Improvement of prostate cancer detection combining a computer-aided diagnostic system with TRUS-MRI targeted biopsy

PurposeTo validate a novel consensus method, called target-in-target, combining human analysis of mpMRI with automated CAD system analysis, with the aim to increasing the prostate cancer detection rate of targeted biopsies.MethodsA cohort of 420 patients was enrolled and 253 patients were rolled out, due to exclusion criteria. 167 patients, underwent diagnostic 3T MpMRI. Two expert radiologists evaluated the exams adopting PI-RADSv2 and CAD system. When a CAD target overlapped with a radiologic one, we performed the biopsy in the overlapping area which we defined as target-in-target. Targeted TRUS-MRI fusion biopsy was performed in 63 patients with a total of 212 targets. The MRI data of all targets were quantitatively analyzed, and diagnostic findings were compared to pathologist’s biopsy reports.ResultsCAD system diagnostic performance exhibited sensitivity and specificity scores of 55.2% and 74.1% [AUC = 0.63 (0.54 ÷ 0.71)] , respectively. Human readers achieved an AUC value, in ROC analysis, of 0.71 (0.63 ÷ 0.79). The target-in-target method provided a detection rate per targeted biopsy core of 81.8 % vs. a detection rate per targeted biopsy core of 68.6 % for pure PI-RADS based on target definitions. The higher per-core detection rate of the target-in-target approach was achieved irrespective of the presence of technical flaws and artifacts.ConclusionsA novel consensus method combining human reader evaluation with automated CAD system analysis of mpMRI to define prostate biopsy targets was shown to improve the detection rate per biopsy core of TRUS-MRI fusion biopsies. Results suggest that the combination of CAD system analysis and human reader evaluation is a winning strategy to improve targeted biopsy efficiency.

[1]  L. Kavoussi,et al.  In patients with a previous negative prostate biopsy and a suspicious lesion on magnetic resonance imaging, is a 12‐core biopsy still necessary in addition to a targeted biopsy? , 2015, BJU international.

[2]  H. G. van der Poel,et al.  EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. , 2017, European urology.

[3]  Mark Emberton,et al.  Image-guided prostate biopsy using magnetic resonance imaging-derived targets: a systematic review. , 2013, European urology.

[4]  Y. Yamashita,et al.  Negative predictive value of multiparametric MRI for prostate cancer detection: outcome of 5-year follow-up in men with negative findings on initial MRI studies. , 2014, European journal of radiology.

[5]  M. Roethke,et al.  Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging , 2016, PloS one.

[6]  Shyam Natarajan,et al.  Prostate cancer detection with magnetic resonance‐ultrasound fusion biopsy: The role of systematic and targeted biopsies , 2016, Cancer.

[7]  M. Parmar,et al.  Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confi rmatory study , 2018 .

[8]  C. Catalano,et al.  Multiparametric magnetic resonance imaging vs. standard care in men being evaluated for prostate cancer: a randomized study. , 2015, Urologic oncology.

[9]  Katarzyna J Macura,et al.  Synopsis of the PI-RADS v2 Guidelines for Multiparametric Prostate Magnetic Resonance Imaging and Recommendations for Use. , 2016, European urology.

[10]  M. Roethke,et al.  Multiparametric Magnetic Resonance Imaging (MRI) and MRI-Transrectal Ultrasound Fusion Biopsy for Index Tumor Detection: Correlation with Radical Prostatectomy Specimen. , 2016, European urology.

[11]  Baris Turkbey,et al.  Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. , 2015, JAMA.

[12]  D. Margolis,et al.  MRI‐Targeted or Standard Biopsy for Prostate‐Cancer Diagnosis , 2018, The New England journal of medicine.

[13]  P. Walsh,et al.  Pathologic and clinical findings to predict tumor extent of nonpalpable (stage T1c) prostate cancer. , 1994, JAMA.

[14]  M. Stifelman,et al.  A prospective, blinded comparison of magnetic resonance (MR) imaging-ultrasound fusion and visual estimation in the performance of MR-targeted prostate biopsy: the PROFUS trial. , 2014, European urology.

[15]  M. Coleman,et al.  Cancer survival in Europe 1999-2007 by country and age: results of EUROCARE--5-a population-based study. , 2014, The Lancet. Oncology.

[16]  H. Huisman,et al.  Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance. , 2013, Radiology.

[17]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[18]  Thomas Hambrock,et al.  Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI , 2010, Physics in medicine and biology.

[19]  C. Catalano,et al.  Negative Multiparametric Magnetic Resonance Imaging for Prostate Cancer: What's Next? , 2018, European urology.

[20]  N. Lawrentschuk,et al.  The role of magnetic resonance imaging in the diagnosis and management of prostate cancer , 2013, BJU international.

[21]  P. Albers,et al.  Predictive power of the ESUR scoring system for prostate cancer diagnosis verified with targeted MR-guided in-bore biopsy. , 2014, European journal of radiology.