Michael Deal1,2, Florian Bardet2, Paul-Michael Walker3,4, Mathilde Funes de la Vega5, Alexandre Cochet3,4, Luc Cormier2, Imad Bentellis6, Romaric Loffroy4,7. 1. Department of Urology and Andrology, Arnault Tzanck Private Institute, Mougins Sophia-Antipolis, Mougins Cedex, France. 2. Department of Urology and Andrology, François-Mitterrand University Hospital, Dijon, France. 3. Department of Spectroscopy and Nuclear Magnetic Resonance, François-Mitterrand University Hospital, Dijon, France. 4. ImViA Laboratory, EA-7535, Training and Research Unit in Health Sciences, University of Bourgogne/Franche-Comté, Dijon, France. 5. Department of Cytology and Pathology, François-Mitterrand University Hospital, Dijon, France. 6. Department of Urology and Andrology, Sophia Antipolis University Hospital, Nice, France. 7. Department of Radiology and Medical Imaging, François-Mitterrand University Hospital, Dijon, France.
Abstract
BACKGROUND: The limitations of the assessment of tumor aggressiveness by Prostate Imaging Reporting and Data System (PI-RADS) and biopsies suggest that the diagnostic algorithm could be improved by quantitative measurements in some chosen indications. We assessed the tumor high-risk predictive performance of 3.0 Tesla (3.0T) multiparametric magnetic resonance imaging (mp-MRI) combined with nuclear magnetic resonance spectroscopic sequences (NMR-S) in order to show that the metabolic analysis could bring out an evocative result for the aggressive form of prostate cancer. METHODS: We conducted a retrospective study of 26 patients (mean age, 62.4 years) who had surgery for prostate cancer between 2009 and 2016 after pre-therapeutic assessment with 3.0T mp-MRI and NMR-S. Groups within the intermediate range of the D'Amico risk classification were divided into two categories, low risk (n=20) and high risk (n=6), according to the International Society of Urological Pathology (ISUP) 2-3 limit. Histoprognostic discordances within various risk groups were compared with the corresponding predictive MRI values. The performance of predictive models was assessed based on sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves. RESULTS: After prostatectomy, histological analysis reclassified 18 patients as high-risk, including 16 who were T3 MRI grade, of whom 13 (81.3%) were found to be pT3. Among the patients who had cT1 or cT2 digital rectal examinations, the T3 MRI factor multiplied by 8.7 [odds ratio (OR), 8.7; 95% confidence interval (CI), 1.3-56.2; P=0.024] the relative risk of being pT3 and by 5.8 (OR, 5.8; 95% CI, 0.95-35.7; P=0.05) the relative risk of being pGleason (pGS) > GS-prostate biopsy. Spectroscopic data showed that the choline concentration was significantly higher (P=0.001) in aggressive disease. CONCLUSIONS: The predictive model of tumor aggressiveness combining mp-MRI plus NMR-S was better than the mp-MRI model alone (AUC, 0.95 vs. 0.86). Information obtained by mp-MRI coupled with spectroscopy may improve the detection of occult aggressive disease, helping in the discrimination of intermediate risks. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: The limitations of the assessment of tumor aggressiveness by Prostate Imaging Reporting and Data System (PI-RADS) and biopsies suggest that the diagnostic algorithm could be improved by quantitative measurements in some chosen indications. We assessed the tumor high-risk predictive performance of 3.0 Tesla (3.0T) multiparametric magnetic resonance imaging (mp-MRI) combined with nuclear magnetic resonance spectroscopic sequences (NMR-S) in order to show that the metabolic analysis could bring out an evocative result for the aggressive form of prostate cancer. METHODS: We conducted a retrospective study of 26 patients (mean age, 62.4 years) who had surgery for prostate cancer between 2009 and 2016 after pre-therapeutic assessment with 3.0T mp-MRI and NMR-S. Groups within the intermediate range of the D'Amico risk classification were divided into two categories, low risk (n=20) and high risk (n=6), according to the International Society of Urological Pathology (ISUP) 2-3 limit. Histoprognostic discordances within various risk groups were compared with the corresponding predictive MRI values. The performance of predictive models was assessed based on sensitivity, specificity, and the area under the curve (AUC) of receiver operating characteristic (ROC) curves. RESULTS: After prostatectomy, histological analysis reclassified 18 patients as high-risk, including 16 who were T3 MRI grade, of whom 13 (81.3%) were found to be pT3. Among the patients who had cT1 or cT2 digital rectal examinations, the T3 MRI factor multiplied by 8.7 [odds ratio (OR), 8.7; 95% confidence interval (CI), 1.3-56.2; P=0.024] the relative risk of being pT3 and by 5.8 (OR, 5.8; 95% CI, 0.95-35.7; P=0.05) the relative risk of being pGleason (pGS) > GS-prostate biopsy. Spectroscopic data showed that the choline concentration was significantly higher (P=0.001) in aggressive disease. CONCLUSIONS: The predictive model of tumor aggressiveness combining mp-MRI plus NMR-S was better than the mp-MRI model alone (AUC, 0.95 vs. 0.86). Information obtained by mp-MRI coupled with spectroscopy may improve the detection of occult aggressive disease, helping in the discrimination of intermediate risks. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Entities:
Keywords:
1H magnetic resonance spectroscopic imaging (1H MRSI); Gleason; International Society of Urological Pathology (ISUP); Prostate Imaging Reporting and Data System (PI-RADS); Prostate cancer; intermediate risks; magnetic resonance imaging (MRI); spectroscopy
Authors: Miriam W Lagemaat; Christian M Zechmann; Jurgen J Fütterer; Elisabeth Weiland; Jianping Lu; Geert M Villeirs; Barbara A Holshouser; Paul van Hecke; Marc Lemort; Heinz-Peter Schlemmer; Jelle O Barentsz; Stefan O Roell; Arend Heerschap; Tom W J Scheenen Journal: J Magn Reson Imaging Date: 2011-09-29 Impact factor: 4.813
Authors: Frederik B Thomsen; Klaus Brasso; Laurence H Klotz; M Andreas Røder; Kasper D Berg; Peter Iversen Journal: J Surg Oncol Date: 2014-03-07 Impact factor: 3.454
Authors: Andrew B Rosenkrantz; Hersh Chandarana; Anthony Gilet; Fang-Ming Deng; James S Babb; Jonathan Melamed; Samir S Taneja Journal: J Magn Reson Imaging Date: 2012-12-12 Impact factor: 4.813
Authors: Olga Starobinets; Jeffry P Simko; Kyle Kuchinsky; John Kornak; Peter R Carroll; Kirsten L Greene; John Kurhanewicz; Susan M Noworolski Journal: NMR Biomed Date: 2017-09-29 Impact factor: 4.478