A novel algorithm to improve pathologic stage prediction of clinically organ‐confined muscle‐invasive bladder cancer

BACKGROUND: An algorithm was created to predict pathologic stage in patients with clinically organ‐confined muscle‐invasive bladder cancer. METHODS: The sample consisted of 133 consecutive patients scheduled to undergo cystectomy. To develop a tool to predict nonorgan‐confined disease before surgery...

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Veröffentlicht in:Cancer 2009-04, Vol.115 (7), p.1459-1464
Hauptverfasser: Margel, David, Harel, Amir, Yossepowitch, Ofer, Baniel, Jack
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container_end_page 1464
container_issue 7
container_start_page 1459
container_title Cancer
container_volume 115
creator Margel, David
Harel, Amir
Yossepowitch, Ofer
Baniel, Jack
description BACKGROUND: An algorithm was created to predict pathologic stage in patients with clinically organ‐confined muscle‐invasive bladder cancer. METHODS: The sample consisted of 133 consecutive patients scheduled to undergo cystectomy. To develop a tool to predict nonorgan‐confined disease before surgery, principal component analysis (PCA) was applied. Patients were stratified into a training set (n = 89) and a validation set (n = 44), and 7 parameters were evaluated: levels of carcinoembryonic antigen, cancer antigen (CA) 125, and carbohydrate antigen (CA) 19‐9; clinical stage; presence of hydronephrosis; presence of carcinoma in situ; and initial tumor size >3 cm. PCA was applied to the training set to determine the weight of each parameter. A PCA score was generated for each patient in the set, and a cutoff defining nonorgan‐confined disease was established. The accuracy of the cutoff was quantified by the area under the receiver operator characteristics curve (AUC). The model was then applied to the validation set without recalculation; the AUC and the positive and negative predictive values of the validation set were calculated. RESULTS: On pathologic evaluation, 71 patients (53%) were found to have organ‐confined tumors and 62 patients (47%) had extravesical disease. The AUC was 0.85 in the training group (95% confidence interval [95% CI], 0.71‐0.97) and 0.84 in the validation group (95% CI, 0.75‐0.93). The positive and negative predictive values in the validation group were 88% (95% CI, 71%‐96%) and 94% (95% CI, 71%‐99%), respectively. CONCLUSIONS: The newly devised, internally validated, algorithm was 85% accurate in predicting nonorgan‐confined bladder disease before cystectomy. Further external validation in a large cohort was recommended as still necessary. Cancer 2009. © 2009 American Cancer Society. In the current study, a unique algorithm was developed to assist in predicting nonorgan‐confined disease in patients with muscle‐invasive urothelial carcinoma. The newly devised formula was 85% accurate, and the findings were internally validated.
doi_str_mv 10.1002/cncr.24138
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METHODS: The sample consisted of 133 consecutive patients scheduled to undergo cystectomy. To develop a tool to predict nonorgan‐confined disease before surgery, principal component analysis (PCA) was applied. Patients were stratified into a training set (n = 89) and a validation set (n = 44), and 7 parameters were evaluated: levels of carcinoembryonic antigen, cancer antigen (CA) 125, and carbohydrate antigen (CA) 19‐9; clinical stage; presence of hydronephrosis; presence of carcinoma in situ; and initial tumor size &gt;3 cm. PCA was applied to the training set to determine the weight of each parameter. A PCA score was generated for each patient in the set, and a cutoff defining nonorgan‐confined disease was established. The accuracy of the cutoff was quantified by the area under the receiver operator characteristics curve (AUC). The model was then applied to the validation set without recalculation; the AUC and the positive and negative predictive values of the validation set were calculated. RESULTS: On pathologic evaluation, 71 patients (53%) were found to have organ‐confined tumors and 62 patients (47%) had extravesical disease. The AUC was 0.85 in the training group (95% confidence interval [95% CI], 0.71‐0.97) and 0.84 in the validation group (95% CI, 0.75‐0.93). The positive and negative predictive values in the validation group were 88% (95% CI, 71%‐96%) and 94% (95% CI, 71%‐99%), respectively. CONCLUSIONS: The newly devised, internally validated, algorithm was 85% accurate in predicting nonorgan‐confined bladder disease before cystectomy. Further external validation in a large cohort was recommended as still necessary. Cancer 2009. © 2009 American Cancer Society. In the current study, a unique algorithm was developed to assist in predicting nonorgan‐confined disease in patients with muscle‐invasive urothelial carcinoma. The newly devised formula was 85% accurate, and the findings were internally validated.</description><identifier>ISSN: 0008-543X</identifier><identifier>EISSN: 1097-0142</identifier><identifier>DOI: 10.1002/cncr.24138</identifier><identifier>PMID: 19152435</identifier><identifier>CODEN: CANCAR</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Algorithms ; Biological and medical sciences ; CA 125 ; CA 19‐9 ; CA-125 Antigen - analysis ; CA-19-9 Antigen - analysis ; carcinoembryonic antigen ; Carcinoembryonic Antigen - analysis ; Female ; Humans ; Male ; mathematical prediction model ; Medical sciences ; Middle Aged ; Neoplasm Invasiveness - pathology ; Neoplasm Staging - methods ; Nephrology. Urinary tract diseases ; Prognosis ; Reproducibility of Results ; tumor markers ; Tumors ; Tumors of the urinary system ; Urinary Bladder Neoplasms - pathology ; Urinary tract. 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METHODS: The sample consisted of 133 consecutive patients scheduled to undergo cystectomy. To develop a tool to predict nonorgan‐confined disease before surgery, principal component analysis (PCA) was applied. Patients were stratified into a training set (n = 89) and a validation set (n = 44), and 7 parameters were evaluated: levels of carcinoembryonic antigen, cancer antigen (CA) 125, and carbohydrate antigen (CA) 19‐9; clinical stage; presence of hydronephrosis; presence of carcinoma in situ; and initial tumor size &gt;3 cm. PCA was applied to the training set to determine the weight of each parameter. A PCA score was generated for each patient in the set, and a cutoff defining nonorgan‐confined disease was established. The accuracy of the cutoff was quantified by the area under the receiver operator characteristics curve (AUC). The model was then applied to the validation set without recalculation; the AUC and the positive and negative predictive values of the validation set were calculated. RESULTS: On pathologic evaluation, 71 patients (53%) were found to have organ‐confined tumors and 62 patients (47%) had extravesical disease. The AUC was 0.85 in the training group (95% confidence interval [95% CI], 0.71‐0.97) and 0.84 in the validation group (95% CI, 0.75‐0.93). The positive and negative predictive values in the validation group were 88% (95% CI, 71%‐96%) and 94% (95% CI, 71%‐99%), respectively. CONCLUSIONS: The newly devised, internally validated, algorithm was 85% accurate in predicting nonorgan‐confined bladder disease before cystectomy. Further external validation in a large cohort was recommended as still necessary. Cancer 2009. © 2009 American Cancer Society. In the current study, a unique algorithm was developed to assist in predicting nonorgan‐confined disease in patients with muscle‐invasive urothelial carcinoma. The newly devised formula was 85% accurate, and the findings were internally validated.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Biological and medical sciences</subject><subject>CA 125</subject><subject>CA 19‐9</subject><subject>CA-125 Antigen - analysis</subject><subject>CA-19-9 Antigen - analysis</subject><subject>carcinoembryonic antigen</subject><subject>Carcinoembryonic Antigen - analysis</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>mathematical prediction model</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Neoplasm Invasiveness - pathology</subject><subject>Neoplasm Staging - methods</subject><subject>Nephrology. Urinary tract diseases</subject><subject>Prognosis</subject><subject>Reproducibility of Results</subject><subject>tumor markers</subject><subject>Tumors</subject><subject>Tumors of the urinary system</subject><subject>Urinary Bladder Neoplasms - pathology</subject><subject>Urinary tract. Prostate gland</subject><subject>urothelial carcinoma</subject><issn>0008-543X</issn><issn>1097-0142</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1uFDEQhS0EIkNgwwGQN7CI1IntttvtZTQKP1IUpAgkdi1PdXli5LYHuydhdhwhZ-QkOMwIdrAq1dOnV6r3CHnJ2SlnTJxBhHwqJG_7R2TBmdEN41I8JgvGWN8o2X45Is9K-VpXLVT7lBxxw5WQrVqQ7-c0plsM1IZ1yn6-meicqJ82uap0Y-ebFNLaAy2zXVch4-hh9inS5CgEHz3YEHY05bWNP3_cQ4rORxzptC0QsCo-3triq9kq2HHETMFGwPycPHE2FHxxmMfk89uLT8v3zeXHdx-W55cNSCH7pu1GI4UC6aRjqI0D1FLpXpoWjbZMtMBd3-nRKGsQ2GocBaxUj0apvrO2PSZv9r71o29bLPMw-QIYgo2YtmXoNFOKG_ZfUNRQdcdkBU_2IORUSkY3bLKfbN4NnA0PhQwPhQy_C6nwq4PrdjXh-Bc9NFCB1wfAlhqlyzUdX_5wggulBeeV43vuzgfc_ePksLxaXu-P_wKDMqbF</recordid><startdate>20090401</startdate><enddate>20090401</enddate><creator>Margel, David</creator><creator>Harel, Amir</creator><creator>Yossepowitch, Ofer</creator><creator>Baniel, Jack</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley-Blackwell</general><scope>IQODW</scope><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>7U7</scope><scope>C1K</scope><scope>7X8</scope></search><sort><creationdate>20090401</creationdate><title>A novel algorithm to improve pathologic stage prediction of clinically organ‐confined muscle‐invasive bladder cancer</title><author>Margel, David ; Harel, Amir ; Yossepowitch, Ofer ; Baniel, Jack</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4248-36d9425c4f4f0e79fce74578493e97a023c1f867d95a9ec0bdd2cb58e95586aa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Biological and medical sciences</topic><topic>CA 125</topic><topic>CA 19‐9</topic><topic>CA-125 Antigen - analysis</topic><topic>CA-19-9 Antigen - analysis</topic><topic>carcinoembryonic antigen</topic><topic>Carcinoembryonic Antigen - analysis</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>mathematical prediction model</topic><topic>Medical sciences</topic><topic>Middle Aged</topic><topic>Neoplasm Invasiveness - pathology</topic><topic>Neoplasm Staging - methods</topic><topic>Nephrology. Urinary tract diseases</topic><topic>Prognosis</topic><topic>Reproducibility of Results</topic><topic>tumor markers</topic><topic>Tumors</topic><topic>Tumors of the urinary system</topic><topic>Urinary Bladder Neoplasms - pathology</topic><topic>Urinary tract. Prostate gland</topic><topic>urothelial carcinoma</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Margel, David</creatorcontrib><creatorcontrib>Harel, Amir</creatorcontrib><creatorcontrib>Yossepowitch, Ofer</creatorcontrib><creatorcontrib>Baniel, Jack</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>MEDLINE - Academic</collection><jtitle>Cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Margel, David</au><au>Harel, Amir</au><au>Yossepowitch, Ofer</au><au>Baniel, Jack</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel algorithm to improve pathologic stage prediction of clinically organ‐confined muscle‐invasive bladder cancer</atitle><jtitle>Cancer</jtitle><addtitle>Cancer</addtitle><date>2009-04-01</date><risdate>2009</risdate><volume>115</volume><issue>7</issue><spage>1459</spage><epage>1464</epage><pages>1459-1464</pages><issn>0008-543X</issn><eissn>1097-0142</eissn><coden>CANCAR</coden><abstract>BACKGROUND: An algorithm was created to predict pathologic stage in patients with clinically organ‐confined muscle‐invasive bladder cancer. METHODS: The sample consisted of 133 consecutive patients scheduled to undergo cystectomy. To develop a tool to predict nonorgan‐confined disease before surgery, principal component analysis (PCA) was applied. Patients were stratified into a training set (n = 89) and a validation set (n = 44), and 7 parameters were evaluated: levels of carcinoembryonic antigen, cancer antigen (CA) 125, and carbohydrate antigen (CA) 19‐9; clinical stage; presence of hydronephrosis; presence of carcinoma in situ; and initial tumor size &gt;3 cm. PCA was applied to the training set to determine the weight of each parameter. A PCA score was generated for each patient in the set, and a cutoff defining nonorgan‐confined disease was established. The accuracy of the cutoff was quantified by the area under the receiver operator characteristics curve (AUC). The model was then applied to the validation set without recalculation; the AUC and the positive and negative predictive values of the validation set were calculated. RESULTS: On pathologic evaluation, 71 patients (53%) were found to have organ‐confined tumors and 62 patients (47%) had extravesical disease. The AUC was 0.85 in the training group (95% confidence interval [95% CI], 0.71‐0.97) and 0.84 in the validation group (95% CI, 0.75‐0.93). The positive and negative predictive values in the validation group were 88% (95% CI, 71%‐96%) and 94% (95% CI, 71%‐99%), respectively. CONCLUSIONS: The newly devised, internally validated, algorithm was 85% accurate in predicting nonorgan‐confined bladder disease before cystectomy. Further external validation in a large cohort was recommended as still necessary. Cancer 2009. © 2009 American Cancer Society. 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source MEDLINE; Wiley Online Library Journals Frontfile Complete; Wiley Free Content; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection
subjects Adult
Aged
Aged, 80 and over
Algorithms
Biological and medical sciences
CA 125
CA 19‐9
CA-125 Antigen - analysis
CA-19-9 Antigen - analysis
carcinoembryonic antigen
Carcinoembryonic Antigen - analysis
Female
Humans
Male
mathematical prediction model
Medical sciences
Middle Aged
Neoplasm Invasiveness - pathology
Neoplasm Staging - methods
Nephrology. Urinary tract diseases
Prognosis
Reproducibility of Results
tumor markers
Tumors
Tumors of the urinary system
Urinary Bladder Neoplasms - pathology
Urinary tract. Prostate gland
urothelial carcinoma
title A novel algorithm to improve pathologic stage prediction of clinically organ‐confined muscle‐invasive bladder cancer
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