AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines

Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, hi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2024-08
Hauptverfasser: Spaanderman, Douwe J, Marzetti, Matthew, Wan, Xinyi, Scarsbrook, Andrew F, Robinson, Philip, Oei, Edwin H G, Visser, Jacob J, Hemke, Robert, Kirsten van Langevelde, Hanff, David F, Geert J L H van Leenders, Verhoef, Cornelis, Gruühagen, Dirk J, Niessen, Wiro J, Klein, Stefan, Starmans, Martijn P A
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Spaanderman, Douwe J
Marzetti, Matthew
Wan, Xinyi
Scarsbrook, Andrew F
Robinson, Philip
Oei, Edwin H G
Visser, Jacob J
Hemke, Robert
Kirsten van Langevelde
Hanff, David F
Geert J L H van Leenders
Verhoef, Cornelis
Gruühagen, Dirk J
Niessen, Wiro J
Klein, Stefan
Starmans, Martijn P A
description Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9\(\pm\)7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1\(\pm\)2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3096438449</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3096438449</sourcerecordid><originalsourceid>FETCH-proquest_journals_30964384493</originalsourceid><addsrcrecordid>eNqNjcFqAjEURUNBqKj_8MD1wDQZrboTUSropuhans6b8CSTtPMSi8v-eYP4AV1duPcezovqa2Peilml9asaiVzLstTTdz2ZmL76XW6BPXRYc3DB8gUdcIuWvYXQgIQmFpFFEgH6Gs7BE8TUhtTJAhDkLpFajHyBjm5MP0A3dCkXmUeL7CXCarfc7h_45ng4fq6L7LSJa3LsSYaq16ATGj1zoMab9WH1UXx14TuRxNM123yeTqacTyszq6q5-d_rD9NdT-A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3096438449</pqid></control><display><type>article</type><title>AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines</title><source>Free E- Journals</source><creator>Spaanderman, Douwe J ; Marzetti, Matthew ; Wan, Xinyi ; Scarsbrook, Andrew F ; Robinson, Philip ; Oei, Edwin H G ; Visser, Jacob J ; Hemke, Robert ; Kirsten van Langevelde ; Hanff, David F ; Geert J L H van Leenders ; Verhoef, Cornelis ; Gruühagen, Dirk J ; Niessen, Wiro J ; Klein, Stefan ; Starmans, Martijn P A</creator><creatorcontrib>Spaanderman, Douwe J ; Marzetti, Matthew ; Wan, Xinyi ; Scarsbrook, Andrew F ; Robinson, Philip ; Oei, Edwin H G ; Visser, Jacob J ; Hemke, Robert ; Kirsten van Langevelde ; Hanff, David F ; Geert J L H van Leenders ; Verhoef, Cornelis ; Gruühagen, Dirk J ; Niessen, Wiro J ; Klein, Stefan ; Starmans, Martijn P A</creatorcontrib><description>Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9\(\pm\)7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1\(\pm\)2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Artificial intelligence ; Availability ; Best practice ; Guidelines ; Medical imaging ; Systematic review ; Tumors ; Workflow</subject><ispartof>arXiv.org, 2024-08</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Spaanderman, Douwe J</creatorcontrib><creatorcontrib>Marzetti, Matthew</creatorcontrib><creatorcontrib>Wan, Xinyi</creatorcontrib><creatorcontrib>Scarsbrook, Andrew F</creatorcontrib><creatorcontrib>Robinson, Philip</creatorcontrib><creatorcontrib>Oei, Edwin H G</creatorcontrib><creatorcontrib>Visser, Jacob J</creatorcontrib><creatorcontrib>Hemke, Robert</creatorcontrib><creatorcontrib>Kirsten van Langevelde</creatorcontrib><creatorcontrib>Hanff, David F</creatorcontrib><creatorcontrib>Geert J L H van Leenders</creatorcontrib><creatorcontrib>Verhoef, Cornelis</creatorcontrib><creatorcontrib>Gruühagen, Dirk J</creatorcontrib><creatorcontrib>Niessen, Wiro J</creatorcontrib><creatorcontrib>Klein, Stefan</creatorcontrib><creatorcontrib>Starmans, Martijn P A</creatorcontrib><title>AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines</title><title>arXiv.org</title><description>Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9\(\pm\)7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1\(\pm\)2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods.</description><subject>Artificial intelligence</subject><subject>Availability</subject><subject>Best practice</subject><subject>Guidelines</subject><subject>Medical imaging</subject><subject>Systematic review</subject><subject>Tumors</subject><subject>Workflow</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjcFqAjEURUNBqKj_8MD1wDQZrboTUSropuhans6b8CSTtPMSi8v-eYP4AV1duPcezovqa2Peilml9asaiVzLstTTdz2ZmL76XW6BPXRYc3DB8gUdcIuWvYXQgIQmFpFFEgH6Gs7BE8TUhtTJAhDkLpFajHyBjm5MP0A3dCkXmUeL7CXCarfc7h_45ng4fq6L7LSJa3LsSYaq16ATGj1zoMab9WH1UXx14TuRxNM123yeTqacTyszq6q5-d_rD9NdT-A</recordid><startdate>20240822</startdate><enddate>20240822</enddate><creator>Spaanderman, Douwe J</creator><creator>Marzetti, Matthew</creator><creator>Wan, Xinyi</creator><creator>Scarsbrook, Andrew F</creator><creator>Robinson, Philip</creator><creator>Oei, Edwin H G</creator><creator>Visser, Jacob J</creator><creator>Hemke, Robert</creator><creator>Kirsten van Langevelde</creator><creator>Hanff, David F</creator><creator>Geert J L H van Leenders</creator><creator>Verhoef, Cornelis</creator><creator>Gruühagen, Dirk J</creator><creator>Niessen, Wiro J</creator><creator>Klein, Stefan</creator><creator>Starmans, Martijn P A</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240822</creationdate><title>AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines</title><author>Spaanderman, Douwe J ; Marzetti, Matthew ; Wan, Xinyi ; Scarsbrook, Andrew F ; Robinson, Philip ; Oei, Edwin H G ; Visser, Jacob J ; Hemke, Robert ; Kirsten van Langevelde ; Hanff, David F ; Geert J L H van Leenders ; Verhoef, Cornelis ; Gruühagen, Dirk J ; Niessen, Wiro J ; Klein, Stefan ; Starmans, Martijn P A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_30964384493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial intelligence</topic><topic>Availability</topic><topic>Best practice</topic><topic>Guidelines</topic><topic>Medical imaging</topic><topic>Systematic review</topic><topic>Tumors</topic><topic>Workflow</topic><toplevel>online_resources</toplevel><creatorcontrib>Spaanderman, Douwe J</creatorcontrib><creatorcontrib>Marzetti, Matthew</creatorcontrib><creatorcontrib>Wan, Xinyi</creatorcontrib><creatorcontrib>Scarsbrook, Andrew F</creatorcontrib><creatorcontrib>Robinson, Philip</creatorcontrib><creatorcontrib>Oei, Edwin H G</creatorcontrib><creatorcontrib>Visser, Jacob J</creatorcontrib><creatorcontrib>Hemke, Robert</creatorcontrib><creatorcontrib>Kirsten van Langevelde</creatorcontrib><creatorcontrib>Hanff, David F</creatorcontrib><creatorcontrib>Geert J L H van Leenders</creatorcontrib><creatorcontrib>Verhoef, Cornelis</creatorcontrib><creatorcontrib>Gruühagen, Dirk J</creatorcontrib><creatorcontrib>Niessen, Wiro J</creatorcontrib><creatorcontrib>Klein, Stefan</creatorcontrib><creatorcontrib>Starmans, Martijn P A</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</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>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Spaanderman, Douwe J</au><au>Marzetti, Matthew</au><au>Wan, Xinyi</au><au>Scarsbrook, Andrew F</au><au>Robinson, Philip</au><au>Oei, Edwin H G</au><au>Visser, Jacob J</au><au>Hemke, Robert</au><au>Kirsten van Langevelde</au><au>Hanff, David F</au><au>Geert J L H van Leenders</au><au>Verhoef, Cornelis</au><au>Gruühagen, Dirk J</au><au>Niessen, Wiro J</au><au>Klein, Stefan</au><au>Starmans, Martijn P A</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines</atitle><jtitle>arXiv.org</jtitle><date>2024-08-22</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9\(\pm\)7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1\(\pm\)2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2024-08
issn 2331-8422
language eng
recordid cdi_proquest_journals_3096438449
source Free E- Journals
subjects Artificial intelligence
Availability
Best practice
Guidelines
Medical imaging
Systematic review
Tumors
Workflow
title AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T05%3A10%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=AI%20in%20radiological%20imaging%20of%20soft-tissue%20and%20bone%20tumours:%20a%20systematic%20review%20evaluating%20against%20CLAIM%20and%20FUTURE-AI%20guidelines&rft.jtitle=arXiv.org&rft.au=Spaanderman,%20Douwe%20J&rft.date=2024-08-22&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3096438449%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3096438449&rft_id=info:pmid/&rfr_iscdi=true