MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer
Purpose: Prostatectomy is one of the main therapeutic options for prostate cancer (PCa). Studies proved the benefit of adjuvant radiotherapy (aRT) on clinical outcomes, with more toxicities when compared to salvage radiotherapy. A better assessment of the likelihood of biochemical recurrence (BCR) w...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-08-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2019.00807/full |
| _version_ | 1819047676731195392 |
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| author | Vincent Bourbonne Vincent Bourbonne Vincent Bourbonne Martin Vallières Martin Vallières François Lucia François Lucia François Lucia Laurent Doucet Dimitris Visvikis Valentin Tissot Olivier Pradier Olivier Pradier Olivier Pradier Mathieu Hatt Ulrike Schick Ulrike Schick Ulrike Schick |
| author_facet | Vincent Bourbonne Vincent Bourbonne Vincent Bourbonne Martin Vallières Martin Vallières François Lucia François Lucia François Lucia Laurent Doucet Dimitris Visvikis Valentin Tissot Olivier Pradier Olivier Pradier Olivier Pradier Mathieu Hatt Ulrike Schick Ulrike Schick Ulrike Schick |
| author_sort | Vincent Bourbonne |
| collection | DOAJ |
| description | Purpose: Prostatectomy is one of the main therapeutic options for prostate cancer (PCa). Studies proved the benefit of adjuvant radiotherapy (aRT) on clinical outcomes, with more toxicities when compared to salvage radiotherapy. A better assessment of the likelihood of biochemical recurrence (BCR) would rationalize performing aRT. Our goal was to assess the prognostic value of MRI-derived radiomics on BCR for PCa with high recurrence risk.Methods: We retrospectively selected patients with a high recurrence risk (T3a/b or T4 and/or R1 and/or Gleason score>7) and excluded patients with a post-operative PSA > 0.04 ng/mL or a lymph-node involvement. We extracted IBSI-compliant radiomic features (shape and first order intensity metrics, as well as second and third order textural features) from tumors delineated in T2 and ADC sequences. After random division (training and testing sets) and machine learning based feature reduction, a univariate and multivariate Cox regression analysis was performed to identify independent factors. The correlation with BCR was assessed using AUC and prediction of biochemical relapse free survival (bRFS) with a Kaplan-Meier analysis.Results: One hundred seven patients were included. With a median follow-up of 52.0 months, 17 experienced BCR. In the training set, no clinical feature was correlated with BCR. One feature from ADC (SZEGLSZM) outperformed with an AUC of 0.79 and a HR 17.9 (p = 0.0001). Lower values of SZEGLSZM are associated with more heterogeneous tumors. In the testing set, this feature remained predictive of BCR and bRFS (AUC 0.76, p = 0.0236).Conclusion: One radiomic feature was predictive of BCR and bRFS after prostatectomy helping to guide post-operative management. |
| first_indexed | 2024-12-21T11:04:09Z |
| format | Article |
| id | doaj.art-71d885624bbd4bd5a525ff3883849db8 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-21T11:04:09Z |
| publishDate | 2019-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-71d885624bbd4bd5a525ff3883849db82022-12-21T19:06:15ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2019-08-01910.3389/fonc.2019.00807471025MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate CancerVincent Bourbonne0Vincent Bourbonne1Vincent Bourbonne2Martin Vallières3Martin Vallières4François Lucia5François Lucia6François Lucia7Laurent Doucet8Dimitris Visvikis9Valentin Tissot10Olivier Pradier11Olivier Pradier12Olivier Pradier13Mathieu Hatt14Ulrike Schick15Ulrike Schick16Ulrike Schick17Department of Radiation Oncology, University Hospital, Brest, FranceLaTIM, INSERM, UMR 1101, Brest University, Brest, FranceUniversité de Bretagne Occidentale, Brest, FranceLaTIM, INSERM, UMR 1101, Brest University, Brest, FranceMedical Physics Unit, McGill University, Montreal, QC, CanadaDepartment of Radiation Oncology, University Hospital, Brest, FranceLaTIM, INSERM, UMR 1101, Brest University, Brest, FranceUniversité de Bretagne Occidentale, Brest, FranceDepartment of Anatomopathology, University Hospital, Brest, FranceLaTIM, INSERM, UMR 1101, Brest University, Brest, FranceDepartment of Radiology, University Hospital, Brest, FranceDepartment of Radiation Oncology, University Hospital, Brest, FranceLaTIM, INSERM, UMR 1101, Brest University, Brest, FranceUniversité de Bretagne Occidentale, Brest, FranceLaTIM, INSERM, UMR 1101, Brest University, Brest, FranceDepartment of Radiation Oncology, University Hospital, Brest, FranceLaTIM, INSERM, UMR 1101, Brest University, Brest, FranceUniversité de Bretagne Occidentale, Brest, FrancePurpose: Prostatectomy is one of the main therapeutic options for prostate cancer (PCa). Studies proved the benefit of adjuvant radiotherapy (aRT) on clinical outcomes, with more toxicities when compared to salvage radiotherapy. A better assessment of the likelihood of biochemical recurrence (BCR) would rationalize performing aRT. Our goal was to assess the prognostic value of MRI-derived radiomics on BCR for PCa with high recurrence risk.Methods: We retrospectively selected patients with a high recurrence risk (T3a/b or T4 and/or R1 and/or Gleason score>7) and excluded patients with a post-operative PSA > 0.04 ng/mL or a lymph-node involvement. We extracted IBSI-compliant radiomic features (shape and first order intensity metrics, as well as second and third order textural features) from tumors delineated in T2 and ADC sequences. After random division (training and testing sets) and machine learning based feature reduction, a univariate and multivariate Cox regression analysis was performed to identify independent factors. The correlation with BCR was assessed using AUC and prediction of biochemical relapse free survival (bRFS) with a Kaplan-Meier analysis.Results: One hundred seven patients were included. With a median follow-up of 52.0 months, 17 experienced BCR. In the training set, no clinical feature was correlated with BCR. One feature from ADC (SZEGLSZM) outperformed with an AUC of 0.79 and a HR 17.9 (p = 0.0001). Lower values of SZEGLSZM are associated with more heterogeneous tumors. In the testing set, this feature remained predictive of BCR and bRFS (AUC 0.76, p = 0.0236).Conclusion: One radiomic feature was predictive of BCR and bRFS after prostatectomy helping to guide post-operative management.https://www.frontiersin.org/article/10.3389/fonc.2019.00807/fullmagnetic resonance imagingprostatic neoplasmsradiomicsmachine learningtreatment failure |
| spellingShingle | Vincent Bourbonne Vincent Bourbonne Vincent Bourbonne Martin Vallières Martin Vallières François Lucia François Lucia François Lucia Laurent Doucet Dimitris Visvikis Valentin Tissot Olivier Pradier Olivier Pradier Olivier Pradier Mathieu Hatt Ulrike Schick Ulrike Schick Ulrike Schick MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer Frontiers in Oncology magnetic resonance imaging prostatic neoplasms radiomics machine learning treatment failure |
| title | MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer |
| title_full | MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer |
| title_fullStr | MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer |
| title_full_unstemmed | MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer |
| title_short | MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer |
| title_sort | mri derived radiomics to guide post operative management for high risk prostate cancer |
| topic | magnetic resonance imaging prostatic neoplasms radiomics machine learning treatment failure |
| url | https://www.frontiersin.org/article/10.3389/fonc.2019.00807/full |
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