1
|
Rende PRF, Pires JM, Nakadaira KS, Lopes S, Vale J, Hecht F, Beltrão FEL, Machado GJR, Kimura ET, Eloy C, Ramos HE. Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model. J Pathol Transl Med 2024; 58:117-126. [PMID: 38684222 PMCID: PMC11106606 DOI: 10.4132/jptm.2024.03.07] [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: 09/08/2023] [Revised: 02/12/2024] [Accepted: 03/06/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration. METHODS We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio. RESULTS This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value. CONCLUSIONS The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.
Collapse
Affiliation(s)
- Pedro R. F. Rende
- Bioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil
| | | | | | - Sara Lopes
- Endocrinology Department, Hospital de Braga, Braga, Portugal
| | - João Vale
- Laboratory of Pathology of the Institute of Pathology and Molecular Immunology, University of Porto, Porto, Portugal
| | - Fabio Hecht
- Department of Biomedical Genetics, University of Rochester, Rochester, New York, USA
| | - Fabyan E. L. Beltrão
- Bioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil
| | - Gabriel J. R. Machado
- Bioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil
| | - Edna T. Kimura
- Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Catarina Eloy
- Laboratory of Pathology of the Institute of Pathology and Molecular Immunology, University of Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - Helton E. Ramos
- Bioregulation Department, Health and Science Institute, Federal University of Bahia, Salvador, Brazil
- Postgraduate Program in Medicine and Health, Bahia Faculty of Medicine, Federal University of Bahia, Salvador, Brazil
| |
Collapse
|
2
|
Kim HK, Han E, Lee J, Yim K, Abdul-Ghafar J, Seo KJ, Seo JW, Gong G, Cho NH, Kim M, Yoo CW, Chong Y. Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid. Cancers (Basel) 2024; 16:1064. [PMID: 38473421 DOI: 10.3390/cancers16051064] [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: 11/29/2023] [Revised: 02/17/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Ascites cytology is a cost-effective test for metastatic colorectal cancer (CRC) in the abdominal cavity. However, metastatic carcinoma of the peritoneum is difficult to diagnose based on biopsy findings, and ascitic aspiration cytology has a low sensitivity and specificity and a high inter-observer variability. The aim of the present study was to apply artificial intelligence (AI) to classify benign and malignant cells in ascites cytology patch images of metastatic CRC using a deep convolutional neural network. Datasets were collected from The OPEN AI Dataset Project, a nationwide cytology dataset for AI research. The numbers of patch images used for training, validation, and testing were 56,560, 7068, and 6534, respectively. We evaluated 1041 patch images of benign and metastatic CRC in the ascitic fluid to compare the performance of pathologists and an AI algorithm, and to examine whether the diagnostic accuracy of pathologists improved with the assistance of AI. This AI method showed an accuracy, a sensitivity, and a specificity of 93.74%, 87.76%, and 99.75%, respectively, for the differential diagnosis of malignant and benign ascites. The diagnostic accuracy and sensitivity of the pathologist with the assistance of the proposed AI method increased from 86.8% to 90.5% and from 73.3% to 79.3%, respectively. The proposed deep learning method may assist pathologists with different levels of experience in diagnosing metastatic CRC cells of ascites.
Collapse
Affiliation(s)
- Hyung Kyung Kim
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
- Department of Pathology, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Eunkyung Han
- Department of Pathology, Soonchunyang University Hospital Bucheon, Bucheon 14584, Republic of Korea
| | - Jeonghyo Lee
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Kwangil Yim
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Jamshid Abdul-Ghafar
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Kyung Jin Seo
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| | - Jang Won Seo
- AI Team, MTS Company Inc., Seoul 06178, Republic of Korea
| | - Gyungyub Gong
- Department of Pathology, Asan Medical Center, Seoul 05505, Republic of Korea
| | - Nam Hoon Cho
- Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Milim Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Chong Woo Yoo
- Department of Pathology, National Cancer Center, Goyang 10408, Republic of Korea
| | - Yosep Chong
- Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul 06591, Republic of Korea
| |
Collapse
|
3
|
Slabaugh G, Beltran L, Rizvi H, Deloukas P, Marouli E. Applications of machine and deep learning to thyroid cytology and histopathology: a review. Front Oncol 2023; 13:958310. [PMID: 38023130 PMCID: PMC10661921 DOI: 10.3389/fonc.2023.958310] [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: 05/31/2022] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner.
Collapse
Affiliation(s)
- Greg Slabaugh
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
| | - Luis Beltran
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
| | - Hasan Rizvi
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Eirini Marouli
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| |
Collapse
|
4
|
Dov D, Elliott Range D, Cohen J, Bell J, Rocke DJ, Kahmke RR, Weiss-Meilik A, Lee WT, Henao R, Carin L, Kovalsky SZ. Deep-Learning-Based Screening and Ancillary Testing for Thyroid Cytopathology. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:1185-1194. [PMID: 37611969 PMCID: PMC10477952 DOI: 10.1016/j.ajpath.2023.05.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 05/06/2023] [Accepted: 05/19/2023] [Indexed: 08/25/2023]
Abstract
Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification.
Collapse
Affiliation(s)
- David Dov
- I-Medata AI Center, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo, Israel; Department of Pathology, Duke University Medical Center, Durham, North Carolina.
| | | | - Jonathan Cohen
- Department of Head and Neck Surgery, Kaplan Medical Center, Rehovot, Israel
| | - Jonathan Bell
- Department of Pathology, Duke University Medical Center, Durham, North Carolina
| | - Daniel J Rocke
- Department of Head and Neck Surgery & Communication Sciences, Duke University Medical Center, Durham, North Carolina
| | - Russel R Kahmke
- Department of Head and Neck Surgery & Communication Sciences, Duke University Medical Center, Durham, North Carolina
| | - Ahuva Weiss-Meilik
- I-Medata AI Center, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo, Israel
| | - Walter T Lee
- Department of Head and Neck Surgery & Communication Sciences, Duke University Medical Center, Durham, North Carolina
| | - Ricardo Henao
- Biological, Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Lawrence Carin
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Shahar Z Kovalsky
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| |
Collapse
|
5
|
Toro-Tobon D, Loor-Torres R, Duran M, Fan JW, Singh Ospina N, Wu Y, Brito JP. Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions. Thyroid 2023; 33:903-917. [PMID: 37279303 PMCID: PMC10440669 DOI: 10.1089/thy.2023.0132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.
Collapse
Affiliation(s)
- David Toro-Tobon
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Ricardo Loor-Torres
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Mayra Duran
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jungwei W. Fan
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Naykky Singh Ospina
- Division of Endocrinology, Department of Medicine, University of Florida, Gainesville, Florida, USA
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Juan P. Brito
- Division of Endocrinology, Diabetes, Metabolism and Nutrition, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
6
|
Thakur N, Alam MR, Abdul-Ghafar J, Chong Y. Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review. Cancers (Basel) 2022; 14:cancers14143529. [PMID: 35884593 PMCID: PMC9316753 DOI: 10.3390/cancers14143529] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 11/27/2022] Open
Abstract
Simple Summary Artificial intelligence (AI) has attracted significant interest in the healthcare sector due to its promising results. Cytological examination is a critical step in the initial diagnosis of cancer. Here, we conducted a systematic review with quantitative analysis to understand the current status of AI applications in non-gynecological (non-GYN) cancer cytology. In our analysis, we found that most of the studies focused on classification and segmentation tasks. Overall, AI showed promising results for non-GYN cancer cytopathology analysis. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation across all studies. Abstract State-of-the-art artificial intelligence (AI) has recently gained considerable interest in the healthcare sector and has provided solutions to problems through automated diagnosis. Cytological examination is a crucial step in the initial diagnosis of cancer, although it shows limited diagnostic efficacy. Recently, AI applications in the processing of cytopathological images have shown promising results despite the elementary level of the technology. Here, we performed a systematic review with a quantitative analysis of recent AI applications in non-gynecological (non-GYN) cancer cytology to understand the current technical status. We searched the major online databases, including MEDLINE, Cochrane Library, and EMBASE, for relevant English articles published from January 2010 to January 2021. The searched query terms were: “artificial intelligence”, “image processing”, “deep learning”, “cytopathology”, and “fine-needle aspiration cytology.” Out of 17,000 studies, only 26 studies (26 models) were included in the full-text review, whereas 13 studies were included for quantitative analysis. There were eight classes of AI models treated of according to target organs: thyroid (n = 11, 39%), urinary bladder (n = 6, 21%), lung (n = 4, 14%), breast (n = 2, 7%), pleural effusion (n = 2, 7%), ovary (n = 1, 4%), pancreas (n = 1, 4%), and prostate (n = 1, 4). Most of the studies focused on classification and segmentation tasks. Although most of the studies showed impressive results, the sizes of the training and validation datasets were limited. Overall, AI is also promising for non-GYN cancer cytopathology analysis, such as pathology or gynecological cytology. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation found across all studies. Future studies with larger datasets with high-quality annotations and external validation are required.
Collapse
|
7
|
Xu Z, Shen J, Qu Y, Chen H, Zhou X, Hong H, Sun H, Lin H, Deng W, Wu F. Using simple and easy water quality parameters to predict trihalomethane occurrence in tap water. CHEMOSPHERE 2022; 286:131586. [PMID: 34303907 DOI: 10.1016/j.chemosphere.2021.131586] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 07/11/2021] [Accepted: 07/15/2021] [Indexed: 06/13/2023]
Abstract
Monitoring of disinfection by-products (DBPs) in water supply system is important to ensure safety of drinking water. Yet it is a laborious job. Developing predictive DBPs models using simple and easy parameters is a promising way. Yet current models could not be well applied into practice because of the improper dataset (e.g. not from real tap water) they used or involving the parameters that are difficult to measure or require expensive instruments. In this study, four simple and easy water quality parameters (temperature, pH, UVA254 and Cl2) were used to predict trihalomethane (THMs) occurrence in tap water. Linear/log linear regression models (LRM) and radial basis function artificial neural network (RBF ANN) were adopted to develop the THMs models. 64 observations from tap water samples were used to develop and test models. Results showed that only one or two parameters entered LRMs, and their prediction ability was very limited (testing datasets: N25 = 46-69%, rp = 0.334-0.459). Different from LRM, the prediction accuracy of RBF ANNs developed with pH, temperature, UVA254 and Cl2 can be improved continuously by tweaking the maximum number of neuron (MN) and Gaussian function spread (S) until it reached best. The optimum RBF ANNs of T-THMs, TCM and BDCM were obtained when setting MN = 20, S = 100, 100.1 and 60, respectively, where the N25 and rp values for testing datasets reached 85-92% and 0.813-0.886, respectively. Accurate predictions of THMs by RBF ANNs with these four simple and easy parameters paved an economic and convenient way for THMs monitoring in real water supply system.
Collapse
Affiliation(s)
- Zeqiong Xu
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | - Jiao Shen
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | - Yuqing Qu
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | | | - Xiaoling Zhou
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | - Huachang Hong
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China.
| | - Hongjie Sun
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China
| | - Hongjun Lin
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China.
| | - Wenjing Deng
- Department of Science and Environmental Studies, The Education University of Hong Kong, Tai Po, N.T, Hong Kong
| | - Fuyong Wu
- College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, PR China
| |
Collapse
|