Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in context
Summary: Background: Lymph node metastasis (LNM) assessment in patients with papillary thyroid carcinoma (PTC) is of great value. This study aimed to develop a deep learning model applied to intraoperative frozen section for prediction of LNM in PTC patients. Methods: We established a deep-learning...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | EClinicalMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537023001840 |
| _version_ | 1797823723086020608 |
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| author | Yihao Liu Fenghua Lai Bo Lin Yunquan Gu Lili Chen Gang Chen Han Xiao Shuli Luo Yuyan Pang Dandan Xiong Bin Li Sui Peng Weiming Lv Erik K. Alexander Haipeng Xiao |
| author_facet | Yihao Liu Fenghua Lai Bo Lin Yunquan Gu Lili Chen Gang Chen Han Xiao Shuli Luo Yuyan Pang Dandan Xiong Bin Li Sui Peng Weiming Lv Erik K. Alexander Haipeng Xiao |
| author_sort | Yihao Liu |
| collection | DOAJ |
| description | Summary: Background: Lymph node metastasis (LNM) assessment in patients with papillary thyroid carcinoma (PTC) is of great value. This study aimed to develop a deep learning model applied to intraoperative frozen section for prediction of LNM in PTC patients. Methods: We established a deep-learning model (ThyNet-LNM) with the multiple-instance learning framework to predict LNM using whole slide images (WSIs) from intraoperative frozen sections of PTC. Data for the development and validation of ThyNet-LNM were retrospectively derived from four hospitals from January 2018 to December 2021. The ThyNet-LNM was trained using 1987 WSIs from 1120 patients obtained at the First Affiliated Hospital of Sun Yat-sen University. The ThyNet-LNM was then validated in the independent internal test set (479 WSIs from 280 patients) as well as three external test sets (1335 WSIs from 692 patients). The performance of ThyNet-LNM was further compared with preoperative ultrasound and computed tomography (CT). Findings: The area under the receiver operating characteristic curves (AUCs) of ThyNet-LNM were 0.80 (95% CI 0.74–0.84), 0.81 (95% CI 0.77–0.86), 0.76 (95% CI 0.68–0.83), and 0.81 (95% CI 0.75–0.85) in internal test set and three external test sets, respectively. The AUCs of ThyNet-LNM were significantly higher than those of ultrasound and CT or their combination in all four test sets (all P < 0.01). Of 397 clinically node-negative (cN0) patients, the rate of unnecessary lymph node dissection decreased from 56.4% to 14.9% by ThyNet-LNM. Interpretation: The ThyNet-LNM showed promising efficacy as a potential novel method in evaluating intraoperative LNM status, providing real-time guidance for decision. Furthermore, this led to a reduction of unnecessary lymph node dissection in cN0 patients. Funding: National Natural Science Foundation of China, Guangzhou Science and Technology Project, and Guangxi Medical High-level Key Talents Training “139” Program. |
| first_indexed | 2024-03-13T10:28:12Z |
| format | Article |
| id | doaj.art-b0e22bfb0bd1437d9d0b6443265bbfbb |
| institution | Directory Open Access Journal |
| issn | 2589-5370 |
| language | English |
| last_indexed | 2024-03-13T10:28:12Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj.art-b0e22bfb0bd1437d9d0b6443265bbfbb2023-05-19T04:46:20ZengElsevierEClinicalMedicine2589-53702023-06-0160102007Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in contextYihao Liu0Fenghua Lai1Bo Lin2Yunquan Gu3Lili Chen4Gang Chen5Han Xiao6Shuli Luo7Yuyan Pang8Dandan Xiong9Bin Li10Sui Peng11Weiming Lv12Erik K. Alexander13Haipeng Xiao14Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaClinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumour Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumour Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumour Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaClinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaClinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Corresponding author. Department of Breast and Thyroid surgery, The First Affiliated Hospital of Sun Yat-sen University, No. 58, ZhongShan Second Road, Guangzhou, 510080, China.Thyroid Section, Brigham & Women's Hospital, Harvard Medical School, Boston, USA; Corresponding author. Thyroid Section, Brigham & Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Corresponding author. Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, No. 58, ZhongShan Second Road, Guangzhou, 510080, China.Summary: Background: Lymph node metastasis (LNM) assessment in patients with papillary thyroid carcinoma (PTC) is of great value. This study aimed to develop a deep learning model applied to intraoperative frozen section for prediction of LNM in PTC patients. Methods: We established a deep-learning model (ThyNet-LNM) with the multiple-instance learning framework to predict LNM using whole slide images (WSIs) from intraoperative frozen sections of PTC. Data for the development and validation of ThyNet-LNM were retrospectively derived from four hospitals from January 2018 to December 2021. The ThyNet-LNM was trained using 1987 WSIs from 1120 patients obtained at the First Affiliated Hospital of Sun Yat-sen University. The ThyNet-LNM was then validated in the independent internal test set (479 WSIs from 280 patients) as well as three external test sets (1335 WSIs from 692 patients). The performance of ThyNet-LNM was further compared with preoperative ultrasound and computed tomography (CT). Findings: The area under the receiver operating characteristic curves (AUCs) of ThyNet-LNM were 0.80 (95% CI 0.74–0.84), 0.81 (95% CI 0.77–0.86), 0.76 (95% CI 0.68–0.83), and 0.81 (95% CI 0.75–0.85) in internal test set and three external test sets, respectively. The AUCs of ThyNet-LNM were significantly higher than those of ultrasound and CT or their combination in all four test sets (all P < 0.01). Of 397 clinically node-negative (cN0) patients, the rate of unnecessary lymph node dissection decreased from 56.4% to 14.9% by ThyNet-LNM. Interpretation: The ThyNet-LNM showed promising efficacy as a potential novel method in evaluating intraoperative LNM status, providing real-time guidance for decision. Furthermore, this led to a reduction of unnecessary lymph node dissection in cN0 patients. Funding: National Natural Science Foundation of China, Guangzhou Science and Technology Project, and Guangxi Medical High-level Key Talents Training “139” Program.http://www.sciencedirect.com/science/article/pii/S2589537023001840Deep learningLymph node metastasisIntraoperative frozen sectionPapillary thyroid carcinoma |
| spellingShingle | Yihao Liu Fenghua Lai Bo Lin Yunquan Gu Lili Chen Gang Chen Han Xiao Shuli Luo Yuyan Pang Dandan Xiong Bin Li Sui Peng Weiming Lv Erik K. Alexander Haipeng Xiao Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in context EClinicalMedicine Deep learning Lymph node metastasis Intraoperative frozen section Papillary thyroid carcinoma |
| title | Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in context |
| title_full | Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in context |
| title_fullStr | Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in context |
| title_full_unstemmed | Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in context |
| title_short | Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic studyResearch in context |
| title_sort | deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma a multicentre diagnostic studyresearch in context |
| topic | Deep learning Lymph node metastasis Intraoperative frozen section Papillary thyroid carcinoma |
| url | http://www.sciencedirect.com/science/article/pii/S2589537023001840 |
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