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mpMRI detection of suspected prostate cancer with a negative biopsy: can radiomic features help radiologists?

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Τίτλος: mpMRI detection of suspected prostate cancer with a negative biopsy: can radiomic features help radiologists?
Συγγραφείς: Alessandro Bevilacqua, Margherita Mottola, Fabio Ferroni, Giampaolo Gavelli, Domenico Barone
Συνεισφορές: Alessandro Bevilacqua, Margherita Mottola, Fabio Ferroni, Giampaolo Gavelli, Domenico Barone
Έτος έκδοσης: 2020
Συλλογή: IRIS Università degli Studi di Bologna (CRIS - Current Research Information System)
Θεματικοί όροι: prostate cancer, negative biopsy, machine learning, MRI, automatic detection, artificial intelligence
Περιγραφή: Purpose: To investigate whether DWI-based radiomics features could differentiate patients with a clinical suspicion of PCa and negative TRUS-biopsy that have a positive mpMRI from patients where mpMRIs do not show any evidence. Materials and Methods: The records of 17 patients undergoing 3T-mpMRI for suspected PCa subsequently not confirmed at TRUS-biopsy were extracted from our institutional database. The ground truth was available for only a few. 7 patients did not reported evidence at mpMRI, while 10 patients showed suspected PCa lesions, contoured in consensus by two radiologists. 84 image-based radiomic features were computed on high b-value DW-MRI sequences of all patients of the two groups. The ROC curve was computed for each feature and the one yielding the highest AUC was selected. Its discrimination power was also assessed via a Wilcoxon rank-sum test (p<0.001). Results: The mean of local skewness (SL-m), related to local inhomogeneities of DWI values, confirms radiologist reports in 94% of cases, with AUC=0.93 (95% CI, 0.56-1.00), specificity=100% and sensitivity=86% (one false positive only). Median SL-m values in patients with suspected PCa were greater than 30% (p~10-4) with respect to patients showing no evidences at mpMRI. Conclusions: DWI-based radiomic features strongly support mpMRI evidences in case of suspected, and for some patients clear, PCa although TRUS-biopsy is negative. These outcomes suggest further investigation on the role that these features are extremely promising could have in PCa patient’s stratification. Limitations: Although it confirmed the mpMRI evidence to be PCa for the few patients where the ground-truth was available, for most of them it was not at our disposal because did not belong to a dedicated study. Ethics committee approval: IRB approval, written informed consent was waived. Funding: No funding was received for this work.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: ELETTRONICO
Γλώσσα: English
Relation: volume:11; issue:Suppl 1; firstpage:492; lastpage:493; numberofpages:1; journal:INSIGHTS INTO IMAGING; https://hdl.handle.net/11585/759096
Διαθεσιμότητα: https://hdl.handle.net/11585/759096
Rights: info:eu-repo/semantics/openAccess ; license:Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Αριθμός Καταχώρησης: edsbas.2D4B4292
Βάση Δεδομένων: BASE
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  Data: mpMRI detection of suspected prostate cancer with a negative biopsy: can radiomic features help radiologists?
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  Data: Alessandro Bevilacqua, Margherita Mottola, Fabio Ferroni, Giampaolo Gavelli, Domenico Barone
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  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22prostate+cancer%22&quot;&gt;prostate cancer&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22negative+biopsy%22&quot;&gt;negative biopsy&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22machine+learning%22&quot;&gt;machine learning&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22MRI%22&quot;&gt;MRI&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22automatic+detection%22&quot;&gt;automatic detection&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22artificial+intelligence%22&quot;&gt;artificial intelligence&lt;/searchLink&gt;
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  Data: Purpose: To investigate whether DWI-based radiomics features could differentiate patients with a clinical suspicion of PCa and negative TRUS-biopsy that have a positive mpMRI from patients where mpMRIs do not show any evidence. Materials and Methods: The records of 17 patients undergoing 3T-mpMRI for suspected PCa subsequently not confirmed at TRUS-biopsy were extracted from our institutional database. The ground truth was available for only a few. 7 patients did not reported evidence at mpMRI, while 10 patients showed suspected PCa lesions, contoured in consensus by two radiologists. 84 image-based radiomic features were computed on high b-value DW-MRI sequences of all patients of the two groups. The ROC curve was computed for each feature and the one yielding the highest AUC was selected. Its discrimination power was also assessed via a Wilcoxon rank-sum test (p&lt;0.001). Results: The mean of local skewness (SL-m), related to local inhomogeneities of DWI values, confirms radiologist reports in 94% of cases, with AUC=0.93 (95% CI, 0.56-1.00), specificity=100% and sensitivity=86% (one false positive only). Median SL-m values in patients with suspected PCa were greater than 30% (p~10-4) with respect to patients showing no evidences at mpMRI. Conclusions: DWI-based radiomic features strongly support mpMRI evidences in case of suspected, and for some patients clear, PCa although TRUS-biopsy is negative. These outcomes suggest further investigation on the role that these features are extremely promising could have in PCa patient’s stratification. Limitations: Although it confirmed the mpMRI evidence to be PCa for the few patients where the ground-truth was available, for most of them it was not at our disposal because did not belong to a dedicated study. Ethics committee approval: IRB approval, written informed consent was waived. Funding: No funding was received for this work.
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      – Text: English
    Subjects:
      – SubjectFull: prostate cancer
        Type: general
      – SubjectFull: negative biopsy
        Type: general
      – SubjectFull: machine learning
        Type: general
      – SubjectFull: MRI
        Type: general
      – SubjectFull: automatic detection
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      – SubjectFull: artificial intelligence
        Type: general
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      – TitleFull: mpMRI detection of suspected prostate cancer with a negative biopsy: can radiomic features help radiologists?
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              Y: 2020
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