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Kim CA, An HR, Yoo J, Lee YM, Sung TY, Kim WG, Song DE. Morphometric Analysis of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Digital Pathology. Endocr Pathol 2024; 35:113-121. [PMID: 38064165 DOI: 10.1007/s12022-023-09790-0] [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] [Accepted: 11/02/2023] [Indexed: 06/14/2024]
Abstract
Digital pathology uses digitized images for cancer research. We aimed to assess morphometric parameters using digital pathology for predicting recurrence in patients with papillary thyroid carcinoma (PTC) and lateral cervical lymph node (LN) metastasis. We analyzed 316 PTC patients and assessed the longest diameter and largest area of metastatic focus in LNs using a whole slide imaging scanner. In digital pathology assessment, the longest diameters and largest areas of metastatic foci in LNs were positively correlated with traditional optically measured diameters (R = 0.928 and R2 = 0.727, p < 0.001 and p < 0.001, respectively). The optimal cutoff diameter was 8.0 mm in both traditional microscopic (p = 0.009) and digital pathology (p = 0.016) evaluations, with significant differences in progression-free survival (PFS) observed at this cutoff (p = 0.006 and p = 0.002, respectively). The predictive area's cutoff was 35.6 mm2 (p = 0.005), which significantly affected PFS (p = 0.015). Using an 8.0-mm cutoff in traditional microscopic evaluation and a 35.6-mm2 cutoff in digital pathology showed comparable predictive results using the proportion of variation explained (PVE) methods (2.6% vs. 2.4%). Excluding cases with predominant cystic changes in LNs, the largest metastatic areas by digital pathology had the highest PVE at 3.9%. Furthermore, high volume of LN metastasis (p = 0.001), extranodal extension (p = 0.047), and high ratio of metastatic LNs (p = 0.006) were associated with poor prognosis. Both traditional microscopic and digital pathology evaluations effectively measured the longest diameter of metastatic foci in LNs. Moreover, digital pathology offers limited advantages in predicting PFS of patients with lateral cervical LN metastasis of PTC, especially those without predominant cystic changes in LNs.
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Affiliation(s)
- Chae A Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyeong Rok An
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jungmin Yoo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yu-Mi Lee
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Tae-Yon Sung
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Won Gu Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Dong Eun Song
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Caulley L, Eskander A, Yang W, Auh E, Cairncross L, Cho NL, Golbon B, Iyer S, Liu JC, Lee PJ, Lindeman B, Meltzer C, Molin N, Moore A, Noel J, Nozolino H, Pasternak J, Price B, Ramsay T, Rolighed L, Sajisevi M, Sharma A, Sinclair C, Sorensen M, Tae K, Tang AL, Tsao G, Williams M, Wrenn S, Xing MH, Zafereo M, Stack BC, Randolph G, Davies L. Trends in Diagnosis of Noninvasive Follicular Thyroid Neoplasm With Papillarylike Nuclear Features and Total Thyroidectomies for Patients With Papillary Thyroid Neoplasms. JAMA Otolaryngol Head Neck Surg 2021; 148:99-106. [PMID: 34817546 DOI: 10.1001/jamaoto.2021.3277] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Importance Increasing detection of early-stage papillary thyroid neoplasms without improvements in mortality has prompted development of strategies to prevent or mitigate overtreatment. Objective To determine adoption rates of 2 recent strategies developed to limit overtreatment of low-risk thyroid cancers: (1) a new classification, noninvasive follicular thyroid neoplasm with papillarylike nuclear features (NIFTP), and (2) hemithyroidectomy for selected papillary thyroid carcinomas (PTCs) up to 4 cm in size. Design, Setting, and Participants This is a cross-sectional analysis of 3368 pathology records of 2 cohorts of patients from 18 hospitals in 6 countries during 2 time periods (2015 and 2019). Participating hospitals were included from the US (n = 12), Canada (n = 2), Denmark (n = 1), South Korea (n = 1), South Africa (n = 1), and India (n = 1). The records of the first 100 patients per institution for each year who underwent thyroid-directed surgery (hemithyroidectomy, total thyroidectomy, or completion thyroidectomy) were reviewed. Main Outcomes and Measures Frequency of diagnosis of NIFTP, PTCs, and thyroidectomies during the study period. Results Of the 790 papillary thyroid neoplasms captured in the 2019 cohort, 38 (4.8%) were diagnosed as NIFTP. Diagnosis of NIFTP was observed in the US, South Africa, and India. There was minimal difference in the total proportion of PTCs in the 2015 cohort compared with the 2019 cohort (778 [47.1%] vs 752 [44.5%]; difference, 2.6% [95% CI, -16.9% to 22.1%]). The proportion of PTCs eligible for hemithyroidectomy but treated with total thyroidectomy in the 2 cohorts demonstrated a decreasing trend from 2015 to 2019 (341 of 453 [75.3%] vs 253 of 434 [58.3%]; difference, 17.0% [95% CI, -1.2% to 35.2%]). Conclusions and Relevance Results of this cohort study showed that the 2 mitigation strategies for preventing overtreatment of early-stage thyroid cancer have had mixed success. The diagnosis of NIFTP has only been applied to a small proportion of thyroid neoplasms compared with expected rates. However, more patients eligible for hemithyroidectomy received it in 2019 compared with 2015, showing some success with this deescalation strategy.
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Affiliation(s)
- Lisa Caulley
- Department of Otolaryngology-Head and Neck Surgery, University of Ottawa, Ottawa, Ontario, Canada.,Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Department of Otolaryngology-Head and Neck Surgery, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Antoine Eskander
- Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Weining Yang
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Edel Auh
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire
| | - Lydia Cairncross
- Department of Surgery, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Nancy L Cho
- Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bahar Golbon
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Subramania Iyer
- Department of Head and Neck Surgery, Amrita Institute of Medical Sciences, Kochi, India
| | - Jeffrey C Liu
- Division of Head and Neck Surgery, Fox Chase Cancer Center, Philadelphia, Pennsylvania.,Department of Otolaryngology-Head and Neck Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | - Paul J Lee
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, Ohio
| | - Brenessa Lindeman
- Department of General Surgery, School of Medicine, University of Alabama at Birmingham, Birmingham
| | | | - Nicole Molin
- Department of Otolaryngology-Head and Neck Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | - Alessandra Moore
- Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Julia Noel
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Stanford University, Stanford, California
| | - Halie Nozolino
- Department of General Surgery, School of Medicine, University of Alabama at Birmingham, Birmingham
| | - Jesse Pasternak
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Brendon Price
- Division of Anatomical Pathology, Department of Pathology, Faculty of Health Sciences, University of Cape Town/National Health Laboratory Services, Groote Schuur Hospital, Cape Town, South Africa
| | - Tim Ramsay
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Lars Rolighed
- Department of Otorhinolaryngology, Head and Neck Surgery, Aarhus University Hospital, Aarhus, Denmark
| | - Mirabelle Sajisevi
- Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, Larner College of Medicine, University of Vermont Medical Center, Burlington
| | - Arun Sharma
- Department of Otolaryngology-Head and Neck Surgery, Southern Illinois University School of Medicine, Springfield
| | - Catherine Sinclair
- Department of Otolaryngology-Head and Neck Surgery, Mount Sinai Hospital, New York, New York
| | - Meredith Sorensen
- Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Kyung Tae
- Department of Otolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Alice L Tang
- Department of Otolaryngology-Head and Neck Surgery, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Gabriel Tsao
- The Permanente Medical Group, Santa Rosa, California
| | - Michelle Williams
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Sean Wrenn
- Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Monica H Xing
- The Thyroid, Head, and Neck Cancer Foundation, New York, New York
| | - Mark Zafereo
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Brendan C Stack
- Department of Otolaryngology-Head and Neck Surgery, Mount Sinai Hospital, New York, New York
| | - Greg Randolph
- Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts
| | - Louise Davies
- VA Outcomes Group, White River Junction, Vermont.,Section of Otolaryngology-Head and Neck Surgery, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
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Li LR, Du B, Liu HQ, Chen C. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Front Oncol 2021; 10:604051. [PMID: 33634025 PMCID: PMC7899964 DOI: 10.3389/fonc.2020.604051] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/21/2020] [Indexed: 12/12/2022] Open
Abstract
Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.
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Affiliation(s)
- Ling-Rui Li
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Du
- School of Computer Science, Wuhan University, Wuhan, China.,Institute of Artificial Intelligence, Wuhan University, Wuhan, China
| | - Han-Qing Liu
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chuang Chen
- Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China
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Girolami I, Marletta S, Pantanowitz L, Torresani E, Ghimenton C, Barbareschi M, Scarpa A, Brunelli M, Barresi V, Trimboli P, Eccher A. Impact of image analysis and artificial intelligence in thyroid pathology, with particular reference to cytological aspects. Cytopathology 2020; 31:432-444. [PMID: 32248583 DOI: 10.1111/cyt.12828] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 03/27/2020] [Accepted: 03/27/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Thyroid pathology has great potential for automated/artificial intelligence algorithm application as the incidence of thyroid nodules is increasing and the indeterminate interpretation rate of fine-needle aspiration remains relatively high. The aim of the study is to review the published literature on automated image analysis and artificial intelligence applications to thyroid pathology with whole-slide imaging. METHODS Systematic search was carried out in electronic databases. Studies dealing with thyroid pathology and use of automated algorithms applied to whole-slide imaging were included. Quality of studies was assessed with a modified QUADAS-2 tool. RESULTS Of 919 retrieved articles, 19 were included. The main themes addressed were the comparison of automated assessment of immunohistochemical staining with manual pathologist's assessment, quantification of differences in cellular and nuclear parameters among tumour entities, and discrimination between benign and malignant nodules. Correlation coefficients with manual assessment were higher than 0.76 and diagnostic performance of automated models was comparable with an expert pathologist diagnosis. Computational difficulties were related to the large size of whole-slide images. CONCLUSIONS Overall, the results are promising and it is likely that, with the resolution of technical issues, the application of automated algorithms in thyroid pathology will increase and be adopted following suitable validation studies.
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Affiliation(s)
- Ilaria Girolami
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Stefano Marletta
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Liron Pantanowitz
- Department of Pathology, UPMC Shadyside Hospital, University of Pittsburgh, Pittsburgh, PA, USA
| | - Evelin Torresani
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Claudio Ghimenton
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | | | - Aldo Scarpa
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Matteo Brunelli
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Valeria Barresi
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Pierpaolo Trimboli
- Clinic for Nuclear Medicine and Competence Centre for Thyroid Disease, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
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5
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Ulisse S, Tuccilli C, Sorrenti S, Antonelli A, Fallahi P, D'Armiento E, Catania A, Tartaglia F, Amabile MI, Giacomelli L, Metere A, Cornacchini N, Pironi D, Carbotta G, Vergine M, Monti M, Baldini E. PD-1 Ligand Expression in Epithelial Thyroid Cancers: Potential Clinical Implications. Int J Mol Sci 2019; 20:ijms20061405. [PMID: 30897754 PMCID: PMC6471477 DOI: 10.3390/ijms20061405] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 03/14/2019] [Accepted: 03/17/2019] [Indexed: 12/12/2022] Open
Abstract
The new immunotherapy targeting the programmed cell death 1 (PD-1) receptor and its cognate ligand PD-L1 has renewed hopes of eradicating the most difficult human cancers to treat. Among these, there are the poorly differentiated and anaplastic thyroid cancers, unresponsive to all the therapies currently in use. In the present review we will summarize information regarding the expression of PD-L1 in the different thyroid cancer histotypes, its correlation with clinicopathological features, and its potential prognostic value. Then, we will evaluate the available data indicating the PD-1/PD-L1 axis as a promising target for thyroid cancer therapy.
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Affiliation(s)
- Salvatore Ulisse
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Chiara Tuccilli
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Salvatore Sorrenti
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Alessandro Antonelli
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy.
| | - Poupak Fallahi
- Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy.
| | - Eleonora D'Armiento
- Department of Internal Medicine and Medical Specialties, Sapienza University of Rome, 00161 Rome, Italy.
| | - Antonio Catania
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Francesco Tartaglia
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Maria Ida Amabile
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Laura Giacomelli
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Alessio Metere
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Nicola Cornacchini
- Department of Surgery, S. Kliment Ohridski University, 1504 Sofia, Bulgaria.
| | - Daniele Pironi
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Giovanni Carbotta
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Massimo Vergine
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Massimo Monti
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
| | - Enke Baldini
- Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.
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