Explore the vast range of titles available.

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability ( p = 0.0003) and smaller uncertainty areas ( p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.

PurposeTo compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard.MethodsWe retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as “low risk,” a PI-RADSv2 score ≥ 4 as “high risk” for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3.Results119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score (“low” vs. “high”) was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively.ConclusionsADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.

Rationale and objectives The study aims to propose an optimal workflow in patients with a PI‐RADS 3 (PR‐3) assessment category (AC) through determining the timing and type of pathology interrogation used for the detection of clinically significant prostate cancer (csPCa) in these men based upon a 5‐year retrospective review in a large academic medical center. Materials and methods This United States Health Insurance Probability and Accountability Act (HIPAA)‐compliant, institutional review board‐approved retrospective study included men without prior csPCa diagnosis who received PR‐3 AC on magnetic resonance (MR) imaging (MRI). Subsequent incidence and time to csPCa diagnosis and number/type of prostate interventions was recorded. Categorical data were compared using Fisher's exact test and continuous data using ANOVA omnibus F‐test. Results Our cohort of 3238 men identified 332 who received PR‐3 as their highest AC on MRI, 240 (72.3%) of whom had pathology follow‐up within 5 years. csPCa was detected in 76/240 (32%) and non‐csPCa in 109/240 (45%) within 9.0 ± 10.6 months. Using a non‐targeted trans‐rectal ultrasound biopsy as the initial approach (n = 55), another diagnostic procedure was required to diagnose csPCa in 42/55 (76.4%) of men, compared with 3/21(14.3%) men with an initial MR targeted‐biopsy approach (n = 21); (p < 0.0001). Those with csPCa had higher median serum prostate‐specific antigen (PSA) and PSA density, and lower median prostate volume (p < 0.003) compared with non‐csPCa/no PCa. Conclusion Most patients with PR‐3 AC underwent prostate pathology exams within 5 years, 32% of whom were found to have csPCa within 1 year of MRI, most often with a higher PSA density and a prior non‐csPCa diagnosis. Addition of a targeted biopsy approach initially reduced the need for a second biopsy to reach a for csPCa diagnosis. Thus, a combination of systematic and targeted biopsy is advised in men with PR‐3 and a co‐existing abnormal PSA and PSA density.

Currently there is a lot of interest in the use of a “biparametric” or “abbreviated” prostate MR protocol, which usually refers to removal of the dynamic contrast-enhanced (DCE) MRI, in the detection of clinically significant prostate cancer. In this article we describe the benefits of DCE as part of the PI-RADS lexicon, with particular reference to its role in PI-RADS V2 category 3 peripheral zone lesions. We also discuss the benefits of triplanar T2-weighted images, and finally discuss how a mpMRI protocol is of benefit in prostate cancer staging, in evaluating for local disease recurrence, and as a biomarker for neoadjuvant therapy response.

INTRODUCTION Uterine sarcomas are rare uterine neoplasms that comprise a heterogeneous histological group of tumors, including leiomyosarcoma (LMS) (the most common subtype), followed by endometrial stromal sarcoma (ESS) (including low-grade and high-grade variants), and rarer subtypes, such as adenosarcoma, undifferentiated uterine sarcomas (UUS), and tumors of uncertain malignant potential including perivascular epithelioid cell tumors (PEComa) and neurotrophic tropomyosin-receptor kinase (NTRK)-rearranged gynecological sarcomas.1 2 They are diagnosed predominantly between the fourth and sixth decades of life and typically exhibit aggressive behavior including risk of distant metastases, even in early stages, and are associated with a poor prognosis in a significant proportion of patients with high-grade tumors. An adapted version of the ‘Infectious Diseases Society of America-United States Public Health Service Grading System’ was used to define the level of evidence and grade of recommendation for each of the recommendations (see Figure 2).7 8 In the absence of any clear scientific evidence, judgment was based on the professional experience and consensus of the international development group. Treatment planning should be multidisciplinary (within a tumor board, composed according to local guidelines) and supported by all available evidence including an understanding and appreciation of prognostic and predictive factors, potential adverse effects of treatments, and quality of life (IV, A). Given the complexity of uterine sarcomas and pathologic evaluation, the diagnosis should be confirmed by a pathologist subspecialized in gynecologic pathology and/or with experience in diagnosing uterine mesenchymal tumors, preferably at a sarcoma reference center where molecular diagnostics are available and routinely used.9–11 The diagnosis of uterine sarcomas should adhere to the guidelines outlined in the fifth edition of the WHO Classification of Female Genital Tumors and the International Collaboration on Cancer Reporting (ICCR) datasets.1 12 Adherence to ICCR guidelines by meticulous macroscopic examination and extensive tumor sampling is recommended.12 This is critical for the evaluation of differential diagnoses, such as sarcoma vs carcinosarcoma, LG-ESS vs HG-ESS, and smooth muscle tumor of uncertain malignant potential (STUMP) vs LMS.

PurposePI-RADS v2 dictates that dynamic contrast-enhanced (DCE) imaging be used to further classify peripheral zone (PZ) cases that receive a diffusion-weighted imaging equivocal score of three (DWI3), a positive DCE resulting in an increase in overall assessment score to a four, indicative of clinically significant prostate cancer (csPCa). However, the accuracy of DCE in predicting csPCa in DWI3 PZ cases is unknown. This study sought to determine the frequency with which DCE changes the PI-RADS v2 DWI3 assessment category, and to determine the overall accuracy of DCE-MRI in equivocal PZ DWI3 lesions.Materials and MethodsThis is a retrospective study of patients with pathologically proven PCa who underwent prostate mpMRI at 3T and subsequent radical prostatectomy. PI-RADS v2 assessment categories were determined by a radiologist, aware of a diagnosis of PCa, but blinded to final pathology. csPCa was defined as a Gleason score ≥ 7 or extra prostatic extension at pathology review. Performance characteristics and diagnostic accuracy of DCE in assigning a csPCa assessment in PZ lesions were calculated.ResultsA total of 271 men with mean age of 59 ± 6 years mean PSA 6.7 ng/mL were included. csPCa was found in 212/271 (78.2%) cases at pathology, 209 of which were localized in the PZ. DCE was necessary to further classify (45/209) of patients who received a score of DWI3. DCE was positive in 29/45 cases, increasing the final PI-RADS v2 assessment category to a category 4, with 16/45 having a negative DCE. When compared with final pathology, DCE was correct in increasing the assessment category in 68.9% ± 7% (31/45) of DWI3 cases.ConclusionDCE increases the accuracy of detection of csPCa in the majority of PZ lesions that receive an equivocal PI-RADS v2 assessment category using DWI.

PurposeThe purpose of this study was to determine the Magnetic Resonance (MR) imaging features that best differentiate leiomyosarcoma (LMS) from leiomyoma, and to explore a scoring system to preoperatively identify those at highest risk of having LMS. MethodsOur Institutional Review Board approved this retrospective HIPAA-compliant study with a waiver for written informed consent. Institutional Research Patient Data Registry identified patients with histopathologically-proven LMS (n = 19) or leiomyoma (n = 25) and a pelvic MRI within six months prior to surgery. Qualitative differentiating MRI features were selected based on prior publications and clinical experience. Patient and MRI characteristics for leiomyomas versus LMS were compared using Wilcoxon rank-sum tests or Fisher’s exact tests and using a basic classification tree. Hypothesis testing was two-tailed, with a p value < 0.001 used to determine inclusion of variables into an MR imaging predictive (MRP) score. Diagnostic performance [sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)] of the MRP in diagnosis of LMS used all possible scores as cutoffs.ResultsSeven out of 15 MRI features were found to have an association with LMS. The final MRP scores ranged from 0 to 7: a score of 0–3 was associated with 100% NPV for LMS, and a MRP score of 6–7 with 100% PPV for LMS.ConclusionSeven qualitative MR imaging features, extracted from a standard MR imaging protocol, allow differentiation of LMS from leiomyoma. An exploratory risk stratification MRP score can be used to determine the likelihood of LMS being present.

Objectives The Prostate Imaging Quality (PI-QUAL) score is a new metric to evaluate the diagnostic quality of multiparametric magnetic resonance imaging (MRI) of the prostate. This study assesses the impact of an intervention, namely a prostate MRI quality training lecture, on the participant’s ability to apply PI-QUAL. Methods Sixteen participants (radiologists, urologists, physicists, and computer scientists) of varying experience in reviewing diagnostic prostate MRI all assessed the image quality of ten examinations from different vendors and machines. Then, they attended a dedicated lecture followed by a hands-on workshop on MRI quality assessment using the PI-QUAL score. Five scans assessed by the participants were evaluated in the workshop using the PI-QUAL score for teaching purposes. After the course, the same participants evaluated the image quality of a new set of ten scans applying the PI-QUAL score. Results were assessed using receiver operating characteristic analysis. The reference standard was the PI-QUAL score assessed by one of the developers of PI-QUAL. Results There was a significant improvement in average area under the curve for the evaluation of image quality from baseline (0.59 [95 % confidence intervals: 0.50–0.66]) to post-teaching (0.96 [0.92–0.98]), an improvement of 0.37 [0.21–0.41] ( p < 0.001). Conclusions A teaching course (dedicated lecture + hands-on workshop) on PI-QUAL significantly improved the application of this scoring system to assess the quality of prostate MRI examinations. Key Points • A significant improvement in the application of PI-QUAL for the assessment of prostate MR image quality was observed after an educational intervention. • Appropriate training on image quality can be delivered to those involved in the acquisition and interpretation of prostate MRI. • Further investigation will be needed to understand the impact on improving the acquisition of high-quality diagnostic prostate MR examinations.

The most recent edition of the prostate imaging reporting and data system (PI-RADS version 2) was developed based on expert consensus of the international working group on prostate cancer. It provides the minimum acceptable technical standards for MR image acquisition and suggests a structured method for multiparametric prostate MRI (mpMRI) reporting. T1-weighted, T2-weighted (T2W), diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging are the suggested sequences to include in mpMRI. The PI-RADS version 2 scoring system enables the reader to assess and rate all focal lesions detected at mpMRI to determine the likelihood of a clinically significant cancer. According to PI-RADS v2, a lesion with a Gleason score ≥7, volume >0.5 cc, or extraprostatic extension is considered clinically significant. PI-RADS v2 uses the concept of a dominant MR sequence based on zonal location of the lesion rather than summing each component score, as was the case in version 1. The dominant sequence in the peripheral zone is DWI and the corresponding apparent diffusion coefficient (ADC) map, with a secondary role for DCE in equivocal cases (PI-RADS score 3). For lesions in the transition zone, T2W images are the dominant sequence with DWI/ADC images playing a supporting role in the case of an equivocal lesion.