Loading…
Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test
Background Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clin...
Saved in:
| Published in: | Prostate cancer and prostatic diseases 2018-11, Vol.21 (4), p.594-603 |
|---|---|
| Main Authors: | , , , , , , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c470t-400221659c56ad6c85d1be682e50277fc59e9b13bd09ba4b1ce569d81765abde3 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c470t-400221659c56ad6c85d1be682e50277fc59e9b13bd09ba4b1ce569d81765abde3 |
| container_end_page | 603 |
| container_issue | 4 |
| container_start_page | 594 |
| container_title | Prostate cancer and prostatic diseases |
| container_volume | 21 |
| creator | Donovan, Michael J. Fernandez, Gerardo Scott, Richard Khan, Faisal M. Zeineh, Jack Koll, Giovanni Gladoun, Nataliya Charytonowicz, Elizabeth Tewari, Ash Cordon-Cardo, Carlos |
| description | Background
Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.
Methods
A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan–Meier, and hazards ratio.
Results
In the training cohort (
n
= 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76–0.86], HR:6.7, [95% CI: 3.59–12.45],
p
|
| doi_str_mv | 10.1038/s41391-018-0067-4 |
| format | article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_2085671722</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A573239413</galeid><sourcerecordid>A573239413</sourcerecordid><originalsourceid>FETCH-LOGICAL-c470t-400221659c56ad6c85d1be682e50277fc59e9b13bd09ba4b1ce569d81765abde3</originalsourceid><addsrcrecordid>eNp1kl1v1iAYhhujcR_6AzwxJCZmJ51AC7SHy9RpssQTPSYUnrYsFF6Bvsn-jz9Uunc6ZzQcQPJc9_PFXVWvCD4nuOnepZY0Pakx6WqMuajbJ9UxaQWvGcfd0_JuOKtFx-hRdZLSDca4Jz1-Xh01GHeipfy4-vEe9uDCbgGfkfIG7ZWzRmUbPAojUsiHEkdqzWFRGQy6cqBSCU5RGbhTLMGBXp2KaBfDaB2gPKuMDIzWFwLNdprdbQmCsTrbPWxcyiUb0spruNNNEVLaiio3hWjzvNSDSqVehpRfVM9G5RK8vL9Pq28fP3y9_FRff7n6fHlxXetW4Fy3GFNKOOs148pw3TFDBuAdBYapEKNmPfQDaQaD-0G1A9HAeG86IjhTg4HmtDo75C0NfV9LYbnYpME55SGsSVLcMS6IoLSgb_5Cb8IafelOUsIIxS3u8QM1KQfS-jHkqPSWVF4w0dCmLz9YqPN_UOUYWKwOHralPha8_UMwg3J5TsGt26-lxyA5gLpsPEUY5S7aRcVbSbDcLCQPFpLFQnKzkGyL5vX9ZOuwgPmt-OWZAtADkErITxAfRv9_1p_xDtG9</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2151204090</pqid></control><display><type>article</type><title>Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test</title><source>Springer Nature</source><creator>Donovan, Michael J. ; Fernandez, Gerardo ; Scott, Richard ; Khan, Faisal M. ; Zeineh, Jack ; Koll, Giovanni ; Gladoun, Nataliya ; Charytonowicz, Elizabeth ; Tewari, Ash ; Cordon-Cardo, Carlos</creator><creatorcontrib>Donovan, Michael J. ; Fernandez, Gerardo ; Scott, Richard ; Khan, Faisal M. ; Zeineh, Jack ; Koll, Giovanni ; Gladoun, Nataliya ; Charytonowicz, Elizabeth ; Tewari, Ash ; Cordon-Cardo, Carlos</creatorcontrib><description>Background
Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.
Methods
A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan–Meier, and hazards ratio.
Results
In the training cohort (
n
= 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76–0.86], HR:6.7, [95% CI: 3.59–12.45],
p
< 0.00001. Results were confirmed in validation (
n
= 284) with a C-index 0.77 [95% CI: 0.72–0.81], HR = 5.4, [95% CI: 2.74–10.52],
p
< 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.
Conclusions
Precise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.</description><identifier>ISSN: 1365-7852</identifier><identifier>EISSN: 1476-5608</identifier><identifier>DOI: 10.1038/s41391-018-0067-4</identifier><identifier>PMID: 30087426</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>13/1 ; 13/51 ; 14/35 ; 631/67 ; 692/53 ; Algorithms ; Analysis ; Artificial intelligence ; Biological markers ; Biomarkers ; Biomedical and Life Sciences ; Biomedicine ; Cancer ; Cancer metastasis ; Cancer Research ; Cancer surgery ; Cancer therapies ; Cancer treatment ; Chemotherapy ; Deprivation ; Development and progression ; Digital imaging ; Evaluation ; Failure ; Hazards ; Health risks ; Image analysis ; Image processing ; Learning algorithms ; Machine learning ; Mathematical models ; Medical research ; Medical schools ; Metastases ; Patients ; Pattern analysis ; Predictions ; Prostate cancer ; Prostatectomy ; Radiation ; Radiation (Physics) ; Risk assessment ; Surgery ; Training ; Urological surgery</subject><ispartof>Prostate cancer and prostatic diseases, 2018-11, Vol.21 (4), p.594-603</ispartof><rights>Springer Nature Limited 2018</rights><rights>COPYRIGHT 2018 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Nov 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c470t-400221659c56ad6c85d1be682e50277fc59e9b13bd09ba4b1ce569d81765abde3</citedby><cites>FETCH-LOGICAL-c470t-400221659c56ad6c85d1be682e50277fc59e9b13bd09ba4b1ce569d81765abde3</cites><orcidid>0000-0003-0858-5624</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30087426$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Donovan, Michael J.</creatorcontrib><creatorcontrib>Fernandez, Gerardo</creatorcontrib><creatorcontrib>Scott, Richard</creatorcontrib><creatorcontrib>Khan, Faisal M.</creatorcontrib><creatorcontrib>Zeineh, Jack</creatorcontrib><creatorcontrib>Koll, Giovanni</creatorcontrib><creatorcontrib>Gladoun, Nataliya</creatorcontrib><creatorcontrib>Charytonowicz, Elizabeth</creatorcontrib><creatorcontrib>Tewari, Ash</creatorcontrib><creatorcontrib>Cordon-Cardo, Carlos</creatorcontrib><title>Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test</title><title>Prostate cancer and prostatic diseases</title><addtitle>Prostate Cancer Prostatic Dis</addtitle><addtitle>Prostate Cancer Prostatic Dis</addtitle><description>Background
Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.
Methods
A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan–Meier, and hazards ratio.
Results
In the training cohort (
n
= 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76–0.86], HR:6.7, [95% CI: 3.59–12.45],
p
< 0.00001. Results were confirmed in validation (
n
= 284) with a C-index 0.77 [95% CI: 0.72–0.81], HR = 5.4, [95% CI: 2.74–10.52],
p
< 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.
Conclusions
Precise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.</description><subject>13/1</subject><subject>13/51</subject><subject>14/35</subject><subject>631/67</subject><subject>692/53</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer</subject><subject>Cancer metastasis</subject><subject>Cancer Research</subject><subject>Cancer surgery</subject><subject>Cancer therapies</subject><subject>Cancer treatment</subject><subject>Chemotherapy</subject><subject>Deprivation</subject><subject>Development and progression</subject><subject>Digital imaging</subject><subject>Evaluation</subject><subject>Failure</subject><subject>Hazards</subject><subject>Health risks</subject><subject>Image analysis</subject><subject>Image processing</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Medical research</subject><subject>Medical schools</subject><subject>Metastases</subject><subject>Patients</subject><subject>Pattern analysis</subject><subject>Predictions</subject><subject>Prostate cancer</subject><subject>Prostatectomy</subject><subject>Radiation</subject><subject>Radiation (Physics)</subject><subject>Risk assessment</subject><subject>Surgery</subject><subject>Training</subject><subject>Urological surgery</subject><issn>1365-7852</issn><issn>1476-5608</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kl1v1iAYhhujcR_6AzwxJCZmJ51AC7SHy9RpssQTPSYUnrYsFF6Bvsn-jz9Uunc6ZzQcQPJc9_PFXVWvCD4nuOnepZY0Pakx6WqMuajbJ9UxaQWvGcfd0_JuOKtFx-hRdZLSDca4Jz1-Xh01GHeipfy4-vEe9uDCbgGfkfIG7ZWzRmUbPAojUsiHEkdqzWFRGQy6cqBSCU5RGbhTLMGBXp2KaBfDaB2gPKuMDIzWFwLNdprdbQmCsTrbPWxcyiUb0spruNNNEVLaiio3hWjzvNSDSqVehpRfVM9G5RK8vL9Pq28fP3y9_FRff7n6fHlxXetW4Fy3GFNKOOs148pw3TFDBuAdBYapEKNmPfQDaQaD-0G1A9HAeG86IjhTg4HmtDo75C0NfV9LYbnYpME55SGsSVLcMS6IoLSgb_5Cb8IafelOUsIIxS3u8QM1KQfS-jHkqPSWVF4w0dCmLz9YqPN_UOUYWKwOHralPha8_UMwg3J5TsGt26-lxyA5gLpsPEUY5S7aRcVbSbDcLCQPFpLFQnKzkGyL5vX9ZOuwgPmt-OWZAtADkErITxAfRv9_1p_xDtG9</recordid><startdate>20181101</startdate><enddate>20181101</enddate><creator>Donovan, Michael J.</creator><creator>Fernandez, Gerardo</creator><creator>Scott, Richard</creator><creator>Khan, Faisal M.</creator><creator>Zeineh, Jack</creator><creator>Koll, Giovanni</creator><creator>Gladoun, Nataliya</creator><creator>Charytonowicz, Elizabeth</creator><creator>Tewari, Ash</creator><creator>Cordon-Cardo, Carlos</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7Z</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0858-5624</orcidid></search><sort><creationdate>20181101</creationdate><title>Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test</title><author>Donovan, Michael J. ; Fernandez, Gerardo ; Scott, Richard ; Khan, Faisal M. ; Zeineh, Jack ; Koll, Giovanni ; Gladoun, Nataliya ; Charytonowicz, Elizabeth ; Tewari, Ash ; Cordon-Cardo, Carlos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c470t-400221659c56ad6c85d1be682e50277fc59e9b13bd09ba4b1ce569d81765abde3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>13/1</topic><topic>13/51</topic><topic>14/35</topic><topic>631/67</topic><topic>692/53</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Artificial intelligence</topic><topic>Biological markers</topic><topic>Biomarkers</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cancer</topic><topic>Cancer metastasis</topic><topic>Cancer Research</topic><topic>Cancer surgery</topic><topic>Cancer therapies</topic><topic>Cancer treatment</topic><topic>Chemotherapy</topic><topic>Deprivation</topic><topic>Development and progression</topic><topic>Digital imaging</topic><topic>Evaluation</topic><topic>Failure</topic><topic>Hazards</topic><topic>Health risks</topic><topic>Image analysis</topic><topic>Image processing</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Medical research</topic><topic>Medical schools</topic><topic>Metastases</topic><topic>Patients</topic><topic>Pattern analysis</topic><topic>Predictions</topic><topic>Prostate cancer</topic><topic>Prostatectomy</topic><topic>Radiation</topic><topic>Radiation (Physics)</topic><topic>Risk assessment</topic><topic>Surgery</topic><topic>Training</topic><topic>Urological surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Donovan, Michael J.</creatorcontrib><creatorcontrib>Fernandez, Gerardo</creatorcontrib><creatorcontrib>Scott, Richard</creatorcontrib><creatorcontrib>Khan, Faisal M.</creatorcontrib><creatorcontrib>Zeineh, Jack</creatorcontrib><creatorcontrib>Koll, Giovanni</creatorcontrib><creatorcontrib>Gladoun, Nataliya</creatorcontrib><creatorcontrib>Charytonowicz, Elizabeth</creatorcontrib><creatorcontrib>Tewari, Ash</creatorcontrib><creatorcontrib>Cordon-Cardo, Carlos</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Biotechnology and BioEngineering Abstracts</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>MEDLINE - Academic</collection><jtitle>Prostate cancer and prostatic diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Donovan, Michael J.</au><au>Fernandez, Gerardo</au><au>Scott, Richard</au><au>Khan, Faisal M.</au><au>Zeineh, Jack</au><au>Koll, Giovanni</au><au>Gladoun, Nataliya</au><au>Charytonowicz, Elizabeth</au><au>Tewari, Ash</au><au>Cordon-Cardo, Carlos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test</atitle><jtitle>Prostate cancer and prostatic diseases</jtitle><stitle>Prostate Cancer Prostatic Dis</stitle><addtitle>Prostate Cancer Prostatic Dis</addtitle><date>2018-11-01</date><risdate>2018</risdate><volume>21</volume><issue>4</issue><spage>594</spage><epage>603</epage><pages>594-603</pages><issn>1365-7852</issn><eissn>1476-5608</eissn><abstract>Background
Postoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.
Methods
A prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan–Meier, and hazards ratio.
Results
In the training cohort (
n
= 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76–0.86], HR:6.7, [95% CI: 3.59–12.45],
p
< 0.00001. Results were confirmed in validation (
n
= 284) with a C-index 0.77 [95% CI: 0.72–0.81], HR = 5.4, [95% CI: 2.74–10.52],
p
< 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.
Conclusions
Precise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>30087426</pmid><doi>10.1038/s41391-018-0067-4</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-0858-5624</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1365-7852 |
| ispartof | Prostate cancer and prostatic diseases, 2018-11, Vol.21 (4), p.594-603 |
| issn | 1365-7852 1476-5608 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2085671722 |
| source | Springer Nature |
| subjects | 13/1 13/51 14/35 631/67 692/53 Algorithms Analysis Artificial intelligence Biological markers Biomarkers Biomedical and Life Sciences Biomedicine Cancer Cancer metastasis Cancer Research Cancer surgery Cancer therapies Cancer treatment Chemotherapy Deprivation Development and progression Digital imaging Evaluation Failure Hazards Health risks Image analysis Image processing Learning algorithms Machine learning Mathematical models Medical research Medical schools Metastases Patients Pattern analysis Predictions Prostate cancer Prostatectomy Radiation Radiation (Physics) Risk assessment Surgery Training Urological surgery |
| title | Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T18%3A43%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20and%20validation%20of%20a%20novel%20automated%20Gleason%20grade%20and%20molecular%20profile%20that%20define%20a%20highly%20predictive%20prostate%20cancer%20progression%20algorithm-based%20test&rft.jtitle=Prostate%20cancer%20and%20prostatic%20diseases&rft.au=Donovan,%20Michael%20J.&rft.date=2018-11-01&rft.volume=21&rft.issue=4&rft.spage=594&rft.epage=603&rft.pages=594-603&rft.issn=1365-7852&rft.eissn=1476-5608&rft_id=info:doi/10.1038/s41391-018-0067-4&rft_dat=%3Cgale_proqu%3EA573239413%3C/gale_proqu%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c470t-400221659c56ad6c85d1be682e50277fc59e9b13bd09ba4b1ce569d81765abde3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2151204090&rft_id=info:pmid/30087426&rft_galeid=A573239413&rfr_iscdi=true |