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Wang R, Li Q, Shi G, Li Q, Zhong D. A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles. PLoS One 2024; 19:e0306549. [PMID: 39083516 PMCID: PMC11290691 DOI: 10.1371/journal.pone.0306549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024] Open
Abstract
Endometrial cancer screening is crucial for clinical treatment. Currently, cytopathologists analyze cytopathology images is considered a popular screening method, but manual diagnosis is time-consuming and laborious. Deep learning can provide objective guidance efficiency. But endometrial cytopathology images often come from different medical centers with different staining styles. It decreases the generalization ability of deep learning models in cytopathology images analysis, leading to poor performance. This study presents a robust automated screening framework for endometrial cancer that can be applied to cytopathology images with different staining styles, and provide an objective diagnostic reference for cytopathologists, thus contributing to clinical treatment. We collected and built the XJTU-EC dataset, the first cytopathology dataset that includes segmentation and classification labels. And we propose an efficient two-stage framework for adapting different staining style images, and screening endometrial cancer at the cellular level. Specifically, in the first stage, a novel CM-UNet is utilized to segment cell clumps, with a channel attention (CA) module and a multi-level semantic supervision (MSS) module. It can ignore staining variance and focus on extracting semantic information for segmentation. In the second stage, we propose a robust and effective classification algorithm based on contrastive learning, ECRNet. By momentum-based updating and adding labeled memory banks, it can reduce most of the false negative results. On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. Our method robustly predicts endometrial cancer on cytopathologic images with different staining styles, which will further advance research in endometrial cancer screening and provide early diagnosis for patients. The code will be available on GitHub.
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Affiliation(s)
- Ruijie Wang
- School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
| | - Qing Li
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
| | - Guizhi Shi
- Laboratory Animal Center, Institute of Biophysics, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences, Beijing, China
| | - Qiling Li
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
| | - Dexing Zhong
- School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, P.R. China
- Pazhou Laboratory, Guangzhou, P.R. China
- Research Institute of Xi’an Jiaotong University, Zhejiang, Hangzhou, P.R. China
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Russo L, Bottazzi S, Kocak B, Zormpas-Petridis K, Gui B, Stanzione A, Imbriaco M, Sala E, Cuocolo R, Ponsiglione A. Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools. Eur Radiol 2024:10.1007/s00330-024-10947-6. [PMID: 39014086 DOI: 10.1007/s00330-024-10947-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/15/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
OBJECTIVE To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). METHODS We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. RESULTS Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9-14) and METRICS score of 67.6% (IQR, 58.8-76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. CONCLUSIONS Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. CLINICAL RELEVANCE STATEMENT Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. KEY POINTS The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.
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Affiliation(s)
- Luca Russo
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Bottazzi
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey
| | - Konstantinos Zormpas-Petridis
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Evis Sala
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
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Fechter T, Sachpazidis I, Baltas D. The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data. Z Med Phys 2024; 34:180-196. [PMID: 36376203 PMCID: PMC11156786 DOI: 10.1016/j.zemedi.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022]
Abstract
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally, we summarised the most recent developments. For better understanding, we provide explanations of key terms and approaches to solving common deep learning problems. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work is on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly present in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. The conclusion of our analysis is that deep learning can positively change the workflow of interventional radiotherapy but there is still room for improvements when it comes to reproducible results and standardised evaluation methods.
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Affiliation(s)
- Tobias Fechter
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany.
| | - Ilias Sachpazidis
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
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4
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Leo E, Stanzione A, Miele M, Cuocolo R, Sica G, Scaglione M, Camera L, Maurea S, Mainenti PP. Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows. J Clin Med 2023; 13:226. [PMID: 38202233 PMCID: PMC10779496 DOI: 10.3390/jcm13010226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.
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Affiliation(s)
- Elisabetta Leo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Mariaelena Miele
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Luigi Camera
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), 80131 Naples, Italy
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5
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Kido A, Himoto Y, Kurata Y, Minamiguchi S, Nakamoto Y. Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023. J Magn Reson Imaging 2023. [PMID: 38146775 DOI: 10.1002/jmri.29161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023] Open
Abstract
The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Aki Kido
- Department Radiology, Toyama University Hospital, Toyama, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
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Ying J, Huang W, Fu L, Yang H, Cheng J. Weakly supervised segmentation of uterus by scribble labeling on endometrial cancer MR images. Comput Biol Med 2023; 167:107582. [PMID: 37922606 DOI: 10.1016/j.compbiomed.2023.107582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/28/2023] [Accepted: 10/15/2023] [Indexed: 11/07/2023]
Abstract
Uterine segmentation of endometrial cancer MR images can be a valuable diagnostic tool for gynecologists. However, uterine segmentation based on deep learning relies on artificial pixel-level annotation, which is time-consuming, laborious and subjective. To reduce the dependence on pixel-level annotation, a method of weakly supervised uterine segmentation on endometrial cancer MRI slices is proposed, which only requires scribble label and is enhanced by pseudo-label technology, exponential geodesic distance loss and input disturbance strategy. Specifically, the limitations caused by the shortage of supervision are addressed by dynamically mixing the two outputs of the dual branch network to generate pseudo-labels, expanding supervision information and promoting mutual supervision training. On the other hand, considering the large difference of grayscale intensity between the uterus and surrounding tissues, the exponential geodesic distance loss is introduced to enhance the ability of the network to capture the edge of the uterus. Input disturbance strategies are incorporated to adapt to the flexible and variable characteristics of the uterus and further improve the segmentation performance of the network. The proposed method is evaluated on MRI images from 135 cases of endometrial cancer. Compared with other four weakly supervised segmentation methods, the performance of the proposed method is the best, whose mean DI, HD95, Recall, Precision, ADP are 92.8%, 11.632, 92.7%, 93.6%, 6.5% and increasing by 2.1%, 9.144, 0.6%, 2.4%, 2.9% respectively. The experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.
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Affiliation(s)
- Jie Ying
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
| | - Wei Huang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Le Fu
- Department of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Haima Yang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jiangzihao Cheng
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
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7
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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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Xiong L, Chen C, Lin Y, Mao W, Song Z. A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images. Biomed Eng Online 2023; 22:103. [PMID: 37907955 PMCID: PMC10617104 DOI: 10.1186/s12938-023-01169-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 10/24/2023] [Indexed: 11/02/2023] Open
Abstract
To classify early endometrial cancer (EC) on sagittal T2-weighted images (T2WI) by determining the depth of myometrial infiltration (MI) using a computer-aided diagnosis (CAD) method based on a multi-stage deep learning (DL) model. This study retrospectively investigated 154 patients with pathologically proven early EC at the institution between January 1, 2018, and December 31, 2020. Of these patients, 75 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA and 79 were in FIGO stage IB. An SSD-based detection model and an Attention U-net-based segmentation model were trained to select, crop, and segment magnetic resonance imaging (MRl) images. Then, an ellipse fitting algorithm was used to generate a uterine cavity line (UCL) to obtain MI depth for classification. In the independent test datasets, the uterus and tumor detection model achieves an average precision rate of 98.70% and 87.93%, respectively. Selecting the optimal MRI slices method yields an accuracy of 97.83%. The uterus and tumor segmentation model with mean IOU of 0.738 and 0.655, mean PA of 0.867 and 0.749, and mean DSC of 0.845 and 0.779, respectively. Finally, the CAD method based on the calculated MI depth reaches an accuracy of 86.9%, a sensitivity of 81.8%, and a specificity of 91.7% for early EC classification. In this study, the CAD method implements an end-to-end early EC classification and is found to be on par with radiologists in terms of performance. It is more intuitive and interpretable than previous DL-based CAD methods.
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Affiliation(s)
- Liu Xiong
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No. 600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China
| | - Chunxia Chen
- Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, No.18 Daoshan Road, Gulou District, Fuzhou, 350001, Fujian, China
| | - Yongping Lin
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No. 600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China.
| | - Wei Mao
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No. 600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China
| | - Zhiyu Song
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No. 600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China
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Petrila O, Stefan AE, Gafitanu D, Scripcariu V, Nistor I. The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies-A Systematic Review. Diagnostics (Basel) 2023; 13:2592. [PMID: 37568955 PMCID: PMC10416838 DOI: 10.3390/diagnostics13152592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/22/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: "endometrial cancer artificial intelligence", "endometrial cancer AI", "endometrial cancer MRI artificial intelligence", "endometrial cancer machine learning", and "endometrial cancer machine learning MRI". We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients.
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Affiliation(s)
- Octavia Petrila
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Radiology, “Sfantul Spiridon” Hospital, 700111 Iasi, Romania
| | - Anca-Elena Stefan
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Nephrology, “Dr. C.I. Parhon” Hospital, 700503 Iasi, Romania
| | - Dumitru Gafitanu
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Obstetrics and Gynecology, “Elena Doamna” Hospital, 700398 Iasi, Romania
| | - Viorel Scripcariu
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Regional Institute of Oncology, 700483 Iasi, Romania
| | - Ionut Nistor
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Nephrology, “Dr. C.I. Parhon” Hospital, 700503 Iasi, Romania
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Butler D, Wang H, Zhang Y, To MS, Condous G, Leonardi M, Knox S, Avery J, Hull ML, Carneiro G. The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification *†. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083681 DOI: 10.1109/embc40787.2023.10340504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Endometriosis is a debilitating condition affecting 5% to 10% of the women worldwide, where early detection and treatment are the best tools to manage the condition. Early detection can be done via surgery, but multi-modal medical imaging is preferable given the simpler and faster process. However, imaging-based endometriosis diagnosis is challenging as 1) there are few capable clinicians; and 2) it is characterised by small lesions unconfined to a specific location. These two issues challenge the development of endometriosis classifiers as the training datasets tend to be small and contain difficult samples, which leads to overfitting. Hence, it is important to consider generalisation techniques to mitigate this problem, particularly self-supervised pre-training methods that have shown outstanding results in computer vision and natural language processing applications. The main goal of this paper is to study the effectiveness of modern self-supervised pre-training techniques to overcome the two issues mentioned above for the classification of endometriosis from multi-modal imaging data. We also introduce a new masking image modelling self-supervised pre-training method that works with 3D multi-modal medical imaging. Furthermore, to the best of our knowledge, this paper presents the first endometriosis classifier, fine-tuned from the pre-trained model above, which works with multi-modal (i.e., T1 and T2) magnetic resonance imaging (MRI) data. Our results show that self-supervised pre-training improves endometriosis classification by as much as 31%, when compared with classifiers trained from scratch.
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11
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Quantitative evaluation of myometrial infiltration depth ratio for early endometrial cancer based on deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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12
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Di Donato V, Kontopantelis E, Cuccu I, Sgamba L, Golia D'Augè T, Pernazza A, Della Rocca C, Manganaro L, Catalano C, Perniola G, Palaia I, Tomao F, Giannini A, Muzii L, Bogani G. Magnetic resonance imaging-radiomics in endometrial cancer: a systematic review and meta-analysis. Int J Gynecol Cancer 2023:ijgc-2023-004313. [PMID: 37094971 DOI: 10.1136/ijgc-2023-004313] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
OBJECTIVE Endometrial carcinoma is the most common gynecological tumor in developed countries. Clinicopathological factors and molecular subtypes are used to stratify the risk of recurrence and to tailor adjuvant treatment. The present study aimed to assess the role of radiomics analysis in pre-operatively predicting molecular or clinicopathological prognostic factors in patients with endometrial carcinoma. METHODS Literature was searched for publications reporting radiomics analysis in assessing diagnostic performance of MRI for different outcomes. Diagnostic accuracy performance of risk prediction models was pooled using the metandi command in Stata. RESULTS A search of MEDLINE (PubMed) resulted in 153 relevant articles. Fifteen articles met the inclusion criteria, for a total of 3608 patients. MRI showed pooled sensitivity and specificity 0.785 and 0.814, respectively, in predicting high-grade endometrial carcinoma, deep myometrial invasion (pooled sensitivity and specificity 0.743 and 0.816, respectively), lymphovascular space invasion (pooled sensitivity and specificity 0.656 and 0.753, respectively), and nodal metastasis (pooled sensitivity and specificity 0.831 and 0.736, respectively). CONCLUSIONS Pre-operative MRI-radiomics analyses in patients with endometrial carcinoma is a good predictor of tumor grading, deep myometrial invasion, lymphovascular space invasion, and nodal metastasis.
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Affiliation(s)
- Violante Di Donato
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Evangelos Kontopantelis
- Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - Ilaria Cuccu
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovica Sgamba
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Tullio Golia D'Augè
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Angelina Pernazza
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Carlo Della Rocca
- Department of Medical-Surgical Sciences and Biotechnologies, University of Rome Sapienza, Rome, Italy
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgia Perniola
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Innocenza Palaia
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Federica Tomao
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Andrea Giannini
- Department of Medical and Surgical Sciences and Translational Medicine, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Ludovico Muzii
- Department of Maternal, Child Health and Urological Sciences, Policlinico Umberto I, University of Rome Sapienza, Rome, Italy
| | - Giorgio Bogani
- Department of Gynecologic Oncology, IRCCS National Cancer Institute, Milan, Italy
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13
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Zhao A, Du X, Yuan S, Shen W, Zhu X, Wang W. Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13081409. [PMID: 37189510 DOI: 10.3390/diagnostics13081409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/31/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023] Open
Abstract
Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.
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Affiliation(s)
- Aihua Zhao
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
| | - Xin Du
- Department of Gynecology, Maternal and Child Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China
| | - Suzhen Yuan
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenfeng Shen
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
| | - Xin Zhu
- Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
| | - Wenwen Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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14
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Lefebvre TL, Ciga O, Bhatnagar SR, Ueno Y, Saif S, Winter-Reinhold E, Dohan A, Soyer P, Forghani R, Siddiqi K, Seuntjens J, Reinhold C, Savadjiev P. Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI. Diagn Interv Imaging 2023; 104:142-152. [PMID: 36328942 DOI: 10.1016/j.diii.2022.10.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI). PATIENTS AND METHODS This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances. RESULTS With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology. CONCLUSION The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.
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Affiliation(s)
- Thierry L Lefebvre
- Medical Physics Unit, McGill University, Montreal, QC H4A 3J1, Canada; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Ozan Ciga
- School of Computer Science and Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada; Department of Medical Biophysics, University of Toronto, Toronto ON M5G 1L7, Canada
| | - Sahir Rai Bhatnagar
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada
| | - Yoshiko Ueno
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Department of Radiology, Kobe University Graduate School of Medicine, Kobe City, Hyogo, 650-0017, Japan
| | - Sameh Saif
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada
| | - Eric Winter-Reinhold
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada
| | - Anthony Dohan
- Department of Radiology, Hopital Cochin, AP-HP, 75014, Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
| | - Philippe Soyer
- Department of Radiology, Hopital Cochin, AP-HP, 75014, Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
| | - Reza Forghani
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada
| | - Kaleem Siddiqi
- School of Computer Science and Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada
| | - Jan Seuntjens
- Medical Physics Unit, McGill University, Montreal, QC H4A 3J1, Canada
| | - Caroline Reinhold
- Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada; Montreal Imaging Experts Inc., Montreal, QC H9R 5K3, Canada
| | - Peter Savadjiev
- School of Computer Science and Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada; Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada.
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15
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Malani SN, Shrivastava D, Raka MS. A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology. Cureus 2023; 15:e34891. [PMID: 36925982 PMCID: PMC10013256 DOI: 10.7759/cureus.34891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 02/12/2023] [Indexed: 03/18/2023] Open
Abstract
The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.
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Affiliation(s)
- Sagar N Malani
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Deepti Shrivastava
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Mayur S Raka
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
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16
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Farzaneh F, Jafari Ashtiani A, Hashemi M, Hosseini MS, Arab M, Ashrafganjoei T, Hooshmand Chayjan S. Endometrial cancer in women with abnormal uterine bleeding: Data mining classification methods. CASPIAN JOURNAL OF INTERNAL MEDICINE 2023; 14:526-533. [PMID: 37520874 PMCID: PMC10379791 DOI: 10.22088/cjim.14.3.526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/16/2022] [Accepted: 08/14/2022] [Indexed: 08/01/2023]
Abstract
Background Over the last decade, artificial intelligence in medicine has been growing. Since endometrial cancer can be treated with early diagnosis, finding a non-invasive method for screening patients, especially high-risk ones, could have a particular value. Regarding the importance of this issue, we aimed to investigate the risk factors related to endometrial cancer and find a tool to predict it using machine learning. Methods In this cross-sectional study, 972 patients with abnormal uterine bleeding from January 2016 to January 2021 were studied, and the essential characteristics of each patient, along with the findings of curettage pathology, were analyzed using statistical methods and machine learning algorithms, including artificial neural networks, classification and regression trees, support vector machine, and logistic regression. Results Out of 972 patients with a mean age of 45.77 ± 10.70 years, 920 patients had benign pathology, and 52 patients had endometrial cancer. In terms of endometrial cancer prediction, the logistic regression model had the best performance (sensitivity of 100% and 98%, specificity of 98.83% and 98.7%, for trained and test data sets respectively,) followed by the classification and regression trees model. Conclusion Based on the results, artificial intelligence-based algorithms can be applied as a non-invasive screening method for predicting endometrial cancer.
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Affiliation(s)
- Farah Farzaneh
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Jafari Ashtiani
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Hashemi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Maryam Sadat Hosseini
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maliheh Arab
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Tahereh Ashrafganjoei
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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17
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On the use of spectroscopy, prediction machines and cybernetics for an affordable and proactive care approach for endometrial cancer. BIOMEDICAL ENGINEERING ADVANCES 2022. [DOI: 10.1016/j.bea.2022.100057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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18
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A Case Study of Multiple Maintenance Efficacy in Gynaecological Surgery Assessed by Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8574000. [PMID: 35979051 PMCID: PMC9377963 DOI: 10.1155/2022/8574000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Deep learning is a new learning concept and a highly effective way of learning, which is still being explored in the field of nursing education. This paper analyses the effectiveness of interventions in perioperative gynaecological care using humanised care in the operating theatre and the impact of this model of care on patients’ psychological well-being and sleep quality. A deep learning-based vision robot was designed to provide higher quality of care for our human care and simplify our approach to gynaecological surgery. The anxiety and depression scores of the two groups were significantly improved after and before care, and the scores of the observation group were lower than those of the control group, with a statistically significant difference (
). The humanised care for gynaecological surgery patients in the perioperative period is more conducive to the improvement of their negative emotions and at the same time can improve the sleep quality of patients, so it can be further promoted.
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19
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Bhardwaj V, Sharma A, Parambath SV, Gul I, Zhang X, Lobie PE, Qin P, Pandey V. Machine Learning for Endometrial Cancer Prediction and Prognostication. Front Oncol 2022; 12:852746. [PMID: 35965548 PMCID: PMC9365068 DOI: 10.3389/fonc.2022.852746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.
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Affiliation(s)
- Vipul Bhardwaj
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Arundhiti Sharma
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | | | - Ijaz Gul
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Xi Zhang
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peter E. Lobie
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peiwu Qin
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Vijay Pandey
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- *Correspondence: Vijay Pandey,
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Development and Validation of an MRI-based Radiomics Nomogram for Assessing Deep Myometrial Invasion in Early Stage Endometrial Adenocarcinoma. Acad Radiol 2022; 30:668-679. [PMID: 35778306 DOI: 10.1016/j.acra.2022.05.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/23/2022] [Accepted: 05/28/2022] [Indexed: 11/20/2022]
Abstract
RATIONALE AND OBJECTIVES To establish a radiomics nomogram for detecting deep myometrial invasion (DMI) in early stage endometrioid adenocarcinoma (EAC). MATERIALS AND METHODS A total of 266 patients with stage I EAC were divided into training (n = 185) and test groups (n = 81). Logistic regression were used to identify clinical predictors. Radiomics features were extracted and selected from multiparameter MR images. The important clinical factors and radiomics features were integrated into a nomogram. A receiver operating characteristic curve was used to evaluate the nomogram. Two radiologists evaluated MR images with or without the help of the nomogram to detect DMI. The clinical benefit of using the nomogram was evaluated by decision curve analysis (DCA) and by calculating net reclassification index (NRI) and integrated discrimination index (IDI). RESULTS Age and CA125 were independent clinical predictors. The area under the curves of the clinical parameters, radiomics signature and nomogram in evaluating DMI were 0.744, 0.869 and 0.883, respectively. The accuracies of the two radiologists increased from 79.0% and 80.2% to 90.1% and 92.5% when they used the nomogram. The NRI of the two radiologists were 0.262 and 0.318, and the IDI were 0.322 and 0.405. According to DCA, the nomogram showed a higher net benefit than the radiomics signature or unaided radiologists. Cross-validation showed the outcome of radiomics analysis may not be influenced by changes in field strength. CONCLUSION The radiomics nomogram based on radiomics features and clinical factors can help radiologists evaluate DMI and improve their accuracy in predicting DMI in early stage EAC.
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21
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Zhao M, Wen F, Shi J, Song J, Zhao J, Song Q, Lai Q, Luo Y, Yu T, Jiang X, Jiang W, Dong Y. MRI-based radiomics nomogram for the preoperative prediction of deep myometrial invasion of FIGO I stage endometrial carcinoma. Med Phys 2022; 49:6505-6516. [PMID: 35758644 DOI: 10.1002/mp.15835] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/11/2022] [Accepted: 06/10/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Endometrial carcinoma (EC) is one of the most common gynecological malignancies with an increasing incidence, and an accurate preoperative diagnosis of deep myometrial invasion (DMI) is crucial for personalized treatment. OBJECTIVE To determine the predictive value of an MRI-based radiomics nomogram for the presence of DMI in FIGO I stage EC. METHODS We retrospectively collected 163 patients with pathologically confirmed stage I EC from two centers and divided all samples into a training group (center 1) and a validation group (center 2). Clinical and routine imaging indicators were analyzed by logistical regression to construct a conventional diagnostic model (M1). Radiomics features extracted from the axial T2-weighted (T2W) and axial contrast-enhanced T1-weighted (CE-T1W) images were treated with the intraclass correlation coefficient, Mann-Whitney U test, least absolute shrinkage and selection operator (LASSO), and logistic regression analysis with Akaike information criterion (AIC) to build a combined radiomics signature (M2). A nomogram (M3) was constructed by M1 and M2. Calibration and decision curves were drawn to evaluate the nomogram in the training and validation cohorts. The diagnostic performance of each indicator and model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULT The four most significant radiomics features were finally selected from the CE-T1W MRI. For the diagnosis of DMI, the AUCT /AUCV of M1 was 0.798/0.738, the AUCT /AUCV of M2 was 0.880/0.852, and the AUCT /AUCV of M3 was 0.936/0.871 in the training and validation groups, respectively. The calibration curves showed that M3 was in good agreement with the ideal values. The decision curve analysis suggested potential clinical application values of the nomogram. CONCLUSION A nomogram based on MRI radiomics and clinical imaging indicators can improve the diagnosis of DMI in patients with FIGO I stage EC. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mingli Zhao
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Feng Wen
- Radiology Department, Shengjing Hospital, China Medical University, Shenyang, Liaoning, 110122, China
| | - Jiaxin Shi
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, 110001, China
| | - Jing Song
- Radiology Department, Shengjing Hospital, China Medical University, Shenyang, Liaoning, 110122, China
| | - Jiaqi Zhao
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Qingling Song
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Qingyuan Lai
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Yahong Luo
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Tao Yu
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, 110001, China
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
| | - Yue Dong
- Radiology Department, Liaoning Cancer Hospital & Institute, China Medical University, Shenyang, Liaoning, 110042, China
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22
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Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning Techniques. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127384. [PMID: 35742633 PMCID: PMC9224242 DOI: 10.3390/ijerph19127384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/07/2022] [Accepted: 06/14/2022] [Indexed: 02/04/2023]
Abstract
Triaging of medical referrals can be completed using various machine learning techniques, but trained models with historical datasets may not be relevant as the clinical criteria for triaging are regularly updated and changed. This paper proposes the use of machine learning techniques coupled with the clinical prioritisation criteria (CPC) of Queensland (QLD), Australia, to deliver better triaging for referrals in accordance with the CPC’s updates. The unique feature of the proposed model is its non-reliance on the past datasets for model training. Medical Natural Language Processing (NLP) was applied in the proposed approach to process the medical referrals, which are unstructured free text. The proposed multiclass classification approach achieved a Micro F1 score = 0.98. The proposed approach can help in the processing of two million referrals that the QLD health service receives annually; therefore, they can deliver better and more efficient health services.
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Urushibara A, Saida T, Mori K, Ishiguro T, Inoue K, Masumoto T, Satoh T, Nakajima T. The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists. BMC Med Imaging 2022; 22:80. [PMID: 35501705 PMCID: PMC9063362 DOI: 10.1186/s12880-022-00808-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/21/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs. Results The AUC of the CNNs pertaining to the single and combined image sets were 0.88–0.95 and 0.87–0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88–0.95. Conclusion CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.
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Affiliation(s)
- Aiko Urushibara
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Tsukasa Saida
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
| | - Kensaku Mori
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Toshitaka Ishiguro
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Kei Inoue
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Tomohiko Masumoto
- Department of Diagnostic Radiology, Toranomon Hospital, 2-2-2 Toranomon, Minato-ku, Tokyo, 105-8470, Japan
| | - Toyomi Satoh
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Takahito Nakajima
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
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Hoivik EA, Hodneland E, Dybvik JA, Wagner-Larsen KS, Fasmer KE, Berg HF, Halle MK, Haldorsen IS, Krakstad C. A radiogenomics application for prognostic profiling of endometrial cancer. Commun Biol 2021; 4:1363. [PMID: 34873276 PMCID: PMC8648740 DOI: 10.1038/s42003-021-02894-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 11/09/2021] [Indexed: 12/12/2022] Open
Abstract
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.
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Affiliation(s)
- Erling A Hoivik
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Julie A Dybvik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kari S Wagner-Larsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kristine E Fasmer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hege F Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Mari K Halle
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
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Kim BW, Choi MC, Kim MK, Lee JW, Kim MT, Noh JJ, Park H, Jung SG, Joo WD, Song SH, Lee C. Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers. Cancers (Basel) 2021; 13:cancers13225670. [PMID: 34830824 PMCID: PMC8616351 DOI: 10.3390/cancers13225670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Recurrent patients with gynecologic cancer experience a difficult situation when using immune checkpoint inhibitors based on mismatch repair gene immunohistochemistry and microsatellite instability. Six machine learning algorithms were used to create predictive models with seven prospective features (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This provides novel and baseline results of patients with recurrent gynecologic cancer using immune checkpoint inhibitors by using machine learning methods based on Lynch syndrome-related screening markers. Abstract To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). Following that, predictive models were built based on these variables using six machine learning algorithms: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), gradient boosting (GB), and extreme gradient boosting (EGB) (XGBoost). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This study provides novel and baseline results about predicting the recurrence of gynecologic cancer in patients using ICI by using machine learning methods based on Lynch syndrome-related screening markers.
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Affiliation(s)
- Byung Wook Kim
- Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea; (B.W.K.); (M.T.K.)
| | - Min Chul Choi
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Min Kyu Kim
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Korea
- Correspondence: (M.K.K.); (J.-W.L.)
| | - Jeong-Won Lee
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Correspondence: (M.K.K.); (J.-W.L.)
| | - Min Tae Kim
- Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea; (B.W.K.); (M.T.K.)
| | - Joseph J. Noh
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
| | - Hyun Park
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Sang Geun Jung
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Won Duk Joo
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Seung Hun Song
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
| | - Chan Lee
- Comprehensive Gynecologic Cancer Center, CHA Bundang Medical Center, CHA University, Seongnam 13497, Gyeonggido, Korea; (M.C.C.); (H.P.); (S.G.J.); (W.D.J.); (S.H.S.); (C.L.)
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Akazawa M, Hashimoto K. Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review. Artif Intell Med 2021; 120:102164. [PMID: 34629152 DOI: 10.1016/j.artmed.2021.102164] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/28/2021] [Accepted: 08/31/2021] [Indexed: 11/30/2022]
Abstract
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.
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Affiliation(s)
- Munetoshi Akazawa
- Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
| | - Kazunori Hashimoto
- Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
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Toğaçar M. Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Comput Biol Med 2021; 136:104659. [PMID: 34329863 DOI: 10.1016/j.compbiomed.2021.104659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 12/01/2022]
Abstract
Uterine cancer consists of cells of a layer that forms the inside of the uterus. Sometimes, as a result of abnormal growth of normal cells, it can damage the surrounding tissues and cause the formation of cancerous cells. In the USA, according to the projections for 2021, approximately 66 thousand new cases of uterine cancer will be detected and approximately 13 thousand of these cancer patients are expected to die from uterine cancer. Early diagnosis of cancer is important. Recently, artificial intelligence-based technologies have been used in the diagnosis and treatment processes of various diseases. In this study, five categories of datasets including normal, abnormal, and benign cells were used. The dataset consists of cellular images and is publicly available. The proposed approach consists of three steps. In the first step, the Hotspot method was used to detect the tumor cells in the images. In the second step, tumor cells that were brought to the fore by segmentation were trained by deep learning models, and activation sets of five types from each deep learning model were created. In the last step, the best activation sets were selected among the activation sets obtained by deep learning models of each type (for five dataset types). Pigeon-Inspired Optimization was used for this selection. Thus, the activation sets with the best performance of the five types were classified by the Softmax method. The overall accuracy success achieved with the approach suggested as a result of the classification was 99.65%.
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Affiliation(s)
- Mesut Toğaçar
- Computer Technologies Department, Technical Sciences Vocational School, Fırat University Elazığ, Turkey.
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Lecointre L, Dana J, Lodi M, Akladios C, Gallix B. Artificial intelligence-based radiomics models in endometrial cancer: A systematic review. Eur J Surg Oncol 2021; 47:2734-2741. [PMID: 34183201 DOI: 10.1016/j.ejso.2021.06.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/03/2021] [Accepted: 06/20/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Radiological preoperative assessment of endometrial cancer (EC) is in some cases not precise enough and its performances improvement could lead to a clinical benefit. Radiomics is a recent field of application of artificial intelligence (AI) in radiology. AIMS To investigate the contribution of radiomics on the radiological preoperative assessment of patients with EC; and to establish a simple and reproducible AI Quality Score applicable to Machine Learning and Deep Learning studies. METHODS We conducted a systematic review of current literature including original articles that studied EC through imaging-based AI techniques. Then, we developed a novel Simplified and Reproducible AI Quality score (SRQS) based on 10 items which ranged to 0 to 20 points in total which focused on clinical relevance, data collection, model design and statistical analysis. SRQS cut-off was defined at 10/20. RESULTS We included 17 articles which studied different radiological parameters such as deep myometrial invasion, lympho-vascular space invasion, lymph nodes involvement, etc. One article was prospective, and the others were retrospective. The predominant technique was magnetic resonance imaging. Two studies developed Deep Learning models, while the others machine learning ones. We evaluated each article with SRQS by 2 independent readers. Finally, we kept only 7 high-quality articles with clinical impact. SRQS was highly reproducible (Kappa = 0.95 IC 95% [0.907-0.988]). CONCLUSION There is currently insufficient evidence on the benefit of radiomics in EC. Nevertheless, this field is promising for future clinical practice. Quality should be a priority when developing these new technologies.
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Affiliation(s)
- Lise Lecointre
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; I-Cube UMR 7357 - Laboratoire des Sciences de L'ingénieur, de L'informatique et de L'imagerie, Université de Strasbourg, Strasbourg, France; Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France.
| | - Jérémy Dana
- Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France; Inserm U1110, Institut de Recherche sur Les Maladies Virales et Hépatiques, Strasbourg, France
| | - Massimo Lodi
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Chérif Akladios
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Benoît Gallix
- I-Cube UMR 7357 - Laboratoire des Sciences de L'ingénieur, de L'informatique et de L'imagerie, Université de Strasbourg, Strasbourg, France; Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France; Department of Diagnostic Radiology, McGill University, Montreal, Canada
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Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble. Comput Biol Med 2021; 134:104487. [PMID: 34022489 DOI: 10.1016/j.compbiomed.2021.104487] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 04/25/2021] [Accepted: 05/07/2021] [Indexed: 11/21/2022]
Abstract
The depth of myometrial invasion affects the treatment and prognosis of patients with endometrial cancer (EC), conventionally evaluated using MR imaging (MRI). However, only a few computer-aided diagnosis methods have been reported for identifying deep myometrial invasion (DMI) using MRI. Moreover, these existing methods exhibit relatively unsatisfactory sensitivity and specificity. This study proposes a novel computerized method to facilitate the accurate detection of DMI on MRI. This method requires only the corpus uteri region provided by humans or computers instead of the tumor region. We also propose a geometric feature called LS to describe the irregularity of the tissue structure inside the corpus uteri triggered by EC, which has not been leveraged for the DMI prediction model in other studies. Texture features are extracted and then automatically selected by recursive feature elimination. Utilizing a feature fusion strategy of strong and weak features devised in this study, multiple probabilistic support vector machines incorporate LS and texture features, which are then merged to form the ensemble model EPSVM. The model performance is evaluated via leave-one-out cross-validation. We make the following comparisons, EPSVM versus the commonly used classifiers such as random forest, logistic regression, and naive Bayes; EPSVM versus the models using LS or texture features alone. The results show that EPSVM attains an accuracy, sensitivity, specificity, and F1 score of 93.7%, 94.7%, 93.3%, and 87.8%, all of which are higher than those of the commonly used classifiers and the models using LS or texture features alone. Compared with the methods in existing studies, EPSVM exhibits high performance in terms of both sensitivity and specificity. Moreover, LS can achieve an accuracy, sensitivity, and specificity of 89.9%, 89.5%, and 90.0%. Thus, the devised geometric feature LS is significant for DMI detection. The fusion of LS and texture features in the proposed EPSVM can provide more reliable prediction. The computer-aided classification based on the proposed method can assist radiologists in accurately identifying DMI on MRI.
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Sone K, Toyohara Y, Taguchi A, Miyamoto Y, Tanikawa M, Uchino-Mori M, Iriyama T, Tsuruga T, Osuga Y. Application of artificial intelligence in gynecologic malignancies: A review. J Obstet Gynaecol Res 2021; 47:2577-2585. [PMID: 33973305 DOI: 10.1111/jog.14818] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 04/25/2021] [Indexed: 12/28/2022]
Abstract
With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi-omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.
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Affiliation(s)
- Kenbun Sone
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yusuke Toyohara
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ayumi Taguchi
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yuichiro Miyamoto
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Michihiro Tanikawa
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mayuyo Uchino-Mori
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takayuki Iriyama
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tetsushi Tsuruga
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yutaka Osuga
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
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Guerriero S, Pascual M, Ajossa S, Neri M, Musa E, Graupera B, Rodriguez I, Alcazar JL. Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis. Eur J Obstet Gynecol Reprod Biol 2021; 261:29-33. [PMID: 33873085 DOI: 10.1016/j.ejogrb.2021.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 04/06/2021] [Accepted: 04/11/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES The aim of this study was to compare the accuracy of seven classical Machine Learning (ML) models trained with ultrasound (US) soft markers to raise suspicion of endometriotic bowel involvement. MATERIALS AND METHODS Input data to the models was retrieved from a database of a previously published study on bowel endometriosis performed on 333 patients. The following models have been tested: k-nearest neighbors algorithm (k-NN), Naive Bayes, Neural Networks (NNET-neuralnet), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The data driven strategy has been to split randomly the complete dataset in two different datasets. The training dataset and the test dataset with a 67 % and 33 % of the original cases respectively. All models were trained on the training dataset and the predictions have been evaluated using the test dataset. The best model was chosen based on the accuracy demonstrated on the test dataset. The information used in all the models were: age; presence of US signs of uterine adenomyosis; presence of an endometrioma; adhesions of the ovary to the uterus; presence of "kissing ovaries"; absence of sliding sign. All models have been trained using CARET package in R with ten repeated 10-fold cross-validation. Accuracy, Sensitivity, Specificity, positive (PPV) and negative (NPV) predictive value were calculated using a 50 % threshold. Presence of intestinal involvement was defined in all cases in the test dataset with an estimated probability greater than 0.5. RESULTS In our previous study from where the inputs were retrieved, 106 women had a final expert US diagnosis of rectosigmoid endometriosis. In term of diagnostic accuracy the best model was the Neural Net (Accuracy, 0.73; sensitivity, 0.72; specificity 0.73; PPV 0.52; and NPV 0.86) but without significant difference with the others. CONCLUSIONS The accuracy of ultrasound soft markers in raising suspicion of rectosigmoid endometriosis using Artificial Intelligence (AI) models showed similar results to the logistic model.
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Affiliation(s)
- Stefano Guerriero
- Centro Integrato di Procreazione Medicalmente Assistita (PMA) e Diagnostica Ostetrico-Ginecologica, Policlinico Universitario Duilio Casula, Monserrato, Cagliari, Italy; University of Cagliari, Cagliari, Italy.
| | - MariaAngela Pascual
- Department of Obstetrics, Gynecology, and Reproduction, Hospital Universitari Dexeus, Spain
| | - Silvia Ajossa
- Department of Obstetrics and Gynecology, University of Cagliari, Policlinico Universitario Duilio Casula, Monserrato, Cagliari, Italy
| | - Manuela Neri
- Department of Obstetrics and Gynecology, University of Cagliari, Policlinico Universitario Duilio Casula, Monserrato, Cagliari, Italy
| | - Eleonora Musa
- Department of Obstetrics and Gynecology, University of Cagliari, Policlinico Universitario Duilio Casula, Monserrato, Cagliari, Italy
| | - Betlem Graupera
- Department of Obstetrics, Gynecology, and Reproduction, Hospital Universitari Dexeus, Spain
| | - Ignacio Rodriguez
- Unidad Epidemiología y Estadística, Departamento de Obstetricia, Ginecología y Reproducción, Hospital Universitario Quirón Dexeus, Barcelona, Spain
| | - Juan Luis Alcazar
- Department of Obstetrics and Gynecology, Clínica Universidad de Navarra, School of Medicine, University of Navarra, Pamplona, Spain
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Visalaxi S, Punnoose D, Muthu T. Lesion Extraction of Endometriotic images using Open Computer Vision. 2021 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SMART SYSTEMS (ICAIS) 2021. [DOI: 10.1109/icais50930.2021.9395822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
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Zhou J, Zeng ZY, Li L. Progress of Artificial Intelligence in Gynecological Malignant Tumors. Cancer Manag Res 2020; 12:12823-12840. [PMID: 33364831 PMCID: PMC7751777 DOI: 10.2147/cmar.s279990] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is a sort of new technical science which can simulate, extend and expand human intelligence by developing theories, methods and application systems. In the last five years, the application of AI in medical research has become a hot topic in modern science and technology. Gynecological malignant tumors involves a wide range of knowledge, and AI can play an important part in these aspects, such as medical image recognition, auxiliary diagnosis, drug research and development, treatment scheme formulation and other fields. The purpose of this paper is to describe the progress of AI in gynecological malignant tumors and discuss some problems in its application. It is believed that AI improves the efficiency of diagnosis, reduces the burden of clinicians, and improves the effect of treatment and prognosis. AI will play an irreplaceable role in the field of gynecological malignant oncology and will promote the development of medicine and further promote the transformation from traditional medicine to precision medicine and preventive medicine. However, there are also some problems in the application of AI in gynecologic malignant tumors. For example, AI, inseparable from human participation, still needs to be more “humanized”, and needs to further protect patients’ privacy and health, improve legal and insurance protection, and further improve according to local ethnic conditions and national conditions. However, it is believed that with the continuous development of AI, especially ensemble classifier, and deep learning will have a profound influence on the future of medical technology, which is a powerful driving force for future medical innovation and reform.
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Affiliation(s)
- Jie Zhou
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Nanning 530021, Guangxi, People's Republic of China.,Department of Gynecology, The Second Affiliated Hospital, University of South China, Hengyang 421001, Hunan, People's Republic of China
| | - Zhi Ying Zeng
- Department of Anesthesiology, The Second Affiliated Hospital, University of South China, Hengyang 421001, Hunan, People's Republic of China
| | - Li Li
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Nanning 530021, Guangxi, People's Republic of China
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