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Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women
Background To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women. Methods The subjects were 310 Japanese female outpatients who...
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| Published in: | Breast cancer (Tokyo, Japan) Japan), 2020-07, Vol.27 (4), p.642-651 |
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| container_end_page | 651 |
| container_issue | 4 |
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| container_title | Breast cancer (Tokyo, Japan) |
| container_volume | 27 |
| creator | Sasaki, Michiro Tozaki, Mitsuhiro Rodríguez-Ruiz, Alejandro Yotsumoto, Daisuke Ichiki, Yumi Terawaki, Aiko Oosako, Shunichi Sagara, Yasuaki Sagara, Yoshiaki |
| description | Background
To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.
Methods
The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions.
Results
The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11;
P
|
| doi_str_mv | 10.1007/s12282-020-01061-8 |
| format | article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_2354736400</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A712280245</galeid><sourcerecordid>A712280245</sourcerecordid><originalsourceid>FETCH-LOGICAL-c504t-726f50092efa3fc8b359834d90d371560672e071c96a0d76e70f6a4cf8468d5a3</originalsourceid><addsrcrecordid>eNp9kc-KFDEQxhtR3HX1BTxIwIuXXitJd5LxNiyuf1hRcD2HTLoym2U6aZM0Ok_i625mehUEkRwqVH7fl6K-pnlO4ZwCyNeZMqZYCwxaoCBoqx40p1QpaDvG-cN65x20Qgl10jzJ-Rag4xLE4-aEM-gZp_S0-bVOxTtvvdkRHwrudn6LwSJxMZFNQpMLsaY2EhmwoC0-hgqS0Yxj3CYz3ezfEPw5YfJHWXRkzsdSbpB8tQkxfInVmXzCwdv6y3UyIU8mGZL3ueB4cOMUyEczmYBV-yOOGJ42j5zZZXx2X8-ab5dvry_et1ef3324WF-1toeutJIJ1wOsGDrDnVUb3q8U74YVDFzSXoCQDEFSuxIGBilQghOms051Qg294WfNq8V3SvH7jLno0Wdb11BniXPWjPed5KIDqOjLBd2aHWofXCzJ2AOu1_IQBbCur9T5P6h6Bhy9jQGdr_2_BGwR2BRzTuj0lPxo0l5T0Iec9ZKzrjnrY85aVdGL-7HnzYjDH8nvYCvAFyDXp7DFpG_jnEJd5f9s7wA3ArKH</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2354736400</pqid></control><display><type>article</type><title>Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women</title><source>Springer Nature</source><creator>Sasaki, Michiro ; Tozaki, Mitsuhiro ; Rodríguez-Ruiz, Alejandro ; Yotsumoto, Daisuke ; Ichiki, Yumi ; Terawaki, Aiko ; Oosako, Shunichi ; Sagara, Yasuaki ; Sagara, Yoshiaki</creator><creatorcontrib>Sasaki, Michiro ; Tozaki, Mitsuhiro ; Rodríguez-Ruiz, Alejandro ; Yotsumoto, Daisuke ; Ichiki, Yumi ; Terawaki, Aiko ; Oosako, Shunichi ; Sagara, Yasuaki ; Sagara, Yoshiaki</creatorcontrib><description>Background
To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.
Methods
The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions.
Results
The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11;
P
< 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively.
Conclusions
Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.</description><identifier>ISSN: 1340-6868</identifier><identifier>EISSN: 1880-4233</identifier><identifier>DOI: 10.1007/s12282-020-01061-8</identifier><identifier>PMID: 32052311</identifier><language>eng</language><publisher>Tokyo: Springer Japan</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Artificial Intelligence ; Breast - diagnostic imaging ; Breast cancer ; Breast Neoplasms - diagnosis ; Cancer ; Cancer Research ; Comparative analysis ; Decision Support Systems, Clinical ; Diagnosis ; Early Detection of Cancer - methods ; Early Detection of Cancer - standards ; Female ; Humans ; Japan ; Mammography ; Mammography - methods ; Mammography - standards ; Medicine ; Medicine & Public Health ; Middle Aged ; Oncology ; Original Article ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiologists - standards ; Reference Standards ; Retrospective Studies ; ROC Curve ; Surgery ; Surgical Oncology ; Women ; Young Adult</subject><ispartof>Breast cancer (Tokyo, Japan), 2020-07, Vol.27 (4), p.642-651</ispartof><rights>The Japanese Breast Cancer Society 2020</rights><rights>COPYRIGHT 2020 Springer</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c504t-726f50092efa3fc8b359834d90d371560672e071c96a0d76e70f6a4cf8468d5a3</citedby><cites>FETCH-LOGICAL-c504t-726f50092efa3fc8b359834d90d371560672e071c96a0d76e70f6a4cf8468d5a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32052311$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sasaki, Michiro</creatorcontrib><creatorcontrib>Tozaki, Mitsuhiro</creatorcontrib><creatorcontrib>Rodríguez-Ruiz, Alejandro</creatorcontrib><creatorcontrib>Yotsumoto, Daisuke</creatorcontrib><creatorcontrib>Ichiki, Yumi</creatorcontrib><creatorcontrib>Terawaki, Aiko</creatorcontrib><creatorcontrib>Oosako, Shunichi</creatorcontrib><creatorcontrib>Sagara, Yasuaki</creatorcontrib><creatorcontrib>Sagara, Yoshiaki</creatorcontrib><title>Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women</title><title>Breast cancer (Tokyo, Japan)</title><addtitle>Breast Cancer</addtitle><addtitle>Breast Cancer</addtitle><description>Background
To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.
Methods
The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions.
Results
The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11;
P
< 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively.
Conclusions
Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Artificial Intelligence</subject><subject>Breast - diagnostic imaging</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnosis</subject><subject>Cancer</subject><subject>Cancer Research</subject><subject>Comparative analysis</subject><subject>Decision Support Systems, Clinical</subject><subject>Diagnosis</subject><subject>Early Detection of Cancer - methods</subject><subject>Early Detection of Cancer - standards</subject><subject>Female</subject><subject>Humans</subject><subject>Japan</subject><subject>Mammography</subject><subject>Mammography - methods</subject><subject>Mammography - standards</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiologists - standards</subject><subject>Reference Standards</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Surgery</subject><subject>Surgical Oncology</subject><subject>Women</subject><subject>Young Adult</subject><issn>1340-6868</issn><issn>1880-4233</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kc-KFDEQxhtR3HX1BTxIwIuXXitJd5LxNiyuf1hRcD2HTLoym2U6aZM0Ok_i625mehUEkRwqVH7fl6K-pnlO4ZwCyNeZMqZYCwxaoCBoqx40p1QpaDvG-cN65x20Qgl10jzJ-Rag4xLE4-aEM-gZp_S0-bVOxTtvvdkRHwrudn6LwSJxMZFNQpMLsaY2EhmwoC0-hgqS0Yxj3CYz3ezfEPw5YfJHWXRkzsdSbpB8tQkxfInVmXzCwdv6y3UyIU8mGZL3ueB4cOMUyEczmYBV-yOOGJ42j5zZZXx2X8-ab5dvry_et1ef3324WF-1toeutJIJ1wOsGDrDnVUb3q8U74YVDFzSXoCQDEFSuxIGBilQghOms051Qg294WfNq8V3SvH7jLno0Wdb11BniXPWjPed5KIDqOjLBd2aHWofXCzJ2AOu1_IQBbCur9T5P6h6Bhy9jQGdr_2_BGwR2BRzTuj0lPxo0l5T0Iec9ZKzrjnrY85aVdGL-7HnzYjDH8nvYCvAFyDXp7DFpG_jnEJd5f9s7wA3ArKH</recordid><startdate>20200701</startdate><enddate>20200701</enddate><creator>Sasaki, Michiro</creator><creator>Tozaki, Mitsuhiro</creator><creator>Rodríguez-Ruiz, Alejandro</creator><creator>Yotsumoto, Daisuke</creator><creator>Ichiki, Yumi</creator><creator>Terawaki, Aiko</creator><creator>Oosako, Shunichi</creator><creator>Sagara, Yasuaki</creator><creator>Sagara, Yoshiaki</creator><general>Springer Japan</general><general>Springer</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>7X8</scope></search><sort><creationdate>20200701</creationdate><title>Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women</title><author>Sasaki, Michiro ; Tozaki, Mitsuhiro ; Rodríguez-Ruiz, Alejandro ; Yotsumoto, Daisuke ; Ichiki, Yumi ; Terawaki, Aiko ; Oosako, Shunichi ; Sagara, Yasuaki ; Sagara, Yoshiaki</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c504t-726f50092efa3fc8b359834d90d371560672e071c96a0d76e70f6a4cf8468d5a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Artificial Intelligence</topic><topic>Breast - diagnostic imaging</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - diagnosis</topic><topic>Cancer</topic><topic>Cancer Research</topic><topic>Comparative analysis</topic><topic>Decision Support Systems, Clinical</topic><topic>Diagnosis</topic><topic>Early Detection of Cancer - methods</topic><topic>Early Detection of Cancer - standards</topic><topic>Female</topic><topic>Humans</topic><topic>Japan</topic><topic>Mammography</topic><topic>Mammography - methods</topic><topic>Mammography - standards</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiologists - standards</topic><topic>Reference Standards</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Surgery</topic><topic>Surgical Oncology</topic><topic>Women</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sasaki, Michiro</creatorcontrib><creatorcontrib>Tozaki, Mitsuhiro</creatorcontrib><creatorcontrib>Rodríguez-Ruiz, Alejandro</creatorcontrib><creatorcontrib>Yotsumoto, Daisuke</creatorcontrib><creatorcontrib>Ichiki, Yumi</creatorcontrib><creatorcontrib>Terawaki, Aiko</creatorcontrib><creatorcontrib>Oosako, Shunichi</creatorcontrib><creatorcontrib>Sagara, Yasuaki</creatorcontrib><creatorcontrib>Sagara, Yoshiaki</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Breast cancer (Tokyo, Japan)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sasaki, Michiro</au><au>Tozaki, Mitsuhiro</au><au>Rodríguez-Ruiz, Alejandro</au><au>Yotsumoto, Daisuke</au><au>Ichiki, Yumi</au><au>Terawaki, Aiko</au><au>Oosako, Shunichi</au><au>Sagara, Yasuaki</au><au>Sagara, Yoshiaki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women</atitle><jtitle>Breast cancer (Tokyo, Japan)</jtitle><stitle>Breast Cancer</stitle><addtitle>Breast Cancer</addtitle><date>2020-07-01</date><risdate>2020</risdate><volume>27</volume><issue>4</issue><spage>642</spage><epage>651</epage><pages>642-651</pages><issn>1340-6868</issn><eissn>1880-4233</eissn><abstract>Background
To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.
Methods
The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions.
Results
The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11;
P
< 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively.
Conclusions
Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.</abstract><cop>Tokyo</cop><pub>Springer Japan</pub><pmid>32052311</pmid><doi>10.1007/s12282-020-01061-8</doi><tpages>10</tpages></addata></record> |
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| source | Springer Nature |
| subjects | Adult Aged Aged, 80 and over Artificial Intelligence Breast - diagnostic imaging Breast cancer Breast Neoplasms - diagnosis Cancer Cancer Research Comparative analysis Decision Support Systems, Clinical Diagnosis Early Detection of Cancer - methods Early Detection of Cancer - standards Female Humans Japan Mammography Mammography - methods Mammography - standards Medicine Medicine & Public Health Middle Aged Oncology Original Article Radiographic Image Interpretation, Computer-Assisted - methods Radiologists - standards Reference Standards Retrospective Studies ROC Curve Surgery Surgical Oncology Women Young Adult |
| title | Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women |
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