Cardiovascular disease (CVD) risk is higher in patients with nonalcoholic fatty liver disease (NAFLD).
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.
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.
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%
47.1%, p=0.013), but not fibrosis (12.9%
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.
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.
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.
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.
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.
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.
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%,
= 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6,
= 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.
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.
Background
Radiomics is expected to improve the management of metastatic colorectal cancer (CRC). We aimed at evaluating the impact of liver lesion contouring as a source of variability on radiomic ...features (RFs).
Methods
After Ethics Committee approval, 70 liver metastases in 17 CRC patients were segmented on contrast-enhanced computed tomography scans by two residents and checked by experienced radiologists. RFs from grey level co-occurrence and run length matrices were extracted from three-dimensional (3D) regions of interest (ROIs) and the largest two-dimensional (2D) ROIs. Inter-reader variability was evaluated with Dice coefficient and Hausdorff distance, whilst its impact on RFs was assessed using mean relative change (MRC) and intraclass correlation coefficient (ICC). For the main lesion of each patient, one reader also segmented a circular ROI on the same image used for the 2D ROI.
Results
The best inter-reader contouring agreement was observed for 2D ROIs according to both Dice coefficient (median 0.85, interquartile range 0.78–0.89) and Hausdorff distance (0.21 mm, 0.14–0.31 mm). Comparing RF values, MRC ranged 0–752% for 2D and 0–1567% for 3D. For 24/32 RFs (75%), MRC was lower for 2D than for 3D. An ICC > 0.90 was observed for more RFs for 2D (53%) than for 3D (34%). Only 2/32 RFs (6%) showed a variability between 2D and circular ROIs higher than inter-reader variability.
Conclusions
A 2D contouring approach may help mitigate overall inter-reader variability, albeit stable RFs can be extracted from both 3D and 2D segmentations of CRC liver metastases.
Aims
The angiotensin receptor neprilysin inhibitor (ARNI) sacubitril/valsartan reduces mortality and hospitalizations in patients with heart failure and reduced ejection fraction (HFrEF). Favourable ...effects on haemodynamic and functional parameters have been observed in patients with HFrEF undergoing ARNI therapy, using standard transthoracic echocardiography. Global longitudinal strain (GLS) assessment uses a semi‐automatic procedure to provide a reliable and repeatable method that improves the detection of early changes of contractile function. We aimed to assess the effects of ARNI on GLS and myocardial mechanics in patients with HFrEF.
Methods and results
Thirty patients with New York Heart Association class II–III HFrEF were treated with ARNI and monitored using standard echocardiographic examination and GLS measurements at baseline, 3 months, and 6 months. ARNI therapy resulted in a significant reduction of ventricular volumes and a significant increase in left ventricular ejection fraction at 6 months but not 3 months by standard transthoracic echocardiography (left ventricular ejection fraction from 28 ± 8% at baseline to 34 ± 12% at 6 months, P < 0.001). Non‐significant differences in the size of the left atrium, right ventricular function, and pulmonary pressures were found at 6 months. By using GLS, there was a progressive improvement of all strain parameters by 3 months. The improvement showed a progressive trend over time and maintained significance at 6 months: GLS 4ch −7.2 ± 4.8% at baseline vs. −7.5 ± 3.9% at 3 months (P = 0.025) and − 9.2 ± 5.2% at 6 months (P = 0.0001); AVG GLS −6.9 ± 4.3 at baseline vs. −7.9 ± 4.2 at 3 months (P = 0.04) and − 8.8 ± 4.4 at 6 months (P = 0.035); GLS endo 8.2 ± 4.8 at baseline vs. −9.0 ± 4.8 at 3 months (P = 0.05) and − 10.1 ± 5.1 at 6 months (P = 0.001).
Conclusions
Sacubitril/valsartan induces an early benefit on left ventricular remodelling, which is captured by myocardial strain and not by standard echocardiography. Strain method represents a practical tool to assess early and minimal variations of left ventricular systolic function.
The recent definition of an intermediate clinical phenotype of heart failure (HF) based on an ejection fraction (EF) of between 40% and 49%, namely HF with mid-range EF (HFmrEF), has fuelled ...investigations into the clinical profile and prognosis of this patient group. HFmrEF shares common clinical features with other HF phenotypes, such as a high prevalence of ischaemic aetiology, as in HF with reduced EF (HFrEF), or hypertension and diabetes, as in HF with preserved EF (HFpEF), and benefits from the cornerstone drugs indicated for HFrEF. Among the HF phenotypes, HFmrEF is characterised by the highest rate of transition to either recovery or worsening of the severe systolic dysfunction profile that is the target of disease-modifying therapies, with opposite prognostic implications. This article focuses on the epidemiology, clinical characteristics and therapeutic approaches for HFmrEF, and discusses the major determinants of transition to HFpEF or HFrEF.
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