Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis
Neoadjuvant cisplatin-based combination chemotherapy (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscle-invasive bladder cancer (MIBC). Prediction of response to NAC is essential for clinical decision-making regarding alternatives in case of non-...
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Veröffentlicht in: | Canadian Urological Association journal 2024-09, Vol.18 (9), p.E276-E284 |
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creator | Suartz, Caio Vinícius Martinez, Lucas Motta Cordeiro, Maurício Dener Flores, Hunter Ausley Kodama, Sarah Cardili, Leonardo Mota, José Maurício Coelho, Fernando Morbeck Almeida de Bessa Junior, José Camargo, Cristina Pires Teoh, Jeremy Yuen-Chun Shariat, Shahrokh F Toren, Paul Nahas, William Carlos Ribeiro-Filho, Leopoldo Alves |
description | Neoadjuvant cisplatin-based combination chemotherapy (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscle-invasive bladder cancer (MIBC). Prediction of response to NAC is essential for clinical decision-making regarding alternatives in case of non-response, and bladder-sparing in case of complete response. This research aimed to assess the performance of machine learning in predicting therapeutic response following NAC treatment in patients with MIBC.
A systematic review adhering to the PRISMA guidelines was conducted until July 2023. The study integrated articles relating to artificial intelligence and NAC response in MIBC from various databases. The quality of articles was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A meta-analysis was subsequently performed on selected studies to determine the sensitivity and specificity of machine learning algorithms in predicting NAC response.
Of 655 articles identified, 12 studies comprising 1523 patients were included, and four studies were eligible for meta-analysis. The sensitivity and specificity of the studies were 0.62 (95% confidence interval [CI] 0.50-0.72) and 0.82 (95% CI 0.72-0.89), respectively, with a heterogeneity score (I
) of 38.5%. The machine learning algorithms used computed tomography, genetic, and anatomopathologic data as input and exhibited promising potential for predicting NAC response.
Machine-learning algorithms, especially those using computed tomography, genetic, and pathologic data, demonstrate significant potential for predicting NAC response in MIBC. Standardization of methodologic data analysis and response criteria are needed as validation studies. |
doi_str_mv | 10.5489/cuaj.8681 |
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A systematic review adhering to the PRISMA guidelines was conducted until July 2023. The study integrated articles relating to artificial intelligence and NAC response in MIBC from various databases. The quality of articles was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A meta-analysis was subsequently performed on selected studies to determine the sensitivity and specificity of machine learning algorithms in predicting NAC response.
Of 655 articles identified, 12 studies comprising 1523 patients were included, and four studies were eligible for meta-analysis. The sensitivity and specificity of the studies were 0.62 (95% confidence interval [CI] 0.50-0.72) and 0.82 (95% CI 0.72-0.89), respectively, with a heterogeneity score (I
) of 38.5%. The machine learning algorithms used computed tomography, genetic, and anatomopathologic data as input and exhibited promising potential for predicting NAC response.
Machine-learning algorithms, especially those using computed tomography, genetic, and pathologic data, demonstrate significant potential for predicting NAC response in MIBC. Standardization of methodologic data analysis and response criteria are needed as validation studies.</description><identifier>ISSN: 1911-6470</identifier><identifier>EISSN: 1920-1214</identifier><identifier>DOI: 10.5489/cuaj.8681</identifier><identifier>PMID: 39190175</identifier><language>eng</language><publisher>Canada: Canadian Urological Association</publisher><subject>Adjuvant treatment ; Algorithms ; Artificial intelligence ; Bladder cancer ; Cancer ; Care and treatment ; Complications and side effects ; CT imaging ; Data mining ; Drug therapy, Combination ; Health aspects ; Information management ; Machine learning ; Measurement ; Medical research ; Medicine, Experimental ; Neoadjuvant therapy ; Patient outcomes ; Radiation ; Review</subject><ispartof>Canadian Urological Association journal, 2024-09, Vol.18 (9), p.E276-E284</ispartof><rights>COPYRIGHT 2024 Canadian Urological Association</rights><rights>2024 CANADIAN UROLOGICAL ASSOCIATION 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-1364-5508</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/PMC11404678/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11404678/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39190175$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Suartz, Caio Vinícius</creatorcontrib><creatorcontrib>Martinez, Lucas Motta</creatorcontrib><creatorcontrib>Cordeiro, Maurício Dener</creatorcontrib><creatorcontrib>Flores, Hunter Ausley</creatorcontrib><creatorcontrib>Kodama, Sarah</creatorcontrib><creatorcontrib>Cardili, Leonardo</creatorcontrib><creatorcontrib>Mota, José Maurício</creatorcontrib><creatorcontrib>Coelho, Fernando Morbeck Almeida</creatorcontrib><creatorcontrib>de Bessa Junior, José</creatorcontrib><creatorcontrib>Camargo, Cristina Pires</creatorcontrib><creatorcontrib>Teoh, Jeremy Yuen-Chun</creatorcontrib><creatorcontrib>Shariat, Shahrokh F</creatorcontrib><creatorcontrib>Toren, Paul</creatorcontrib><creatorcontrib>Nahas, William Carlos</creatorcontrib><creatorcontrib>Ribeiro-Filho, Leopoldo Alves</creatorcontrib><title>Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis</title><title>Canadian Urological Association journal</title><addtitle>Can Urol Assoc J</addtitle><description>Neoadjuvant cisplatin-based combination chemotherapy (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscle-invasive bladder cancer (MIBC). Prediction of response to NAC is essential for clinical decision-making regarding alternatives in case of non-response, and bladder-sparing in case of complete response. This research aimed to assess the performance of machine learning in predicting therapeutic response following NAC treatment in patients with MIBC.
A systematic review adhering to the PRISMA guidelines was conducted until July 2023. The study integrated articles relating to artificial intelligence and NAC response in MIBC from various databases. The quality of articles was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A meta-analysis was subsequently performed on selected studies to determine the sensitivity and specificity of machine learning algorithms in predicting NAC response.
Of 655 articles identified, 12 studies comprising 1523 patients were included, and four studies were eligible for meta-analysis. The sensitivity and specificity of the studies were 0.62 (95% confidence interval [CI] 0.50-0.72) and 0.82 (95% CI 0.72-0.89), respectively, with a heterogeneity score (I
) of 38.5%. The machine learning algorithms used computed tomography, genetic, and anatomopathologic data as input and exhibited promising potential for predicting NAC response.
Machine-learning algorithms, especially those using computed tomography, genetic, and pathologic data, demonstrate significant potential for predicting NAC response in MIBC. Standardization of methodologic data analysis and response criteria are needed as validation studies.</description><subject>Adjuvant treatment</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Bladder cancer</subject><subject>Cancer</subject><subject>Care and treatment</subject><subject>Complications and side effects</subject><subject>CT imaging</subject><subject>Data mining</subject><subject>Drug therapy, Combination</subject><subject>Health aspects</subject><subject>Information management</subject><subject>Machine learning</subject><subject>Measurement</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Neoadjuvant therapy</subject><subject>Patient outcomes</subject><subject>Radiation</subject><subject>Review</subject><issn>1911-6470</issn><issn>1920-1214</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNptkstu1DAUhiMEoqWw4AWQBRKCRQZ7nIu9qkYVl0oVLIC1deKcTDxK7KntDMxr8MQ4tFQz0sgLW8efv8XvP8teMrooCyE_6Ak2C1EJ9ig7Z3JJc7ZkxeP5zFheFTU9y56FsKG0SpP6aXbGJZOU1eV59mflo-mMNjAQYyMOg1mj1Ug658nWY2t0NHZNPIatswFJdMSig3Yz7cBGonscXezRw3b_700zQNuiJxqSxZMV0W5Mnh5tMDskYR8ijhCNTsqdwV8EbEtGjJCDhWEfTHiePelgCPjifr_Ifn76-OPqS37z7fP11eom11zSmGPX1g3HqijLlle8aUrB6iUtlx0wJgQKKeqOSqBc1gJZU1clq5pGtBVrqOw0v8gu77zbqRmx1Wijh0FtvRnB75UDo45vrOnV2u0UYwUtqlokw7t7g3e3E4aoRhN0yhBSRFNQnMq6kGXBeULf3KFrGFAZ27mk1DOuVoJKyqtlMVOvT1B6a27VIbQ4AaXV4mi0s9iZND-yvj96kJiIv-MaphDU9fevx-zbA7ZHGGIf3DBFk37_pFR7F4LH7iE4RtXcSjW3Us2tTOyrw6QfyP815H8Bi4Dd9w</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Suartz, Caio Vinícius</creator><creator>Martinez, Lucas Motta</creator><creator>Cordeiro, Maurício Dener</creator><creator>Flores, Hunter Ausley</creator><creator>Kodama, Sarah</creator><creator>Cardili, Leonardo</creator><creator>Mota, José Maurício</creator><creator>Coelho, Fernando Morbeck Almeida</creator><creator>de Bessa Junior, José</creator><creator>Camargo, Cristina Pires</creator><creator>Teoh, Jeremy Yuen-Chun</creator><creator>Shariat, Shahrokh F</creator><creator>Toren, Paul</creator><creator>Nahas, William Carlos</creator><creator>Ribeiro-Filho, Leopoldo Alves</creator><general>Canadian Urological Association</general><general>Canadian Medical Association</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1364-5508</orcidid></search><sort><creationdate>20240901</creationdate><title>Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis</title><author>Suartz, Caio Vinícius ; Martinez, Lucas Motta ; Cordeiro, Maurício Dener ; Flores, Hunter Ausley ; Kodama, Sarah ; Cardili, Leonardo ; Mota, José Maurício ; Coelho, Fernando Morbeck Almeida ; de Bessa Junior, José ; Camargo, Cristina Pires ; Teoh, Jeremy Yuen-Chun ; Shariat, Shahrokh F ; Toren, Paul ; Nahas, William Carlos ; Ribeiro-Filho, Leopoldo Alves</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-efd7b3e6455d363bb58172052fa1188e8987f09a03978e1b76516bb8d61b09fc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adjuvant treatment</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Bladder cancer</topic><topic>Cancer</topic><topic>Care and treatment</topic><topic>Complications and side effects</topic><topic>CT imaging</topic><topic>Data mining</topic><topic>Drug therapy, Combination</topic><topic>Health aspects</topic><topic>Information management</topic><topic>Machine learning</topic><topic>Measurement</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Neoadjuvant therapy</topic><topic>Patient outcomes</topic><topic>Radiation</topic><topic>Review</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Suartz, Caio Vinícius</creatorcontrib><creatorcontrib>Martinez, Lucas Motta</creatorcontrib><creatorcontrib>Cordeiro, Maurício Dener</creatorcontrib><creatorcontrib>Flores, Hunter Ausley</creatorcontrib><creatorcontrib>Kodama, Sarah</creatorcontrib><creatorcontrib>Cardili, Leonardo</creatorcontrib><creatorcontrib>Mota, José Maurício</creatorcontrib><creatorcontrib>Coelho, Fernando Morbeck Almeida</creatorcontrib><creatorcontrib>de Bessa Junior, José</creatorcontrib><creatorcontrib>Camargo, Cristina Pires</creatorcontrib><creatorcontrib>Teoh, Jeremy Yuen-Chun</creatorcontrib><creatorcontrib>Shariat, Shahrokh F</creatorcontrib><creatorcontrib>Toren, Paul</creatorcontrib><creatorcontrib>Nahas, William Carlos</creatorcontrib><creatorcontrib>Ribeiro-Filho, Leopoldo Alves</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Canadian Urological Association journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Suartz, Caio Vinícius</au><au>Martinez, Lucas Motta</au><au>Cordeiro, Maurício Dener</au><au>Flores, Hunter Ausley</au><au>Kodama, Sarah</au><au>Cardili, Leonardo</au><au>Mota, José Maurício</au><au>Coelho, Fernando Morbeck Almeida</au><au>de Bessa Junior, José</au><au>Camargo, Cristina Pires</au><au>Teoh, Jeremy Yuen-Chun</au><au>Shariat, Shahrokh F</au><au>Toren, Paul</au><au>Nahas, William Carlos</au><au>Ribeiro-Filho, Leopoldo Alves</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis</atitle><jtitle>Canadian Urological Association journal</jtitle><addtitle>Can Urol Assoc J</addtitle><date>2024-09-01</date><risdate>2024</risdate><volume>18</volume><issue>9</issue><spage>E276</spage><epage>E284</epage><pages>E276-E284</pages><issn>1911-6470</issn><eissn>1920-1214</eissn><abstract>Neoadjuvant cisplatin-based combination chemotherapy (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscle-invasive bladder cancer (MIBC). Prediction of response to NAC is essential for clinical decision-making regarding alternatives in case of non-response, and bladder-sparing in case of complete response. This research aimed to assess the performance of machine learning in predicting therapeutic response following NAC treatment in patients with MIBC.
A systematic review adhering to the PRISMA guidelines was conducted until July 2023. The study integrated articles relating to artificial intelligence and NAC response in MIBC from various databases. The quality of articles was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A meta-analysis was subsequently performed on selected studies to determine the sensitivity and specificity of machine learning algorithms in predicting NAC response.
Of 655 articles identified, 12 studies comprising 1523 patients were included, and four studies were eligible for meta-analysis. The sensitivity and specificity of the studies were 0.62 (95% confidence interval [CI] 0.50-0.72) and 0.82 (95% CI 0.72-0.89), respectively, with a heterogeneity score (I
) of 38.5%. The machine learning algorithms used computed tomography, genetic, and anatomopathologic data as input and exhibited promising potential for predicting NAC response.
Machine-learning algorithms, especially those using computed tomography, genetic, and pathologic data, demonstrate significant potential for predicting NAC response in MIBC. Standardization of methodologic data analysis and response criteria are needed as validation studies.</abstract><cop>Canada</cop><pub>Canadian Urological Association</pub><pmid>39190175</pmid><doi>10.5489/cuaj.8681</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-1364-5508</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adjuvant treatment Algorithms Artificial intelligence Bladder cancer Cancer Care and treatment Complications and side effects CT imaging Data mining Drug therapy, Combination Health aspects Information management Machine learning Measurement Medical research Medicine, Experimental Neoadjuvant therapy Patient outcomes Radiation Review |
title | Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis |
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