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Lee CY, Kim H, Degani I, Lee H, Sandoval A, Nam Y, Pascavis M, Park HG, Randall T, Ly A, Castro CM, Lee H. Empowering the on-site detection of nucleic acids by integrating CRISPR and digital signal processing. Nat Commun 2024; 15:6271. [PMID: 39054353 PMCID: PMC11272939 DOI: 10.1038/s41467-024-50588-3] [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: 12/29/2023] [Accepted: 07/15/2024] [Indexed: 07/27/2024] Open
Abstract
Addressing the global disparity in cancer care necessitates the development of rapid and affordable nucleic acid (NA) testing technologies. This need is particularly critical for cervical cancer, where molecular detection of human papillomavirus (HPV) has emerged as an accurate screening method. However, implementing this transition in low- and middle-income countries has been challenging due to the high costs and centralized facilities required for current NA tests. Here, we present CreDiT (CRISPR Enhanced Digital Testing) for on-site NA detection. The CreDiT platform integrates i) a one-pot CRISPR strategy that simultaneously amplifies both target NAs and analytical signals and ii) a robust fluorescent detection based on digital communication (encoding/decoding) technology. These features enable a rapid assay (<35 minutes) in a single streamlined workflow. We demonstrate the sensitive detection of cell-derived HPV DNA targets down to single copies and accurate identification of HPV types in clinical cervical brushing specimens (n = 121).
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Affiliation(s)
- Chang Yeol Lee
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Bionanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
| | - Hyunho Kim
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ismail Degani
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hanna Lee
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Angel Sandoval
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Yoonho Nam
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Chemical and Biomolecular Engineering (BK21 Four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Madeleine Pascavis
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA
- CaNCURE program, College of Science, Northeastern University, Boston, MA, USA
| | - Hyun Gyu Park
- Department of Chemical and Biomolecular Engineering (BK21 Four), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Thomas Randall
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA
| | - Amy Ly
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Cesar M Castro
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Hakho Lee
- Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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2
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Tandon R, Agrawal S, Rathore NPS, Mishra AK, Jain SK. A systematic review on deep learning-based automated cancer diagnosis models. J Cell Mol Med 2024; 28:e18144. [PMID: 38426930 PMCID: PMC10906380 DOI: 10.1111/jcmm.18144] [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: 06/28/2023] [Revised: 12/08/2023] [Accepted: 01/16/2024] [Indexed: 03/02/2024] Open
Abstract
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
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Affiliation(s)
| | | | | | - Abhinava K. Mishra
- Molecular, Cellular and Developmental Biology DepartmentUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
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3
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Jiang Y, Wang C, Zhou S. Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology. Semin Cancer Biol 2023; 96:82-99. [PMID: 37783319 DOI: 10.1016/j.semcancer.2023.09.005] [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: 12/17/2022] [Revised: 08/27/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.
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Affiliation(s)
- Yuting Jiang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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4
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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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5
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Gupta R, Gupta S. Point-of-care tests for human papillomavirus detection in uterine cervical samples: A review of advances in resource-constrained settings. Indian J Med Res 2023; 158:509-521. [PMID: 38236008 PMCID: PMC10878492 DOI: 10.4103/ijmr.ijmr_1143_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Indexed: 01/19/2024] Open
Abstract
Incidence of cervical cancer and associated mortality are still high in resource-constrained countries due to the lack of infrastructural facilities and trained workforce. Human papillomavirus (HPV)-based screening tests offer a better sensitivity (>90%) for the detection of cervical high-grade lesions. However, these tests usually require an extensive laboratory set-up and trained technical staff. Moreover, the high cost of the currently available and approved HPV tests precludes their use in the cervical cancer screening programmes in resource-limited settings. Hence, there is a felt need for a low-cost point-of-care (POC) HPV test with good performance characteristics to help augment cervical cancer screening in such settings. A recent meta-analysis demonstrated a good sensitivity and specificity for two of the commercially available POC HPV tests. The present review discusses the merits and limitations of the current commercially available POC and near-POC devices for HPV-based cervical cancer screening. The technologies that have the potential to be developed into low-cost POC tests and newer promising modalities for HPV-based POC or near POC have also been highlighted. This review underscores the need for collaborative and coordinated research for development of POC or near-POC HPV-based tests to be used in cervical cancer screening. Efforts need to be focussed on technologies that offer ease of performance without the requirement of sophisticated equipment or extensive sample pre-processing coupled with a good sensitivity and cost-effectiveness.
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Affiliation(s)
- Ruchika Gupta
- Division of Cytopathology, ICMR-National Institute for Cancer Prevention and Research, Noida, Uttar Pradesh, India
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6
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Nakisige C, de Fouw M, Kabukye J, Sultanov M, Nazrui N, Rahman A, de Zeeuw J, Koot J, Rao AP, Prasad K, Shyamala G, Siddharta P, Stekelenburg J, Beltman JJ. Artificial intelligence and visual inspection in cervical cancer screening. Int J Gynecol Cancer 2023; 33:1515-1521. [PMID: 37666527 PMCID: PMC10579490 DOI: 10.1136/ijgc-2023-004397] [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: 02/23/2023] [Accepted: 08/07/2023] [Indexed: 09/06/2023] Open
Abstract
INTRODUCTION Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm. METHODS A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values. RESULTS Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively. CONCLUSION This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.
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Affiliation(s)
| | - Marlieke de Fouw
- Gynecology, Leiden University Medical Center department of Gynecology, Leiden, Zuid-Holland, Netherlands
| | | | - Marat Sultanov
- University Medical Center Groningen, University of Groningen, Groningen, Netherlands, Groningen, Netherlands
| | | | - Aminur Rahman
- ICDDRB Public Health Sciences Division, Dhaka, Dhaka District, Bangladesh
| | - Janine de Zeeuw
- University Medical Center Groningen, University of Groningen, Groningen, Netherlands, Groningen, Netherlands
| | - Jaap Koot
- University Medical Center Groningen, University of Groningen, Groningen, Netherlands, Groningen, Netherlands
| | - Arathi P Rao
- Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India, Manipal, India
| | - Keerthana Prasad
- Manipal Academy of Higher Education School of Life Sciences, Manipal, Karnataka, India
| | - Guruvare Shyamala
- Manipal Academy of Higher Education - Mangalore Campus, Mangalore, Karnataka, India
| | - Premalatha Siddharta
- Gynecological Oncology, St John's National Academy of Health Sciences, Bangalore, Karnataka, India
| | - Jelle Stekelenburg
- University Medical Center Groningen, University of Groningen, Groningen, Netherlands, Groningen, Netherlands
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7
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Yao H, Zhang X. A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples. Front Microbiol 2023; 14:1232295. [PMID: 37529327 PMCID: PMC10387549 DOI: 10.3389/fmicb.2023.1232295] [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: 05/31/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Human papillomavirus (HPV) is a sexually transmitted virus. Cervical cancer is one of the highest incidences of cancer, almost all patients are accompanied by HPV infection. In addition, the occurrence of a variety of cancers is also associated with HPV infection. HPV vaccination has gained widespread popularity in recent years with the increase in public health awareness. In this context, HPV testing not only needs to be sensitive and specific but also needs to trace the source of HPV infection. Through machine learning and deep learning, information from medical examinations can be used more effectively. In this review, we discuss recent advances in HPV testing in combination with machine learning and deep learning.
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Affiliation(s)
- Huanchun Yao
- Department of Cancer, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xinglong Zhang
- Department of Hematology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
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8
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Burrow DT, Heggestad JT, Kinnamon DS, Chilkoti A. Engineering Innovative Interfaces for Point-of-Care Diagnostics. Curr Opin Colloid Interface Sci 2023; 66:101718. [PMID: 37359425 PMCID: PMC10247612 DOI: 10.1016/j.cocis.2023.101718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023]
Abstract
The ongoing Coronavirus disease 2019 (COVID-19) pandemic illustrates the need for sensitive and reliable tools to diagnose and monitor diseases. Traditional diagnostic approaches rely on centralized laboratory tests that result in long wait times to results and reduce the number of tests that can be given. Point-of-care tests (POCTs) are a group of technologies that miniaturize clinical assays into portable form factors that can be run both in clinical areas --in place of traditional tests-- and outside of traditional clinical settings --to enable new testing paradigms. Hallmark examples of POCTs are the pregnancy test lateral flow assay and the blood glucose meter. Other uses for POCTs include diagnostic assays for diseases like COVID-19, HIV, and malaria but despite some successes, there are still unsolved challenges for fully translating these lower cost and more versatile solutions. To overcome these challenges, researchers have exploited innovations in colloid and interface science to develop various designs of POCTs for clinical applications. Herein, we provide a review of recent advancements in lateral flow assays, other paper based POCTs, protein microarray assays, microbead flow assays, and nucleic acid amplification assays. Features that are desirable to integrate into future POCTs, including simplified sample collection, end-to-end connectivity, and machine learning, are also discussed in this review.
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Affiliation(s)
- Damon T Burrow
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708 USA
| | - Jacob T Heggestad
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708 USA
| | - David S Kinnamon
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708 USA
| | - Ashutosh Chilkoti
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708 USA
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9
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Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification. APPL INTELL 2023; 53:7201-7215. [PMID: 35875199 PMCID: PMC9289654 DOI: 10.1007/s10489-022-03893-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/13/2022] [Indexed: 11/18/2022]
Abstract
COVID-19 has become a pandemic for the entire world, and it has significantly affected the world economy. The importance of early detection and treatment of the infection cannot be overstated. The traditional diagnosis techniques take more time in detecting the infection. Although, numerous deep learning-based automated solutions have recently been developed in this regard, nevertheless, the limitation of computational and battery power in resource-constrained devices makes it difficult to deploy trained models for real-time inference. In this paper, to detect the presence of COVID-19 in CT-scan images, an important weights-only transfer learning method has been proposed for devices with limited runt-time resources. In the proposed method, the pre-trained models are made point-of-care devices friendly by pruning less important weight parameters of the model. The experiments were performed on two popular VGG16 and ResNet34 models and the empirical results showed that pruned ResNet34 model achieved 95.47% accuracy, 0.9216 sensitivity, 0.9567 F-score, and 0.9942 specificity with 41.96% fewer FLOPs and 20.64% fewer weight parameters on the SARS-CoV-2 CT-scan dataset. The results of our experiments showed that the proposed method significantly reduces the run-time resource requirements of the computationally intensive models and makes them ready to be utilized on the point-of-care devices.
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10
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Gupta A, Parveen A, Kumar A, Yadav P. Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer. Curr Genomics 2022; 23:234-245. [PMID: 36777879 PMCID: PMC9875539 DOI: 10.2174/1389202923666220511155939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/20/2022] [Accepted: 03/25/2022] [Indexed: 11/22/2022] Open
Abstract
Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.
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Affiliation(s)
- Akshat Gupta
- Department of Biotechnology, Motilal Nehru National Institute of Technology, Allahabad, Prayagraj, 211004, India
| | - Alisha Parveen
- Rudolf-Zenker, Institute of Experimental Surgery, Rostock University Medical Center, Rostock, Germany
| | - Abhishek Kumar
- Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India;,Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, 576104, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Jodhpur, 342037 India,Address correspondence to this author at the Department of Bioscience and Bioengineering, Indian Institute of Technology, Jodhpur, 342037 Rajasthan, India; Tel: +91 (0) 291 280-1211; E-mail:
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11
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Hou X, Shen G, Zhou L, Li Y, Wang T, Ma X. Artificial Intelligence in Cervical Cancer Screening and Diagnosis. Front Oncol 2022; 12:851367. [PMID: 35359358 PMCID: PMC8963491 DOI: 10.3389/fonc.2022.851367] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/10/2022] [Indexed: 12/11/2022] Open
Abstract
Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.
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Affiliation(s)
- Xin Hou
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Guangyang Shen
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Liqiang Zhou
- Cancer Centre and Center of Reproduction, Development and Aging, Faculty of Health Sciences, University of Macau, Macau, Macau SAR, China
| | - Yinuo Li
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Tian Wang
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangyi Ma
- Department of Obstetrics and Gynecology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Xiangyi Ma,
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12
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Yang Z, Francisco J, Reese AS, Spriggs DR, Im H, Castro CM. Addressing cervical cancer screening disparities through advances in artificial intelligence and nanotechnologies for cellular profiling. BIOPHYSICS REVIEWS 2021; 2:011303. [PMID: 33842926 PMCID: PMC8015256 DOI: 10.1063/5.0043089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/15/2021] [Indexed: 06/12/2023]
Abstract
Almost all cases of cervical cancer are caused by the human papilloma virus (HPV). Detection of pre-cancerous cervical changes provides a window of opportunity for cure of an otherwise lethal disease when metastatic. With a greater understanding of the biology and natural course of high-risk HPV infections, screening methods have shifted beyond subjective Pap smears toward more sophisticated and objective tactics. This has led to a substantial growth in the breadth and depth of HPV-based cervical cancer screening tests, especially in developed countries without constrained resources. Many low- and middle-income countries (LMICs) have less access to advanced laboratories and healthcare resources, so new point-of-care (POC) technologies have been developed to provide test results in real time, improve the efficiency of techniques, and increase screening adoption. In this Review, we will discuss how novel decentralized screening technologies and computational strategies improve upon traditional methods and how their realized promise could further democratize cervical cancer screening and promote greater disease prevention.
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Affiliation(s)
| | | | - Alexandra S. Reese
- Cancer Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - David R. Spriggs
- Cancer Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Hyungsoon Im
- Authors to whom all correspondence should be addressed: and
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Cao R, Yang F, Ma SC, Liu L, Zhao Y, Li Y, Wu DH, Wang T, Lu WJ, Cai WJ, Zhu HB, Guo XJ, Lu YW, Kuang JJ, Huan WJ, Tang WM, Huang K, Huang J, Yao J, Dong ZY. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer. Am J Cancer Res 2020; 10:11080-11091. [PMID: 33042271 PMCID: PMC7532670 DOI: 10.7150/thno.49864] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/25/2020] [Indexed: 12/21/2022] Open
Abstract
Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
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