Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer.

OBJECTIVES: The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. METHODS: This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. RESULTS: MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. CONCLUSION: MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. CLINICAL RELEVANCE STATEMENT: The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. KEY POINTS: • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI.

In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.

MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes.

PURPOSE: The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDMs) and standard vocabularies have appeared in an attempt to offer harmonized access to the available information, with Observational Medical Outcomes Partnership (OMOP)-CDM being one of the most prominent ones, allowing the standardization and harmonization of health care information. However, despite its flexibility, still capturing imaging metadata along with the corresponding clinical data continues to pose a challenge. This challenge arises from the absence of a comprehensive standard representation for image-related information and subsequent image curation processes and their interlinkage with the respective clinical information. Successful resolution of this challenge holds the potential to enable imaging and clinical data to become harmonized, quality-checked, annotated, and ready to be used in conjunction, in the development of artificial intelligence models and other data-dependent use cases. METHODS: To address this challenge, we introduce medical imaging (MI)-CDM-an extension of the OMOP-CDM specifically designed for registering medical imaging data and curation-related processes. Our modeling choices were the result of iterative numerous discussions among clinical and AI experts to enable the integration of imaging and clinical data in the context of the ProCAncer-I project, for answering a set of clinical questions across the prostate cancer's continuum. RESULTS: Our MI-CDM extension has been successfully implemented for the use case of prostate cancer for integrating imaging and curation metadata along with clinical information by using the OMOP-CDM and its oncology extension. CONCLUSION: By using our proposed terminologies and standardized attributes, we demonstrate how diverse imaging modalities can be seamlessly integrated in the future.

Virtual biopsy in abdominal pathology: where do we stand?

In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient's phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits.

Hemodynamic effects of heart rate lowering in patients admitted for acute heart failure: the RedRate-HF Study (Reduction of heart Rate in Heart Failure).

BACKGROUND: In patients admitted for acute heart failure (HF) indication for drugs which reduce the heart rate (HR) is debated. The multicentre prospective study Reduction of heart Rate in Heart Failure (RedRate-HF) was designed to analyse the hemodynamic effects of an early reduction of HR in acute HF. METHODS: Hemodynamic parameters were recorded by using the bioimpedance technique, which was shown to be accurate, highly reproducible and sensitive to intra-observer changes. Lowering HR was obtained by ivabradine 5 mg bd, given 48-72 h after admission on the top of optimized treatment. Patients were followed at 24, 48, 72 h after drug assumption and at hospital discharge. RESULTS: Twenty patients of a mean age of 67 ±â€Š15 years, BNP at entry 1348 ±â€Š1198 pg/ml were enrolled. Despite a clinical stabilization, after 48-72 h from admission, HR was persistently >70 bpm. Ivabradine was well tolerated in all patients with a significant increase in RR interval from 747 ±â€Š69 ms at baseline to 948 ±â€Š121 ms at discharge, P < 0.0001. Change in HR was associated with a significant increase in stroke volume (baseline 73 ±â€Š22 vs. 84 ±â€Š19 ml at discharge, P = 0.03), and reduction in left cardiac work index (baseline 3.6 ±â€Š1.2 vs. 3.1 ±â€Š1.1 kg/m2 at discharge, P = 0.04). Other measures of heart work were also significantly affected while cardiac output remained unchanged. CONCLUSION: The strategy of an early lowering of HR in patients admitted for acute HF on top of usual care is feasible and safe. The HR reduction causes a positive increase in stroke volume and may contribute to saving energy without affecting cardiac output.

A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.

The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.

Effect of dose splitting of a low-volume bowel preparation macrogol-based solution on CT colonography tagging quality.

PURPOSE: To compare examination quality and acceptability of three different low-volume bowel preparation regimens differing in scheduling of the oral administration of a Macrogol-based solution, in patients undergoing computed tomographic colonography (CTC). The secondary aim was to compare CTC quality according to anatomical and patient variables (dolichocolon, colonic diverticulosis, functional and secondary constipation). METHODS: One-hundred-eighty patients were randomized into one of three regimens where PEG was administered, respectively: in a single dose the day prior to (A), or in a fractionated dose 2 (B) and 3 days (C) before the examination. Two experienced radiologists evaluated fecal tagging (FT) density and homogeneity both qualitatively and quantitatively by assessing mean segment density (MSD) and relative standard deviation (RSD). Tolerance to the regimens and patient variables were also recorded. RESULTS: Compared to B and C, regimen A showed a lower percentage of segments with inadequate FT and a significantly higher median FT density and/or homogeneity scores as well as significantly higher MSD values in some colonic segments. No statistically significant differences were found in tolerance of the preparations. A higher number of inadequate segments were observed in patients with dolichocolon (p < 0.01) and secondary constipation (p < 0.01). Interobserver agreement was high for the assessment of both FT density (k = 0.887) and homogeneity (k = 0.852). CONCLUSION: The best examination quality was obtained when PEG was administered the day before CTC in a single session. The presence of dolichocolon and secondary constipation represent a risk factor for the presence of inadequately tagged colonic segments.

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study.

BACKGROUND: Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15-30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. METHODS: Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. RESULTS: Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. CONCLUSION: Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

Twenty-year trends in heart failure among U.S. adults, 1999-2018: The growing impact of obesity and diabetes.

BACKGROUND: The aim of this study is to evaluate trends in heart failure (HF) prevalence, impact of accompanying risk factors and use of effective therapeutic regimens during the last two decades in the general adult US population. METHODS: We analyzed data obtained from the 1999-2018 cycles of the National Health and Nutrition Examination Survey (NHANES). Among a total of 34,403 participants 40 years or older who attended the mobile examination center visit, 1690 reported a diagnosis of HF. Trends in participant features across calendar periods were assessed by linear regression for continuous variables and logistic regression for binary variables. RESULTS: Prevalence of self-reported HF did not change significantly from 1999 to 2002 to 2015-2018 (~3.5%), while obesity and diabetes showed a progressive increase in prevalence, affecting ~65% and ~ 45% of patients with HF in the most recent calendar period, respectively. In parallel, use of glucose lowering drugs (especially metformin and insulin) as well as statins increased from 1999 to 2010, with significant improvement of the lipid control. A modest improvement in blood pressure control was achieved in association with a significant increase in the use of angiotensin receptor blockers and beta-blockers. CONCLUSIONS: In the last 20 years, the prevalence of HF in US adults remained stable, while both obesity and diabetes increased, with the two conditions affecting half of patients with HF. Improvements in the control of dyslipidemia and, to a lesser extent, blood pressure, was detected; nonetheless, a significant gap remains in guideline-directed use of HF and diabetes medications.

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases.

The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R-) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R- lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies.

Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer.

Colorectal cancer (CRC) has the second-highest tumor incidence and is a leading cause of death by cancer. Nearly 20% of patients with CRC will have metastases (mts) at the time of diagnosis, and more than 50% of patients with CRC develop metastases during their disease. Unfortunately, only 45% of patients after a chemotherapy will respond to treatment. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts, using CT scans. Understanding which mts will respond or not will help clinicians in providing a more efficient per-lesion treatment based on patient specific response and not only following a standard treatment. A group of 92 patients was enrolled from two Italian institutions. CT scans were collected, and the portal venous phase was manually segmented by an expert radiologist. Then, 75 radiomics features were extracted both from 7x7 ROIs that moved across the image and from the whole 3D mts. Feature selection was performed using a genetic algorithm. Results are presented as a comparison of the two different approaches of features extraction and different classification algorithms. Accuracy (ACC), sensitivity (SE), specificity (SP), negative and positive predictive values (NPV and PPV) were evaluated for all lesions (per-lesion analysis) and patients (per-patient analysis) in the construction and validation sets. Best results were obtained in the per-lesion analysis from the 3D approach using a Support Vector Machine as classifier. We reached on the training set an ACC of 81%, while on test set, we obtained SE of 76%, SP of 67%, PPV of 69% and NPV of 75%. On the validation set a SE of 61%, SP of 60%, PPV of 57% and NPV of 64% were reached. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to develop a radiomics signatures predicting single liver mts response to therapy. A personalized mts approach is important to avoid unnecessary toxicity offering more suitable treatments and a better quality of life to oncological patients.

Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil.

Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance- This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection.

Virtual biopsy in prostate cancer: can machine learning distinguish low and high aggressive tumors on MRI?

In the last decades, MRI was proven a useful tool for the diagnosis and characterization of Prostate Cancer (PCa). In the literature, many studies focused on characterizing PCa aggressiveness, but a few have distinguished between low-aggressive (Gleason Grade Group (GG) <=2) and high-aggressive (GG>=3) PCas based on biparametric MRI (bpMRI). In this study, 108 PCas were collected from two different centers and were divided into training, testing, and validation set. From Apparent Diffusion Coefficient (ADC) map and T2-Weighted Image (T2WI), we extracted texture features, both 3D and 2D, and we implemented three different methods of Feature Selection (FS): Minimum Redundance Maximum Relevance (MRMR), Affinity Propagation (AP), and Genetic Algorithm (GA). From the resulting subsets of predictors, we trained Support Vector Machine (SVM), Decision Tree, and Ensemble Learning classifiers on the training set, and we evaluated their prediction ability on the testing set. Then, for each FS method, we chose the best classifier, based on both training and testing performances, and we further assessed their generalization capability on the validation set. Between the three best models, a Decision Tree was trained using only two features extracted from the ADC map and selected by MRMR, achieving, on the validation set, an Area Under the ROC (AUC) equal to 81%, with sensitivity and specificity of 77% and 93%, respectively.Clinical Relevance- Our best model demonstrated to be able to distinguish low-aggressive from high-aggressive PCas with high accuracy. Potentially, this approach could help clinician to noninvasively distinguish between PCas that might need active treatment and those that could potentially benefit from active surveillance, avoiding biopsy-related complications.

A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation.

In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.

Nonalcoholic Fatty Liver Disease, Liver Fibrosis and Cardiovascular Disease in the Adult US Population.

Background: Cardiovascular disease (CVD) risk is higher in patients with nonalcoholic fatty liver disease (NAFLD). Aim: To evaluate whether this can be attributed to the link between NAFLD and known CVD risk factors or to an independent contribution of liver steatosis and fibrosis. Methods: This is an analysis of data from the 2017-2018 cycle of the National Health and Nutrition Examination Survey. We included participants older than 40 years with available data on vibration-controlled transient elastography (VCTE) and without viral hepatitis and significant alcohol consumption. Steatosis and fibrosis were diagnosed by the median value of controlled attenuation parameter (CAP) and liver stiffness measurement (LSM), respectively. History of CVD was self-reported and defined as a composite of coronary artery disease and stroke/transient ischemic attacks. Results: Among the 2734 included participants, prevalence of NAFLD was 48.6% (95% CI 45.1-51.4), 316 participants (9.7%, 95% CI 8.1-11.6) had evidence of significant liver fibrosis and 371 (11.5%, 95% CI 9.5-13.9) had a history of CVD. In univariate analysis, patients with CVD had a higher prevalence of steatosis (59.6% vs 47.1%, p=0.013), but not fibrosis (12.9% vs 9.3%, p=0.123). After adjustment for potential confounders in a multivariable logistic regression model, neither steatosis nor significant fibrosis were independently associated with CVD and heart failure. Conclusions: In this population-based study, we did not identify an independent association between steatosis and fibrosis and CVD. Large prospective cohort studies are needed to provide a more definitive evidence on this topic.

Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers.

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

Diagnostic Accuracy of Single-plane Biparametric and Multiparametric Magnetic Resonance Imaging in Prostate Cancer: A Randomized Noninferiority Trial in Biopsy-naïve Men.

BACKGROUND: Urological guidelines recommend multiparametric magnetic resonance imaging (mpMRI) in men with a suspicion of prostate cancer (PCa). The resulting increase in MRI demand might place health care systems under substantial stress. OBJECTIVE: To determine whether single-plane biparametric MRI (fast MRI) workup could represent an alternative to mpMRI in the detection of clinically significant (cs) PCa. DESIGN, SETTING, AND PARTICIPANTS: Between April 2018 and February 2020, 311 biopsy-naïve men aged ≤75 yr with PSA ≤15 ng/ml and negative digital rectal examination were randomly assigned to 1.5-T fast MRI (n = 213) or mpMRI (n = 98). INTERVENTION: All MRI examinations were classified according to Prostate Imaging-Reporting and Data System (PI-RADS) version 2. Men scored PI-RADS 1-2 underwent 12-core standard biopsy (SBx) and those with PI-RADS 4-5 on fast MRI or PI-RADS 3-5 on mpMRI underwent targeted biopsy in combination with SBx. Equivocal cases on fast MRI (PI-RADS 3) underwent mpMRI and then biopsy according to the findings. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was to compare the detection rate of csPCa in both study arms, setting a 10% difference for noninferiority. The secondary outcome was to assess the role of prostate-specific antigen density (PSAD) in ruling out men who could avoid biopsy among those with equivocal findings on fast MRI. RESULTS AND LIMITATIONS: The overall MRI detection rate for csPCa was 23.5% (50/213; 95% confidence interval [CI] 18.0-29.8%) with fast MRI and 32.7% (32/98; 95% CI 23.6-42.9%) with mpMRI (difference 9.2%; p = 0.09). The reproducibility of the study could have been affected by its single-center nature. CONCLUSIONS: Fast MRI followed by mpMRI in equivocal cases is not inferior to mpMRI in the detection of csPCa among biopsy-naïve men aged ≤75 yr with PSA ≤15 ng/ml and negative digital rectal examination. These findings could pave the way to broader use of MRI for PCa diagnosis. PATIENT SUMMARY: A faster MRI (magnetic resonance imaging) protocol with no contrast agent and fewer scan sequences for examination of the prostate is not inferior to the typical MRI approach in the detection of clinically significant prostate cancer. If our findings are confirmed in other studies, fast MRI could represent a time-saving and less invasive examination for men with suspicion of prostate cancer. This trial is registered at ClinicalTrials.gov as NCT03693703.

Compassionate use of ruxolitinib in patients with SARS-Cov-2 infection not on mechanical ventilation: Short-term effects on inflammation and ventilation.

Ruxolitinib is an anti-inflammatory drug that inhibits the Janus kinase-signal transducer (JAK-STAT) pathway on the surface of immune cells. The potential targeting of this pathway using JAK inhibitors is a promising approach in patients affected by coronavirus disease 2019 (COVID-19). Ruxolitinib was provided as a compassionate use in patients consecutively admitted to our institution for severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection. Inclusion criteria were oxygen saturation less than or equal to 92%, signs of interstitial pneumonia, and no need of mechanical ventilation. Patients received 5 mg b.i.d. of ruxolitinib for 15 days, data were collected at baseline and on days 4, 7, and 15 during treatment. Two main targets were identified, C-reactive protein (CRP) and PaO2 /FiO2 ratio. In the 31 patients who received ruxolitinib, symptoms improved (dyspnea scale) on day 7 in 25 of 31 patients (80.6%); CRP decreased progressively from baseline (79.1 ± 73.4 mg/dl) to day 15 (18.6 ± 33.2, p = 0.022). In parallel with CRP, PO2/FiO2 ratio increased progressively during the 3 steps from 183 ± 95 to 361 ± 144 mmHg (p < 0.001). In those patients with a reduction of polymerase chain reaction less than or equal to 80%, delta increase of the PO2/FiO2 ratio was significantly more pronounced (129 ± 118 vs. 45 ± 35 mmHg, p = 0.02). No adverse side effects were recorded during treatment. In patients hospitalized for COVID-19, compassionate-use of ruxolitinib determined a significant reduction of biomarkers of inflammation, which was associated with a more effective ventilation and reduced need for oxygen support. Data on ruxolitinib reinforces the hypothesis that targeting the hyperinflammation state, may be of prognostic benefit in patients with SARS-CoV-2 infection. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Some evidence suggest that patients affected by coronavirus disease 2019 (COVID-19) present an exuberant inflammatory response represented by a massive production of type I interferons and different pro-inflammatory cytokines. Nonetheless, as for the present, there are no proven therapeutic agents for COVID-19, in particular anti-inflammatory and antiviral, with a significant and reproducible positive clinical response. WHAT QUESTION DID THIS STUDY ADDRESS? Targeted therapeutic management of pro-inflammatory pathways appears to be a promising strategy against COVID-19, and ruxolitinib, due to its established broad and fast anti-inflammatory effect, appears to be a promising candidate worthy of focused investigations in this field. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Ruxolitinib rapidly reduces the systemic inflammation, which accompanies the disease, thereby improving respiratory function and the need of oxygen support. This effect may contribute to avoid progression of the disease and the use of invasive ventilation. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? Data on ruxolitinib contributes the reinforcement of the hypothesis that it is crucial to counteract the early hyperinflammation state, particularly of the lungs, induced by COVID-19 infection.