:102164. https://doi.org/10.1016/j.artmed.2021.102164
Aljakouch K, Hilal Z, Daho I, Schuler M, Kraus SD, Yosef HK et al (2019) Fast and noninvasive diagnosis of cervical cancer by coherent anti-stokes Raman scattering. Anal Chem 91(21):13900–13906. https://doi.org/10.1021/acs.analchem.9b03395
Allison KH, Reed SD, Voigt LF, Jordan CD, Newton KM, Garcia RL (2008) Diagnosing endometrial hyperplasia. Am J Surg Pathol 32(5):691–698. https://doi.org/10.1097/pas.0b013e318159a2a0
Asaduzzaman S, Ahmed MR, Rehana H, Chakraborty S, Islam MS, Bhuiyan T (2021) Machine learning to reveal an astute risk predictive framework for gynecologic cancer and its impact on women psychology: Bangladeshi perspective. BMC Bioinformatics. https://doi.org/10.1186/s12859-021-04131-6
Asiedu MN, Simhal A, Chaudhary U, Mueller JL, Lam CT, Schmitt JW et al (2019) Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, pocket colposcope. IEEE Trans Biomed Eng 66(8):2306–2318. https://doi.org/10.1109/tbme.2018.2887208
Bagaria M, Wentzensen N, Clarke M, Hopkins MR, Ahlberg LJ, Guire LJM et al (2021) Quantifying procedural pain associated with office gynecologic tract sampling methods. Gynecol Oncol 162(1):128–133. https://doi.org/10.1016/j.ygyno.2021.04.033
Bao H, Bi H, Zhang X, Zhao Y, Dong Y, Luo X et al (2020) Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: a multicenter, clinical-based, observational study. Gynecol Oncol 159(1):171–178. https://doi.org/10.1016/j.ygyno.2020.07.099
Barnabas GD, Bahar-Shany K, Sapoznik S, Helpman L, Kadan Y, Beiner M et al (2019) Microvesicle proteomic profiling of uterine liquid biopsy for ovarian cancer early detection. Mol Cell Proteomics 18(5):865–875. https://doi.org/10.1074/mcp.ra119.001362
Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G et al (2021) Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer. https://doi.org/10.1186/s12885-021-08773-w
BenTaieb A, Li-Chang H, Huntsman D, Hamarneh G (2017) A structured latent model for ovarian carcinoma subtyping from histopathology slides. Med Image Anal 39:194–205. https://doi.org/10.1016/j.media.2017.04.008
Boyce B (2017) An update on the validation of whole slide imaging systems following FDA approval of a system for a routine pathology diagnostic service in the united states. Biotech Histochem 92(6):381–389. https://doi.org/10.1080/10520295.2017.1355476
Brereton P, Kitchenham BA, Budgen D, Turner M, Khalil M (2007) Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw 80(4):571–583. https://doi.org/10.1016/j.jss.2006.07.009
Brüggmann D, Ouassou K, Klingelhöfer D, Bohlmann MK, Jaque J, Groneberg DA (2020) Endometrial cancer: mapping the global landscape of research. J Transl Med. https://doi.org/10.1186/s12967-020-02554-y
Burg L, Timmermans M, van der Aa M, Boll D, Rovers K, de Hingh I, van Altena A (2020) Incidence and predictors of peritoneal metastases of gynecological origin: a population-based study in the Netherlands. J Gynecol Oncol. https://doi.org/10.3802/jgo.2020.31.e58
Chan H-P, Hadjiiski LM, Samala RK (2020) Computer-aided diagnosis in the era of deep learning. Med Phys. https://doi.org/10.1002/mp.13764
Chardin L, Leary A (2021) Immunotherapy in ovarian cancer: thinking beyond PD-1/PD-l1. Front Oncol. https://doi.org/10.3389/fonc.2021.795547
Chen D, Xing K, Henson D, Sheng L, Schwartz AM, Cheng X (2009) Developing prognostic systems of cancer patients by ensemble clustering. J Biomed Biotechnol. https://doi.org/10.1155/2009/632786
Chen X, Wang Y, Shen M, Yang B, Zhou Q, Yi Y et al (2020) Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution. Eur Radiol 30(9):4985–4994. https://doi.org/10.1007/s00330-020-06870-1
Chen C, Qin Y, Chen H, Zhu D, Gao F, Zhou X (2021) A metaanalysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients. Insights into Imaging. https://doi.org/10.1186/s13244-021-01034-1
Cheng H, Wu K, Ma K, Tian J, Xu R, Gu C, Guan X (2020) Double attention for pathology image diagnosis network with visual interpretability. In: International joint conference on neural networks (IJCNN). IEEE. https://doi.org/10.1109/ijcnn48605.2020.9206603
Cramer DW (2012) The epidemiology of endometrial and ovarian cancer. Hematol Oncol Clin N Am 26(1):1–12. https://doi.org/10.1016/j.hoc.2011.10.009
dos Santos FLC, Wojciechowska U, Michalek IM, Didkowska J (2023) Survival of patients with cancers of the female genital organs in Poland, 2000–2019. Sci Rep. https://doi.org/10.1038/s41598-023-35749-6
Downing MJ, Papke DJ, Tyekucheva S, Mutter GL (2019) A new classification of benign, premalignant, and malignant endometrial tissues using machine learning applied to 1413 candidate variables. Int J Gynecol Pathol 39(4):333–343. https://doi.org/10.1097/pgp.0000000000000615
Duque J, Moreira JJ, Costa J (2023) Data mining to support decision-making-a research approach. Intelligent sustainable systems. Springer, Singapore, pp 553–563
Elias KM, Fendler W, Stawiski K, Fiascone SJ, Vitonis AF, Berkowitz RS, et al (2017) Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer. eLife. https://doi.org/10.7554/elife.28932
Esfandiari N, Babavalian MR, Moghadam A-ME, Tabar VK (2014) Knowledge discovery in medicine: current issue and future trend. Expert Syst Appl 41(9):4434–4463. https://doi.org/10.1016/j.eswa.2014.01.011
Eusebi P (2013) Diagnostic accuracy measures. Cerebrovasc Dis 36(4):267–272. https://doi.org/10.1159/000353863
everhobbes (n.d.) Ovarian Cancer Key Stats* – worldovariancancercoalition. org. https://worldovariancancercoalition.org/about-ovarian-cancer/key-stats/. Accessed 22 May 2022
Farooq A, Abdelkader A, Javakhishivili N, Moreno GA, Kuderer P, Polley M et al (2021) Assessing the value of second opinion pathology review. Int J Qual Health Care. https://doi.org/10.1093/intqhc/mzab032
Fiscutean A (2021) Clarifying the burden of ovarian cancer. Nature 600(7889):S48–S49. https://doi.org/10.1038/d41586-021-03719-5
Fresard ME, Erices R, Bravo ML, Cuello M, Owen GI, Ibanez C, Rodriguez- Fernandez M (2020) Multi-objective optimization for personalized prediction of venous thromboembolism in ovarian cancer patients. IEEE J Biomed Health Inform 24(5):1500–1508. https://doi.org/10.1109/jbhi.2019.2943499
Gao Y, Zeng S, Xu X, Li H, Yao S, Song K et al (2022) Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in china: a retrospective, multicentre, diagnostic study. Lancet Digital Health 4(3):e179–e187. https://doi.org/10.1016/s2589-7500(21)00278-8
Genta RM (2014) Same specimen, different diagnoses. Adv Anat Pathol 21(3):188–190. https://doi.org/10.1097/pap.0000000000000023
Girolamo F, Lante I, Muraca M, Putignani L (2013) The role of mass spectrometry in the “omics” era. Curr Org Chem 17(23):2891–2905. https://doi.org/10.2174/1385272817888131118162725
Gravitt PE, Silver MI, Hussey HM, Arrossi S, Huchko M, Jeronimo J et al (2021) Achieving equity in cervical cancer screening in low- and middle-income countries (LMICs): strengthening health systems using a systems thinking approach. Prev Med 144:106322. https://doi.org/10.1016/j.ypmed.2020.106322
Greyson D, Rafferty E, Slater L, MacDonald N, Bettinger JA, Dubé È, MacDonald SE et al (2019) Systematic review searches must be systematic, comprehensive, and transparent: a critique of perman. BMC Public Health. https://doi.org/10.1186/s12889-018-6275-y
Grimley PM, Liu Z, Darcy KM, Hueman MT, Wang H, Sheng L et al (2021) A prognostic system for epithelial ovarian carcinomas using machine learning. Acta Obstet Gynecol Scand 100(8):1511–1519. https://doi.org/10.1111/aogs.14137
Guo L, Boukir S (2015) Fast data selection for SVM training using ensemble margin. Pattern Recogn Lett 51:112–119. https://doi.org/10.1016/j.patrec.2014.08.003
Guo P, Banerjee K, Stanley RJ, Long R, Antani S, Thoma G et al (2016) Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. IEEE J Biomed Health Inform 20(6):1595–1607. https://doi.org/10.1109/jbhi.2015.2483318
Hanna MG, Reuter VE, Ardon O, Kim D, Sirintrapun SJ, Schüffler PJ et al (2020) Validation of a digital pathology system including remote review during the COVID-19 pandemic. Mod Pathol 33(11):2115–2127. https://doi.org/10.1038/s41379-020-0601-5
Henderson JT, Webber EM, Sawaya GF (2018) Screening for ovarian cancer. JAMA 319(6):595. https://doi.org/10.1001/jama.2017.21421
Hirschberg C, Edinger M, Holmfred E, Rantanen J, Boetker J (2020) Image-based artificial intelligence methods for product control of tablet coating quality. Pharmaceutics 12(9):877. https://doi.org/10.3390/pharmaceutics12090877
Holsbeke CV, Calster BV, Bourne T, Ajossa S, Testa AC, Guerriero S et al (2012) External validation of diagnostic models to estimate the risk of malignancy in adnexal masses. Clin Cancer Res 18(3):815–825. https://doi.org/10.1158/1078-0432.ccr-11-0879
Horvath S, George E, Herzog TJ (2013) Unintended consequences: surgical complications in gynecologic cancer. Womens Health 9(6):595–604. https://doi.org/10.2217/whe.13.60
Hosni M, Abnane I, Idri A, de Gea JMC, Alemán JLF (2019) Reviewing ensemble classification methods in breast cancer. Comput Methods Programs Biomed 177:89–112. https://doi.org/10.1016/j.cmpb.2019.05.019
Hsiao Y-W, Tao C-L, Chuang EY, Lu T-P (2021) A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models. J Adv Res 30:113–122. https://doi.org/10.1016/j.jare.2020.11.006
Huang S et al (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics. https://doi.org/10.21873/cgp.20063
Huang P, Tan X, Chen C, Lv X, Li Y (2020) AF-SENet: classification of cancer in cervical tissue pathological images based on fusing deep convolution features. Sensors 21(1):122. https://doi.org/10.3390/s21010122
Huang P, Zhang S, Li M, Wang J, Ma C, Wang B, Lv X (2020) Classification of cervical biopsy images based on LASSO and EL-SVM. IEEE Access 8:24219–24228. https://doi.org/10.1109/access.2020.2970121
Hull R, Mbele M, Makhafola T, Hicks C, Wang S, Reis R et al (2020) Cervical cancer in low and middle-income countries (review). Oncol Lett 20(3):2058–2074. https://doi.org/10.3892/ol.2020.11754
Idlahcen F, Idri A (2022) Systematic map of data mining for gynecologic oncology. Information systems and technologies. Springer, pp 466-475
Idri A, Amazal F, Abran A (2015) Analogy-based software development effort estimation: a systematic mapping and review. Inf Softw Technol 58:206–230. https://doi.org/10.1016/j.infsof.2014.07.013
Idri A, Hosni M, Abran A (2016) Systematic literature review of ensemble effort estimation. J Syst Softw 118:151–175. https://doi.org/10.1016/j.jss.2016.05.016
Idri A, Benhar H, Fernández-Alemán J, Kadi I (2018) A systematic map of medical data preprocessing in knowledge discovery. Comput Methods Programs Biomed 162:69–85. https://doi.org/10.1016/j.cmpb.2018.05.007
Idri A, Chlioui I, Ouassif BE (2018) A systematic map of data analytics in breast cancer. In: Proceedings of the Australasian computer science week multiconference. ACM. https://doi.org/10.1145/3167918.3167930
Jha AK, Mithun S, Purandare NC, Kumar R, Rangarajan V, Wee L, Dekker A (2022) Radiomics: a quantitative imaging biomarker in precision oncology. Nucl Med Commun 43(5):483–493. https://doi.org/10.1097/mnm.0000000000001543
Jiang H-K, Liang Y (2020) Penalized logistic regression based on l1/2 penalty for high-dimensional DNA methylation data. Technol Health Care 28:161–171. https://doi.org/10.3233/thc-209016
Jo S (2022) The use of multiple imputation to handle missing data in secondary datasets: suggested approaches when missing data results from the survey structure. INQUIRY. https://doi.org/10.1177/00469580221088627
Kanavati F, Hirose N, Ishii T, Fukuda A, Ichihara S, Tsuneki M (2022) A deep learning model for cervical cancer screening on liquid-based cytology specimens in whole slide images. Cancers 14(5):1159. https://doi.org/10.3390/cancers14051159
Kawakami E, Tabata J, Yanaihara N, Ishikawa T, Koseki K, Iida Y et al (2019) Application of artificial intelligence for preoperative diagnostic and prognostic prediction in epithelial ovarian cancer based on blood biomarkers. Clin Cancer Res 25(10):3006–3015. https://doi.org/10.1158/1078-0432.ccr-18-3378
Keele S, et al (2007) Guidelines for performing systematic literature reviews in software engineering. Technical report, ver. 2.3 EBSE technical report. ebse
Kehoe S, Bhatla N (2021) FIGO cancer report 2021. Int J Gynecol Obstet 155(S1):5–6. https://doi.org/10.1002/ijgo.13882
Kitchenham B (n.d.) Evidence-based Software Engineering – keele.ac.uk. https://www.keele.ac.uk/research/ourresearch/computerscienceandmathematics/evidence-basedsoftwareengineering/#!. Accessed 22 May 2022
Kitchenham B, Dyba T, Jorgensen M (2004) Evidence-based software engineering. In: Proceedings 26th international conference on software engineering. IEEE Comput. Soc., pp 273–281. https://doi.org/10.1109/icse.2004.1317449
Krakauer EL, Kwete X, Kane K, Afshan G, Bazzett-Matabele L, Bien-Aimé DDR et al (2021) Cervical cancer-associated suffering: Estimating the palliative care needs of a highly vulnerable population. JCO Glob Oncol 7:862–872. https://doi.org/10.1200/go.21.00025
Kumar S, Rana ML, Verma K, Singh N, Sharma AK, Maria AK et al (2014) PrediQt-cx: Post treatment health related quality of life prediction model for cervical cancer patients. PLoS ONE 9(2):e89851. https://doi.org/10.1371/journal.pone.0089851
Kusy M, Obrzut B, Kluska J (2013) Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients. Med Biol Eng Comput 51(12):1357–1365. https://doi.org/10.1007/s11517-013-1108-8
Laios A, Gryparis A, DeJong D, Hutson R, Theophilou G, Leach C (2020) Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models. J Ovarian Res. https://doi.org/10.1186/s13048-020-00700-0
Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Tommaso LD (2021) Artificial intelligence & tissue biomarkers: advantages, risks and perspectives for pathology. Cells 10(4):787. https://doi.org/10.3390/cells10040787
LaVigne AW, Triedman SA, Randall TC, Trimble EL, Viswanathan AN (2017) Cervical cancer in low and middle income countries: addressing barriers to radiotherapy delivery. Gynecol Oncol Rep 22:16–20. https://doi.org/10.1016/j.gore.2017.08.004
Lee CKH, Tse YK, Ho G, Chung S (2021) Uncovering insights from healthcare archives to improve operations: an association analysis for cervical cancer screening. Technol Forecast Soc Chang 162:120375. https://doi.org/10.1016/j.techfore.2020.120375
Li C, Chen H, Zhang L, Xu N, Xue D, Hu Z et al (2019) Cervical histopathology image classification using multilayer hidden conditional random fields and weakly supervised learning. IEEE Access 7:90378–90397. https://doi.org/10.1109/access.2019.2924467
Li Y, Chen J, Xue P, Tang C, Chang J, Chu C et al (2020) Computer-aided cervical cancer diagnosis using timelapsed colposcopic images. IEEE Trans Med Imaging 39(11):3403–3415. https://doi.org/10.1109/tmi.2020.2994778
Li S, Chen H, Zhang T, Li R, Yin X, Man J et al (2022) Spatiotemporal trends in burden of uterine cancer and its attribution to body mass index in 204 countries and territories from 1990 to 2019. Cancer Med 11(12):2467–2481. https://doi.org/10.1002/cam4.4608
Liang LA, Einzmann T, Franzen A, Schwarzer K, Schauberger G, Schriefer D et al (2021) Cervical cancer screening: comparison of conventional pap smear test, liquid-based cytology, and human papillomavirus testing as stand-alone or cotesting strategies. Cancer Epidemiol Biomarkers Prev 30(3):474–484. https://doi.org/10.1158/1055-9965.epi-20-1003
Liang Y, Jiao H, Qu L, Liu H (2022) Association between hormone replacement therapy and development of endometrial cancer: results from a prospective US cohort study. Front Med. https://doi.org/10.3389/fmed.2021.802959
Liu Y, Ma L, Yang X, Bie J, Li D, Sun C et al (2019) Menopausal hormone replacement therapy and the risk of ovarian cancer: a meta-analysis. Front Endocrinol. https://doi.org/10.3389/fendo.2019.00801
Liu X, Xiao Z, Song Y, Zhang R, Li X, Du Z (2021) A machine learning-aided framework to predict outcomes of anti-PD-1 therapy for patients with gynecological cancer on incomplete post-marketing surveillance dataset. IEEE Access 9:120464–120480. https://doi.org/10.1109/access.2021.3107498
Lõhmussaar K, Boretto M, Clevers H (2020) Human-derived model systems in gynecological cancer research. Trends Cancer 6(12):1031–1043. https://doi.org/10.1016/j.trecan.2020.07.007
Lopez C, Tucker S, Salameh T, Tucker C (2018) An unsupervised machine learning method for discovering patient clusters based on genetic signatures. J Biomed Inform 85:30–39. https://doi.org/10.1016/j.jbi.2018.07.004
Luchini C, Pea A, Scarpa A (2021) Artificial intelligence in oncology: current applications and future perspectives. Br J Cancer 126(1):4–9. https://doi.org/10.1038/s41416-021-01633-1
Luo Y-H, Xi IL, Wang R, Abdallah HO, Wu J, Vance AZ et al (2020) Deep learning based on MR imaging for predicting outcome of uterine fibroid embolization. J Vasc Interv Radiol 31(6):1010-1017.e3. https://doi.org/10.1016/j.jvir.2019.11.032
Ma J-H, Huang Y, Liu L-Y, Feng Z (2021) An 8-gene DNA methylation signature predicts the recurrence risk of cervical cancer. J Int Med Res 49(5):030006052110184. https://doi.org/10.1177/03000605211018443
Mabwa D, Gajjar K, Furniss D, Schiemer R, Crane R, Fallaize C et al (2021) Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples. Analyst 146(18):5631–5642. https://doi.org/10.1039/d1an00833a
Malla RR, Patnala K, Kumar DKG, Marni R (2021) Drug resistance in gynecologic cancers: emphasis on noncoding RNAs and drug efflux mechanisms. Overcoming drug resistance in gynecologic cancers. Elsevier, pp 155–168. https://doi.org/10.1016/b978-0-12-824299-5.00018-6
Mallik S, Mukhopadhyay A, Maulik U, Bandyopadhyay S (2013) Integrated analysis of gene expression and genome-wide DNA methylation for tumor prediction: an association rule mining-based approach. In: IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB). IEEE. https://doi.org/10.1109/cibcb.2013.6595397
Manteghinejad A, Javanmard SH (2021) Challenges and opportunities of digital health in a post-covid19 world. J Res Med Sci 26
Martinic MK, Pieper D, Glatt A, Puljak L (2019) Definition of a systematic review used in overviews of systematic reviews, metaepidemiological studies and textbooks. BMC Med Res Methodol. https://doi.org/10.1186/s12874-019-0855-0
Medhin LB, Tekle LA, Achila OO, Said S (2020) Incidence of cervical, ovarian and uterine cancer in eritrea: data from the national health laboratory, 2011–2017. Sci Rep. https://doi.org/10.1038/s41598-020-66096-5
Melton BL (2017) Systematic review of medical informatics-supported medication decision making. Biomed Inform Insights. https://doi.org/10.1177/1178222617697975
Meng Z, Zhao Z, Li B, Su F, Guo L (2021) A cervical histopathology dataset for computer aided diagnosis of precancerous lesions. IEEE Trans Med Imaging 40(6):1531–1541. https://doi.org/10.1109/tmi.2021.3059699
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M et al (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMAp) 2015 statement. Syst Rev. https://doi.org/10.1186/2046-4053-4-1
Momenimovahed Z, Tiznobaik A, Taheri S, Salehiniya H (2019) Ovarian cancer in the world: epidemiology and risk factors. Int J Women’s Health 11:287–299. https://doi.org/10.2147/ijwh.s197604
Morganti S, Tarantino P, Ferraro E, D’Amico P, Viale G, Trapani D, et al (2019) Role of next-generation sequencing technologies in personalized medicine. P5 eHealth: an agenda for the health technologies of the future. Springer, pp 125–154
Mostafa S, Mondal D, Beck MA, Bidinosti CP, Henry CJ, Stavness I (2022) Leveraging guided backpropagation to select convolutional neural networks for plant classification. Front Artif Intell. https://doi.org/10.3389/frai.2022.871162
Nakagawa M, Nakaura T, Namimoto T, Iyama Y, Kidoh M, Hirata K et al (2019) A multiparametric MRI based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with 18f-FDG PET/CT. Clin Radiol 74(2):167.e1-167.e7. https://doi.org/10.1016/j.crad.2018.10.010
Nees LK, Heublein S, Steinmacher S, Juhasz-Böss I, Brucker S, Tempfer CB, Wallwiener M (2022) Endometrial hyperplasia as a risk factor of endometrial cancer. Arch Gynecol Obstet 306(2):407–421. https://doi.org/10.1007/s00404-021-06380-5
Nie X, Song L, Li X, Wang Y, Qu B (2021) Prognostic signature of ovarian cancer based on 14 tumor microenvironment-related genes. Medicine 100(28):e26574. https://doi.org/10.1097/md.0000000000026574
Oyelade J, Isewon I, Oladipupo F, Aromolaran O, Uwoghiren E, Ameh F et al (2016) Clustering algorithms: their application to gene expression data. Bioinformatics Biol Insights. https://doi.org/10.4137/bbi.s38316
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. https://doi.org/10.1136/bmj.n71
Peng G, Dong H, Liang T, Li L, Liu J (2021) Diagnosis of cervical precancerous lesions based on multimodal feature changes. Comput Biol Med 130:104209. https://doi.org/10.1016/j.compbiomed.2021.104209
Petersen K, Vakkalanka S, Kuzniarz L (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol 64:1–18. https://doi.org/10.1016/j.infsof.2015.03.007
Praiss AM, Huang Y, Clair CMS, Tergas AI, Melamed A, Khoury-Collado F et al (2020) Using machine learning to create prognostic systems for endometrial cancer. Gynecol Oncol 159(3):744–750. https://doi.org/10.1016/j.ygyno.2020.09.047
Rahman R, Clark MD, Collins Z, Traore F, Dioukhane EM, Thiam H et al (2019) Cervical cancer screening decentralized policy adaptation: an African rural-context-specific systematic literature review. Glob Health Action 12(1):1587894. https://doi.org/10.1080/16549716.2019.1587894
Rajula HSR, Verlato G, Manchia M, Antonucci N, Fanos V (2020) Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment. Medicina 56(9):455. https://doi.org/10.3390/medicina56090455
Razzak MI, Imran M, Xu G (2019) Big data analytics for preventive medicine. Neural Comput Appl 32(9):4417–4451. https://doi.org/10.1007/s00521-019-04095-y
Reijnen C, Gogou E, Visser NCM, Engerud H, Ramjith J, van der Putten LJM et al (2020) Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: a development and validation study. PLoS Med 17(5):e1003111. https://doi.org/10.1371/journal.pmed.1003111
Rodriguez JPM, Rodriguez R, Silva VWK, Kitamura FC, Corradi GCA, de Marchi ACB, Rieder R (2022) Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: a systematic review. J Pathol Inform 13:100138. https://doi.org/10.1016/j.jpi.2022.100138
Sarana A, Subhashini R (2023) A systematic review of explainable artificial intelligence models and applications: recent developments and future trends. Decis Analyt J 7:100230. https://doi.org/10.1016/j.dajour.2023.100230
Sarica A, Cerasa A, Quattrone A (2017) Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2017.00329
Schiavo JH (2019) PROSPERO: an international register of systematic review protocols. Med Ref Serv Q 38(2):171–180. https://doi.org/10.1080/02763869.2019.1588072
Shao J, Zhang Z, Liu H, Song Y, Yan Z, Wang X, Hou Z (2020) DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction. Comput Biol Med 118:103634. https://doi.org/10.1016/j.compbiomed.2020.103634
Shen W-C, Chen S-W, Wu K-C, Hsieh T-C, Liang J-A, Hung Y-C et al (2019) Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18f]-fluorodeoxyglucose positron emission tomography/computed tomography. Eur Radiol 29(12):6741–6749. https://doi.org/10.1007/s00330-019-06265-x
Shin SJ, You SC, Jeon H, Jung JW, An MH, Park RW, Roh J (2021) Style transfer strategy for developing a generalizable deep learning application in digital pathology. Comput Methods Programs Biomed 198:105815. https://doi.org/10.1016/j.cmpb.2020.105815
Shrestha P, Poudyal B, Yadollahi S, Wright DE, Gregory AV, Warner JD et al (2022) A systematic review on the use of artificial intelligence in gynecologic imaging–background, state of the art, and future directions. Gynecol Oncol 166(3):596–605. https://doi.org/10.1016/j.ygyno.2022.07.024
Sidey-Gibbons JAM, Sidey-Gibbons CJ (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol. https://doi.org/10.1186/s12874-019-0681-4
Smeltzer MP, Lee Y-S, Faris Nicholas RM, Fehnel C, Akinbobola O, Meadows-Taylor M et al (2021) Trends in accuracy and comprehensiveness of pathology reports for resected NSCLC in a high mortality area of the united states. J Thorac Oncol 16(10):1663–1671. https://doi.org/10.1016/j.jtho.2021.06.027
Smrz SA, Calo C, Fisher JL, Salani R (2021) An ecological evaluation of the increasing incidence of endometrial cancer and the obesity epidemic. Am J Obstet Gynecol 224(5):506.e1-506.e8. https://doi.org/10.1016/j.ajog.2020.10.042
Song H-J, Yang E-S, Kim J-D, Park C-Y, Kyung M-S, Kim Y-S (2018) Best serum biomarker combination for ovarian cancer classification. BioMed Eng. https://doi.org/10.1186/s12938-018-0581-6
Stoler MH (2001) Interobserver reproducibility of cervical cytologic and histologic interpretations. JAMA 285(11):1500. https://doi.org/10.1001/jama.285.11.1500
Sun H, Zeng X, Xu T, Peng G, Ma Y (2020) Computer-aided diagnosis in histopathological images of the endometrium using a convolutional neural network and attention mechanisms. IEEE J Biomed Health Inform 24(6):1664–1676. https://doi.org/10.1109/jbhi.2019.2944977
Suzuki A, Aoki M, Miyagawa C, Murakami K, Takaya H, Kotani Y et al (2019) Differential diagnosis of uterine leiomyoma and uterine sarcoma using magnetic resonance images: a literature review. Healthcare 7(4):158. https://doi.org/10.3390/healthcare7040158
Tian Z, Yen A, Zhou Z, Shen C, Albuquerque K, Hrycushko B (2019) A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies. Brachytherapy 18(4):530–538. https://doi.org/10.1016/j.brachy.2019.04.004
Torheim T, Malinen E, Kvaal K, Lyng H, Indahl UG, Andersen EKF, Futsaether CM (2014) Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE Trans Med Imaging 33(8):1648–1656. https://doi.org/10.1109/tmi.2014.2321024
Troisi J, Sarno L, Landolfi A, Scala G, Martinelli P, Venturella R et al (2018) Metabolomic signature of endometrial cancer. J Proteome Res 17(2):804–812. https://doi.org/10.1021/acs.jproteome.7b00503
Troisi J, Raffone A, Travaglino A, Belli G, Belli C, Anand S et al (2020) Development and validation of a serum metabolomic signature for endometrial cancer screening in postmenopausal women. JAMA Netw Open 3(9):e2018327. https://doi.org/10.1001/jamanetworkopen.2020.18327
Tsai M-H, Chen M-Y, Huang SG, Hung Y-C, Wang H-C (2014) A bio-inspired computing model for ovarian carcinoma classification and oncogene detection. Bioinformatics 31(7):1102–1110. https://doi.org/10.1093/bioinformatics/btu782
Urushibara A, Saida T, Mori K, Ishiguro T, Sakai M, Masuoka S et al (2021) Diagnosing uterine cervical cancer on a single t2-weighted image: comparison between deep learning versus radiologists. Eur J Radiol 135:109471. https://doi.org/10.1016/j.ejrad.2020.109471
van Haastrecht M, Sarhan I, Ozkan BY, Brinkhuis M, Spruit M (2021) SYMBALS: a systematic review methodology blending active learning and snowballing. Front Res Metr Anal. https://doi.org/10.3389/frma.2021.685591
van Panhuis WG, Paul P, Emerson C, Grefenstette J, Wilder R, Herbst AJ et al (2014) A systematic review of barriers to data sharing in public health. BMC Public Health. https://doi.org/10.1186/1471-2458-14-1144
Varughese J, Richman S (2010) Cancer care inequity for women in resource-poor countries. Rev Obstet Gynecol 3(3):122–132
Vázquez MA, Mariño IP, Blyuss O, Ryan A, Gentry-Maharaj A, Kalsi J et al (2018) A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer. Biomed Signal Process Control 46:86–93. https://doi.org/10.1016/j.bspc.2018.07.001
Wadghiri M, Idri A, Idrissi TE, Hakkoum H (2022) Ensemble blood glucose prediction in diabetes mellitus: a review. Comput Biol Med 147:105674. https://doi.org/10.1016/j.compbiomed.2022.105674
Wardle J, Robb K, Vernon S, Waller J (2015) Screening for prevention and early diagnosis of cancer. Am Psychol 70(2):119–133. https://doi.org/10.1037/a0037357
Wasnik AP (2013) Multimodality imaging of ovarian cystic lesions: review with an imaging based algorithmic approach. World J Radiol 5(3):113. https://doi.org/10.4329/wjr.v5.i3.113
Wieringa R, Maiden N, Mead N, Rolland C (2005) Requirements engineering paper classification and evaluation criteria: a proposal and a discussion. Requirements Eng 11(1):102–107. https://doi.org/10.1007/s00766-005-0021-6
Wilailak S, Kengsakul M, Kehoe S (2021) Worldwide initiatives to eliminate cervical cancer. Int J Gynecol Obstet 155(S1):102–106. https://doi.org/10.1002/ijgo.13879
Wu W, Zhou H (2017) Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access 5:25189–25195. https://doi.org/10.1109/access.2017.2763984
Wu Q, Wang S, Zhang S, Wang M, Ding Y, Fang J et al (2020) Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer. JAMA Netw Open 3(7):e2011625. https://doi.org/10.1001/jamanetworkopen.2020.11625
Xue Y, Zhou Q, Ye J, Long LR, Antani S, Cornwell C, et al (2019a) Synthetic augmentation and feature-based filtering for improved cervical histopathology image classification. arXiv:1907.10655
Xue Y, Zhou Q, Ye J, Long LR, Antani S, Cornwell C, et al (2019b) Synthetic augmentation and feature-based filtering for improved cervical histopathology image classification. arXiv:1907.10655
Xue D, Zhou X, Li C, Yao Y, Rahaman MM, Zhang J et al (2020) An application of transfer learning and ensemble learning techniques for cervical histopathology image classification. IEEE Access 8:104603–104618. https://doi.org/10.1109/access.2020.2999816
Xue P, Ng MTA, Qiao Y (2020) The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence. BMC Med. https://doi.org/10.1186/s12916-020-01613-x
Xue P, Tang C, Li Q, Li Y, Shen Y, Zhao Y et al (2020) Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies. BMC Med. https://doi.org/10.1186/s12916-020-01860-y
Xue P, Wang J, Qin D, Yan H, Qu Y, Seery S, et al (2022) Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis. npj Digit Med. https://doi.org/10.1038/s41746-022-00559-z
Xue P, Xu H-M, Tang H-P, Wu W-Q, Seery S, Han X et al (2023) Assessing artificial intelligence enabled liquid-based cytology for triaging HPV positive women a population based crosssectional study. Acta Obstet Gynecol Scand 102(8):1026–1033. https://doi.org/10.1111/aogs.14611
Yang X, Stamp M (2021) Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS). Comput Biol Med 138:104874. https://doi.org/10.1016/j.compbiomed.2021.104874
Yang GR, Wang X-J (2020) Artificial neural networks for neuroscientists: a primer. Neuron 107(6):1048–1070. https://doi.org/10.1016/j.neuron.2020.09.005
Yin F-F, Zhao L-J, Ji X-Y, Duan N, Wang Y-K, Zhou J-Y et al (2019) Intra-tumor heterogeneity for endometrial cancer and its clinical significance. Chin Med J 132(13):1550–1562. https://doi.org/10.1097/cm9.0000000000000286
Yu K-H, Hu V, Wang F, Matulonis UA, Mutter GL, Golden JA, Kohane IS (2020) Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks. BMC Med. https://doi.org/10.1186/s12916-020-01684-w
Yu Y, Ma J, Zhao W, Li Z, Ding S (2021) MSCI: a multistate dataset for colposcopy image classification of cervical cancer screening. Int J Med Informatics 146:104352. https://doi.org/10.1016/j.ijmedinf.2020.104352
Yuan C, Yao Y, Cheng B, Cheng Y, Li Y, Li Y et al (2020) The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images. Sci Rep. https://doi.org/10.1038/s41598-020-68252-3
Zeng H, Chen L, Zhang M, Luo Y, Ma X (2021) Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecol Oncol 163(1):171–180. https://doi.org/10.1016/j.ygyno.2021.07.015
Zhang Z, Han Y (2020) Detection of ovarian tumors in obstetric ultrasound imaging using logistic regression classifier with an advanced machine learning approach. IEEE Access 8:44999–45008. https://doi.org/10.1109/access.2020.2977962
Zhang S, Gong T-T, Liu F-H, Jiang Y-T, Sun H, Ma X-X et al (2019) Global, regional, and national burden of endometrial cancer, 1990–2017: results from the global burden of disease study, 2017. Front Oncol. https://doi.org/10.3389/fonc.2019.01440
Zhang H, Chen C, Gao R, Yan Z, Zhu Z, Yang B et al (2021) Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms. Photodiagn Photodyn Ther 33:102104. https://doi.org/10.1016/j.pdpdt.2020.102104
Zhang S, Chen C, Chen C, Chen F, Li M, Yang B et al (2021) Research on application of classification model based on stack generalization in staging of cervical tissue pathological images. IEEE Access 9:48980–48991. https://doi.org/10.1109/access.2021.3064040
Zhang Y, Wang Z, Zhang J, Wang C, Wang Y, Chen H et al (2021) Deep learning model for classifying endometrial lesions. J Transl Med. https://doi.org/10.1186/s12967-020-02660-x
Zhao J, Hu Y, Zhao Y, Chen D, Fang T, Ding M (2021) Risk factors of endometrial cancer in patients with endometrial hyperplasia: implication for clinical treatments. BMC Women’s Health. https://doi.org/10.1186/s12905-021-01452-9
Zhen X, Chen J, Zhong Z, Hrycushko B, Zhou L, Jiang S et al (2017) Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol 62(21):8246–8263. https://doi.org/10.1088/1361-6560/aa8d09