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.

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.

Renal protection: a leading mechanism for cardiovascular benefit in patients treated with SGLT2 inhibitors.

Initially developed as glucose-lowering drugs, sodium-glucose co-transporter type 2 inhibitors (SGLT2i) have demonstrated to be effective agents for the risk reduction of cardiovascular (CV) events in patients with type 2 diabetes mellitus (T2DM). Subsequently, data has emerged showing a significant CV benefit in patients treated with SGLT2i regardless of diabetes status. Renal protection has been initially evaluated in CV randomized trials only as secondary endpoints; nonetheless, the positive results gained have rapidly led to the evaluation of nephroprotection as primary outcome in the CREDENCE trial. Different renal and vascular mechanisms can account for the CV and renal benefits enlightened in recent literature. As clinical guidelines rapidly evolve and the role of SGLT2i appears to become pivotal for CV, T2DM, and kidney disease management, in this review, we analyze the renal effects of SGLT2, the benefits derived from its inhibition, and how this may result in the multiple CV and renal benefits evidenced in recent clinical trials.

Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy.

The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.

Risk stratification tools for heart failure in the diabetes clinic.

The advent of Sodium Glucose Transporter 2-inhibitors (SGLT2-i) in recent years gave endocrinologists the opportunity to actively treat and prevent heart failure (HF) in patients with type 2 diabetes (T2DM). While the relationship between T2DM and HF has been extensively reviewed, previous works focused mostly on epidemiology, pathophysiology and treatment of HF in T2DM. The aim of our work was to aid health care professionals in identifying individuals at high risk for this dreadful complication. Recent guidelines recommend to use drugs with proven cardiovascular benefits (Glucagon-like peptide-1 receptor agonists (GLP1-RA) and SGLT2-i) in patients with previous cardiovascular disease (CVD) and to prefer SGLT2-i in patients with known HF. In everyday clinical practice, the choice between these two drug classes in patients without known HF or atherosclerotic CVD is mostly arbitrary and based on the side effect profile. Recently, risk stratification tools to estimate HF incidence have been developed in order to guide treatment with a view to bring precision medicine into diabetes care. With this purpose, we provide a review of the tools able to predict HF incidence for patients in primary CVD prevention as well as risk of future hospitalizations for patients with known HF.

A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.

OBJECTIVES: To compare unassisted and CAD-assisted detection and time efficiency of radiologists in reporting lung nodules on CT scans taken from patients with extra-thoracic malignancies using a Cloud-based system. MATERIALS AND METHODS: Three radiologists searched for pulmonary nodules in patients with extra-thoracic malignancy who underwent CT (slice thickness/spacing 2 mm/1.7 mm) between September 2015 and March 2016. All nodules detected by unassisted reading were measured and coordinates were uploaded on a cloud-based system. CAD marks were then reviewed by the same readers using the cloud-based interface. To establish the reference standard all nodules ≥ 3 mm detected by at least one radiologist were validated by two additional experienced radiologists in consensus. Reader detection rate and reporting time with and without CAD were compared. The study was approved by the local ethics committee. All patients signed written informed consent. RESULTS: The series included 225 patients (age range 21-90 years, mean 62 years), including 75 patients having at least one nodule, for a total of 215 nodules. Stand-alone CAD sensitivity for lesions ≥ 3 mm was 85% (183/215, 95% CI: 82-91); mean false-positive rate per scan was 3.8. Sensitivity across readers in detecting lesions ≥ 3 mm was statistically higher using CAD: 65% (95% CI: 61-69) versus 88% (95% CI: 86-91, p<0.01). Reading time increased by 11% using CAD (296 s vs. 329 s; p<0.05). CONCLUSION: In patients with extra-thoracic malignancies, CAD-assisted reading improves detection of ≥ 3-mm lung nodules on CT, slightly increasing reading time. KEY POINTS: • CAD-assisted reading improves the detection of lung nodules compared with unassisted reading on CT scans of patients with primary extra-thoracic tumour, slightly increasing reading time. • Cloud-based CAD systems may represent a cost-effective solution since CAD results can be reviewed while a separated cloud back-end is taking care of computations. • Early identification of lung nodules by CAD-assisted interpretation of CT scans in patients with extra-thoracic primary tumours is of paramount importance as it could anticipate surgery and extend patient life expectancy.

Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study.

OBJECTIVES: To compare the performance of experienced readers in detecting prostate cancer (PCa) using likelihood maps generated by a CAD system with that of unassisted interpretation of multiparametric magnetic resonance imaging (mp-MRI). METHODS: Three experienced radiologists reviewed mp-MRI prostate cases twice. First, readers observed CAD marks on a likelihood map and classified as positive those suspicious for cancer. After 6 weeks, radiologists interpreted mp-MRI examinations unassisted, using their favourite protocol. Sensitivity, specificity, reading time and interobserver variability were compared for the two reading paradigms. RESULTS: The dataset comprised 89 subjects of whom 35 with at least one significant PCa. Sensitivity was 80.9% (95% CI 72.1-88.0%) and 87.6% (95% CI 79.8-93.2; p = 0.105) for unassisted and CAD paradigm respectively. Sensitivity was higher with CAD for lesions with GS > 6 (91.3% vs 81.2%; p = 0.046) or diameter ≥10 mm (95.0% vs 80.0%; p = 0.006). Specificity was not affected by CAD. The average reading time with CAD was significantly lower (220 s vs 60 s; p < 0.001). CONCLUSIONS: Experienced readers using likelihood maps generated by a CAD scheme can detect more patients with ≥10 mm PCa lesions than unassisted MRI interpretation; overall reporting time is shorter. To gain more insight into CAD-human interaction, different reading paradigms should be investigated. KEY POINTS: • With CAD, sensitivity increases in patients with prostate tumours ≥10 mm and/or GS > 6. • CAD significantly reduces reporting time of multiparametric MRI. • When using CAD, a marginal increase of inter-reader agreement was observed.

MRI to predict nipple-areola complex (NAC) involvement: An automatic method to compute the 3D distance between the NAC and tumor.

OBJECTIVES: To assess the role in predicting nipple-areola complex (NAC) involvement of a newly developed automatic method which computes the 3D tumor-NAC distance. PATIENTS AND METHODS: Ninety-nine patients scheduled to nipple sparing mastectomy (NSM) underwent magnetic resonance (MR) examination at 1.5 T, including sagittal T2w and dynamic contrast enhanced (DCE)-MR imaging. An automatic method was developed to segment the NAC and the tumor and to compute the 3D distance between them. The automatic measurement was compared with manual axial and sagittal 2D measurements. NAC involvement was defined by the presence of invasive ductal or lobular carcinoma and/or ductal carcinoma in situ or ductal intraepithelial neoplasia (DIN1c - DIN3). RESULTS: Tumor-NAC distance was computed on 95/99 patients (25 NAC+), as three tumors were not correctly segmented (sensitivity = 97%), and 1 NAC was not detected (sensitivity = 99%). The automatic 3D distance reached the highest area under the receiver operating characteristic (ROC) curve (0.830) with respect to the manual axial (0.676), sagittal (0.664), and minimum distances (0.664). At the best cut-off point of 21 mm, the 3D distance obtained sensitivity = 72%, specificity = 80%, positive predictive value = 56%, and negative predictive value = 89%. CONCLUSIONS: This method could provide a reproducible biomarker to preoperatively select breast cancer patients candidates to NSM, thus helping surgical planning and intraoperative management of patients.

Big data in oncologic imaging.

Cancer is a complex disease and unfortunately understanding how the components of the cancer system work does not help understand the behavior of the system as a whole. In the words of the Greek philosopher Aristotle "the whole is greater than the sum of parts." To date, thanks to improved information technology infrastructures, it is possible to store data from each single cancer patient, including clinical data, medical images, laboratory tests, and pathological and genomic information. Indeed, medical archive storage constitutes approximately one-third of total global storage demand and a large part of the data are in the form of medical images. The opportunity is now to draw insight on the whole to the benefit of each individual patient. In the oncologic patient, big data analysis is at the beginning but several useful applications can be envisaged including development of imaging biomarkers to predict disease outcome, assessing the risk of X-ray dose exposure or of renal damage following the administration of contrast agents, and tracking and optimizing patient workflow. The aim of this review is to present current evidence of how big data derived from medical images may impact on the diagnostic pathway of the oncologic patient.

Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard.

OBJECTIVE: To evaluate the sensitivity of multiparametric magnetic resonance imaging (mp-MRI) for detecting prostate cancer foci, including the largest (index) lesions. PATIENTS AND METHODS: In all, 115 patients with biopsy confirmed prostate cancer underwent mp-MRI before radical prostatectomy. A single expert radiologist recorded all prostate cancer foci including the index lesion 'blinded' to the pathologist's biopsy report. Stained whole-mount histological sections were used as the reference standard. All lesions were contoured by an experienced uropathologist who assessed their volume and pathological Gleason score. All lesions with a volume of >0.5 mL and/or pathological Gleason score of >6 were defined as clinically significant prostate cancer. Multivariate analysis was used to ascertain the characteristics of lesions identified by MRI. RESULTS: In all, 104 of 115 index lesions were correctly diagnosed by mp-MRI (sensitivity 90.4%; 95% confidence interval [CI] 83.5-95.1%), including 98/105 clinically significant index lesions (93.3%; 95% CI 86.8-97.3%), among which three of three lesions had a volume of <0.5 mL and Gleason score of >6. Overall, mp-MRI detected 131/206 lesions including 13 of 68 'insignificant' prostate cancers. The multivariate logistic regression modelling showed that pathological Gleason score (odds ratio [OR] 11.7, 95% CI 2.3-59.8; P = 0.003) and lesion volume (OR 4.24, 95% CI 1.3-14.7; P = 0.022) were independently associated with the detection of index lesions at MRI. CONCLUSIONS: This study shows that mp-MRI has a high sensitivity for detecting clinically significant prostate cancer index lesions, while having disappointing results for the detection of small-volume, low Gleason score prostate cancer foci. Thus, mp-MRI could be used to stratify patients according to risk, allowing better treatment selection.

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.

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.

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.

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.

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.

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.

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.

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.

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.