Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review

OBJECTIVE Over the past years, the application of artificial intelligence (AI) in medicine has increased rapidly, especially in diagnostics, and in the near future, the role of AI in medicine will become progressively more important. In this study, we elucidated the state of AI research on gynecologic cancers. METHODS A search was conducted in three databases-PubMed, Web of Science, and Scopus-for research papers dated between January 2010 and December 2020. As keywords, we used "artificial intelligence," "deep learning," "machine learning," and "neural network," combined with "cervical cancer," "endometrial cancer," "uterine cancer," and "ovarian cancer." We excluded genomic and molecular research, as well as automated pap-smear diagnoses and digital colposcopy. RESULTS Of 1632 articles, 71 were eligible, including 34 on cervical cancer, 13 on endometrial cancer, three on uterine sarcoma, and 21 on ovarian cancer. A total of 35 studies (49%) used imaging data and 36 studies (51%) used value-based data as the input data. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, cytology, and hysteroscopy data were used as imaging data, and the patients' backgrounds, blood examinations, tumor markers, and indices in pathological examination were used as value-based data. The targets of prediction were definitive diagnosis and prognostic outcome, including overall survival and lymph node metastasis. The size of the dataset was relatively small because 64 studies (90%) included less than 1000 cases, and the median size was 214 cases. The models were evaluated by accuracy scores, area under the receiver operating curve (AUC), and sensitivity/specificity. Owing to the heterogeneity, a quantitative synthesis was not appropriate in this review. CONCLUSIONS In gynecologic oncology, more studies have been conducted on cervical cancer than on ovarian and endometrial cancers. Prognoses were mainly used in the study of cervical cancer, whereas diagnoses were primarily used for studying ovarian cancer. The proficiency of the study design for endometrial cancer and uterine sarcoma was unclear because of the small number of studies conducted. The small size of the dataset and the lack of a dataset for external validation were indicated as the challenges of the studies.

[1]  Jie Tian,et al.  Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram , 2020, European Radiology.

[2]  G. Bogani,et al.  Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer , 2018, Journal of gynecologic oncology.

[3]  Nishtha Hooda,et al.  Predicting risk of Cervical Cancer : A case study of machine learning , 2019, Journal of Statistics and Management Systems.

[4]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[5]  S. Jeong,et al.  Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis , 2020, Radiation Oncology.

[6]  S. Shiraishi,et al.  A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with 18F-FDG PET/CT. , 2019, Clinical radiology.

[7]  Chih-Jen Tseng,et al.  Application of machine learning to predict the recurrence-proneness for cervical cancer , 2013, Neural Computing and Applications.

[8]  Sanjay Purushotham,et al.  Survival outcome prediction in cervical cancer: Cox models vs deep‐learning model , 2019, American journal of obstetrics and gynecology.

[9]  Xiran Jiang,et al.  Lymph-vascular space invasion prediction in cervical cancer: Exploring radiomics and deep learning multilevel features of tumor and peritumor tissue on multiparametric MRI , 2020, Biomed. Signal Process. Control..

[10]  Sabah Jassim,et al.  Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images , 2019, PloS one.

[11]  Raymond Y Huang,et al.  Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging , 2020, European Radiology.

[12]  A. Gryparis,et al.  Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models , 2020, Journal of Ovarian Research.

[13]  Zhenyu Liu,et al.  Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  Xuan-tao Su,et al.  Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach , 2020, Frontiers in Oncology.

[15]  Marios S. Pattichis,et al.  Computer-Aided Diagnosis in Hysteroscopic Imaging , 2015, IEEE Journal of Biomedical and Health Informatics.

[16]  Jie Tian,et al.  Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. , 2019, European journal of radiology.

[17]  C. Robson,et al.  Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer , 2015, Annals of Surgical Oncology.

[18]  B. Kong,et al.  Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer , 2020, Frontiers in Oncology.

[19]  R. Khorasani,et al.  High-Grade Serous Ovarian Cancer: Use of Machine Learning to Predict Abdominopelvic Recurrence on CT on the Basis of Serial Cancer Antigen 125 Levels. , 2018, Journal of the American College of Radiology.

[20]  Ali Ayhan,et al.  A novel prediction method for lymph node involvement in endometrial cancer: machine learning , 2018, International Journal of Gynecologic Cancer.

[21]  J. Qiang,et al.  Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study , 2020, European Radiology.

[22]  J. Ioannidis,et al.  The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration , 2009, Annals of Internal Medicine [serial online].

[23]  J. She,et al.  Clinical calculator predictive of chemotherapy benefit in stage 1A uterine papillary serous cancers. , 2019, Gynecologic oncology.

[24]  Bogdan Obrzut,et al.  Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods , 2017, BMC Cancer.

[25]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[26]  R. Geetha,et al.  Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier , 2019, Journal of Medical Systems.

[27]  Meiyun Wang,et al.  Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[28]  Fahima A. Maghraby,et al.  Cervical Cancer Diagnosis Using Random Forest Classifier With SMOTE and Feature Reduction Techniques , 2018, IEEE Access.

[29]  Xiaojun Chen,et al.  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 , 2020, European Radiology.

[30]  Hoda A. Abdelhafez,et al.  Predicting Cervical Cancer using Machine Learning Methods , 2020 .

[31]  K Sneha,et al.  Cervical Cancer Detection and Classification using Texture Analysis , 2016 .

[32]  A. Juliet.  Early Warning System for Endometrial Cancer Prediction in PMB Women Using Novel Ensemble Model , 2018 .

[33]  Hong Liu,et al.  Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients , 2020, Comput. Methods Programs Biomed..

[34]  A. F.,et al.  Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer , 2020, Journal of biomedical physics & engineering.

[35]  Chih-Jen Tseng,et al.  Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence , 2017, Artif. Intell. Medicine.

[36]  K. Hashimoto,et al.  The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study , 2020, Obstetrics & gynecology science.

[37]  Wen Wu,et al.  Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches , 2017, IEEE Access.

[38]  A. Villanueva,et al.  Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach. , 2016, Ultrasound in medicine & biology.

[39]  Licong Cui,et al.  Design and Implementation of a Comprehensive Web-based Survey for Ovarian Cancer Survivorship with an Analysis of Prediagnosis Symptoms via Text Mining , 2014, Cancer informatics.

[40]  Qian Liu,et al.  Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks , 2018, Bioscience reports.

[41]  Rebecka Weegar,et al.  Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations , 2020, PloS one.

[42]  Cheng-Chang Chang,et al.  Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study , 2020, International journal of environmental research and public health.

[43]  M. Oghabian,et al.  A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters. , 2019, European journal of radiology.

[44]  G. Collins,et al.  PROBAST : A Tool to Assess the Risk of Bias and Applicability of PredictionModel Studies , 2018 .

[45]  Qing Li,et al.  Postprandial increase in serum CA125 as a surrogate biomarker for early diagnosis of ovarian cancer , 2018, Journal of Translational Medicine.

[46]  J. She,et al.  Long term survival outcomes of stage I mucinous ovarian cancer - A clinical calculator predictive of chemotherapy benefit. , 2020, Gynecologic oncology.

[47]  Taieb Znati,et al.  Using machine learning to predict ovarian cancer , 2020, Int. J. Medical Informatics.

[48]  Lei Zhang,et al.  RETRACTED ARTICLE: Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System , 2019, Journal of Medical Systems.

[49]  K. Hashimoto,et al.  Artificial Intelligence in Ovarian Cancer Diagnosis , 2020, AntiCancer Research.

[50]  S. Purushotham,et al.  A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer , 2017, American journal of obstetrics and gynecology.

[51]  T. Uno,et al.  A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions , 2020, Japanese Journal of Radiology.

[52]  C. Chrelias,et al.  The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. , 2018, Public health.

[53]  Jaime S. Cardoso,et al.  Supervised deep learning embeddings for the prediction of cervical cancer diagnosis , 2018, PeerJ Comput. Sci..

[54]  Zhengyang Zhou,et al.  Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT , 2018, Scientific Reports.

[55]  Jason S. Shapiro,et al.  Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers , 2019, Clinical Cancer Research.

[56]  G. Makris,et al.  Image analysis and multi‐layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions , 2017, Diagnostic cytopathology.

[57]  W. Qian,et al.  Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer , 2020, JAMA network open.

[58]  Knut Kvaal,et al.  Classification of Dynamic Contrast Enhanced MR Images of Cervical Cancers Using Texture Analysis and Support Vector Machines , 2014, IEEE Transactions on Medical Imaging.

[59]  B. Nithya,et al.  Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction , 2019, SN Applied Sciences.

[60]  Jaime S. Cardoso,et al.  Automated Methods for the Decision Support of Cervical Cancer Screening Using Digital Colposcopies , 2018, IEEE Access.

[61]  Renaud de Crevoisier,et al.  Random forests to predict tumor recurrence following cervical cancer therapy using pre- and per-treatment 18F-FDG PET parameters , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[62]  U Rajendra Acharya,et al.  Ovarian Tumor Characterization using 3D Ultrasound , 2012, Technology in cancer research & treatment.

[63]  L. Kaderali,et al.  Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study , 2019, Archives of Gynecology and Obstetrics.

[64]  Jeong Won Lee,et al.  Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods , 2019, Journal of gynecologic oncology.

[65]  Bradley J. Nartowt,et al.  Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence , 2020, Frontiers in Artificial Intelligence.

[66]  Jie Tian,et al.  Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis , 2020, Frontiers in Oncology.

[67]  Muhammad Attique,et al.  Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods , 2020, Sensors.

[68]  Sasank Chilamkurthy,et al.  Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study , 2018, The Lancet.

[69]  M. Gasparri,et al.  When Does Neoadjuvant Chemotherapy Really Avoid Radiotherapy? Clinical Predictors of Adjuvant Radiotherapy in Cervical Cancer , 2015, Annals of Surgical Oncology.

[70]  Ghassan Hamarneh,et al.  A structured latent model for ovarian carcinoma subtyping from histopathology slides , 2017, Medical Image Anal..

[71]  T. Masumoto,et al.  Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists. , 2020, European journal of radiology.

[72]  Lei Wu,et al.  A preoperative radiomics model for the identification of lymph node metastasis in patients with Early-stage cervical squamous cell carcinoma. , 2020, The British journal of radiology.

[73]  E. Lee,et al.  MRI texture features differentiate clinicopathological characteristics of cervical carcinoma , 2020, European Radiology.

[74]  E. Sartori,et al.  RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients , 2017, Scientific Reports.

[75]  Tao Xu,et al.  Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms , 2019, IEEE Journal of Biomedical and Health Informatics.

[76]  Wei-Chun Chang,et al.  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 , 2019, European Radiology.