1
|
Kitaya K, Yasuo T, Yamaguchi T. Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:972. [PMID: 38929589 PMCID: PMC11205857 DOI: 10.3390/medicina60060972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/17/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024]
Abstract
Chronic endometritis (CE) is an inflammatory pathologic condition of the uterine mucosa characterized by unusual infiltration of CD138(+) endometrial stromal plasmacytes (ESPCs). CE is often identified in infertile women with unexplained etiology, tubal factors, endometriosis, repeated implantation failure, and recurrent pregnancy loss. Diagnosis of CE has traditionally relied on endometrial biopsy and histopathologic/immunohistochemistrical detection of ESPCs. Endometrial biopsy, however, is a somewhat painful procedure for the subjects and does not allow us to grasp the whole picture of this mucosal tissue. Meanwhile, fluid hysteroscopy has been recently adopted as a less-invasive diagnostic modality for CE. We launched the ARCHIPELAGO (ARChival Hysteroscopic Image-based Prediction for histopathologic chronic Endometritis in infertile women using deep LeArninG mOdel) study to construct the hysteroscopic CE finding-based prediction tools for histopathologic CE. The development of these deep learning-based novel models and computer-aided detection/diagnosis systems potentially benefits infertile women suffering from this elusive disease.
Collapse
Affiliation(s)
- Kotaro Kitaya
- Infertility Center, Iryouhoujin Kouseikai Mihara Hospital, 6-8 Kamikatsura Miyanogo-cho, Nishikyo-ku, Kyoto 615-8227, Japan
- Iryouhoujin Kouseikai Katsura-ekimae Mihara Clinic, 103 Katsura OS Plaza Building, 133 Katsura Minamitatsumi-cho, Nishikyo-ku, Kyoto 615-8074, Japan
| | - Tadahiro Yasuo
- Department of Obstetrics and Gynecology, Otsu City Hospital, 2-9-9 Motomiya, Otsu 520-0804, Japan
| | - Takeshi Yamaguchi
- Infertility Center, Daigo Watanabe Clinic, 30-15 Daigo Takahata-cho, Fushimi-ku, Kyoto 601-1375, Japan
| |
Collapse
|
2
|
Changhez J, James S, Jamala F, Khan S, Khan MZ, Gul S, Zainab I. Evaluating the Efficacy and Accuracy of AI-Assisted Diagnostic Techniques in Endometrial Carcinoma: A Systematic Review. Cureus 2024; 16:e60973. [PMID: 38910646 PMCID: PMC11193879 DOI: 10.7759/cureus.60973] [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] [Accepted: 05/23/2024] [Indexed: 06/25/2024] Open
Abstract
Diagnosing endometrial carcinoma correctly is essential for appropriate treatment, as it is a major health risk. As machine learning (ML) and artificial intelligence (AI) have grown in popularity, so has interest in their potential to improve cancer diagnosis accuracy. In the context of endometrial cancer, this study attempts to examine the efficacy as well as the accuracy of AI-assisted diagnostic approaches. Additionally, it aims to methodically evaluate the contribution of AI and ML techniques to the improvement of endometrial cancer diagnosis. Following PRISMA guidelines, we performed a thorough search of numerous databases, including Medline via Ovid, PubMed, Scopus, Web of Science, and Google Scholar. Ten years were searched, encompassing both basic and advanced research. Peer-reviewed papers and original research studies that explicitly looked at the application of AI/ML in endometrial cancer diagnosis were the main targets of the well-defined selection criteria. Using the Critical Appraisal Skills Programme (CASP) methodology, two independent researchers conducted a thorough screening process and quality assessment of included studies. The review found a notable inclination towards the effective use of AI in endometrial carcinoma diagnostics, namely in the identification and categorization of endometrial cancer. Artificial intelligence models, particularly Convolutional Neural Networks (CNNs) and deep learning algorithms have shown remarkable precision in detecting endometrial cancer. They frequently achieve or even exceed the diagnostic proficiency of human specialists. The use of artificial intelligence in medical diagnostics signifies revolutionary progress in the field of oncology. AI-assisted diagnostic tools have demonstrated the potential to improve the precision and effectiveness of cancer diagnosis, namely in cases of endometrial carcinoma. This innovation not only enhances the quality of patient care but also indicates a transition towards more individualized and efficient treatment approaches in the field of oncology. The advancement of AI technology is expected to play a crucial role in medical diagnostics, particularly in the field of cancer detection and treatment, perhaps leading to a significant transformation in the approach to these areas.
Collapse
Affiliation(s)
| | - Simran James
- Gynecology, Rehman Medical Institute, Peshawar, PAK
| | - Fazilat Jamala
- Obstetrics and Gynecology, Northwest General Hospital and Research Center, Peshawar, PAK
| | - Shandana Khan
- General Surgery, Medical Teaching Institution (MTI) - Hayatabad Medical Complex, Peshawar, PAK
| | | | - Sana Gul
- Gynecology, Rehman Medical Institute, Peshawar, PAK
| | - Irta Zainab
- Gynecology, Medicsi Hospital, Islamabad, PAK
| |
Collapse
|
3
|
Matsumoto Y, Yamaguchi K, Maruyama M, Sone K, Mori-Uchino M, Hirota Y, Wada-Hiraike O, Osuga Y. Characteristic hysteroscopy appearance considerations for detecting uterine endometrial malignancies. J Obstet Gynaecol Res 2024; 50:566-571. [PMID: 38214188 DOI: 10.1111/jog.15879] [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: 06/29/2023] [Accepted: 12/28/2023] [Indexed: 01/13/2024]
Abstract
AIM The effectiveness of hysteroscopy in diagnosing endometrial lesions has been demonstrated, showing high diagnostic accuracy for malignant endometrial lesions. Although the characteristic appearances of atypical and malignant endometria have been reported, they are not definitive and sometimes complicated. This study aimed to identify a small number of characteristic features to detect endometrial abnormalities using a simple judgment system and analyze the diagnostic characteristics and their accuracy in endometrial malignancy diagnosis. METHODS We performed a retrospective analysis of hysteroscopy video data of 250 patients, of which we selected for analysis based on pathology examination 152 cases with benign changes, 16 with atypical endometrium, and 18 with carcinoma in situ or endometrial cancer. Endometrial characteristics assessed included protrusion, desquamation, extended vessel, atypical vessel, and white/yellow lesion. RESULTS Multivariable analysis revealed that desquamation (p = 0.001, odds ratio [OR] 5.28), atypical vessels (p < 0.001, OR 8.50), and white/yellow lesions (p = 0.011, OR 1.37) were significant predictors for endometrial malignancy. From their contribution status, scoring points of 4, 6, and 1 were settled according to the odds ratio proportions. When scores ≥5 (at least both desquamation and white/yellow lesions or only atypical vessels) were used to define endometrial malignancy, sensitivity and specificity were 100% and 92%, respectively. When detecting cancer, atypical, and benign cases, sensitivity and specificity were 88% and 90%, respectively. CONCLUSION Our characteristics hysteroscopic findings showed a higher predictive ability in detecting endometrial malignancies. However, further examination with more cases would be needed to accurately diagnose endometrial malignancy by hysteroscopy.
Collapse
Affiliation(s)
- Yoko Matsumoto
- Department of Obstetrics and Gynecology, Kanto Rosai Hospital, Kawasaki-shi, Kanagawa, Japan
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kohei Yamaguchi
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masanori Maruyama
- Department of Obstetrics and Gynecology, Maruyama Memorial General Hospital, Saitama, Japan
| | - Kenbun Sone
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mayuyo Mori-Uchino
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yasushi Hirota
- Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Osamu Wada-Hiraike
- 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
| |
Collapse
|
4
|
Raimondo D, Raffone A, Salucci P, Raimondo I, Capobianco G, Galatolo FA, Cimino MGCA, Travaglino A, Maletta M, Ferla S, Virgilio A, Neola D, Casadio P, Seracchioli R. Detection and Classification of Hysteroscopic Images Using Deep Learning. Cancers (Basel) 2024; 16:1315. [PMID: 38610993 PMCID: PMC11011142 DOI: 10.3390/cancers16071315] [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: 02/20/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. AIM To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. METHODS A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. RESULTS We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. CONCLUSION Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.
Collapse
Affiliation(s)
- Diego Raimondo
- Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (D.R.); (P.C.); (R.S.)
| | - Antonio Raffone
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
- Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, 80131 Naples, Italy;
| | - Paolo Salucci
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Ivano Raimondo
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy;
- Gynecology and Breast Care Center, Mater Olbia Hospital, 07026 Olbia, Italy
| | - Giampiero Capobianco
- Gynecologic and Obstetric Unit, Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy;
| | - Federico Andrea Galatolo
- Department of Information Engineering, University of Pisa, 56100 Pisa, Italy; (F.A.G.); (M.G.C.A.C.)
| | | | - Antonio Travaglino
- Unit of Pathology, Department of Medicine and Technological Innovation, University of Insubria, 21100 Varese, Italy;
| | - Manuela Maletta
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Stefano Ferla
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Agnese Virgilio
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Daniele Neola
- Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, 80131 Naples, Italy;
| | - Paolo Casadio
- Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (D.R.); (P.C.); (R.S.)
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Renato Seracchioli
- Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (D.R.); (P.C.); (R.S.)
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| |
Collapse
|
5
|
Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
Collapse
Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| |
Collapse
|
6
|
Taddese AA, Tilahun BC, Awoke T, Atnafu A, Mamuye A, Mengiste SA. Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis. Front Oncol 2024; 13:1216326. [PMID: 38273847 PMCID: PMC10809847 DOI: 10.3389/fonc.2023.1216326] [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: 05/03/2023] [Accepted: 11/13/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications. Methods The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model. Results The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias. Conclusion This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.
Collapse
Affiliation(s)
- Asefa Adimasu Taddese
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Binyam Chakilu Tilahun
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Tadesse Awoke
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Asmamaw Atnafu
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Adane Mamuye
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- School of Information Technology and Engineering, Addis Ababa University, Addis Ababa, Ethiopia
| | - Shegaw Anagaw Mengiste
- Department of Business, History and Social Sciences, University of Southeastern Norway, Vestfold, Vestfold, Norway
| |
Collapse
|
7
|
Mumtaz H, Saqib M, Jabeen S, Muneeb M, Mughal W, Sohail H, Safdar M, Mehmood Q, Khan MA, Ismail SM. Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review. Front Med (Lausanne) 2023; 10:1227168. [PMID: 37849490 PMCID: PMC10577305 DOI: 10.3389/fmed.2023.1227168] [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: 05/22/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023] Open
Abstract
The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.
Collapse
Affiliation(s)
| | | | | | - Muhammad Muneeb
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Wajiha Mughal
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Hassan Sohail
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Myra Safdar
- Armed Forces Institute of Cardiology and National Institute of Heart Diseases (AFIC-NIHD), Rawalpindi, Pakistan
| | - Qasim Mehmood
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Muhammad Ahsan Khan
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | | |
Collapse
|
8
|
Piedimonte S, Rosa G, Gerstl B, Sopocado M, Coronel A, Lleno S, Vicus D. Evaluating the use of machine learning in endometrial cancer: a systematic review. Int J Gynecol Cancer 2023; 33:1383-1393. [PMID: 37666535 DOI: 10.1136/ijgc-2023-004622] [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] [Indexed: 09/06/2023] Open
Abstract
OBJECTIVE To review the literature on machine learning in endometrial cancer, report the most commonly used algorithms, and compare performance with traditional prediction models. METHODS This is a systematic review of the literature from January 1985 to March 2021 on the use of machine learning in endometrial cancer. An extensive search of electronic databases was conducted. Four independent reviewers screened studies initially by title then full text. Quality was assessed using the MINORS (Methodological Index for Non-Randomized Studies) criteria. P values were derived using the Pearson's Χ2 test in JMP 15.0. RESULTS Among 4295 articles screened, 30 studies on machine learning in endometrial cancer were included. The most frequent applications were in patient datasets (33.3%, n=10), pre-operative diagnostics (30%, n=9), genomics (23.3%, n=7), and serum biomarkers (13.3%, n=4). The most commonly used models were neural networks (n=10, 33.3%) and support vector machine (n=6, 20%).The number of publications on machine learning in endometrial cancer increased from 1 in 2010 to 29 in 2021.Eight studies compared machine learning with traditional statistics. Among patient dataset studies, two machine learning models (20%) performed similarly to logistic regression (accuracy: 0.85 vs 0.82, p=0.16). Machine learning algorithms performed similarly to detect endometrial cancer based on MRI (accuracy: 0.87 vs 0.82, p=0.24) while outperforming traditional methods in predicting extra-uterine disease in one serum biomarker study (accuracy: 0.81 vs 0.61). For survival outcomes, one study compared machine learning with Kaplan-Meier and reported no difference in concordance index (83.8% vs 83.1%). CONCLUSION Although machine learning is an innovative and emerging technology, performance is similar to that of traditional regression models in endometrial cancer. More studies are needed to assess its role in endometrial cancer. PROSPERO REGISTRATION NUMBER CRD42021269565.
Collapse
Affiliation(s)
- Sabrina Piedimonte
- Department of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada
| | | | - Brigitte Gerstl
- The Rosa Institute, Sydney, New South Wales, Australia
- The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Mars Sopocado
- The Rosa Institute, Sydney, New South Wales, Australia
| | - Ana Coronel
- The Rosa Institute, Sydney, New South Wales, Australia
| | | | - Danielle Vicus
- Department of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada
- Department of Gynecologic Oncology, Sunnybrook Health Sciences, Toronto, Ontario, Canada
| |
Collapse
|
9
|
Shen L, Du L, Hu Y, Chen X, Hou Z, Yan Z, Wang X. MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study. Acta Radiol 2023; 64:2651-2658. [PMID: 37291882 DOI: 10.1177/02841851231175249] [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] [Indexed: 06/10/2023]
Abstract
BACKGROUND Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs). PURPOSE To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting. MATERIAL AND METHODS Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1-3 were utilized for training and validation, and the images from devices 4-7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models. RESULTS The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists. CONCLUSION Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.
Collapse
Affiliation(s)
- Liting Shen
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
| | - Lixin Du
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen, PR China
| | - Yumin Hu
- Department of Radiology, Lishui Central Hospital, Zhejiang, PR China
| | - Xiaojun Chen
- Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, PR China
| | - Zujun Hou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, PR China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
| | - Xue Wang
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
| |
Collapse
|
10
|
Sun H, Zhang L, Fu P, Liu R. OS and DFS are affected by different diagnostic methods and hysterectomy procedures in endometrial cancer patients: A single-center retrospective study. Cancer Med 2023; 12:19072-19080. [PMID: 37584224 PMCID: PMC10557905 DOI: 10.1002/cam4.6465] [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: 04/15/2023] [Revised: 08/02/2023] [Accepted: 08/09/2023] [Indexed: 08/17/2023] Open
Abstract
PURPOSE We aimed to evaluate whether hysteroscopy increases the risk of intraperitoneal dissemination or worsens the prognosis of endometrial carcinoma (EC) patients and whether radical hysterectomy (RH) improves overall survival (OS) or disease-free survival (DFS) in patients with stage II to III EC and to investigate the effects of different procedures for identifying EC and the effects of different surgical methods on the OS and DFS of endometrial cancer patients. METHODS Four hundred sixty-five women with EC were included in this retrospective study. Log-rank tests and Kaplan-Meier analysis were used for the outcome comparisons of the effects of the EC diagnostic method and different hysterectomy procedures. A Cox proportional hazards model was used for univariate regression analysis. RESULTS Among the three procedures for diagnosing EC (diagnostic curettage, hysteroscopy, and hysterectomy), the incidences of fallopian tube and ovarian invasion were not significantly different (p = 0.506 and 0.066, respectively). The diagnostic methods for EC had no significant effect on OS (p = 0.577) or DFS (p = 0.294). In addition, type II RH and type III RH did not improve the prognosis of patients with FIGO stage II and III disease (log-rank p = 0.914 and 0.810 for OS; log-rank p = 0.707 and 0.771 for DFS, respectively). CONCLUSION Based on the current study evidence, the use of diagnostic hysteroscopy procedures is safe and does not increase the risk of fallopian tube and ovarian invasion of intraperitoneal dissemination or worsen the prognosis of EC patients. Type II and type III RH did not demonstrate a benefit for stage II-III EC patients.
Collapse
Affiliation(s)
- Haiying Sun
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanPeople's Republic of China
| | - Long Zhang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanPeople's Republic of China
| | - Peiying Fu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanPeople's Republic of China
| | - Ronghua Liu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanPeople's Republic of China
| |
Collapse
|
11
|
Yui R, Takahashi M, Noda K, Yoshida K, Sakurai R, Ohira S, Omura K, Otori N, Wada K, Kojima H. Preoperative prediction of sinonasal papilloma by artificial intelligence using nasal video endoscopy: a retrospective study. Sci Rep 2023; 13:12439. [PMID: 37532726 PMCID: PMC10397257 DOI: 10.1038/s41598-023-38913-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/17/2023] [Indexed: 08/04/2023] Open
Abstract
Sinonasal inverted papilloma (IP) is at risk of recurrence and malignancy, and early diagnosis using nasal endoscopy is essential. We thus developed a diagnostic system using artificial intelligence (AI) to identify nasal sinus papilloma. Endoscopic surgery videos of 53 patients undergoing endoscopic sinus surgery were edited to train and evaluate deep neural network models and then a diagnostic system was developed. The correct diagnosis rate based on visual examination by otolaryngologists was also evaluated using the same videos and compared with that of the AI diagnostic system patients. Main outcomes evaluated included the percentage of correct diagnoses compared to AI diagnosis and the correct diagnosis rate for otolaryngologists based on years of practice experience. The diagnostic system had an area under the curve of 0.874, accuracy of 0.843, false positive rate of 0.124, and false negative rate of 0.191. The average correct diagnosis rate among otolaryngologists was 69.4%, indicating that the AI was highly accurate. Evidently, although the number of cases was small, a highly accurate diagnostic system was created. Future studies with larger samples to improve the accuracy of the system and expand the range of diseases that can be detected for more clinical applications are warranted.
Collapse
Affiliation(s)
- Ryosuke Yui
- Department of Otorhinolaryngology, Jikei University School of Medicine, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
- Department of Otolaryngology, Head and Neck Surgery, Toho University Faculty of Medicine, Tokyo, Japan
| | - Masahiro Takahashi
- Department of Otorhinolaryngology, Jikei University School of Medicine, Nishi-Shimbashi, Minato-ku, Tokyo, Japan.
| | - Katsuhiko Noda
- SIOS Technology Inc., Minami-Azabu, Minato-ku, Tokyo, Japan
| | - Kaname Yoshida
- SIOS Technology Inc., Minami-Azabu, Minato-ku, Tokyo, Japan
| | - Rinko Sakurai
- Department of Otorhinolaryngology, Jikei University School of Medicine, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Shinya Ohira
- Department of Otolaryngology, Head and Neck Surgery, Toho University Faculty of Medicine, Tokyo, Japan
| | - Kazuhiro Omura
- Department of Otorhinolaryngology, Jikei University School of Medicine, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Nobuyoshi Otori
- Department of Otorhinolaryngology, Jikei University School of Medicine, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Kota Wada
- Department of Otolaryngology, Head and Neck Surgery, Toho University Faculty of Medicine, Tokyo, Japan
| | - Hiromi Kojima
- Department of Otorhinolaryngology, Jikei University School of Medicine, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Precision Medicine for Chronic Endometritis: Computer-Aided Diagnosis Using Deep Learning Model. Diagnostics (Basel) 2023; 13:diagnostics13050936. [PMID: 36900079 PMCID: PMC10000436 DOI: 10.3390/diagnostics13050936] [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: 02/01/2023] [Revised: 02/15/2023] [Accepted: 02/24/2023] [Indexed: 03/06/2023] Open
Abstract
Chronic endometritis (CE) is a localized mucosal infectious and inflammatory disorder marked by infiltration of CD138(+) endometrial stromal plasmacytes (ESPC). CE is drawing interest in the field of reproductive medicine because of its association with female infertility of unknown etiology, endometriosis, repeated implantation failure, recurrent pregnancy loss, and multiple maternal/newborn complications. The diagnosis of CE has long relied on somewhat painful endometrial biopsy and histopathologic examinations combined with immunohistochemistry for CD138 (IHC-CD138). With IHC-CD138 only, CE may be potentially over-diagnosed by misidentification of endometrial epithelial cells, which constitutively express CD138, as ESPCs. Fluid hysteroscopy is emerging as an alternative, less-invasive diagnostic tool that can visualize the whole uterine cavity in real-time and enables the detection of several unique mucosal findings associated with CE. The biases in the hysteroscopic diagnosis of CE; however, are the inter-observer and intra-observer disagreements on the interpretation of the endoscopic findings. Additionally, due to the variances in the study designs and adopted diagnostic criteria, there exists some dissociation in the histopathologic and hysteroscopic diagnosis of CE among researchers. To address these questions, novel dual immunohistochemistry for CD138 and another plasmacyte marker multiple myeloma oncogene 1 are currently being tested. Furthermore, computer-aided diagnosis using a deep learning model is being developed for more accurate detection of ESPCs. These approaches have the potential to contribute to the reduction in human errors and biases, the improvement of the diagnostic performance of CE, and the establishment of unified diagnostic criteria and standardized clinical guidelines for the disease.
Collapse
|
14
|
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
|
15
|
Song J, Im S, Lee SH, Jang HJ. Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from Whole-Slide Histopathology Images. Diagnostics (Basel) 2022; 12:2623. [PMID: 36359467 PMCID: PMC9689570 DOI: 10.3390/diagnostics12112623] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 10/26/2022] [Accepted: 10/26/2022] [Indexed: 08/11/2023] Open
Abstract
Uterine cervical and endometrial cancers have different subtypes with different clinical outcomes. Therefore, cancer subtyping is essential for proper treatment decisions. Furthermore, an endometrial and endocervical origin for an adenocarcinoma should also be distinguished. Although the discrimination can be helped with various immunohistochemical markers, there is no definitive marker. Therefore, we tested the feasibility of deep learning (DL)-based classification for the subtypes of cervical and endometrial cancers and the site of origin of adenocarcinomas from whole slide images (WSIs) of tissue slides. WSIs were split into 360 × 360-pixel image patches at 20× magnification for classification. Then, the average of patch classification results was used for the final classification. The area under the receiver operating characteristic curves (AUROCs) for the cervical and endometrial cancer classifiers were 0.977 and 0.944, respectively. The classifier for the origin of an adenocarcinoma yielded an AUROC of 0.939. These results clearly demonstrated the feasibility of DL-based classifiers for the discrimination of cancers from the cervix and uterus. We expect that the performance of the classifiers will be much enhanced with an accumulation of WSI data. Then, the information from the classifiers can be integrated with other data for more precise discrimination of cervical and endometrial cancers.
Collapse
Affiliation(s)
- JaeYen Song
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Soyoung Im
- Department of Hospital Pathology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 16247, Korea
| | - Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| | - Hyun-Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
| |
Collapse
|
16
|
Takahashi M, Noda K, Yoshida K, Tsuchida K, Yui R, Nakazawa T, Kurihara S, Baba A, Motegi M, Yamamoto K, Yamamoto Y, Ojiri H, Kojima H. Preoperative prediction by artificial intelligence for mastoid extension in pars flaccida cholesteatoma using temporal bone high-resolution computed tomography: A retrospective study. PLoS One 2022; 17:e0273915. [PMID: 36190937 PMCID: PMC9529134 DOI: 10.1371/journal.pone.0273915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/16/2022] [Indexed: 11/07/2022] Open
Abstract
Cholesteatoma is a progressive middle ear disease that can only be treated surgically but with a high recurrence rate. Depending on the extent of the disease, a surgical approach, such as microsurgery with a retroarticular incision or transcanal endoscopic surgery, is performed. However, the current examination cannot sufficiently predict the progression before surgery, and changes in approach may be made during the surgery. Large amounts of data are typically required to train deep neural network models; however, the prevalence of cholesteatomas is low (1-in-25, 000). Developing analysis methods that improve the accuracy with such a small number of samples is an important issue for medical artificial intelligence (AI) research. This paper presents an AI-based system to automatically detect mastoid extensions using CT. This retrospective study included 164 patients (80 with mastoid extension and 84 without mastoid extension) who underwent surgery. This study adopted a relatively lightweight neural network model called MobileNetV2 to learn and predict the CT images of 164 patients. The training was performed with eight divided groups for cross-validation and was performed 24 times with each of the eight groups to verify accuracy fluctuations caused by randomly augmented learning. An evaluation was performed by each of the 24 single-trained models, and 24 sets of ensemble predictions with 23 models for 100% original size images and 400% zoomed images. Fifteen otolaryngologists diagnosed the images and compared the results. The average accuracy of predicting 400% zoomed images using ensemble prediction model was 81.14% (sensitivity = 84.95%, specificity = 77.33%). The average accuracy of the otolaryngologists was 73.41% (sensitivity, 83.17%; specificity, 64.13%), which was not affected by their clinical experiences. Noteworthily, despite the small number of cases, we were able to create a highly accurate AI. These findings represent an important first step in the automatic diagnosis of the cholesteatoma extension.
Collapse
Affiliation(s)
- Masahiro Takahashi
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
- * E-mail:
| | | | | | - Keisuke Tsuchida
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Ryosuke Yui
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Takara Nakazawa
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Sho Kurihara
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Akira Baba
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Masaomi Motegi
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Kazuhisa Yamamoto
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Yutaka Yamamoto
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Hiroya Ojiri
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Hiromi Kojima
- Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| |
Collapse
|
17
|
Wang L, Zhao Q. Deformation Analysis and Research of Building Envelope by Deep Learning Technology under the Reinforcement of the Diaphragm Wall. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9489445. [PMID: 36156955 PMCID: PMC9492380 DOI: 10.1155/2022/9489445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/17/2022] [Accepted: 08/25/2022] [Indexed: 11/17/2022]
Abstract
The safety analysis of underground buildings is the most crucial problem in the construction industry. This work aims to optimize the safety analysis results of the underground building envelope and comprehensively improve the safety of the underground building. Long short-term memory (LSTM) can make long-term and short-term predictions, thus reducing the model's prediction error. Applying it to the deformation analysis, data prediction of the underground building envelope can improve the accuracy of the deformation prediction of the envelope. This work deeply discusses deep learning technology and the principle of the LSTM model. Based on the safety analysis concept of the underground building envelope, LSTM underground building envelope deformation's prediction model is established and comprehensively evaluated. The results show that in the prediction of horizontal displacement of foundation pit pile of diaphragm wall, the mean relative error (MRE) of the prediction results of the designed model range in 10%-18%, and the calculation time ranges 15-36 s. In the settlement displacement prediction, the model's MRE is within the range of 5%-7%, and the calculation time is within the range of 17-40 s. With the increase of training times, the prediction accuracy of the model increases, and the calculation time becomes relatively stable. Compared with other models, the relative error of prediction results is about 5.4% at the highest and 1.8% at the lowest. This work provides technical support for improving the safety prediction accuracy of the underground building envelope and provides some reference value for the comprehensive development of the underground building industry.
Collapse
Affiliation(s)
- Lijuan Wang
- State Key Laboratory of GeoHazard Prevention and GeoEnvironment Protection, Chengdu University of Technology, Chengdu 610059, China
| | - Qihua Zhao
- State Key Laboratory of GeoHazard Prevention and GeoEnvironment Protection, Chengdu University of Technology, Chengdu 610059, China
| |
Collapse
|
18
|
Zhao F, Dong D, Du H, Guo Y, Su X, Wang Z, Xie X, Wang M, Zhang H, Cao X, He X. Diagnosis of endometrium hyperplasia and screening of endometrial intraepithelial neoplasia in histopathological images using a global-to-local multi-scale convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106906. [PMID: 35671602 DOI: 10.1016/j.cmpb.2022.106906] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/10/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Endometrial hyperplasia (EH), a uterine pathology characterized by an increased gland-to-stroma ratio compared to normal endometrium (NE), may precede the development of endometrial cancer (EC). Particularly, atypical EH also known as endometrial intraepithelial neoplasia (EIN), has been proven to be a precursor of EC. Thus, diagnosing different EH (EIN, hyperplasia without atypia (HwA) and NE) and screening EIN from non-EIN are crucial for the health of female reproductive system. Computer-aided-diagnosis (CAD) was used to diagnose endometrial histological images based on machine learning and deep learning. However, these studies perform single-scale image analysis and thus can only characterize partial endometrial features. Empirically, both global (cytological changes relative to background) and local features (gland-to-stromal ratio and lesion dimension) are helpful in identifying endometrial lesions. METHODS We proposed a global-to-local multi-scale convolutional neural network (G2LNet) to diagnose different EH and to screen EIN in endometrial histological images stained by hematoxylin and eosin (H&E). The G2LNet first used a supervised model in the global part to extract contextual features of endometrial lesions, and simultaneously deployed multi-instance learning in the local part to obtain textural features from multiple image patches. The contextual and textural features were used together to diagnose different endometrial lesions after fusion by a convolutional block attention module. In addition, we visualized the salient regions on both the global image and local images to investigate the interpretability of the model in endometrial diagnosis. RESULTS In the five-fold cross validation on 7812 H&E images from 467 endometrial specimens, G2LNet achieved an accuracy of 97.01% for EH diagnosis and an area-under-the-curve (AUC) of 0.9902 for EIN screening, significantly higher than state-of-the-arts. In external validation on 1631 H&E images from 135 specimens, G2LNet achieved an accuracy of 95.34% for EH diagnosis, which was comparable to that of a mid-level pathologist (95.71%). Specifically, G2LNet had advantages in diagnosing EIN, while humans performed better in identifying NE and HwA. CONCLUSIONS The developed G2LNet that integrated both the global (contextual) and local (textural) features may help pathologists diagnose endometrial lesions in clinical practices, especially to improve the accuracy and efficiency of screening for precancerous lesions.
Collapse
Affiliation(s)
- Fengjun Zhao
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Didi Dong
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Hongyan Du
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China.
| | - Yinan Guo
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Xue Su
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Zhiwei Wang
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Xiaoyang Xie
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Mingjuan Wang
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Haiyan Zhang
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Xin Cao
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Xiaowei He
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China.
| |
Collapse
|
19
|
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.
Collapse
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.)
| |
Collapse
|
20
|
Toğaçar M. Disease type detection in lung and colon cancer images using the complement approach of inefficient sets. Comput Biol Med 2021; 137:104827. [PMID: 34560401 DOI: 10.1016/j.compbiomed.2021.104827] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/25/2021] [Accepted: 08/29/2021] [Indexed: 12/19/2022]
Abstract
Lung and colon cancers are deadly diseases that can develop simultaneously in organs and adversely affect human life in some special cases. Although the frequency of simultaneous occurrence of these two types of cancer is unlikely, there is a high probability of metastasis between the two organs if not diagnosed early. Traditionally, specialists have to go through a lengthy and complicated process to examine histopathological images and diagnose cancer cases; yet, it is now possible to achieve this process faster with the available technological possibilities. In this study, artificial intelligence-supported model and optimization methods were used to realize the classification of lung and colon cancers' histopathological images. The used dataset has five classes of histopathological images consisting of two colon cancer classes and three lung cancer classes. In the proposed approach, the image classes were trained from scratch with the DarkNet-19 model, which is one of the deep learning models. In the feature set extracted from the DarkNet-19 model, selection of the inefficient features was performed by using Equilibrium and Manta Ray Foraging optimization algorithms. Then, the set containing the inefficient features was distinguished from the rest of the set features, creating an efficient feature set (complementary rule insets). The efficient features obtained by the two used optimization algorithms were combined and classified with the Support Vector Machine (SVM) method. The overall accuracy rate obtained in the classification process was 99.69%. Based on the outcomes of this study, it has been observed that using the complementary method together with some optimization methods improved the classification performance of the dataset.
Collapse
Affiliation(s)
- Mesut Toğaçar
- Department of Computer Technology, Technical Sciences Vocational School, Fırat UniversityElazig, Turkey.
| |
Collapse
|
21
|
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%.
Collapse
Affiliation(s)
- Mesut Toğaçar
- Computer Technologies Department, Technical Sciences Vocational School, Fırat University Elazığ, Turkey.
| |
Collapse
|
22
|
Mosallaie S, Rad M, Schiffauerova A, Ebadi A. Discovering the evolution of artificial intelligence in cancer research using dynamic topic modeling. COLLNET JOURNAL OF SCIENTOMETRICS AND INFORMATION MANAGEMENT 2021. [DOI: 10.1080/09737766.2021.1958659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Shahab Mosallaie
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1M8, Montreal, QC, Canada
| | - Mahdi Rad
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1M8, Montreal, QC, Canada
| | - Andrea Schiffauerova
- Concordia Institute for Information Systems Engineering (CIISE), Concordia University, H3G 1M8, Montreal, QC, Canada
| | - Ashkan Ebadi
- Digital Technologies Research Centre, National Research Council Canada, H3T 2B2, Montreal, QC, Canada
- Concordia Institute for Information Systems Engineering, Concordia University, H3G 1M8, Montreal, QC, Canada
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|