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Velez Torres JM, Vaickus LJ, Kerr DA. Thyroid Fine-Needle Aspiration: The Current and Future Landscape of Cytopathology. Surg Pathol Clin 2024; 17:371-381. [PMID: 39129137 DOI: 10.1016/j.path.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Thyroid cytology is a rapidly evolving field that has seen significant advances in recent years. Its main goal is to accurately diagnose thyroid nodules, differentiate between benign and malignant lesions, and risk stratify nodules when a definitive diagnosis is not possible. The current landscape of thyroid cytology includes the use of fine-needle aspiration for the diagnosis of thyroid nodules with the use of uniform, tiered reporting systems such as the Bethesda System for Reporting Thyroid Cytopathology. In recent years, molecular testing has emerged as a reliable preoperative diagnostic tool that stratifies patients into different risk categories (low, intermediate, or high) with varying probabilities of malignancy and helps guide patient treatment.
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Affiliation(s)
- Jaylou M Velez Torres
- University of Miami Hospital, Miller School of Medicine, 1400 NW 12th Avenue, Room 4078, Miami, FL 33136, USA. https://twitter.com/JaylouVelez
| | - Louis J Vaickus
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, NH 03756, USA; Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| | - Darcy A Kerr
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon, NH 03756, USA; Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
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Kim D, Thrall MJ, Michelow P, Schmitt FC, Vielh PR, Siddiqui MT, Sundling KE, Virk R, Alperstein S, Bui MM, Chen-Yost H, Donnelly AD, Lin O, Liu X, Madrigal E, Zakowski MF, Parwani AV, Jenkins E, Pantanowitz L, Li Z. The current state of digital cytology and artificial intelligence (AI): global survey results from the American Society of Cytopathology Digital Cytology Task Force. J Am Soc Cytopathol 2024:S2213-2945(24)00039-5. [PMID: 38744615 DOI: 10.1016/j.jasc.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/25/2024] [Accepted: 04/11/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION The integration of whole slide imaging (WSI) and artificial intelligence (AI) with digital cytology has been growing gradually. Therefore, there is a need to evaluate the current state of digital cytology. This study aimed to determine the current landscape of digital cytology via a survey conducted as part of the American Society of Cytopathology (ASC) Digital Cytology White Paper Task Force. MATERIALS AND METHODS A survey with 43 questions pertaining to the current practices and experiences of WSI and AI in both surgical pathology and cytology was created. The survey was sent to members of the ASC, the International Academy of Cytology (IAC), and the Papanicolaou Society of Cytopathology (PSC). Responses were recorded and analyzed. RESULTS In total, 327 individuals participated in the survey, spanning a diverse array of practice settings, roles, and experiences around the globe. The majority of responses indicated there was routine scanning of surgical pathology slides (n = 134; 61%) with fewer respondents scanning cytology slides (n = 150; 46%). The primary challenge for surgical WSI is the need for faster scanning and cost minimization, whereas image quality is the top issue for cytology WSI. AI tools are not widely utilized, with only 16% of participants using AI for surgical pathology samples and 13% for cytology practice. CONCLUSIONS Utilization of digital pathology is limited in cytology laboratories as compared to surgical pathology. However, as more laboratories are willing to implement digital cytology in the near future, the establishment of practical clinical guidelines is needed.
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Affiliation(s)
- David Kim
- Department of Pathology & Laboratory Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York.
| | - Michael J Thrall
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | - Pamela Michelow
- Department of Anatomical Pathology, National Health Laboratory Service, Johannesburg, South Africa; Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - Fernando C Schmitt
- Department of Pathology, Medical Faculty of Porto University, Porto, Portugal
| | - Philippe R Vielh
- Department of Pathology, Medipath and American Hospital of Paris, Paris, France
| | - Momin T Siddiqui
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Kaitlin E Sundling
- The Wisconsin State Laboratory of Hygiene and Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin
| | - Renu Virk
- Department of Pathology and Cell Biology, Columbia University, New York, New York
| | - Susan Alperstein
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Marilyn M Bui
- The Departments of Pathology and Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, Florida
| | | | - Amber D Donnelly
- University of Nebraska Medical Center, Cytotechnology Education, College of Allied Health Professions, Omaha, Nebraska
| | - Oscar Lin
- Department of Pathology & Laboratory Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Xiaoying Liu
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire
| | - Emilio Madrigal
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts
| | - Maureen F Zakowski
- Department of Pathology, Molecular, and Cell-Based Medicine, Mount Sinai Medical Center, New York, New York
| | - Anil V Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | | | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Zaibo Li
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, Ohio
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Franzén B, Auer G, Lewensohn R. Minimally invasive biopsy-based diagnostics in support of precision cancer medicine. Mol Oncol 2024. [PMID: 38519839 DOI: 10.1002/1878-0261.13640] [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: 09/29/2023] [Revised: 01/31/2024] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Precision cancer medicine (PCM) to support the treatment of solid tumors requires minimally invasive diagnostics. Here, we describe the development of fine-needle aspiration biopsy-based (FNA) molecular cytology which will be increasingly important in diagnostics and adaptive treatment. We provide support for FNA-based molecular cytology having a significant potential to replace core needle biopsy (CNB) as a patient-friendly potent technique for tumor sampling for various tumor types. This is not only because CNB is a more traumatic procedure and may be associated with more complications compared to FNA-based sampling, but also due to the recently developed molecular methods used with FNA. Recent studies show that image-guided FNA in combination with ultrasensitive molecular methods also offers opportunities for characterization of the tumor microenvironment which can aid therapeutic decisions. Here we provide arguments for an increased implementation of molecular FNA-based sampling as a patient-friendly diagnostic method, which may, due to its repeatability, facilitate regular sampling that is needed during different treatment lines, to provide tumor information, supporting treatment decisions, shortening lead times in healthcare, and benefit healthcare economics.
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Affiliation(s)
- Bo Franzén
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Cancer Centre Karolinska (CCK) Foundation, Karolinska University Hospital, Stockholm, Sweden
| | - Gert Auer
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Rolf Lewensohn
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Medical Unit Head and Neck, Lung, and Skin Tumors, Thoracic Oncology Center, Karolinska University Hospital, Stockholm, Sweden
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Dell’Era V, Perotti A, Starnini M, Campagnoli M, Rosa MS, Saino I, Aluffi Valletti P, Garzaro M. Machine Learning Model as a Useful Tool for Prediction of Thyroid Nodules Histology, Aggressiveness and Treatment-Related Complications. J Pers Med 2023; 13:1615. [PMID: 38003930 PMCID: PMC10672369 DOI: 10.3390/jpm13111615] [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: 10/18/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
Thyroid nodules are very common, 5-15% of which are malignant. Despite the low mortality rate of well-differentiated thyroid cancer, some variants may behave aggressively, making nodule differentiation mandatory. Ultrasound and fine-needle aspiration biopsy are simple, safe, cost-effective and accurate diagnostic tools, but have some potential limits. Recently, machine learning (ML) approaches have been successfully applied to healthcare datasets to predict the outcomes of surgical procedures. The aim of this work is the application of ML to predict tumor histology (HIS), aggressiveness and post-surgical complications in thyroid patients. This retrospective study was conducted at the ENT Division of Eastern Piedmont University, Novara (Italy), and reported data about 1218 patients who underwent surgery between January 2006 and December 2018. For each patient, general information, HIS and outcomes are reported. For each prediction task, we trained ML models on pre-surgery features alone as well as on both pre- and post-surgery data. The ML pipeline included data cleaning, oversampling to deal with unbalanced datasets and exploration of hyper-parameter space for random forest models, testing their stability and ranking feature importance. The main results are (i) the construction of a rich, hand-curated, open dataset including pre- and post-surgery features (ii) the development of accurate yet explainable ML models. Results highlight pre-screening as the most important feature to predict HIS and aggressiveness, and that, in our population, having an out-of-range (Low) fT3 dosage at pre-operative examination is strongly associated with a higher aggressiveness of the disease. Our work shows how ML models can find patterns in thyroid patient data and could support clinicians to refine diagnostic tools and improve their accuracy.
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Affiliation(s)
- Valeria Dell’Era
- ENT Division, Novara Maggiore Hospital, 28100 Novara, Italy; (M.S.R.); (I.S.)
| | | | - Michele Starnini
- CENTAI Institute, 10138 Turin, Italy; (A.P.)
- Departament de Fisica, Universitat Politecnica de Catalunya, Campus Nord, 08034 Barcelona, Spain
| | - Massimo Campagnoli
- Department of Otorhinolaryngology, Ss. Trinità Hospital, 28021 Borgomanero, Italy;
| | - Maria Silvia Rosa
- ENT Division, Novara Maggiore Hospital, 28100 Novara, Italy; (M.S.R.); (I.S.)
| | - Irene Saino
- ENT Division, Novara Maggiore Hospital, 28100 Novara, Italy; (M.S.R.); (I.S.)
| | - Paolo Aluffi Valletti
- ENT Division, Health Science Department, School of Medicine, Universitá del Piemonte Orientale, 28100 Novara, Italy; (P.A.V.); (M.G.)
| | - Massimiliano Garzaro
- ENT Division, Health Science Department, School of Medicine, Universitá del Piemonte Orientale, 28100 Novara, Italy; (P.A.V.); (M.G.)
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Sharma R, Mahanti GK, Panda G, Rath A, Dash S, Mallik S, Hu R. A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms. J Imaging 2023; 9:173. [PMID: 37754937 PMCID: PMC10532397 DOI: 10.3390/jimaging9090173] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.
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Affiliation(s)
- Rohit Sharma
- Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur 713209, India; (R.S.); (G.K.M.)
| | - Gautam Kumar Mahanti
- Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur 713209, India; (R.S.); (G.K.M.)
| | - Ganapati Panda
- Department of Electronics and Communication Engineering, C.V. Raman Global University, Bhubaneswar 752054, India;
| | - Adyasha Rath
- Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar 752054, India;
| | - Sujata Dash
- Department of Information Technology, Nagaland University, Dimapur 797112, India;
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA
- Department of Pharmacology & Toxicology, The University of Arizona, Tucson, MA 85721, USA
| | - Ruifeng Hu
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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Kussaibi H, Alsafwani N. Trends in AI-powered Classification of Thyroid Neoplasms Based on Histopathology Images - a Systematic Review. Acta Inform Med 2023; 31:280-286. [PMID: 38379694 PMCID: PMC10875959 DOI: 10.5455/aim.2023.31.280-286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 12/20/2023] [Indexed: 02/22/2024] Open
Abstract
Background Assessment of thyroid nodules histopathology using AI is crucial for an accurate diagnosis. This systematic review analyzes recent works employing deep learning approaches for classifying thyroid nodules based on histopathology images, evaluating their performance, and identifying limitations. Methods Eligibility criteria focused on peer-reviewed English papers published in the last 5 years, applying deep learning to categorize thyroid histopathology images. The PubMed database was searched using relevant keyword combinations. Results Out of 103 articles, 11 studies met inclusion criteria. They used convolutional neural networks to classify thyroid neoplasm. Most studies aimed for basic tumor subtyping; however, 3 studies targeted the prediction of tumor-associated mutation. Accuracy ranged from 77% to 100%, with most over 90%. Discussion The findings from our analysis reveal the effectiveness of deep learning in identifying discriminative morphological patterns from histopathology images, thus enhancing the accuracy of thyroid nodule histopathological classification. Key limitations were small sample sizes, subjective annotation, and limited dataset diversity. Further research with larger diverse datasets, model optimization, and real-world validation is essential to translate these tools into clinical practice.
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Affiliation(s)
- Haitham Kussaibi
- Department of Pathology, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Noor Alsafwani
- Department of Pathology, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
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