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Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer
Purpose The aim of this study is to investigate the role of [ 68 Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology ( PS ISUP) grade in primary prostate cancer (PCa). Methods This retrospective study included 47 PCa patients who underwent [...
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| Published in: | European journal of nuclear medicine and molecular imaging 2023-07, Vol.50 (8), p.2548-2560 |
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| container_title | European journal of nuclear medicine and molecular imaging |
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| creator | Ghezzo, Samuele Mapelli, Paola Bezzi, Carolina Samanes Gajate, Ana Maria Brembilla, Giorgio Gotuzzo, Irene Russo, Tommaso Preza, Erik Cucchiara, Vito Ahmed, Naghia Neri, Ilaria Mongardi, Sofia Freschi, Massimo Briganti, Alberto De Cobelli, Francesco Gianolli, Luigi Scifo, Paola Picchio, Maria |
| description | Purpose
The aim of this study is to investigate the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (
PS
ISUP) grade in primary prostate cancer (PCa).
Methods
This retrospective study included 47 PCa patients who underwent [
68
Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of
PS
ISUP grade: ISUP ≥ 4 vs ISUP 0.05).
Conclusion
These findings support the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of
PS
ISUP grade. |
| doi_str_mv | 10.1007/s00259-023-06187-3 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2823989374</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2825529174</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-1f486bb9182c040e44ad846c89ed7cb1ff99395210b7a83dddc3647e57c0e0013</originalsourceid><addsrcrecordid>eNp9kU9LxDAQxYMouv75Ah4k4MVLdNK0TXKUZV0FxcXdPYmENJ0ulW6rTXvw25u1q4IHTxPIb97Mm0fIKYdLDiCvPECUaAaRYJByJZnYISOecs0kKL3785ZwQA69fwXgKlJ6nxyIVAsBMh4RfGoqpE1Bn1M1tS9Ty2bzh2vGOZ1NFrS1edmsS-dp19C3FvPSdfSt8R3zfbsqna3o3Xw5o6sAIi3rwJRr236EGiDbIXW2dtgek73CVh5PtvWILG8mi_Etu3-c3o2v75kTMukYL2KVZpkOazqIAePY5ipOndKYS5fxotBa6CTikEmrRJ7nTqSxxEQ6wOBOHJGLQTfMf-_Rd2ZdeodVZWtsem8iFQmttJBxQM__oK9N39Zhuw2VJJHmX1Q0UC4Y8i0WZuvQcDCbEMwQggkhmK8QjAhNZ1vpPltj_tPyffUAiAHw4ateYfs7-x_ZT9CKj2k</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2825529174</pqid></control><display><type>article</type><title>Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer</title><source>Springer Nature</source><creator>Ghezzo, Samuele ; Mapelli, Paola ; Bezzi, Carolina ; Samanes Gajate, Ana Maria ; Brembilla, Giorgio ; Gotuzzo, Irene ; Russo, Tommaso ; Preza, Erik ; Cucchiara, Vito ; Ahmed, Naghia ; Neri, Ilaria ; Mongardi, Sofia ; Freschi, Massimo ; Briganti, Alberto ; De Cobelli, Francesco ; Gianolli, Luigi ; Scifo, Paola ; Picchio, Maria</creator><creatorcontrib>Ghezzo, Samuele ; Mapelli, Paola ; Bezzi, Carolina ; Samanes Gajate, Ana Maria ; Brembilla, Giorgio ; Gotuzzo, Irene ; Russo, Tommaso ; Preza, Erik ; Cucchiara, Vito ; Ahmed, Naghia ; Neri, Ilaria ; Mongardi, Sofia ; Freschi, Massimo ; Briganti, Alberto ; De Cobelli, Francesco ; Gianolli, Luigi ; Scifo, Paola ; Picchio, Maria</creatorcontrib><description>Purpose
The aim of this study is to investigate the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (
PS
ISUP) grade in primary prostate cancer (PCa).
Methods
This retrospective study included 47 PCa patients who underwent [
68
Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of
PS
ISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal–Wallis and Mann–Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models’ performance. The predictions of the best performing model were compared against ISUP grade at biopsy.
Results
ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM—Zone Entropy and Shape—Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann–Whitney
p
> 0.05).
Conclusion
These findings support the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of
PS
ISUP grade.</description><identifier>ISSN: 1619-7070</identifier><identifier>EISSN: 1619-7089</identifier><identifier>DOI: 10.1007/s00259-023-06187-3</identifier><identifier>PMID: 36933074</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Advanced Image Analyses (Radiomics and Artificial Intelligence) ; Algorithms ; Biomarkers ; Biopsy ; Cardiology ; Entropy ; Gallium Radioisotopes ; Humans ; Imaging ; Learning algorithms ; Machine learning ; Male ; Medical imaging ; Medicine ; Medicine & Public Health ; Nuclear Medicine ; Oncology ; Original Article ; Orthopedics ; Positron emission ; Positron emission tomography ; Positron Emission Tomography Computed Tomography - methods ; Predictions ; Prostate cancer ; Prostatectomy ; Prostatic Neoplasms - diagnostic imaging ; Prostatic Neoplasms - pathology ; Prostatic Neoplasms - surgery ; Radiology ; Radiomics ; Redundancy ; Retrospective Studies ; Standardization</subject><ispartof>European journal of nuclear medicine and molecular imaging, 2023-07, Vol.50 (8), p.2548-2560</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-1f486bb9182c040e44ad846c89ed7cb1ff99395210b7a83dddc3647e57c0e0013</citedby><cites>FETCH-LOGICAL-c375t-1f486bb9182c040e44ad846c89ed7cb1ff99395210b7a83dddc3647e57c0e0013</cites><orcidid>0000-0002-7532-6211</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27915,27916</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36933074$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ghezzo, Samuele</creatorcontrib><creatorcontrib>Mapelli, Paola</creatorcontrib><creatorcontrib>Bezzi, Carolina</creatorcontrib><creatorcontrib>Samanes Gajate, Ana Maria</creatorcontrib><creatorcontrib>Brembilla, Giorgio</creatorcontrib><creatorcontrib>Gotuzzo, Irene</creatorcontrib><creatorcontrib>Russo, Tommaso</creatorcontrib><creatorcontrib>Preza, Erik</creatorcontrib><creatorcontrib>Cucchiara, Vito</creatorcontrib><creatorcontrib>Ahmed, Naghia</creatorcontrib><creatorcontrib>Neri, Ilaria</creatorcontrib><creatorcontrib>Mongardi, Sofia</creatorcontrib><creatorcontrib>Freschi, Massimo</creatorcontrib><creatorcontrib>Briganti, Alberto</creatorcontrib><creatorcontrib>De Cobelli, Francesco</creatorcontrib><creatorcontrib>Gianolli, Luigi</creatorcontrib><creatorcontrib>Scifo, Paola</creatorcontrib><creatorcontrib>Picchio, Maria</creatorcontrib><title>Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer</title><title>European journal of nuclear medicine and molecular imaging</title><addtitle>Eur J Nucl Med Mol Imaging</addtitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><description>Purpose
The aim of this study is to investigate the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (
PS
ISUP) grade in primary prostate cancer (PCa).
Methods
This retrospective study included 47 PCa patients who underwent [
68
Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of
PS
ISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal–Wallis and Mann–Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models’ performance. The predictions of the best performing model were compared against ISUP grade at biopsy.
Results
ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM—Zone Entropy and Shape—Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann–Whitney
p
> 0.05).
Conclusion
These findings support the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of
PS
ISUP grade.</description><subject>Advanced Image Analyses (Radiomics and Artificial Intelligence)</subject><subject>Algorithms</subject><subject>Biomarkers</subject><subject>Biopsy</subject><subject>Cardiology</subject><subject>Entropy</subject><subject>Gallium Radioisotopes</subject><subject>Humans</subject><subject>Imaging</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Orthopedics</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>Positron Emission Tomography Computed Tomography - methods</subject><subject>Predictions</subject><subject>Prostate cancer</subject><subject>Prostatectomy</subject><subject>Prostatic Neoplasms - diagnostic imaging</subject><subject>Prostatic Neoplasms - pathology</subject><subject>Prostatic Neoplasms - surgery</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Redundancy</subject><subject>Retrospective Studies</subject><subject>Standardization</subject><issn>1619-7070</issn><issn>1619-7089</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kU9LxDAQxYMouv75Ah4k4MVLdNK0TXKUZV0FxcXdPYmENJ0ulW6rTXvw25u1q4IHTxPIb97Mm0fIKYdLDiCvPECUaAaRYJByJZnYISOecs0kKL3785ZwQA69fwXgKlJ6nxyIVAsBMh4RfGoqpE1Bn1M1tS9Ty2bzh2vGOZ1NFrS1edmsS-dp19C3FvPSdfSt8R3zfbsqna3o3Xw5o6sAIi3rwJRr236EGiDbIXW2dtgek73CVh5PtvWILG8mi_Etu3-c3o2v75kTMukYL2KVZpkOazqIAePY5ipOndKYS5fxotBa6CTikEmrRJ7nTqSxxEQ6wOBOHJGLQTfMf-_Rd2ZdeodVZWtsem8iFQmttJBxQM__oK9N39Zhuw2VJJHmX1Q0UC4Y8i0WZuvQcDCbEMwQggkhmK8QjAhNZ1vpPltj_tPyffUAiAHw4ateYfs7-x_ZT9CKj2k</recordid><startdate>20230701</startdate><enddate>20230701</enddate><creator>Ghezzo, Samuele</creator><creator>Mapelli, Paola</creator><creator>Bezzi, Carolina</creator><creator>Samanes Gajate, Ana Maria</creator><creator>Brembilla, Giorgio</creator><creator>Gotuzzo, Irene</creator><creator>Russo, Tommaso</creator><creator>Preza, Erik</creator><creator>Cucchiara, Vito</creator><creator>Ahmed, Naghia</creator><creator>Neri, Ilaria</creator><creator>Mongardi, Sofia</creator><creator>Freschi, Massimo</creator><creator>Briganti, Alberto</creator><creator>De Cobelli, Francesco</creator><creator>Gianolli, Luigi</creator><creator>Scifo, Paola</creator><creator>Picchio, Maria</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><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>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7532-6211</orcidid></search><sort><creationdate>20230701</creationdate><title>Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer</title><author>Ghezzo, Samuele ; Mapelli, Paola ; Bezzi, Carolina ; Samanes Gajate, Ana Maria ; Brembilla, Giorgio ; Gotuzzo, Irene ; Russo, Tommaso ; Preza, Erik ; Cucchiara, Vito ; Ahmed, Naghia ; Neri, Ilaria ; Mongardi, Sofia ; Freschi, Massimo ; Briganti, Alberto ; De Cobelli, Francesco ; Gianolli, Luigi ; Scifo, Paola ; Picchio, Maria</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-1f486bb9182c040e44ad846c89ed7cb1ff99395210b7a83dddc3647e57c0e0013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Advanced Image Analyses (Radiomics and Artificial Intelligence)</topic><topic>Algorithms</topic><topic>Biomarkers</topic><topic>Biopsy</topic><topic>Cardiology</topic><topic>Entropy</topic><topic>Gallium Radioisotopes</topic><topic>Humans</topic><topic>Imaging</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Orthopedics</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Positron Emission Tomography Computed Tomography - methods</topic><topic>Predictions</topic><topic>Prostate cancer</topic><topic>Prostatectomy</topic><topic>Prostatic Neoplasms - diagnostic imaging</topic><topic>Prostatic Neoplasms - pathology</topic><topic>Prostatic Neoplasms - surgery</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Redundancy</topic><topic>Retrospective Studies</topic><topic>Standardization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghezzo, Samuele</creatorcontrib><creatorcontrib>Mapelli, Paola</creatorcontrib><creatorcontrib>Bezzi, Carolina</creatorcontrib><creatorcontrib>Samanes Gajate, Ana Maria</creatorcontrib><creatorcontrib>Brembilla, Giorgio</creatorcontrib><creatorcontrib>Gotuzzo, Irene</creatorcontrib><creatorcontrib>Russo, Tommaso</creatorcontrib><creatorcontrib>Preza, Erik</creatorcontrib><creatorcontrib>Cucchiara, Vito</creatorcontrib><creatorcontrib>Ahmed, Naghia</creatorcontrib><creatorcontrib>Neri, Ilaria</creatorcontrib><creatorcontrib>Mongardi, Sofia</creatorcontrib><creatorcontrib>Freschi, Massimo</creatorcontrib><creatorcontrib>Briganti, Alberto</creatorcontrib><creatorcontrib>De Cobelli, Francesco</creatorcontrib><creatorcontrib>Gianolli, Luigi</creatorcontrib><creatorcontrib>Scifo, Paola</creatorcontrib><creatorcontrib>Picchio, Maria</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Nursing and Allied Health Journals</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Advanced Technologies & Aerospace Database (1962 - 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Academic</collection><jtitle>European journal of nuclear medicine and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ghezzo, Samuele</au><au>Mapelli, Paola</au><au>Bezzi, Carolina</au><au>Samanes Gajate, Ana Maria</au><au>Brembilla, Giorgio</au><au>Gotuzzo, Irene</au><au>Russo, Tommaso</au><au>Preza, Erik</au><au>Cucchiara, Vito</au><au>Ahmed, Naghia</au><au>Neri, Ilaria</au><au>Mongardi, Sofia</au><au>Freschi, Massimo</au><au>Briganti, Alberto</au><au>De Cobelli, Francesco</au><au>Gianolli, Luigi</au><au>Scifo, Paola</au><au>Picchio, Maria</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer</atitle><jtitle>European journal of nuclear medicine and molecular imaging</jtitle><stitle>Eur J Nucl Med Mol Imaging</stitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><date>2023-07-01</date><risdate>2023</risdate><volume>50</volume><issue>8</issue><spage>2548</spage><epage>2560</epage><pages>2548-2560</pages><issn>1619-7070</issn><eissn>1619-7089</eissn><abstract>Purpose
The aim of this study is to investigate the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (
PS
ISUP) grade in primary prostate cancer (PCa).
Methods
This retrospective study included 47 PCa patients who underwent [
68
Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of
PS
ISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal–Wallis and Mann–Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models’ performance. The predictions of the best performing model were compared against ISUP grade at biopsy.
Results
ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM—Zone Entropy and Shape—Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann–Whitney
p
> 0.05).
Conclusion
These findings support the role of [
68
Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of
PS
ISUP grade.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>36933074</pmid><doi>10.1007/s00259-023-06187-3</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-7532-6211</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1619-7070 |
| ispartof | European journal of nuclear medicine and molecular imaging, 2023-07, Vol.50 (8), p.2548-2560 |
| issn | 1619-7070 1619-7089 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2823989374 |
| source | Springer Nature |
| subjects | Advanced Image Analyses (Radiomics and Artificial Intelligence) Algorithms Biomarkers Biopsy Cardiology Entropy Gallium Radioisotopes Humans Imaging Learning algorithms Machine learning Male Medical imaging Medicine Medicine & Public Health Nuclear Medicine Oncology Original Article Orthopedics Positron emission Positron emission tomography Positron Emission Tomography Computed Tomography - methods Predictions Prostate cancer Prostatectomy Prostatic Neoplasms - diagnostic imaging Prostatic Neoplasms - pathology Prostatic Neoplasms - surgery Radiology Radiomics Redundancy Retrospective Studies Standardization |
| title | Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T21%3A32%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Role%20of%20%5B68Ga%5DGa-PSMA-11%20PET%20radiomics%20to%20predict%20post-surgical%20ISUP%20grade%20in%20primary%20prostate%20cancer&rft.jtitle=European%20journal%20of%20nuclear%20medicine%20and%20molecular%20imaging&rft.au=Ghezzo,%20Samuele&rft.date=2023-07-01&rft.volume=50&rft.issue=8&rft.spage=2548&rft.epage=2560&rft.pages=2548-2560&rft.issn=1619-7070&rft.eissn=1619-7089&rft_id=info:doi/10.1007/s00259-023-06187-3&rft_dat=%3Cproquest_cross%3E2825529174%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c375t-1f486bb9182c040e44ad846c89ed7cb1ff99395210b7a83dddc3647e57c0e0013%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2825529174&rft_id=info:pmid/36933074&rfr_iscdi=true |