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Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training
Purpose This study aimed to validate a deep learning model’s diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model’s clinical utility for resident training. Methods The perfor...
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| Published in: | European radiology 2020-06, Vol.30 (6), p.3066-3072 |
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| container_issue | 6 |
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| container_title | European radiology |
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| creator | Lee, Jeong Hoon Ha, Eun Ju Kim, DaYoung Jung, Yong Jun Heo, Subin Jang, Yong-ho An, Sung Hyun Lee, Kyungmin |
| description | Purpose
This study aimed to validate a deep learning model’s diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model’s clinical utility for resident training.
Methods
The performance of eight deep learning models was validated using 3838 axial CT images from 698 consecutive patients with thyroid cancer who underwent preoperative CT imaging between January and August 2018 (3606 and 232 images from benign and malignant lymph nodes, respectively). Six trainees viewed the same patient images (
n
= 242), and their diagnostic performance and confidence level (5-point scale) were assessed before and after computer-aided diagnosis (CAD) was included.
Results
The overall area under the receiver operating characteristics (AUROC) of the eight deep learning algorithms was 0.846 (range 0.784–0.884). The best performing model was Xception, with an AUROC of 0.884. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of Xception were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. After introducing the CAD system, underperforming trainees received more help from artificial intelligence than the higher performing trainees (
p
= 0.046), and overall confidence levels significantly increased from 3.90 to 4.30 (
p
|
| doi_str_mv | 10.1007/s00330-019-06652-4 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2356595748</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2406446163</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-7646f5af556901589d1329b8cc0bb9a5701e3ceb5e09c987b4677cd13a332cf83</originalsourceid><addsrcrecordid>eNp90c1u1DAUBWALgehQeAEWyBIbNgE7_ovZVSNakCqxKevIcW5mXCV2sJ2WeSZeEqcpILFAsuSFv3Nt6yD0mpL3lBD1IRHCGKkI1RWRUtQVf4J2lLO6oqThT9GOaNZUSmt-hl6kdEsI0ZSr5-iM1aT4RuzQz4t5Hp012QWPw4B7gBmPYKJ3_oBzwPkIuHfm4ENyaRUW4l0JjHg8TfMR-9ADniCbVFYRQwxTCZ1icD22xheO710-4v3NRww_MkRfsndmdP12qfHFjc4_zFyyG10-4SFEHCG5HnzGORq3PuclejaYMcGrx_0cfbv8dLP_XF1_vfqyv7iuLFMiV0pyOQgzCCE1oaLRPWW17hprSddpIxShwCx0Aoi2ulEdl0rZggxjtR0ado7ebXPnGL4vkHI7uWRhHI2HsKS2ZkIKLRRf6dt_6G1Y1h8WxYnkXFLJiqo3ZWNIKcLQztFNJp5aStq1ynarsi1Vtg9VtryE3jyOXroJ-j-R390VwDaQypE_QPx793_G_gI58axE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2406446163</pqid></control><display><type>article</type><title>Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training</title><source>Springer Link</source><creator>Lee, Jeong Hoon ; Ha, Eun Ju ; Kim, DaYoung ; Jung, Yong Jun ; Heo, Subin ; Jang, Yong-ho ; An, Sung Hyun ; Lee, Kyungmin</creator><creatorcontrib>Lee, Jeong Hoon ; Ha, Eun Ju ; Kim, DaYoung ; Jung, Yong Jun ; Heo, Subin ; Jang, Yong-ho ; An, Sung Hyun ; Lee, Kyungmin</creatorcontrib><description>Purpose
This study aimed to validate a deep learning model’s diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model’s clinical utility for resident training.
Methods
The performance of eight deep learning models was validated using 3838 axial CT images from 698 consecutive patients with thyroid cancer who underwent preoperative CT imaging between January and August 2018 (3606 and 232 images from benign and malignant lymph nodes, respectively). Six trainees viewed the same patient images (
n
= 242), and their diagnostic performance and confidence level (5-point scale) were assessed before and after computer-aided diagnosis (CAD) was included.
Results
The overall area under the receiver operating characteristics (AUROC) of the eight deep learning algorithms was 0.846 (range 0.784–0.884). The best performing model was Xception, with an AUROC of 0.884. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of Xception were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. After introducing the CAD system, underperforming trainees received more help from artificial intelligence than the higher performing trainees (
p
= 0.046), and overall confidence levels significantly increased from 3.90 to 4.30 (
p
< 0.001).
Conclusion
The deep learning–based CAD system used in this study for CT diagnosis of cervical LNM from thyroid cancer was clinically validated with an AUROC of 0.884. This approach may serve as a training tool to help resident physicians to gain confidence in diagnosis.
Key Points
• A deep learning-based CAD system for CT diagnosis of cervical LNM from thyroid cancer was validated using data from a clinical cohort. The AUROC for the eight tested algorithms ranged from 0.784 to 0.884.
• Of the eight models, the Xception algorithm was the best performing model for the external validation dataset with 0.884 AUROC. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively.
• The CAD system exhibited potential to improve diagnostic specificity and accuracy in underperforming trainees (3 of 6 trainees, 50.0%). This approach may have clinical utility as a training tool to help trainees to gain confidence in diagnoses.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-019-06652-4</identifier><identifier>PMID: 32065285</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Algorithms ; Artificial intelligence ; Cancer ; Computed tomography ; Confidence intervals ; Deep learning ; Diagnosis ; Diagnostic Radiology ; Diagnostic systems ; Head and Neck ; Imaging ; Internal Medicine ; Interventional Radiology ; Learning algorithms ; Lymph nodes ; Lymphatic system ; Machine learning ; Medical diagnosis ; Medical imaging ; Medicine ; Medicine & Public Health ; Metastases ; Metastasis ; Neuroradiology ; Physicians ; Radiology ; Sensitivity ; Thyroid ; Thyroid cancer ; Training ; Ultrasound</subject><ispartof>European radiology, 2020-06, Vol.30 (6), p.3066-3072</ispartof><rights>European Society of Radiology 2019</rights><rights>European Society of Radiology 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-7646f5af556901589d1329b8cc0bb9a5701e3ceb5e09c987b4677cd13a332cf83</citedby><cites>FETCH-LOGICAL-c375t-7646f5af556901589d1329b8cc0bb9a5701e3ceb5e09c987b4677cd13a332cf83</cites><orcidid>0000-0002-1234-2919</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32065285$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Jeong Hoon</creatorcontrib><creatorcontrib>Ha, Eun Ju</creatorcontrib><creatorcontrib>Kim, DaYoung</creatorcontrib><creatorcontrib>Jung, Yong Jun</creatorcontrib><creatorcontrib>Heo, Subin</creatorcontrib><creatorcontrib>Jang, Yong-ho</creatorcontrib><creatorcontrib>An, Sung Hyun</creatorcontrib><creatorcontrib>Lee, Kyungmin</creatorcontrib><title>Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Purpose
This study aimed to validate a deep learning model’s diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model’s clinical utility for resident training.
Methods
The performance of eight deep learning models was validated using 3838 axial CT images from 698 consecutive patients with thyroid cancer who underwent preoperative CT imaging between January and August 2018 (3606 and 232 images from benign and malignant lymph nodes, respectively). Six trainees viewed the same patient images (
n
= 242), and their diagnostic performance and confidence level (5-point scale) were assessed before and after computer-aided diagnosis (CAD) was included.
Results
The overall area under the receiver operating characteristics (AUROC) of the eight deep learning algorithms was 0.846 (range 0.784–0.884). The best performing model was Xception, with an AUROC of 0.884. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of Xception were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. After introducing the CAD system, underperforming trainees received more help from artificial intelligence than the higher performing trainees (
p
= 0.046), and overall confidence levels significantly increased from 3.90 to 4.30 (
p
< 0.001).
Conclusion
The deep learning–based CAD system used in this study for CT diagnosis of cervical LNM from thyroid cancer was clinically validated with an AUROC of 0.884. This approach may serve as a training tool to help resident physicians to gain confidence in diagnosis.
Key Points
• A deep learning-based CAD system for CT diagnosis of cervical LNM from thyroid cancer was validated using data from a clinical cohort. The AUROC for the eight tested algorithms ranged from 0.784 to 0.884.
• Of the eight models, the Xception algorithm was the best performing model for the external validation dataset with 0.884 AUROC. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively.
• The CAD system exhibited potential to improve diagnostic specificity and accuracy in underperforming trainees (3 of 6 trainees, 50.0%). This approach may have clinical utility as a training tool to help trainees to gain confidence in diagnoses.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Cancer</subject><subject>Computed tomography</subject><subject>Confidence intervals</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Head and Neck</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Learning algorithms</subject><subject>Lymph nodes</subject><subject>Lymphatic system</subject><subject>Machine learning</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Neuroradiology</subject><subject>Physicians</subject><subject>Radiology</subject><subject>Sensitivity</subject><subject>Thyroid</subject><subject>Thyroid cancer</subject><subject>Training</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp90c1u1DAUBWALgehQeAEWyBIbNgE7_ovZVSNakCqxKevIcW5mXCV2sJ2WeSZeEqcpILFAsuSFv3Nt6yD0mpL3lBD1IRHCGKkI1RWRUtQVf4J2lLO6oqThT9GOaNZUSmt-hl6kdEsI0ZSr5-iM1aT4RuzQz4t5Hp012QWPw4B7gBmPYKJ3_oBzwPkIuHfm4ENyaRUW4l0JjHg8TfMR-9ADniCbVFYRQwxTCZ1icD22xheO710-4v3NRww_MkRfsndmdP12qfHFjc4_zFyyG10-4SFEHCG5HnzGORq3PuclejaYMcGrx_0cfbv8dLP_XF1_vfqyv7iuLFMiV0pyOQgzCCE1oaLRPWW17hprSddpIxShwCx0Aoi2ulEdl0rZggxjtR0ado7ebXPnGL4vkHI7uWRhHI2HsKS2ZkIKLRRf6dt_6G1Y1h8WxYnkXFLJiqo3ZWNIKcLQztFNJp5aStq1ynarsi1Vtg9VtryE3jyOXroJ-j-R390VwDaQypE_QPx793_G_gI58axE</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Lee, Jeong Hoon</creator><creator>Ha, Eun Ju</creator><creator>Kim, DaYoung</creator><creator>Jung, Yong Jun</creator><creator>Heo, Subin</creator><creator>Jang, Yong-ho</creator><creator>An, Sung Hyun</creator><creator>Lee, Kyungmin</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1234-2919</orcidid></search><sort><creationdate>20200601</creationdate><title>Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training</title><author>Lee, Jeong Hoon ; Ha, Eun Ju ; Kim, DaYoung ; Jung, Yong Jun ; Heo, Subin ; Jang, Yong-ho ; An, Sung Hyun ; Lee, Kyungmin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-7646f5af556901589d1329b8cc0bb9a5701e3ceb5e09c987b4677cd13a332cf83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Cancer</topic><topic>Computed tomography</topic><topic>Confidence intervals</topic><topic>Deep learning</topic><topic>Diagnosis</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Head and Neck</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Learning algorithms</topic><topic>Lymph nodes</topic><topic>Lymphatic system</topic><topic>Machine learning</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Neuroradiology</topic><topic>Physicians</topic><topic>Radiology</topic><topic>Sensitivity</topic><topic>Thyroid</topic><topic>Thyroid cancer</topic><topic>Training</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Jeong Hoon</creatorcontrib><creatorcontrib>Ha, Eun Ju</creatorcontrib><creatorcontrib>Kim, DaYoung</creatorcontrib><creatorcontrib>Jung, Yong Jun</creatorcontrib><creatorcontrib>Heo, Subin</creatorcontrib><creatorcontrib>Jang, Yong-ho</creatorcontrib><creatorcontrib>An, Sung Hyun</creatorcontrib><creatorcontrib>Lee, Kyungmin</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest 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(Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Jeong Hoon</au><au>Ha, Eun Ju</au><au>Kim, DaYoung</au><au>Jung, Yong Jun</au><au>Heo, Subin</au><au>Jang, Yong-ho</au><au>An, Sung Hyun</au><au>Lee, Kyungmin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>30</volume><issue>6</issue><spage>3066</spage><epage>3072</epage><pages>3066-3072</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Purpose
This study aimed to validate a deep learning model’s diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model’s clinical utility for resident training.
Methods
The performance of eight deep learning models was validated using 3838 axial CT images from 698 consecutive patients with thyroid cancer who underwent preoperative CT imaging between January and August 2018 (3606 and 232 images from benign and malignant lymph nodes, respectively). Six trainees viewed the same patient images (
n
= 242), and their diagnostic performance and confidence level (5-point scale) were assessed before and after computer-aided diagnosis (CAD) was included.
Results
The overall area under the receiver operating characteristics (AUROC) of the eight deep learning algorithms was 0.846 (range 0.784–0.884). The best performing model was Xception, with an AUROC of 0.884. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of Xception were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively. After introducing the CAD system, underperforming trainees received more help from artificial intelligence than the higher performing trainees (
p
= 0.046), and overall confidence levels significantly increased from 3.90 to 4.30 (
p
< 0.001).
Conclusion
The deep learning–based CAD system used in this study for CT diagnosis of cervical LNM from thyroid cancer was clinically validated with an AUROC of 0.884. This approach may serve as a training tool to help resident physicians to gain confidence in diagnosis.
Key Points
• A deep learning-based CAD system for CT diagnosis of cervical LNM from thyroid cancer was validated using data from a clinical cohort. The AUROC for the eight tested algorithms ranged from 0.784 to 0.884.
• Of the eight models, the Xception algorithm was the best performing model for the external validation dataset with 0.884 AUROC. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 82.8%, 80.2%, 83.0%, 83.0%, and 80.2%, respectively.
• The CAD system exhibited potential to improve diagnostic specificity and accuracy in underperforming trainees (3 of 6 trainees, 50.0%). This approach may have clinical utility as a training tool to help trainees to gain confidence in diagnoses.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32065285</pmid><doi>10.1007/s00330-019-06652-4</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0002-1234-2919</orcidid></addata></record> |
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| ispartof | European radiology, 2020-06, Vol.30 (6), p.3066-3072 |
| issn | 0938-7994 1432-1084 |
| language | eng |
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| source | Springer Link |
| subjects | Accuracy Algorithms Artificial intelligence Cancer Computed tomography Confidence intervals Deep learning Diagnosis Diagnostic Radiology Diagnostic systems Head and Neck Imaging Internal Medicine Interventional Radiology Learning algorithms Lymph nodes Lymphatic system Machine learning Medical diagnosis Medical imaging Medicine Medicine & Public Health Metastases Metastasis Neuroradiology Physicians Radiology Sensitivity Thyroid Thyroid cancer Training Ultrasound |
| title | Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training |
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