Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study
Abstract In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate...
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| Veröffentlicht in: | PNAS nexus 2023-03, Vol.2 (3), p.pgad026-pgad026 |
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| creator | Shapira, Nadav Donovan, Kevin Mei, Kai Geagan, Michael Roshkovan, Leonid Gang, Grace J Abed, Mohammed Linna, Nathaniel B Cranston, Coulter P O'Leary, Cathal N Dhanaliwala, Ali H Kontos, Despina Litt, Harold I Stayman, J Webster Shinohara, Russell T Noël, Peter B |
| description | Abstract
In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03–0.29, using a 1–5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint’s production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms.
Classification: CT lung phantoms, reader study |
| doi_str_mv | 10.1093/pnasnexus/pgad026 |
| format | Article |
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In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03–0.29, using a 1–5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint’s production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms.
Classification: CT lung phantoms, reader study</description><identifier>ISSN: 2752-6542</identifier><identifier>EISSN: 2752-6542</identifier><identifier>DOI: 10.1093/pnasnexus/pgad026</identifier><identifier>PMID: 36909822</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>3D printing ; Algorithms ; Biological, Health, and Medical Sciences ; CT imaging ; Diagnosis ; Lung diseases ; Medical imaging equipment ; Medical research ; Medicine, Experimental ; Methods ; Technology application</subject><ispartof>PNAS nexus, 2023-03, Vol.2 (3), p.pgad026-pgad026</ispartof><rights>The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences. 2023</rights><rights>The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.</rights><rights>COPYRIGHT 2023 Oxford University Press</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c503t-661fc052236ebe8e320bb7ca72372b3edb396e2fb192fbc378596309fdc92c003</citedby><cites>FETCH-LOGICAL-c503t-661fc052236ebe8e320bb7ca72372b3edb396e2fb192fbc378596309fdc92c003</cites><orcidid>0000-0001-8627-8203 ; 0000-0002-9294-7001 ; 0000-0002-9671-6171</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992761/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992761/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1598,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36909822$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Yooseph, Shibu</contributor><creatorcontrib>Shapira, Nadav</creatorcontrib><creatorcontrib>Donovan, Kevin</creatorcontrib><creatorcontrib>Mei, Kai</creatorcontrib><creatorcontrib>Geagan, Michael</creatorcontrib><creatorcontrib>Roshkovan, Leonid</creatorcontrib><creatorcontrib>Gang, Grace J</creatorcontrib><creatorcontrib>Abed, Mohammed</creatorcontrib><creatorcontrib>Linna, Nathaniel B</creatorcontrib><creatorcontrib>Cranston, Coulter P</creatorcontrib><creatorcontrib>O'Leary, Cathal N</creatorcontrib><creatorcontrib>Dhanaliwala, Ali H</creatorcontrib><creatorcontrib>Kontos, Despina</creatorcontrib><creatorcontrib>Litt, Harold I</creatorcontrib><creatorcontrib>Stayman, J Webster</creatorcontrib><creatorcontrib>Shinohara, Russell T</creatorcontrib><creatorcontrib>Noël, Peter B</creatorcontrib><title>Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study</title><title>PNAS nexus</title><addtitle>PNAS Nexus</addtitle><description>Abstract
In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03–0.29, using a 1–5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint’s production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms.
Classification: CT lung phantoms, reader study</description><subject>3D printing</subject><subject>Algorithms</subject><subject>Biological, Health, and Medical Sciences</subject><subject>CT imaging</subject><subject>Diagnosis</subject><subject>Lung diseases</subject><subject>Medical imaging equipment</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Methods</subject><subject>Technology application</subject><issn>2752-6542</issn><issn>2752-6542</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqNkV1rHCEUhqW0NGGbH9CbIvSmF5nEj4yuvSiE0C8I5Ca5to5znLXMqNWZ0v33cdntkkAviqByfN7Xc3gRekvJBSWKX6ZgSoA_S7lMg-kJEy_QKZMta0R7xV4-uZ-gs1J-EkKYlJReta_RCReKqDVjp-jH_SYDNL2fIBQfgxlxyj7MPgw4OpzM7CHMTUlgvfMW2zilZYYez3GKQzZps8XjUuG0MaHWykdscAbTQ8ZlXvrtG_TKmbHA2eFcoYcvn-9vvjW3d1-_31zfNrYlfG6EoM6SljEuoIM1cEa6TlojGZes49B3XAlgrqOqbpbLdasEJ8r1VjFLCF-hT3vftHQT9LZ2nc2o6zCTyVsdjdfPX4Lf6CH-1kopJgWtBh8OBjn-WqDMevLFwjiaAHEpmsm1aGkr67cr9H6PDmYE7YOL1dHucH0tpaJUCr6jLv5B1dXD5G0M4HytPxPQvcDmWEoGd-yeEr3LXB8z14fMq-bd07GPir8JV-B8D8Ql_YffI513vM8</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Shapira, Nadav</creator><creator>Donovan, Kevin</creator><creator>Mei, Kai</creator><creator>Geagan, Michael</creator><creator>Roshkovan, Leonid</creator><creator>Gang, Grace J</creator><creator>Abed, Mohammed</creator><creator>Linna, Nathaniel B</creator><creator>Cranston, Coulter P</creator><creator>O'Leary, Cathal N</creator><creator>Dhanaliwala, Ali H</creator><creator>Kontos, Despina</creator><creator>Litt, Harold I</creator><creator>Stayman, J Webster</creator><creator>Shinohara, Russell T</creator><creator>Noël, Peter B</creator><general>Oxford University Press</general><scope>TOX</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8627-8203</orcidid><orcidid>https://orcid.org/0000-0002-9294-7001</orcidid><orcidid>https://orcid.org/0000-0002-9671-6171</orcidid></search><sort><creationdate>20230301</creationdate><title>Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study</title><author>Shapira, Nadav ; Donovan, Kevin ; Mei, Kai ; Geagan, Michael ; Roshkovan, Leonid ; Gang, Grace J ; Abed, Mohammed ; Linna, Nathaniel B ; Cranston, Coulter P ; O'Leary, Cathal N ; Dhanaliwala, Ali H ; Kontos, Despina ; Litt, Harold I ; Stayman, J Webster ; Shinohara, Russell T ; Noël, Peter B</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c503t-661fc052236ebe8e320bb7ca72372b3edb396e2fb192fbc378596309fdc92c003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>3D printing</topic><topic>Algorithms</topic><topic>Biological, Health, and Medical Sciences</topic><topic>CT imaging</topic><topic>Diagnosis</topic><topic>Lung diseases</topic><topic>Medical imaging equipment</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Methods</topic><topic>Technology application</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shapira, Nadav</creatorcontrib><creatorcontrib>Donovan, Kevin</creatorcontrib><creatorcontrib>Mei, Kai</creatorcontrib><creatorcontrib>Geagan, Michael</creatorcontrib><creatorcontrib>Roshkovan, Leonid</creatorcontrib><creatorcontrib>Gang, Grace J</creatorcontrib><creatorcontrib>Abed, Mohammed</creatorcontrib><creatorcontrib>Linna, Nathaniel B</creatorcontrib><creatorcontrib>Cranston, Coulter P</creatorcontrib><creatorcontrib>O'Leary, Cathal N</creatorcontrib><creatorcontrib>Dhanaliwala, Ali H</creatorcontrib><creatorcontrib>Kontos, Despina</creatorcontrib><creatorcontrib>Litt, Harold I</creatorcontrib><creatorcontrib>Stayman, J Webster</creatorcontrib><creatorcontrib>Shinohara, Russell T</creatorcontrib><creatorcontrib>Noël, Peter B</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PNAS nexus</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shapira, Nadav</au><au>Donovan, Kevin</au><au>Mei, Kai</au><au>Geagan, Michael</au><au>Roshkovan, Leonid</au><au>Gang, Grace J</au><au>Abed, Mohammed</au><au>Linna, Nathaniel B</au><au>Cranston, Coulter P</au><au>O'Leary, Cathal N</au><au>Dhanaliwala, Ali H</au><au>Kontos, Despina</au><au>Litt, Harold I</au><au>Stayman, J Webster</au><au>Shinohara, Russell T</au><au>Noël, Peter B</au><au>Yooseph, Shibu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study</atitle><jtitle>PNAS nexus</jtitle><addtitle>PNAS Nexus</addtitle><date>2023-03-01</date><risdate>2023</risdate><volume>2</volume><issue>3</issue><spage>pgad026</spage><epage>pgad026</epage><pages>pgad026-pgad026</pages><issn>2752-6542</issn><eissn>2752-6542</eissn><abstract>Abstract
In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03–0.29, using a 1–5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint’s production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms.
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| subjects | 3D printing Algorithms Biological, Health, and Medical Sciences CT imaging Diagnosis Lung diseases Medical imaging equipment Medical research Medicine, Experimental Methods Technology application |
| title | Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study |
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