Reliability of a risk-factor questionnaire for osteoporosis: a primary care survey study with dual energy x-ray absorptiometry ground truth

(1) Purpose: Predisposing factors to osteoporosis (OP) as well as dual-source x-ray densitometry (DXA) steer therapeutic decisions by determining the FRAX index. This study examines the reliability of a standard risk factor questionnaire in OP-screening. (2) Methods: n = 553 eligible questionnaires encompassed 24 OP-predisposing factors. Reliability was assessed using DXA as a gold standard. Multiple logistic regression and Spearman’s correlations, as well as the confounding influence of age and body mass index, were analyzed in SPSS (IBM Corporation, Armonk, NY, USA). (3) Results: Our study revealed low patient self-awareness regarding OP and its risk factors. One out of every four patients reported a positive history for osteoporosis not confirmed by DXA. The extraordinarily high incidence of rheumatoid arthritis and thyroid disorders likely reflect confusion with other diseases or health anxiety. FRAX-determining risk factors such as malnutrition, liver insufficiency, prior fracture without trauma, and glucocorticoid therapy did not correlate with increased OP incidence, altogether demonstrating how inaccurate survey information could influence therapeutic decisions on osteoporosis. (4) Conclusions: Contradictive results and a low level of patient self-awareness suggest a high degree of uncertainty and low reliability of the current OP risk factor survey

The role of gadolinium in magnetic resonance imaging for early prostate cancer diagnosis: A diagnostic accuracy study.

ObjectiveProstate lesions detected with multiparametric magnetic resonance imaging (mpMRI) are classified for their malignant potential according to the Prostate Imaging-Reporting And Data System (PI-RADS™2). In this study, we evaluate the diagnostic accuracy of the mpMRI with and without gadolinium, with emphasis on the added diagnostic value of the dynamic contrast enhancement (DCE).Materials and methodsThe study was retrospective for 286 prostate lesions / 213 eligible patients, n = 116/170, and 49/59% malignant for the peripheral (Pz) and transitional zone (Tz), respectively. A stereotactic MRI-guided prostate biopsy served as the histological ground truth. All patients received a mpMRI with DCE. The influence of DCE in the prediction of malignancy was analyzed by blinded assessment of the imaging protocol without DCE and the DCE separately.ResultsSignificant (CSPca) and insignificant (IPca) prostate cancers were evaluated separately to enhance the potential effects of the DCE in the detection of CSPca. The Receiver Operating Characteristics Area Under Curve (ROC-AUC), sensitivity (Se) and specificity (Spe) of PIRADS-without-DCE in the Pz was 0.70/0.47/0.86 for all cancers (IPca and CSPca merged) and 0.73/0.54/0.82 for CSPca. PIRADS-with-DCE for the same patients showed ROC-AUC/Se/Spe of 0.70/0.49/0.86 for all Pz cancers and 0.69/0.54/0.81 for CSPca in the Pz, respectively, p>0.05 chi-squared test. Similar results for the Tz, AUC/Se/Spe for PIRADS-without-DCE was 0.75/0.61/0.79 all cancers and 0.67/0.54/0.71 for CSPca, not influenced by DCE (0.66/0.47/0.81 for all Tz cancers and 0.61/0.39/0.75 for CSPca in Tz). The added Se and Spe of DCE for the detection of CSPca was 88/34% and 78/33% in the Pz and Tz, respectively.ConclusionDCE showed no significant added diagnostic value and lower specificity for the prediction of CSPca compared to the non-enhanced sequences. Our results support that gadolinium might be omitted without mitigating the diagnostic accuracy of the mpMRI for prostate cancer

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth

<div><p>Background</p><p>Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary^™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade.</p><p>Aim/Objective</p><p>To assess the performance of Watson Elementary^™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies.</p><p>Methods</p><p>The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary^™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth.</p><p>Results</p><p>The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (<i>P</i> 0.06, <i>χ</i>² test).</p><p>Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (<i>P</i> 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (<i>P</i> 0.60, Pearson´s correlation).</p><p>Conclusion</p><p>The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.</p></div

Sequences used for multiparametric magnetic resonance imaging.

<p>Sequences used for multiparametric magnetic resonance imaging.</p

CAD performance is reliable for lesions larger than 1 ml.

<p>(A) Counts of classified and non classified cores in relation to their volume. Volume distribution of all lesions (number, %): 0–0.5ml (51, 49.04), 0.5–1.0ml (38, 36.54) and larger than 1.0ml (15, 14.42). True positive (TP, blue) lesions include classified PCa, true negatives (TN, cyan) are the non classified benign cores, false positives (FP, yellow) are the classified benign cores and false negatives (FN, red) the non classified PCa. The CAD-sensitivity increases and the number of FN decreases towards larger lesion volumes: (sensitivity % / FNR %) 27.27/31.37 for 0–0.5ml lesions, 53.33/18.42 for 0.5–1.0ml lesions and 80.00/13.33% for lesions larger than 1.0ml. (B) MAI score with lesion volume. A strong trend for a positive correlation between lesion size and MAI-score was found for TP lesions (P 0.057, Pearson´s correlation) but not for any of the remaining categories (TN, FP and FN, P > 0.1, Pearson´s correlation). (C) Lesions smaller than 0.5 ml show the same malignancy incidence and comparable aggressiveness compared to larger lesions (Ci) Lesions smaller than 0.5 ml (number, %) benign (29, 56.86) malignant (22, 43.14), (Cii) Gleason histogram for malignant lesions smaller than 0.5 ml, (Ciii) Lesions larger than 0.5 ml (number, %) benign (28, 57.14) malignant (21, 42.86), (Civ) Gleason histogram for malignant lesions larger than 0.5 ml.</p

Receiver operating characteristic (ROC) trade-off curve for ADC, MAI and PI-RADS.

<p>ADC mean (black), MAI median (dark green), MAI mean (light blue) and MAI median/mean ratio (green). MAI is compared with the ADC performance alone and with the observer´s performance according to PI-RADS^™ v1 (dark blue) and PI-RADS^™ v2 (violet). The area under the curve (AUC) is 0.64±0.057 (mean, SEM) with 95% CI 0.53–0.75 and <i>P</i> 0.02 for MAI mean, 0.63±0.058 with 95% CI 0.52–0.74 and <i>P</i> 0.02 for MAI median and 0.59±0.058 with 95% CI 0.47–0.70 and <i>P</i> 0.13 for the MAI median/mean ratio. Corresponding values for the mean ADC lesion value are AUC 0.79±0.05 with 95% CI 0.70–0.88, <i>P</i> < 0.0001. Observer´s performance for PI-RADS^™ v1 was AUC 0.67±0.05 with CI 0.58–0.76, <i>P</i> 0.003 and for PI-RADS^™ v2 AUC 0.68±0.04 with CI 0.59–0.76, P 0.002. <i>N</i> malignant/benign cores 47/57. MAI and PI-RADS (v1, v2) reveal comparable performances in malignancy detection, <i>P</i> 0.60 for MAI vs PI-RADS v1 and P 0.53 for MAI vs PI-RADS v2, chi-squared test. ADC is superior to MAI in malignancy prediction, <i>P</i> 0.04, chi-squared test.</p

Automated mpMRI classification and histological ground truth.

<p>Target lesions were manually drawn by human observers. Sample images for (A-C) classified and (D, E) non classified PCa and benign biopsy cores. (A) Prostatitis, (B) adenocarcinoma (PCa) Gleason grade 6, (C) PCa Gleason grade 9, (D) atypical small acinar proliferation (ASAP) and (E) PCa Gleason grade 7. From left to right: (i) T2w with MAI-heatmap and outlined lesions (white line), (ii) Hematoxylin-Eosin histopathology of the corresponding biopsy cores and (iii) MAI histograms. Warm colors in MAI heatmaps (i) represent higher values in a scale 0–1. Classified lesions revealed a “warm-colored” MAI-map and a left-skewed histogram.</p

CAD sensitivity and specificity for PCa detection.

<p>CAD sensitivity and specificity for PCa detection.</p

Patient demographics and database description.

<p>Patient demographics and database description.</p

Receiver operating characteristic (ROC) analysis for MAI and PI-RADS.

<p>Receiver operating characteristic (ROC) analysis for MAI and PI-RADS.</p