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Hou H, Carns J, Schwarz RA, Gillenwater AM, Anandasabapathy S, Richards-Kortum RR. Use of topical methylene blue to image nuclear morphometry with a low-cost scanning darkfield microendoscope. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:050501. [PMID: 38774711 PMCID: PMC11107336 DOI: 10.1117/1.jbo.29.5.050501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/24/2024]
Abstract
Significance Fiber-optic microendoscopy is a promising approach to noninvasively visualize epithelial nuclear morphometry for early cancer and precancer detection. However, the broader clinical application of this approach is limited by a lack of topical contrast agents available for in vivo use. Aim The aim of this study was to evaluate the ability to image nuclear morphometry in vivo with a novel fiber-optic microendoscope used together with topical application of methylene blue (MB), a dye with FDA approval for use in chromoendoscopy in the gastrointestinal tract. Approach The low-cost, high-resolution microendoscope implements scanning darkfield imaging without complex optomechanical components by leveraging programmable illumination and the rolling shutter of the image sensor. We validate the integration of our system and MB staining for visualizing epithelial cell nuclei by performing ex vivo imaging on fresh animal specimens and in vivo imaging on healthy volunteers. Results The results indicate that scanning darkfield imaging significantly reduces specular reflection and resolves epithelial nuclei with enhanced image contrast and spatial resolution compared to non-scanning widefield imaging. The image quality of darkfield images with MB staining is comparable to that of fluorescence images with proflavine staining. Conclusions Our approach enables real-time microscopic evaluation of nuclear patterns and has the potential to be a powerful noninvasive tool for early cancer detection.
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Affiliation(s)
- Huayu Hou
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Jennifer Carns
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Richard A. Schwarz
- Rice University, Department of Bioengineering, Houston, Texas, United States
| | - Ann M. Gillenwater
- The University of Texas M.D. Anderson Cancer Center, Department of Head and Neck Surgery, Houston, Texas, United States
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Vargas-Cardona HD, Rodriguez-Lopez M, Arrivillaga M, Vergara-Sanchez C, García-Cifuentes JP, Bermúdez PC, Jaramillo-Botero A. Artificial intelligence for cervical cancer screening: Scoping review, 2009-2022. Int J Gynaecol Obstet 2024; 165:566-578. [PMID: 37811597 DOI: 10.1002/ijgo.15179] [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: 06/22/2023] [Revised: 09/04/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images. OBJECTIVES To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). SEARCH STRATEGY Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords. SELECTION CRITERIA Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases. DATA COLLECTION AND ANALYSIS A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance. MAIN RESULTS We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%. CONCLUSION We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.
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Affiliation(s)
| | - Mérida Rodriguez-Lopez
- Faculty of Health Sciences, Universidad Icesi, Cali, Colombia
- Fundación Valle del Lili, Centro de Investigaciones Clínicas, Cali, Colombia
| | | | | | | | | | - Andres Jaramillo-Botero
- OMICAS Research Institute (iOMICAS), Pontificia Universidad Javeriana Cali, Cali, Colombia
- Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA
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Aswathy R, Sumathi S. The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence. Mol Biotechnol 2024:10.1007/s12033-024-01124-7. [PMID: 38573545 DOI: 10.1007/s12033-024-01124-7] [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: 12/19/2023] [Accepted: 02/15/2024] [Indexed: 04/05/2024]
Abstract
Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and middle-income countries (LMICs), where CC prevalence is high, innovative, and cost-effective approaches for prevention, diagnosis, and treatment are vital. These approaches must ensure high response rates with minimal side effects to improve outcomes. The study aims to compile the latest developments in the field of CC, providing insights into the promising future of CC management along with the research gaps and challenges. Integrating biotechnology and artificial intelligence (AI) holds immense potential to revolutionize CC care, from MobileODT screening to precision medicine and innovative therapies. AI enhances healthcare accuracy and improves patient outcomes, especially in CC screening, where its use has increased over the years, showing promising results. Also, combining newly developed strategies with conventional treatment options presents an optimal approach to address the limitations associated with conventional methods. However, further clinical studies are essential for practically implementing these advancements in society. By leveraging these cutting-edge technologies and approaches, there is a substantial opportunity to reduce the global burden of this preventable malignancy, ultimately improving the lives of women in LMICs and beyond.
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Affiliation(s)
- Raghu Aswathy
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Bharathi Park Rd, Near Forest College Campus, Saibaba Colony, Coimbatore, Tamil Nadu, 641043, India
| | - Sundaravadivelu Sumathi
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Bharathi Park Rd, Near Forest College Campus, Saibaba Colony, Coimbatore, Tamil Nadu, 641043, India.
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Hou H, Mitbander R, Tang Y, Azimuddin A, Carns J, Schwarz RA, Richards-Kortum RR. Optical imaging technologies for in vivo cancer detection in low-resource settings. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 28:100495. [PMID: 38406798 PMCID: PMC10883072 DOI: 10.1016/j.cobme.2023.100495] [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] [Indexed: 02/27/2024]
Abstract
Cancer continues to affect underserved populations disproportionately. Novel optical imaging technologies, which can provide rapid, non-invasive, and accurate cancer detection at the point of care, have great potential to improve global cancer care. This article reviews the recent technical innovations and clinical translation of low-cost optical imaging technologies, highlighting the advances in both hardware and software, especially the integration of artificial intelligence, to improve in vivo cancer detection in low-resource settings. Additionally, this article provides an overview of existing challenges and future perspectives of adapting optical imaging technologies into clinical practice, which can potentially contribute to novel insights and programs that effectively improve cancer detection in low-resource settings.
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Affiliation(s)
- Huayu Hou
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Ruchika Mitbander
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Yubo Tang
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Ahad Azimuddin
- School of Medicine, Texas A&M University, Houston, TX 77030, USA
| | - Jennifer Carns
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Richard A Schwarz
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
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Liu M, Lu J, Zhi Y, Ruan Y, Cao G, Xu X, An X, Gao J, Li F. Microendoscopy in vivo for the pathological diagnosis of cervical precancerous lesions and early cervical cancer. Infect Agent Cancer 2023; 18:26. [PMID: 37101242 PMCID: PMC10134531 DOI: 10.1186/s13027-023-00498-8] [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: 10/30/2022] [Accepted: 03/16/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Cervical cancer is an important public health problem. Conventional colposcopy is inefficient in the diagnosis of cervical lesions and massive biopsies result in trauma. There is an urgent need for a new clinical strategy to triage women with abnormal cervical screening results immediately and effectively. In this study, the high-resolution microendoscopy combined with methylene blue cell staining technology was used to perform real-time in vivo imaging of the cervix for the first time. METHODS A total of 41 patients were enrolled in the study. All patients underwent routine colposcopy and cervical biopsy, and high-resolution images of methylene blue-stained cervical lesions were obtained in vivo using microendoscopy. The cell morphological features of benign and neoplastic cervical lesions stained with methylene blue under microendoscopy were analyzed and summarized. The microendoscopy and histopathology findings of the high-grade squamous intraepithelial lesion (HSIL) and more severe lesions were compared. RESULTS The overall consistency of microendoscopy diagnosis with pathological diagnosis was 95.12% (39/41). Diagnostic cell morphological features of cervicitis, low-grade squamous intraepithelial lesion (LSIL), HSIL, adenocarcinoma in situ, and invasive cancer were clearly demonstrated in methylene blue stained microendoscopic images. In HSIL and more severe lesions, microendoscopic methylene blue cell staining technology can show the microscopic diagnostic features consistent with histopathology. CONCLUSIONS This study was an initial exercise in the application of the microendoscopy imaging system combined with methylene blue cell staining technology to cervical precancerous lesions and cervical cancer screening. The results provided the basis for a novel clinical strategy for triage of women with abnormal cervical screening results using in vivo non-invasive optical diagnosis technology.
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Affiliation(s)
- Min Liu
- Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo Road, Shanghai, 200120, China
| | - Jianqiao Lu
- Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo Road, Shanghai, 200120, China
| | - Yong Zhi
- Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo Road, Shanghai, 200120, China
| | - Yetian Ruan
- Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo Road, Shanghai, 200120, China
| | - Guangxu Cao
- Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo Road, Shanghai, 200120, China
| | - Xinxin Xu
- Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo Road, Shanghai, 200120, China
| | - Xin An
- OptoMedic Technologies Inc., Foshan, 528200, China
| | - Jinli Gao
- Department of Pathology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Fang Li
- Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo Road, Shanghai, 200120, China.
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Chen X, Chen C, Liu L, Dai W, Zhang J, Han C, Zhou S. Evaluation of p16/Ki-67 dual-stain as triage test for high-risk HPV-positive women: A hospital-based cross-sectional study. Cancer Cytopathol 2022; 130:955-963. [PMID: 35852302 DOI: 10.1002/cncy.22628] [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: 03/13/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND Most human papillomavirus (HPV)-positive women recover from infections and do not develop cervical intraepithelial neoplasia (CIN) and cervical cancer. Additional triage approaches are needed to reduce unnecessary colposcopy referrals. The aim of this study is to determine the high-risk HPV prevalence in a hospital-based population and to evaluate the performance of p16/Ki-67 dual-stain test for the triage of high-risk HPV-positive women to detect precursor lesions and cervical cancer compared with the ThinPrep cytologic test (TCT). METHODS In a hospital-based population, 100,801 women were provided with a primary HPV DNA test and only women with high-risk HPV infections were triaged using TCT and p16/Ki-67 dual-stain test. CIN2 or worse (≥CIN2) or CIN3 or worse (≥CIN3) were defined as the clinical end points. RESULTS The p16/Ki-67 dual-stain indicated a statistically significant higher sensitivity (82.8% vs. 66.7%%), specificity (51.6% vs. 44.4%), positive predictive value (33.2% vs. 25.8%), negative predictive value (91.2% vs. 82.1%), and accuracy (58.6% vs. 49.4%) compared with TCT examination within ≥CIN2 cases. Similar patterns were observed for the ≥CIN3 end point. CONCLUSIONS Our study demonstrated that p16/Ki-67 dual-stain test could achieve better performance compared with TCT examination for ≥CIN2 or ≥ CIN3 detection, representing a promising approach as a specific and efficient triage strategy for high-risk HPV-positive women.
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Affiliation(s)
| | - Chen Chen
- Yaneng Bioscience, Co, Ltd, Shenzhen, China
| | - Liyi Liu
- Department of Clinical Laboratory, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China
| | - Wenjun Dai
- Yaneng Bioscience, Co, Ltd, Shenzhen, China
| | - Jing Zhang
- Department of Clinical Laboratory, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China
| | - Chunyan Han
- Department of Clinical Laboratory, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China
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Allahqoli L, Laganà AS, Mazidimoradi A, Salehiniya H, Günther V, Chiantera V, Karimi Goghari S, Ghiasvand MM, Rahmani A, Momenimovahed Z, Alkatout I. Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review. Diagnostics (Basel) 2022; 12:2771. [PMID: 36428831 PMCID: PMC9689914 DOI: 10.3390/diagnostics12112771] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/06/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. MATERIALS AND METHODS Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively. CONCLUSION The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.
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Affiliation(s)
- Leila Allahqoli
- Midwifery Department, Ministry of Health and Medical Education, Tehran 1467664961, Iran
| | - Antonio Simone Laganà
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Afrooz Mazidimoradi
- Neyriz Public Health Clinic, Shiraz University of Medical Sciences, Shiraz 7134814336, Iran
| | - Hamid Salehiniya
- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand 9717853577, Iran
| | - Veronika Günther
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
| | - Vito Chiantera
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Shirin Karimi Goghari
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran 1411713114, Iran
| | - Mohammad Matin Ghiasvand
- Department of Computer Engineering, Amirkabir University of Technology (AUT), Tehran 1591634311, Iran
| | - Azam Rahmani
- Nursing and Midwifery Care Research Centre, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran 141973317, Iran
| | - Zohre Momenimovahed
- Reproductive Health Department, Qom University of Medical Sciences, Qom 3716993456, Iran
| | - Ibrahim Alkatout
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
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8
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Istasy P, Lee WS, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. J Med Internet Res 2022; 24:e39748. [PMID: 36005841 PMCID: PMC9667381 DOI: 10.2196/39748] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/11/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. OBJECTIVE We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?" METHODS Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. RESULTS Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. CONCLUSIONS Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.
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Affiliation(s)
- Paul Istasy
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Rotman Institute of Philosophy, Western University, London, ON, Canada
| | - Wen Shen Lee
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | | | - Ross Upshur
- Division of Clinical Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Bridgepoint Collaboratory for Research and Innovation, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Bishal Gyawali
- Division of Cancer Care and Epidemiology, Department of Oncology, Queen's University, Kingston, ON, Canada
- Division of Cancer Care and Epidemiology, Department of Public Health Sciences, Queen's University, Kingston, ON, Canada
| | - Jacquelyn Burkell
- Faculty of Information and Media Studies, Western University, London, ON, Canada
| | - Bekim Sadikovic
- Department of Pathology & Laboratory Medicine, Schulich School of Medicine, Western University, London, ON, Canada
| | - Alejandro Lazo-Langner
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Benjamin Chin-Yee
- Rotman Institute of Philosophy, Western University, London, ON, Canada
- Division of Hematology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Division of Hematology, Department of Medicine, London Health Sciences Centre, London, ON, Canada
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9
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Coole JB, Brenes D, Possati-Resende JC, Antoniazzi M, Fonseca BDO, Maker Y, Kortum A, Vohra IS, Schwarz RA, Carns J, Borba Souza KC, Vidigal Santana IV, Kreitchmann R, Salcedo MP, Ramanujam N, Schmeler KM, Richards-Kortum R. Development of a multimodal mobile colposcope for real-time cervical cancer detection. BIOMEDICAL OPTICS EXPRESS 2022; 13:5116-5130. [PMID: 36425643 PMCID: PMC9664871 DOI: 10.1364/boe.463253] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Cervical cancer remains a leading cause of cancer death among women in low-and middle-income countries. Globally, cervical cancer prevention programs are hampered by a lack of resources, infrastructure, and personnel. We describe a multimodal mobile colposcope (MMC) designed to diagnose precancerous cervical lesions at the point-of-care without the need for biopsy. The MMC integrates two complementary imaging systems: 1) a commercially available colposcope and 2) a high speed, high-resolution, fiber-optic microendoscope (HRME). Combining these two image modalities allows, for the first time, the ability to locate suspicious cervical lesions using widefield imaging and then to obtain co-registered high-resolution images across an entire lesion. The MMC overcomes limitations of high-resolution imaging alone; widefield imaging can be used to guide the placement of the high-resolution imaging probe at clinically suspicious regions and co-registered, mosaicked high-resolution images effectively increase the field of view of high-resolution imaging. Representative data collected from patients referred for colposcopy at Barretos Cancer Hospital in Brazil, including 22,800 high resolution images and 9,900 colposcope images, illustrate the ability of the MMC to identify abnormal cervical regions, image suspicious areas with subcellular resolution, and distinguish between high-grade and low-grade dysplasia.
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Affiliation(s)
- Jackson B. Coole
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | - David Brenes
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | | | - Márcio Antoniazzi
- Barretos Cancer Hospital, Department of Prevention, Barretos, Brazil
| | | | - Yajur Maker
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | - Alex Kortum
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | - Imran S. Vohra
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | | | - Jennifer Carns
- Rice University, Department of Bioengineering, Houston, TX 77005, USA
| | | | | | - Regis Kreitchmann
- Federal University of Health Sciences of Porto Alegre (UFCSPA)/Santa Casa Hospital of Porto Alegre, Department of Obstetrics and Gynecology, Porto Alegre, Brazil
| | - Mila P. Salcedo
- Federal University of Health Sciences of Porto Alegre (UFCSPA)/Santa Casa Hospital of Porto Alegre, Department of Obstetrics and Gynecology, Porto Alegre, Brazil
- The University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77005, USA
| | - Nirmala Ramanujam
- Duke University, Department of Biomedical Engineering, Durham, NC 27708, USA
| | - Kathleen M. Schmeler
- The University of Texas MD Anderson Cancer Center, Department of Gynecologic Oncology and Reproductive Medicine, Houston, TX 77005, USA
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10
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Brenes D, Barberan CJ, Hunt B, Parra SG, Salcedo MP, Possati-Resende JC, Cremer ML, Castle PE, Fregnani JHTG, Maza M, Schmeler KM, Baraniuk R, Richards-Kortum R. Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer. Comput Med Imaging Graph 2022; 97:102052. [PMID: 35299096 PMCID: PMC9250128 DOI: 10.1016/j.compmedimag.2022.102052] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/04/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
Abstract
Cervical cancer is a public health emergency in low- and middle-income countries where resource limitations hamper standard-of-care prevention strategies. The high-resolution endomicroscope (HRME) is a low-cost, point-of-care device with which care providers can image the nuclear morphology of cervical lesions. Here, we propose a deep learning framework to diagnose cervical intraepithelial neoplasia grade 2 or more severe from HRME images. The proposed multi-task convolutional neural network uses nuclear segmentation to learn a diagnostically relevant representation. Nuclear segmentation was trained via proxy labels to circumvent the need for expensive, manually annotated nuclear masks. A dataset of images from over 1600 patients was used to train, validate, and test our algorithm; data from 20% of patients were reserved for testing. An external evaluation set with images from 508 patients was used to further validate our findings. The proposed method consistently outperformed other state-of-the art architectures achieving a test per patient area under the receiver operating characteristic curve (AUC-ROC) of 0.87. Performance was comparable to expert colposcopy with a test sensitivity and specificity of 0.94 (p = 0.3) and 0.58 (p = 1.0), respectively. Patients with recurrent human papillomavirus (HPV) infections are at a higher risk of developing cervical cancer. Thus, we sought to incorporate HPV DNA test results as a feature to inform prediction. We found that incorporating patient HPV status improved test specificity to 0.71 at a sensitivity of 0.94.
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Affiliation(s)
| | | | - Brady Hunt
- Rice University, Houston, TX 77005, USA.
| | | | - Mila P Salcedo
- University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | | | | | | | | | - Mauricio Maza
- Basic Health International, San Savlador, El Salvador.
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Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis. NPJ Digit Med 2022; 5:19. [PMID: 35169217 PMCID: PMC8847584 DOI: 10.1038/s41746-022-00559-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022] Open
Abstract
Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.
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Hunt B, Coole J, Brenes D, Kortum A, Mitbander R, Vohra I, Carns J, Schwarz R, Richards-Kortum R. High frame rate video mosaicking microendoscope to image large regions of intact tissue with subcellular resolution. BIOMEDICAL OPTICS EXPRESS 2021; 12:2800-2812. [PMID: 34123505 PMCID: PMC8176790 DOI: 10.1364/boe.425527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
High-resolution microendoscopy (HRME) is a low-cost strategy to acquire images of intact tissue with subcellular resolution at frame rates ranging from 11 to 18 fps. Current HRME imaging strategies are limited by the small microendoscope field of view (∼0.5 mm2); multiple images must be acquired and reliably registered to assess large regions of clinical interest. Image mosaics have been assembled from co-registered frames of video acquired as a microendoscope is slowly moved across the tissue surface, but the slow frame rate of previous HRME systems made this approach impractical for acquiring quality mosaicked images from large regions of interest. Here, we present a novel video mosaicking microendoscope incorporating a high frame rate CMOS sensor and optical probe holder to enable high-speed, high quality interrogation of large tissue regions of interest. Microendoscopy videos acquired at >90 fps are assembled into an image mosaic. We assessed registration accuracy and image sharpness across the mosaic for images acquired with a handheld probe over a range of translational speeds. This high frame rate video mosaicking microendoscope enables in vivo probe translation at >15 millimeters per second while preserving high image quality and accurate mosaicking, increasing the size of the region of interest that can be interrogated at high resolution from 0.5 mm2 to >30 mm2. Real-time deployment of this high-frame rate system is demonstrated in vivo and source code made publicly available.
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Affiliation(s)
- Brady Hunt
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Jackson Coole
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - David Brenes
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Alex Kortum
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Ruchika Mitbander
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Imran Vohra
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Jennifer Carns
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
| | - Richard Schwarz
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX 77025, USA
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