Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement
Objectives To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization. Methods Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening...
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| Veröffentlicht in: | European radiology 2022-02, Vol.32 (2), p.1054-1064 |
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| creator | Park, Sohee Park, Hyunho Lee, Sang Min Ahn, Yura Kim, Wooil Jung, Kyuhwan Seo, Joon Beom |
| description | Objectives
To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization.
Methods
Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics.
Results
The five readers reported 139–151 negative screening results without CAD and 126–142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (
p
< 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%;
p
= 0.004).
Conclusions
Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases.
Key Points
• Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65.
• The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%).
• In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004). |
| doi_str_mv | 10.1007/s00330-021-08202-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2557233197</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2623197378</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-5f37aba24a44705823963b637ae2eb3ad75a738d4f084eb5433149ca2c78c8cf3</originalsourceid><addsrcrecordid>eNp9kc1u1DAYRS0EokPLC3SBLLFhY_Bfxkl3o-FXGgmJlrXlOJ8jV4md2skCHoDnxjNpqdQFK0v2OdeffRG6ZPQ9o1R9yJQKQQnljNCaU07EM7RhUnDCaC2fow1tRE1U08gz9CrnW0ppw6R6ic6EFIIxxjfoz26aBm_N7GPA0WEbx2mZIRHjO-hw500fYvYZu5jwYQk9-bH7eI2LAH1M_vcq-oD3NzjbBBB86E_wUODCBQvpCoNzYGd8Qo_pCUwHCZu-GCOE-QK9cGbI8Pp-PUc_P3-62X8lh-9fvu13B2KFqmZSOaFMa7g0Uipa1Vw0W9FuyyZwaIXpVGWUqDvpygdAWx2fKRtruFW1ra0T5-jdmjuleLdAnvXos4VhMAHikjWvKsWL1KiCvn2C3sYlhTKd5lt-QlRdKL5SNsWcEzg9JT-a9Eszqo8t6bUlXVrSp5a0KNKb--ilHaH7pzzUUgCxArkchR7S493_if0LpkidMw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2623197378</pqid></control><display><type>article</type><title>Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Park, Sohee ; Park, Hyunho ; Lee, Sang Min ; Ahn, Yura ; Kim, Wooil ; Jung, Kyuhwan ; Seo, Joon Beom</creator><creatorcontrib>Park, Sohee ; Park, Hyunho ; Lee, Sang Min ; Ahn, Yura ; Kim, Wooil ; Jung, Kyuhwan ; Seo, Joon Beom</creatorcontrib><description>Objectives
To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization.
Methods
Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics.
Results
The five readers reported 139–151 negative screening results without CAD and 126–142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (
p
< 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%;
p
= 0.004).
Conclusions
Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases.
Key Points
• Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65.
• The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%).
• In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-021-08202-3</identifier><identifier>PMID: 34331112</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agreements ; Cancer screening ; Chest ; Classification ; Computed tomography ; Computers ; Confidence intervals ; Diagnosis ; Diagnostic Radiology ; Early Detection of Cancer ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Lung - diagnostic imaging ; Lung cancer ; Lung Neoplasms - diagnostic imaging ; Medical diagnosis ; Medical imaging ; Medical screening ; Medicine ; Medicine & Public Health ; Neuroradiology ; Nodules ; Observer Variation ; Radiology ; Retrospective Studies ; Risk ; Sensitivity ; Tomography, X-Ray Computed ; Ultrasound</subject><ispartof>European radiology, 2022-02, Vol.32 (2), p.1054-1064</ispartof><rights>European Society of Radiology 2021</rights><rights>2021. European Society of Radiology.</rights><rights>European Society of Radiology 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-5f37aba24a44705823963b637ae2eb3ad75a738d4f084eb5433149ca2c78c8cf3</citedby><cites>FETCH-LOGICAL-c375t-5f37aba24a44705823963b637ae2eb3ad75a738d4f084eb5433149ca2c78c8cf3</cites><orcidid>0000-0001-7627-2000</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-021-08202-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-021-08202-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34331112$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Park, Sohee</creatorcontrib><creatorcontrib>Park, Hyunho</creatorcontrib><creatorcontrib>Lee, Sang Min</creatorcontrib><creatorcontrib>Ahn, Yura</creatorcontrib><creatorcontrib>Kim, Wooil</creatorcontrib><creatorcontrib>Jung, Kyuhwan</creatorcontrib><creatorcontrib>Seo, Joon Beom</creatorcontrib><title>Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization.
Methods
Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics.
Results
The five readers reported 139–151 negative screening results without CAD and 126–142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (
p
< 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%;
p
= 0.004).
Conclusions
Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases.
Key Points
• Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65.
• The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%).
• In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).</description><subject>Agreements</subject><subject>Cancer screening</subject><subject>Chest</subject><subject>Classification</subject><subject>Computed tomography</subject><subject>Computers</subject><subject>Confidence intervals</subject><subject>Diagnosis</subject><subject>Diagnostic Radiology</subject><subject>Early Detection of Cancer</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Lung - diagnostic imaging</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - diagnostic imaging</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medical screening</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Nodules</subject><subject>Observer Variation</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Risk</subject><subject>Sensitivity</subject><subject>Tomography, X-Ray Computed</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kc1u1DAYRS0EokPLC3SBLLFhY_Bfxkl3o-FXGgmJlrXlOJ8jV4md2skCHoDnxjNpqdQFK0v2OdeffRG6ZPQ9o1R9yJQKQQnljNCaU07EM7RhUnDCaC2fow1tRE1U08gz9CrnW0ppw6R6ic6EFIIxxjfoz26aBm_N7GPA0WEbx2mZIRHjO-hw500fYvYZu5jwYQk9-bH7eI2LAH1M_vcq-oD3NzjbBBB86E_wUODCBQvpCoNzYGd8Qo_pCUwHCZu-GCOE-QK9cGbI8Pp-PUc_P3-62X8lh-9fvu13B2KFqmZSOaFMa7g0Uipa1Vw0W9FuyyZwaIXpVGWUqDvpygdAWx2fKRtruFW1ra0T5-jdmjuleLdAnvXos4VhMAHikjWvKsWL1KiCvn2C3sYlhTKd5lt-QlRdKL5SNsWcEzg9JT-a9Eszqo8t6bUlXVrSp5a0KNKb--ilHaH7pzzUUgCxArkchR7S493_if0LpkidMw</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Park, Sohee</creator><creator>Park, Hyunho</creator><creator>Lee, Sang Min</creator><creator>Ahn, Yura</creator><creator>Kim, Wooil</creator><creator>Jung, Kyuhwan</creator><creator>Seo, Joon Beom</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><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/0000-0001-7627-2000</orcidid></search><sort><creationdate>20220201</creationdate><title>Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement</title><author>Park, Sohee ; Park, Hyunho ; Lee, Sang Min ; Ahn, Yura ; Kim, Wooil ; Jung, Kyuhwan ; Seo, Joon Beom</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-5f37aba24a44705823963b637ae2eb3ad75a738d4f084eb5433149ca2c78c8cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Agreements</topic><topic>Cancer screening</topic><topic>Chest</topic><topic>Classification</topic><topic>Computed tomography</topic><topic>Computers</topic><topic>Confidence intervals</topic><topic>Diagnosis</topic><topic>Diagnostic Radiology</topic><topic>Early Detection of Cancer</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Lung - diagnostic imaging</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medical screening</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Nodules</topic><topic>Observer Variation</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Risk</topic><topic>Sensitivity</topic><topic>Tomography, X-Ray Computed</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Park, Sohee</creatorcontrib><creatorcontrib>Park, Hyunho</creatorcontrib><creatorcontrib>Lee, Sang Min</creatorcontrib><creatorcontrib>Ahn, Yura</creatorcontrib><creatorcontrib>Kim, Wooil</creatorcontrib><creatorcontrib>Jung, Kyuhwan</creatorcontrib><creatorcontrib>Seo, Joon Beom</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</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>Park, Sohee</au><au>Park, Hyunho</au><au>Lee, Sang Min</au><au>Ahn, Yura</au><au>Kim, Wooil</au><au>Jung, Kyuhwan</au><au>Seo, Joon Beom</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>32</volume><issue>2</issue><spage>1054</spage><epage>1064</epage><pages>1054-1064</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To evaluate the effects of computer-aided diagnosis (CAD) on inter-reader agreement in Lung Imaging Reporting and Data System (Lung-RADS) categorization.
Methods
Two hundred baseline CT scans covering all Lung-RADS categories were randomly selected from the National Lung Cancer Screening Trial. Five radiologists independently reviewed the CT scans and assigned Lung-RADS categories without CAD and with CAD. The CAD system presented up to five of the most risk-dominant nodules with measurements and predicted Lung-RADS category. Inter-reader agreement was analyzed using multirater Fleiss κ statistics.
Results
The five readers reported 139–151 negative screening results without CAD and 126–142 with CAD. With CAD, readers tended to upstage (average, 12.3%) rather than downstage Lung-RADS category (average, 4.4%). Inter-reader agreement of five readers for Lung-RADS categorization was moderate (Fleiss kappa, 0.60 [95% confidence interval, 0.57, 0.63]) without CAD, and slightly improved to substantial (Fleiss kappa, 0.65 [95% CI, 0.63, 0.68]) with CAD. The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% [201/371] vs. 63.6% [232/365]). The proportion of disagreement in nodule size measurement was reduced from 5.1% (102/2000) to 3.1% (62/2000) with the use of CAD (
p
< 0.001). In 31 cancer-positive cases, substantial management discrepancies (category 1/2 vs. 4A/B) between reader pairs decreased with application of CAD (pooled sensitivity, 85.2% vs. 91.6%;
p
= 0.004).
Conclusions
Application of CAD demonstrated a minor improvement in inter-reader agreement of Lung-RADS category, while showing the potential to reduce measurement variability and substantial management change in cancer-positive cases.
Key Points
• Inter-reader agreement of five readers for Lung-RADS categorization was minimally improved by application of CAD, with a Fleiss kappa value of 0.60 to 0.65.
• The major cause for disagreement was assignment of different risk-dominant nodules in the reading sessions without and with CAD (54.2% vs. 63.6%).
• In 31 cancer-positive cases, substantial management discrepancies between reader pairs, referring to a difference in follow-up interval of at least 9 months (category 1/2 vs. 4A/B), were reduced in half by application of CAD (32/310 to 16/310) (pooled sensitivity, 85.2% vs. 91.6%; p = 0.004).</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34331112</pmid><doi>10.1007/s00330-021-08202-3</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-7627-2000</orcidid></addata></record> |
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| subjects | Agreements Cancer screening Chest Classification Computed tomography Computers Confidence intervals Diagnosis Diagnostic Radiology Early Detection of Cancer Humans Imaging Internal Medicine Interventional Radiology Lung - diagnostic imaging Lung cancer Lung Neoplasms - diagnostic imaging Medical diagnosis Medical imaging Medical screening Medicine Medicine & Public Health Neuroradiology Nodules Observer Variation Radiology Retrospective Studies Risk Sensitivity Tomography, X-Ray Computed Ultrasound |
| title | Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement |
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