Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis.

Background: Targeted magnetic resonance (MR) with ultrasound (US) fusion-guided biopsy has been shown to improve detection of prostate cancer. The implementation of this approach requires integration of skills from radiologists and urologists. Objective methods for assessment of learning curves, such as cumulative sum (CUSUM) analysis, may be helpful in identifying the presence and duration of a learning curve. The aim of this study is to determine the learning curve for MR/US fusion-guided biopsy in detecting clinically significant prostate cancer using CUSUM analysis. Materials and methods: Retrospective analysis was performed in this institutional review board-approved study. Two urologists implemented an MR/US fusion-guided prostate biopsy program between March 2015 and September 2017. The primary outcome measure was cancer detection rate (CDR) stratified by Prostate Imaging Reporting and Data System (PI-RADS) scores assigned on the MR imaging. Cumulative sum analysis quantified actual cancer detection versus a predetermined target satisfactory CDR of MR/US fusion biopsies in a sequential case-by-case basis. For this analysis, satisfactory performance was defined as >80% CDR in patients with PI-RADS 5, >50% in PI-RADS 4, and <20% in PI-RADS 1-3. Results: Complete data were available for MR/US fusion-guided biopsies performed on 107 patients. The CUSUM learning curve analysis demonstrated intermittent underperformance until approximately 50 cases. After this inflection point, there was consistently good performance, evidence that no further learning curve was being encountered. Conclusions: At a new center implementing MR/US fusion-guided prostate biopsy, the learning curve was approximately 50 cases before a consistently high performance for prostate cancer detection.

Performance of PI-RADS v2 assessment categories assigned prior to MR-US fusion biopsy in a new fusion biopsy program.

OBJECTIVE: To validate the performance of PI-RADS v2 for detection of clinically significant prostate cancer (csPca, Gleason ≥7) within the context of a new fusion biopsy program. MATERIAL AND METHODS: Patients with a PI-RADS v2 assessment category assigned on pre-biopsy mpMRI between March 2015 and November 2017 were identified. Diagnostic performance of PI-RADS v2 was calculated using fusion biopsy results as reference standard using receiver operating characteristic curve analysis. Patient and lesion characteristics were analyzed with one-way ANOVA and Wilcoxon rank sum test. RESULTS: Of 83 patients with 175 lesions, 115/175 (65.7%) were benign, 21/175 (12%) were Gleason 6, and 39/175 (22.3%) were Gleason ≥7. csPCa rates were 0% (0/5) for PI-RADS 1, 7.4% (2/27) for PI-RADS 2, 5.8% (3/52) for PI-RADS 3, 31.2% (24/77) for PI-RADS 4, and 71.4% (10/14) for PI-RADS 5 (p < 0.0001). For prediction of csPCa, patient-level AUC was 0.68 and lesion-level AUC was 0.77. Biopsy threshold of PI-RADS ≥3 was 92.6% sensitive and 22.1% specific. A threshold of PI-RADS ≥4 was 87.2% sensitive and 58.1% specific. Rate of csPca detection on concurrent standard 12 core biopsy only was 6.7%. CONCLUSION: PI-RADS v2 assessment categories assigned prior to biopsy predict pathologic outcome reasonably well in a new prostate fusion biopsy program. Biopsy threshold of PI-RADS ≥3 is highly sensitive. A threshold of ≥4 increases specificity but misses some csPCa.

Breast Cancer Tissue Markers, Genomic Profiling, and Other Prognostic Factors: A Primer for Radiologists.

An understanding of prognostic factors in breast cancer is imperative for guiding patient care. Increased tumor size and more advanced nodal status are established independent prognostic factors of poor outcomes and are incorporated into the American Joint Committee on Cancer (AJCC) TNM (primary tumor, regional lymph node, distant metastasis) staging system. However, other factors including imaging findings, histologic evaluation results, and molecular findings can have a direct effect on a patient's prognosis, including risk of recurrence and relative survival. Several microarray panels for gene profiling of tumors are approved by the U.S. Food and Drug Administration and endorsed by the American Society of Clinical Oncology. This article highlights prognostic factors currently in use for individualizing and guiding breast cancer therapy and is divided into four sections. The first section addresses patient considerations, in which modifiable and nonmodifiable prognostic factors including age, race and ethnicity, and lifestyle factors are discussed. The second part is focused on imaging considerations such as multicentric and/or multifocal disease, an extensive intraductal component, and skin or chest wall involvement and their effect on treatment and prognosis. The third section is about histopathologic findings such as the grade and presence of lymphovascular invasion. Last, tumor biomarkers and tumor biology are discussed, namely hormone receptors, proliferative markers, and categorization of tumors into four recognized molecular subtypes including luminal A, luminal B, human epidermal growth factor receptor 2-enriched, and triple-negative tumors. By understanding the clinical effect of these prognostic factors, radiologists, along with a multidisciplinary team, can use these tools to achieve individualized patient care and to improve patient outcomes. ©RSNA, 2018.