Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method
To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method. Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022. Glass slides of cases diagnose...
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| Veröffentlicht in: | Journal of the College of Physicians and Surgeons--Pakistan 2023-05, Vol.33 (5), p.544-547 |
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| container_title | Journal of the College of Physicians and Surgeons--Pakistan |
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| creator | Zehra, Talat Shams, Mahin Ahmad, Zubair Chundriger, Qurratulain Ahmed, Arsalan Jaffar, Nazish |
| description | To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method.
Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022.
Glass slides of cases diagnosed as invasive ductal carcinoma (IDC) were obtained from the Agha Khan Medical University Hospital, selected retrospectively and randomly from 60 patients. They were stained with the Ki67 antibody. An expert pathologist evaluated the Ki67 index in the hotspot fields using eyeball method. Digital images were taken from the hotspots using a camera attached to the microscope. The images were uploaded in the Mindpeak software to detect the exact percentage of Ki67-positive cells. The results obtained through automated detection were compared with the results reported by expert pathologists to see the differential outcome.
The manual and automated scoring methods showed strong positive concordance (p |
| doi_str_mv | 10.29271/jcpsp.2023.05.544 |
| format | Article |
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Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022.
Glass slides of cases diagnosed as invasive ductal carcinoma (IDC) were obtained from the Agha Khan Medical University Hospital, selected retrospectively and randomly from 60 patients. They were stained with the Ki67 antibody. An expert pathologist evaluated the Ki67 index in the hotspot fields using eyeball method. Digital images were taken from the hotspots using a camera attached to the microscope. The images were uploaded in the Mindpeak software to detect the exact percentage of Ki67-positive cells. The results obtained through automated detection were compared with the results reported by expert pathologists to see the differential outcome.
The manual and automated scoring methods showed strong positive concordance (p <0.001).
Automated scoring of Ki-67 staining has tremendous potential as the issues of lack of consistency, reproducibility, and accuracy can be eliminated. In the era of personalised medicine, pathologists can efficiently give a precise clinical diagnosis with the support of AI.
Artificial intelligence, Algorithms, Breast cancer, Deep learning, Image detection, Ki-67.</description><identifier>ISSN: 1022-386X</identifier><identifier>EISSN: 1681-7168</identifier><identifier>DOI: 10.29271/jcpsp.2023.05.544</identifier><identifier>PMID: 37190690</identifier><language>eng</language><publisher>Pakistan: College of Physicians and Surgeons Pakistan</publisher><subject>Artificial Intelligence ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - pathology ; Computer programs ; Diagnosis ; Diagnostic imaging ; Female ; Humans ; Image processing ; Ki-67 Antigen ; Measurement ; Methods ; Reproducibility of Results ; Retrospective Studies ; Software ; Technology application</subject><ispartof>Journal of the College of Physicians and Surgeons--Pakistan, 2023-05, Vol.33 (5), p.544-547</ispartof><rights>COPYRIGHT 2023 College of Physicians and Surgeons Pakistan</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-a68e3ed8e716f42a6e53de065bdb3b2098de58b3105d6e30a1e06c737a9ee86e3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37190690$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zehra, Talat</creatorcontrib><creatorcontrib>Shams, Mahin</creatorcontrib><creatorcontrib>Ahmad, Zubair</creatorcontrib><creatorcontrib>Chundriger, Qurratulain</creatorcontrib><creatorcontrib>Ahmed, Arsalan</creatorcontrib><creatorcontrib>Jaffar, Nazish</creatorcontrib><title>Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method</title><title>Journal of the College of Physicians and Surgeons--Pakistan</title><addtitle>J Coll Physicians Surg Pak</addtitle><description>To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method.
Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022.
Glass slides of cases diagnosed as invasive ductal carcinoma (IDC) were obtained from the Agha Khan Medical University Hospital, selected retrospectively and randomly from 60 patients. They were stained with the Ki67 antibody. An expert pathologist evaluated the Ki67 index in the hotspot fields using eyeball method. Digital images were taken from the hotspots using a camera attached to the microscope. The images were uploaded in the Mindpeak software to detect the exact percentage of Ki67-positive cells. The results obtained through automated detection were compared with the results reported by expert pathologists to see the differential outcome.
The manual and automated scoring methods showed strong positive concordance (p <0.001).
Automated scoring of Ki-67 staining has tremendous potential as the issues of lack of consistency, reproducibility, and accuracy can be eliminated. In the era of personalised medicine, pathologists can efficiently give a precise clinical diagnosis with the support of AI.
Artificial intelligence, Algorithms, Breast cancer, Deep learning, Image detection, Ki-67.</description><subject>Artificial Intelligence</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Breast Neoplasms - pathology</subject><subject>Computer programs</subject><subject>Diagnosis</subject><subject>Diagnostic imaging</subject><subject>Female</subject><subject>Humans</subject><subject>Image processing</subject><subject>Ki-67 Antigen</subject><subject>Measurement</subject><subject>Methods</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Software</subject><subject>Technology application</subject><issn>1022-386X</issn><issn>1681-7168</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNptkV1rFDEUhgdRbK3-AS8k4E1vZszHJJO5XNfaLrYUUcG7kEnObFNnkjXJUPrvzXarIJRAEk7eJ5zDU1VvCW5oTzvy4dbs0q6hmLIG84a37bPqmAhJ6q7sz8sdU1ozKX4eVa9SusWYcSLly-qIdaTHosfH1a8vrhYd-rpon93ojM4ueOQ8-hhBp4zW2huIaLhHn9zWZT2hzay3zm_RaoO-hTHf6QhIe4tcTmgdvAnR7hl05_INutJ-KcwV5JtgX1cvRj0lePN4nlQ_Pp99X1_Ul9fnm_XqsjYtaXOthQQGVkKZYmypFsCZBSz4YAc2UNxLC1wOjGBuBTCsSXk0Het0DyBL5aQ6Pfy7i-H3Aimr2SUD06Q9hCUpKknLqaCYl-j7Q3SrJ1DOjyFHbfZxteo4FoSKvi2p5olUWRZmZ4KH0ZX6fwA9ACaGlCKMahfdrOO9Ilg9uFMP7tTencJcFXcFevfY9jLMYP8hf2WxPw18lEI</recordid><startdate>202305</startdate><enddate>202305</enddate><creator>Zehra, Talat</creator><creator>Shams, Mahin</creator><creator>Ahmad, Zubair</creator><creator>Chundriger, Qurratulain</creator><creator>Ahmed, Arsalan</creator><creator>Jaffar, Nazish</creator><general>College of Physicians and Surgeons Pakistan</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>202305</creationdate><title>Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method</title><author>Zehra, Talat ; Shams, Mahin ; Ahmad, Zubair ; Chundriger, Qurratulain ; Ahmed, Arsalan ; Jaffar, Nazish</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c414t-a68e3ed8e716f42a6e53de065bdb3b2098de58b3105d6e30a1e06c737a9ee86e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial Intelligence</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Breast Neoplasms - pathology</topic><topic>Computer programs</topic><topic>Diagnosis</topic><topic>Diagnostic imaging</topic><topic>Female</topic><topic>Humans</topic><topic>Image processing</topic><topic>Ki-67 Antigen</topic><topic>Measurement</topic><topic>Methods</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Software</topic><topic>Technology application</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zehra, Talat</creatorcontrib><creatorcontrib>Shams, Mahin</creatorcontrib><creatorcontrib>Ahmad, Zubair</creatorcontrib><creatorcontrib>Chundriger, Qurratulain</creatorcontrib><creatorcontrib>Ahmed, Arsalan</creatorcontrib><creatorcontrib>Jaffar, Nazish</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>Journal of the College of Physicians and Surgeons--Pakistan</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zehra, Talat</au><au>Shams, Mahin</au><au>Ahmad, Zubair</au><au>Chundriger, Qurratulain</au><au>Ahmed, Arsalan</au><au>Jaffar, Nazish</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method</atitle><jtitle>Journal of the College of Physicians and Surgeons--Pakistan</jtitle><addtitle>J Coll Physicians Surg Pak</addtitle><date>2023-05</date><risdate>2023</risdate><volume>33</volume><issue>5</issue><spage>544</spage><epage>547</epage><pages>544-547</pages><issn>1022-386X</issn><eissn>1681-7168</eissn><abstract>To validate the concordance of automated detection of Ki67 in digital images of breast cancer with the manual eyeball / hotspot method.
Descriptive study. Place and Duration of the Study: Jinnah Sindh Medical University, Karachi, from 1st January to 15th February 2022.
Glass slides of cases diagnosed as invasive ductal carcinoma (IDC) were obtained from the Agha Khan Medical University Hospital, selected retrospectively and randomly from 60 patients. They were stained with the Ki67 antibody. An expert pathologist evaluated the Ki67 index in the hotspot fields using eyeball method. Digital images were taken from the hotspots using a camera attached to the microscope. The images were uploaded in the Mindpeak software to detect the exact percentage of Ki67-positive cells. The results obtained through automated detection were compared with the results reported by expert pathologists to see the differential outcome.
The manual and automated scoring methods showed strong positive concordance (p <0.001).
Automated scoring of Ki-67 staining has tremendous potential as the issues of lack of consistency, reproducibility, and accuracy can be eliminated. In the era of personalised medicine, pathologists can efficiently give a precise clinical diagnosis with the support of AI.
Artificial intelligence, Algorithms, Breast cancer, Deep learning, Image detection, Ki-67.</abstract><cop>Pakistan</cop><pub>College of Physicians and Surgeons Pakistan</pub><pmid>37190690</pmid><doi>10.29271/jcpsp.2023.05.544</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | MEDLINE; EZB-FREE-00999 freely available EZB journals |
| subjects | Artificial Intelligence Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Computer programs Diagnosis Diagnostic imaging Female Humans Image processing Ki-67 Antigen Measurement Methods Reproducibility of Results Retrospective Studies Software Technology application |
| title | Ki-67 Quantification in Breast Cancer by Digital Imaging AI Software and its Concordance with Manual Method |
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