Objective
To prospectively assess the additional value of the hepatobiliary (HB) phase of Gd-EOB-DTPA-MRI in identifying and characterising small (≤2 cm) hepatocellular carcinomas (HCCs) undetermined ...in dynamic phases alone because of their atypical features, according to the AASLD criteria.
Methods
127 cirrhotic patients were evaluated with Gd-EOB-DTPA-MRI in two sets: unenhanced and dynamic phases; unenhanced, dynamic and HB phases. Sixty-two out of 215 nodules (29%) were atypical in 42 patients (33%).
Results
62 atypical nodules were reported at histology: high-grade dysplastic nodules (HGDN)/early HCC (
n
= 20), low-grade DN (LGDN) (
n
= 21), regenerative nodules (
n
= 17) and nodular regenerative hyperplasia (
n
= 4). The sensitivity, specificity, accuracy, positive and negative predictive value (PPV, NPV) were increased by the addition of the HB phase: 88.4–99.4%, 88–95%, 88–98.5%, 97–99%, and 65–97.5%, respectively. Twenty atypical nodules were malignant (32%), 19 of which were characterised only during the HB phase.
Conclusions
The HB phase is 11% more sensitive in the classification of HGDN/early HCC than dynamic MRI, with an added value of 32.5% in the NPV. The high incidence (33%) of atypical nodules and their frequent malignancy (32%) suggest the widespread employment of Gd-EOB-DTPA-MRI in the follow-up of small nodules (≤2 cm) in cirrhosis.
Purpose
This article reviews imaging manifestations of complicated pyelonephritis associated with chronic renal stones disease, in particular xanthogranulomatous pyelonephritis (XGP) and ...emphysematous pyelonephritis (EPN), as potential mimics of other renal diseases and malignances and provides helpful tips and differentiating features that may alert the radiologist to suspect a diagnosis of infection.
Materials and methods
A retrospective review of the records from 6 adult patients (5 females and 1 male, mean age 72,3 years) with diagnosis of XGP associated with chronic nephrolithiasis and 7 adult patients (6 females and 1 male, mean age 59,3 years) with diagnosis of EPN associated with chronic nephrolithiasis from January 2010 to January 2020 was carried out. Computed tomography urography (CTU) with at least an unenhanced scan, and the parenchymal and excretory phases after contrast medium administration performed at our Teaching Hospital were included. When available images related to conventional radiography, ultrasound (US) and magnetic resonance imaging of the same patients, the comparison with CTU images was carried out.
Conclusion
A possible diagnosis of XGP or EPN must always be taken into account when a pyelonephritis is associated with untreated kidney stones, especially whenever clinical presentation is atypical, current therapy is not effective and imaging shows features of dubious interpretation. Due to their rarity and atypical presentation, a multidisciplinary approach is required and an expert radiologist represents a key figure in the multidisciplinary team as he can help to differentiate between benign and malignant lesions and thus avoid unnecessary radical surgical procedures.
Objective
To assess renal dysfunction in chronic kidney diseases using diffusion tensor imaging (DTI).
Methods
Forty-seven patients with impaired renal function (study group) and 17 patients without ...renal diseases (control group) were examined using DTI sequences. Cortical and medullary regions of interest (ROIs) were located to obtain the corresponding values of the apparent diffusion coefficient (ADC) and the fractional anisotropy (FA). The mean values of the ADC and FA, for each ROI site, were obtained in each group and were compared. Furthermore, the correlations between the diffusion parameters and the estimated glomerular filtration rate (eGFR) were determined.
Results
In both the normal and affected kidneys, we obtained the cortico-medullary difference of the ADC and the FA values. The FA value in the medulla was significantly lower (
P
= 0.0149) in patients with renal function impairment as compared to patients with normal renal function. A direct correlation between DTI parameters and the eGFR was not found. Tractography visualised disruption of the regular arrangement of the tracts in patient with renal function alteration.
Conclusion
DTI could be a useful tool in the evaluation of chronic kidney disease and, in particular, the medullary FA value seems to be the main parameter for assessing renal damage.
Key Points
•
Magnetic resonance diffusion tensor imaging (MRDTI) provides new information about renal problems.
•
DTI allows non-invasive repeatable evaluation of the renal parenchyma, without contrast media.
•
DTI could become useful in the management of chronic parenchymal disease.
•
DTI seems more appropriate for renal evaluation than diffusion-weighted imaging.
We aimed to review the current state‐of‐the‐art imaging methods used for primary and secondary staging of prostate cancer, mainly focusing on multiparametric magnetic resonance imaging and ...positron‐emission tomography/computed tomography with new radiotracers. An expert panel of urologists, radiologists and nuclear medicine physicians with wide experience in prostate cancer led a PubMed/MEDLINE search for prospective, retrospective original research, systematic review, meta‐analyses and clinical guidelines for local and systemic staging of the primary tumor and recurrence disease after treatment. Despite magnetic resonance imaging having low sensitivity for microscopic extracapsular extension, it is now a mainstay of prostate cancer diagnosis and local staging, and is becoming a crucial tool in treatment planning. Cross‐sectional imaging for nodal staging, such as computed tomography and magnetic resonance imaging, is clinically useless even in high‐risk patients, but is still suggested by current clinical guidelines. Positron‐emission tomography/computed tomography with newer tracers has some advantage over conventional images, but is not cost‐effective. Bone scan and computed tomography are often useless in early biochemical relapse, when salvage treatments are potentially curative. New imaging modalities, such as prostate‐specific membrane antigen positron‐emission tomography/computed tomography and whole‐body magnetic resonance imaging, are showing promising results for early local and systemic detection. Newer imaging techniques, such as multiparametric magnetic resonance imaging, whole‐body magnetic resonance imaging and positron‐emission tomography/computed tomography with prostate‐specific membrane antigen, have the potential to fill the historical limitations of conventional imaging methods in some clinical situations of primary and secondary staging of prostate cancer.
Purpose
To assess the role of the multiparametric Magnetic Resonance Imaging (mpMRI) in predicting the cribriform pattern in both the peripheral and transition zones (PZ and TZ) clinically ...significant prostate cancers (csPCas).
Material and methods
We retrospectively evaluated 150 patients who underwent radical prostatectomy for csPCa and preoperative mpMRI. Patients with negative (
n
= 25) and positive (
n
= 125) mpMRI, stratified according to the presence of prevalent cribriform pattern (PCP, ≥ 50%) and non-PCP (< 50%) at specimen, were included. Difference between the two groups were evaluated. Multivariate logistic regression was used to identify predictors of PCP among mpMRI parameters. The receiver operating characteristic (ROC) analysis was performed to evaluate the area under the curve (AUC) of apparent diffusion coefficient (ADC) and ADC ratio in detecting lesions harboring PCP.
Results
Considering 135 positive lesions at the mpMRI, 30 (22.2%) and 105 (77.8%) harbored PCP and non-PCP PCa. The PCP lesions had more frequently nodular morphology (83.3% vs 62.9%;
p
= 0.04) and significantly lower mean ADC value (0.87 ± 0.16 vs 0.95 ± 0.18;
p
= 0.03) and ADC ratio (0.52 ± 0.09 vs 0.60 ± 0.14;
p
= 0.003) when compared with non-PCP lesions. At univariate and multivariate analyses, mean ADC and ADC ratio resulted as independent predictors of the presence of the PCP of the PZ tumors(OR: 0.025;
p
= 0.03 and OR: 0.001;
p
= 0.004, respectively). At the ROC analysis, the AUC of mean ADC and ADC ratio to predict the presence of PCP in patients with PZ suspicious lesion at the mpMRI were 0.69 (95% CI 0.56–0.81P,
p
= 0.003) and 0.72 (95% CI 0.62–0.82P,
p
= 0.001), respectively.
Conclusions
The mpMRI may correctly identify PCP tumors of the PZ and the mean ADC value and ADC ratio can predict the presence of the cribriform pattern in the PCa.
Background: We investigated the diagnostic accuracy of the new Prostate Imaging for Recurrence Reporting (PI-RR) score and its inter-observer variability. Secondly, we compared the detection rate of ...PI-RR and PET and analyzed the correlation between Prostate Specific Antigen (PSA) levels and the PI-RR score. Methods: We included in the analysis 134 patients submitted to multiparametric magnetic resonance imaging for suspected local recurrence. The images were independently reviewed by two radiologists, assigning a value from 1 to 5 to the PI-RR score. Inter-observer agreement and diagnostic accuracy of the PI-RR score (compared to histopathological data, available for 19 patients) were calculated. The detection rate was compared to those of choline PET/CT (46 patients) and PSMA PET/CT (22 patients). The distribution of the PSA values in relation to the PI-RR scores was also analyzed. Results: The accuracy of the PI-RR score was 68.4%. The reporting agreement was excellent (K = 0.884, p < 0.001). The PI-RR showed a higher detection rate than choline PET/CT (69.6% versus 19.6%) and PSMA PET-CT (59.1% versus 22.7%). The analysis of the PSA distribution documented an increase in the PI-RR score as the PSA value increased. Conclusion: The excellent reproducibility of the PI-RR score supports its wide use in the clinical practice to standardize recurrence reporting. The detection rate of PI-RR was superior to that of PET, but was linked to the PSA level.
To evaluate multiparametric magnetic resonance imaging (mpMRI) parameters, such as TransPA (transverse prostate maximum sectional area), TransCGA (transverse central gland sectional area), TransPZA ...(transverse peripheral zone sectional area), and TransPAI (TransPZA/TransCGA ratio) in predicting prostate cancer (PCa) in prostate imaging reporting and data system (PI-RADS) 3 lesions.
Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), the area under the receiver operating characteristic curve (AUC), and the best cut-off, were calculated. Univariate and multivariate analyses were carried out to evaluate the capability to predict PCa.
Out of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm
, 9.1cm
, 5.5cm
and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P<0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P<0.001) were independent predictors of PCa. The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa. The best cut-off of TransPA for csPCa was 18 (Sensitivity 88.2%, Specificity 37.2%, PPV 35.7%, NPV 88.9%). The discrimination (AUC) of the multivariate model was 0.627 (95% CI: 0.519-0.734, P<0.031).
In PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy.
The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different ...definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG1,2 and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.
Multiparametric magnetic resonance imaging (mpMRI) is currently the most effective diagnostic tool for detecting prostate cancer (PCa) and evaluating adenocarcinoma-mimicking lesions of the prostate ...gland, among which granulomatous prostatitis (GP) represents the most interesting diagnostic challenge. GP consists of a heterogeneous group of chronic inflammatory lesions that can be differentiated into four types: idiopathic, infective, iatrogenic, and associated with systemic granulomatous disease. The incidence of GP is growing due to the increase in endourological surgical interventions and the adoption of intravesical instillation of Bacillus Calmette-Guerin in patients with non-muscle invasive bladder cancer; therefore, the difficulty lies in identifying specific features of GP on mpMRI to avoid the use of transrectal prostate biopsy as much as possible.
The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging ...and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (
< 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.
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