Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters.

Introduction: Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment. Methods: Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age. Results: The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2. Discussion: This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.

Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy.

Breast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., mm2) in one single image while guaranteeing high lateral resolution (i.e., 0.5 µm). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single-cell level. To handle this informative image content, we introduce elements of fractal geometry as multi-scale analysis method. We show the effectiveness of fractal features in describing and classifying fibroadenoma and breast cancer tissue slides from ten patients with very high accuracy. We reach 94.0 ± 4.2% test accuracy in classifying single images. Above all, we show that combining the decisions of the single images, each patient's slide can be classified with no error. Besides, fractal geometry returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. The proposed automatic method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.

BACKGROUND: Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM: To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS: A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS: One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION: Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

A customized anthropomorphic 3D-printed phantom to reproducibility assessment in computed tomography: an oncological case study.

Introduction: Studies on computed tomography (CT) reproducibility at different acquisition parameters have to take into account radiation dose administered and related ethical issues. 3D-printed phantoms provide the possibility to investigate these features deeply and to foster CT research, also taking advantage by outperforming new generation scanners. The aim of this study is to propose a new anthropomorphic 3D-printed phantom for chest lesions, tailored on a real patient CT scan, to investigate the variability of volume and Hounsfield Unit (HU) measurements at different CT acquisition parameters. Methods: The chest CT of a 75-year-old patient with a paramediastinal lung lesion was segmented based on an eight-compartment approach related to HU ranges (air lung, lung interstitium, fat, muscle, vascular, skin, bone, and lesion). From each mask produced, the 3D.stl model was exported and linked to a different printing infill value, based on a preliminary test and HU ratios derived from the patient scan. Fused deposition modeling (FDM) technology printing was chosen with filament materials in polylactic acid (PLA). Phantom was acquired at 50 mAs and three different tube voltages of 80, 100, and 120 kVp on two different scanners, namely, Siemens Somatom Force (Siemens Healthineers, Erlangen, Germany; same setting of real patient for 80 kVp acquisition) and GE 750 HD CT (GE Healthcare, Chicago, IL). The same segmentation workflow was then applied on each phantom acquisition after coregistration pipeline, and Dice Similarity Coefficient (DSC) and HU averages were extracted and compared for each compartment. Results: DSC comparison among real patient versus phantom scans at different kVp, and on both CT scanners, demonstrated a good overlap of different compartments and lesion vascularization with a higher similarity for lung and lesion masks for each setting (about 0.9 and 0.8, respectively). Although mean HU was not comparable with real data, due to the PLA material, the proportion of intensity values for each compartment remains respected. Discussion: The proposed approach demonstrated the reliability of 3D-printed technology for personalized approaches in CT research, opening to the application of the same workflow to other oncological fields.

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images.

Background and objectives: The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. Methods: A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results: 21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. Conclusions: The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.

An Isoenergetic Multifactorial Diet Reduces Pancreatic Fat and Increases Postprandial Insulin Response in Patients With Type 2 Diabetes: A Randomized Controlled Trial.

OBJECTIVE: To compare the effect of an isocaloric multifactorial diet with a diet rich in monounsaturated fatty acids (MUFA) and similar macronutrient composition on pancreatic fat (PF) and postprandial insulin response in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: According to a randomized controlled parallel-group design, 39 individuals with T2D, 35-75 years old, in satisfactory blood glucose control, were assigned to an 8 week isocaloric intervention with a multifactorial diet rich in MUFA, polyunsaturated fatty acids, fiber, polyphenols, and vitamins (n = 18) or a MUFA-rich diet (n = 21). Before/after the intervention, PF content was measured by the proton-density fat fraction using a three-dimensional mDIXON MRI sequence, and plasma insulin and glucose concentrations were measured over a 4 h test meal with a similar composition as the assigned diet. RESULTS: After 8 weeks, PF significantly decreased after the multifactorial diet (from 15.7 ± 6.5% to 14.1 ± 6.3%; P = 0.024), while it did not change after the MUFA diet (from 17.1 ± 10.1% to 18.6 ± 10.6%; P = 0.139) with a significant difference between diets (P = 0.014). Postprandial glucose response was similar in the two groups. Early postprandial insulin response (incremental postprandial areas under the curve [iAUC0-120]) significantly increased with the multifactorial diet (from 36,340 ± 34,954 to 44,138 ± 31,878 pmol/L/min; P = 0.037), while it did not change significantly in the MUFA diet (from 31,754 ± 18,446 to 26,976 ± 12,265 pmol/L/min; P = 0.178), with a significant difference between diets (P = 0.023). Changes in PF inversely correlated with changes in early postprandial insulin response (r = -0.383; P = 0.023). CONCLUSIONS: In patients with T2D, an isocaloric multifactorial diet, including several beneficial dietary components, markedly reduced PF. This reduction was associated with an improved postprandial insulin response.

A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis.

Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.

Reduction of De Novo Lipogenesis Mediates Beneficial Effects of Isoenergetic Diets on Fatty Liver: Mechanistic Insights from the MEDEA Randomized Clinical Trial.

BACKGROUND: Non-alcoholic liver steatosis (NAS) results from an imbalance between hepatic lipid storage, disposal, and partitioning. A multifactorial diet high in fiber, monounsaturated fatty acids (MUFAs), n-6 and n-3 polyunsaturated fatty acids (PUFAs), polyphenols, and vitamins D, E, and C reduces NAS in people with type 2 diabetes (T2D) by 40% compared to a MUFA-rich diet. We evaluated whether dietary effects on NAS are mediated by changes in hepatic de novo lipogenesis (DNL), stearoyl-CoA desaturase (SCD1) activity, and/or ß-oxidation. METHODS: According to a randomized parallel group study design, 37 individuals with T2D completed an 8-week isocaloric intervention with a MUFA diet (n = 20) or multifactorial diet (n = 17). Before and after the intervention, liver fat content was evaluated by proton magnetic resonance spectroscopy, serum triglyceride fatty acid concentrations measured by gas chromatography, plasma ß-hydroxybutyrate by enzymatic method, and DNL and SCD-1 activity assessed by calculating the palmitic acid/linoleic acid (C16:0/C18:2 n6) and palmitoleic acid/palmitic acid (C16:1/C16:0) ratios, respectively. RESULTS: Compared to baseline, mean ± SD DNL significantly decreased after the multifactorial diet (2.2 ± 0.8 vs. 1.5 ± 0.5, p = 0.0001) but did not change after the MUFA diet (1.9 ± 1.1 vs. 1.9 ± 0.9, p = 0.949), with a significant difference between the two interventions (p = 0.004). The mean SCD-1 activity also decreased after the multifactorial diet (0.13 ± 0.05 vs. 0.10 ± 0.03; p = 0.001), but with no significant difference between interventions (p = 0.205). Fasting plasma ß-hydroxybutyrate concentrations did not change significantly after the MUFA or multifactorial diet. Changes in the DNL index significantly and positively correlated with changes in liver fat (r = 0.426; p = 0.009). CONCLUSIONS: A diet rich in multiple beneficial dietary components (fiber, polyphenols, MUFAs, PUFAs, and other antioxidants) compared to a diet rich only in MUFAs further reduces liver fat accumulation through the inhibition of DNL. Registered under ClinicalTrials.gov no. NCT03380416.

MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contrast images. Radiomic features from T2 images and arterial, portal and tardive post-contrast images from DCE-MRI were extracted by using Pyradiomics. Feature selection was performed using correlation filter, Wilcoxon-rank sum test and mutual information. Predictive models were constructed for HCC differentiation with respect to HT and HCC histopathologic grading used at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. Promising results were obtained from radiomic prediction models, with best AUCs ranging from 71% to 96%. Radiomics MRI based on T2 and DCE-MRI revealed promising results concerning both HCC detection and grading. It may be a suitable tool for personalized treatment of HCC patients and could also be used to develop new prognostic biomarkers useful for HCC assessment without the need for invasive procedures.

A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer.

Pooling radiomic features coming from different centers in a statistical framework is challenging due to the variability in scanner models, acquisition protocols, and reconstruction settings. To remove technical variability, commonly called batch effects, different statistical harmonization strategies have been widely used in genomics but less considered in radiomics. The aim of this work was to develop a framework of analysis to facilitate the harmonization of multicenter radiomic features extracted from prostate T2-weighted magnetic resonance imaging (MRI) and to improve the power of radiomics for prostate cancer (PCa) management in order to develop robust non-invasive biomarkers translating into clinical practice. To remove technical variability and correct for batch effects, we investigated four different statistical methods (ComBat, SVA, Arsynseq, and mixed effect). The proposed approaches were evaluated using a dataset of 210 prostate cancer (PCa) patients from two centers. The impacts of the different statistical approaches were evaluated by principal component analysis and classification methods (LogitBoost, random forest, K-nearest neighbors, and decision tree). The ComBat method outperformed all other methods by achieving 70% accuracy and 78% AUC with the random forest method to automatically classify patients affected by PCa. The proposed statistical framework enabled us to define and develop a standardized pipeline of analysis to harmonize multicenter T2W radiomic features, yielding great promise to support PCa clinical practice.

A Systematic Review on the Role of the Perfusion Computed Tomography in Abdominal Cancer.

BACKGROUND AND PURPOSE: Perfusion Computed Tomography (CTp) is an imaging technique which allows quantitative and qualitative evaluation of tissue perfusion through dynamic CT acquisitions. Since CTp is still considered a research tool in the field of abdominal imaging, the aim of this work is to provide a systematic summary of the current literature on CTp in the abdominal region to clarify the role of this technique for abdominal cancer applications. MATERIALS AND METHODS: A systematic literature search of PubMed, Web of Science, and Scopus was performed to identify original articles involving the use of CTp for clinical applications in abdominal cancer since 2011. Studies were included if they reported original data on CTp and investigated the clinical applications of CTp in abdominal cancer. RESULTS: Fifty-seven studies were finally included in the study. Most of the included articles (33/57) dealt with CTp at the level of the liver, while a low number of studies investigated CTp for oncologic diseases involving UGI tract (8/57), pancreas (8/57), kidneys (3/57), and colon-rectum (5/57). CONCLUSIONS: Our study revealed that CTp could be a valuable functional imaging tool in the field of abdominal oncology, particularly as a biomarker for monitoring the response to anti-tumoral treatment.

Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study.

BACKGROUND: Esophageal cancer (ESCA) is the sixth most common malignancy in the world, and its incidence is rapidly increasing. Recently, several microRNAs (miRNAs) and messenger RNA (mRNA) targets were evaluated as potential biomarkers and regulators of epigenetic mechanisms involved in early diagnosis. In addition, computed tomography (CT) radiomic studies on ESCA improved the early stage identification and the prediction of response to treatment. Radiogenomics provides clinically useful prognostic predictions by linking molecular characteristics such as gene mutations and gene expression patterns of malignant tumors with medical images and could provide more opportunities in the management of patients with ESCA. AIM: To explore the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients. METHODS: Of 15 patients with diagnosed ESCA were included in this study and their CT imaging and transcriptomic data were extracted from The Cancer Imaging Archive and gene expression data from The Cancer Genome Atlas, respectively. Cancer stage, history of significant alcohol consumption and body mass index (BMI) were considered as clinical outcomes. Radiomic analysis was performed on CT images acquired after injection of contrast medium. In total, 1302 radiomics features were extracted from three-dimensional regions of interest by using PyRadiomics. Feature selection was performed using a correlation filter based on Spearman's correlation (ρ) and Wilcoxon-rank sum test respect to clinical outcomes. Radiogenomic analysis involved ρ analysis between radiomic features associated with clinical outcomes and transcriptomic signatures consisting of eight N6-methyladenosine RNA methylation regulators and five up-regulated miRNA. The significance level was set at P < 0.05. RESULTS: Of 25, five and 29 radiomic features survived after feature selection, considering stage, alcohol history and BMI as clinical outcomes, respectively. Radiogenomic analysis with stage as clinical outcome revealed that six of the eight mRNA regulators and two of the five up-regulated miRNA were significantly correlated with ten and three of the 25 selected radiomic features, respectively (-0.61 < ρ < -0.60 and 0.53 < ρ < 0.69, P < 0.05). Assuming alcohol history as clinical outcome, no correlation was found between the five selected radiomic features and mRNA regulators, while a significant correlation was found between one radiomic feature and three up-regulated miRNAs (ρ = -0.56, ρ = -0.64 and ρ = 0.61, P < 0.05). Radiogenomic analysis with BMI as clinical outcome revealed that four mRNA regulators and one up-regulated miRNA were significantly correlated with 10 and two radiomic features, respectively (-0.67 < ρ < -0.54 and 0.53 < ρ < 0.71, P < 0.05). CONCLUSION: Our study revealed interesting relationships between the expression of eight N6-methyladenosine RNA regulators, as well as five up-regulated miRNAs, and CT radiomic features associated with clinical outcomes of ESCA patients.

The impact of MR-based attenuation correction in spinal cord FDG-PET/MR imaging for neurological studies.

PURPOSE: Positron emission tomography (PET) attenuation correction (AC) in positron emission tomography-magnetic resonance (PET/MR) scanners constitutes a critical and barely explored issue in spinal cord investigation, mainly due to the limitations in accounting for highly attenuating bone structures which surround the spinal canal. Our study aims at evaluating the clinical suitability of MR-driven AC (MRAC) for 18-fluorodeoxy-glucose positron emission tomography (18 F-FDG-PET) in spinal cord. METHODS: Thirty-six patients, undergoing positron emission tomography-computed tomography (PET/CT) and PET/MR in the same session for oncological examination, were retrospectively analyzed. For each patient, raw PET data from PET/MR scanner were reconstructed with 4- and 5-class MRAC maps, generated by hybrid PET/MR system (PET_MRAC4 and PET_MRAC5, respectively, where PET_MRAC is PET images reconstructed using MR-based attenuation correction map), and an AC map derived from CT data after a custom co-registration pipeline (PET_rCTAC, where PET_rCTAC is PET images reconstructed using CT-based attenuation correction map), which served as reference. Mean PET standardized uptake values ( SU V m ) were extracted from the three reconstructed PET images by regions of interest (ROIs) identified on T2-weighted MRI, in the spinal cord, lumbar cerebrospinal fluid (CSF), and vertebral marrow at five levels (C2, C5, T6, T12, and L3). SU V m values from PET_MRAC4 and PET_MRAC5 were compared with each other and with the reference by means of paired t-test, and correlated using Pearson's correlation (r) to assess their consistency. Cohen's d was calculated to assess the magnitude of differences between PET images. RESULTS: SU V m values from PET_MRAC4 were lower than those from PET_MRAC5 in almost all analyzed ROIs, with a mean difference ranging from 0.03 to 0.26 (statistically significant in the vertebral marrow at C2 and C5, spinal cord at T6 and T2, and CSF at L3). This was also confirmed by the effect size, with highest values at low spinal levels (d = 0.45 at T12 in spinal cord, d = 0.95 at L3 in CSF). S U V m values from PET_MRAC4 and PET_MRAC5 showed a very good correlation (0.81 < r < 0.97, p < 0.05) in all spinal ROIs. Underestimation of SU V m between PET_MRAC4 and PET_rCTAC was observed at each level, with a mean difference ranging from 0.02 to 0.32 (statistically significant in the vertebral marrow at C2 and T6, and CSF at L3). Although PET_MRAC5 underestimates PET_rCTAC (mean difference ranging from 0.02 to 0.3), an overall decrease in effect size could be observed for PET_MRAC5, mainly at lower spinal levels (T12, L3). SU V m from both PET_MRAC4 and PET_MRAC5 methods showed r value from good to very good with respect to PET_rCTAC (0.67 < r < 0.9 and 0.73 < r < 0.94, p < 0.05, respectively). CONCLUSIONS: Our results showed that neglecting bones in AC can underestimate the FDG uptake measurement of the spinal cord. The inclusion of bones in MRAC is far from negligible and improves the AC in spinal cord, mainly at low spinal levels. Therefore, care must be taken in the spinal canal region, and the use of AC map reconstruction methods accounting for bone structures could be beneficial.

The Role of Non-Gaussian Models of Diffusion Weighted MRI in Hepatocellular Carcinoma: A Systematic Review.

The importance of Diffusion Weighted Imaging (DWI) in hepatocellular carcinoma (HCC) has been widely handled in the literature. Due to the mono-exponential model limitations, several studies recently investigated the role of non-Gaussian DWI models in HCC. However, their results are variable and inconsistent. Therefore, the aim of this systematic review is to summarize current knowledge on non-Gaussian DWI techniques in HCC. A systematic search of the literature, including PubMed, Google Scholar, MEDLINE, and ScienceDirect databases, was performed to identify original articles since 2010 that evaluated the role of non-Gaussian DWI models for HCC diagnosis, grading, response to treatment, and prognosis. Studies were grouped and summarized according to the non-Gaussian DWI models investigated. We focused on the most used non-Gaussian DWI models (Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Stretched Exponential-SE). The quality of included studies was evaluated by using QUADAS-2 and QUIPS tools. Forty-three articles were included, with IVIM and DKI being the most investigated models. Although the role of non-Gaussian DWI models in clinical settings has not fully been established, our findings showed that their parameters may potentially play a role in HCC. Further studies are required to identify a standardized DWI acquisition protocol for HCC diagnosis, grading, response to treatment, and prognosis.

Investigating Dual-Energy CT Post-Contrast Phases for Liver Iron Quantification: A Preliminary Study.

BACKGROUND AND PURPOSE: Quantification of hepatic virtual iron content (VIC) by using Multidetector Dual Energy Computed Tomography (DECT) has been recently investigated since this technique could offer a good compromise between accuracy and non-invasiveness for liver iron content quantification. The aim of our study is to investigate differences in VIC at different DECT time points (namely baseline and arterial, venous and tardive phases), identifying the most reliable and also exploring the underlying temporal trend of these values. MATERIALS AND METHODS: Eleven patients who underwent DECT examination and were characterized by low liver fat content were included in this retrospective study. By using the Syngo.via Frontier-DE IronVNC tool, regions of interest (ROI) were placed on the VIC images at 3 hepatic levels, both in left and right liver lobes, at each DECT time point. Friedman's test followed by Bonferroni-adjusted Wilcoxon signed-rank test for post-hoc analysis was performed to assess differences between DECT timepoints. Page's L test was performed to test the temporal trend of VIC across the 4 examined timepoints. RESULTS: For both liver lobes, Friedman's test followed by Bonferroni-adjusted Wilcoxon signed-rank test revealed that VIC values differed significantly when extracted from ROIs placed at the 4 different timepoints. The Page's L test for multiple comparison revealed a significant growing trend for VIC, from baseline acquisition to the fourth and last time point post-contrast agent injection. CONCLUSIONS: The extraction of hepatic VIC in healthy subjects was found to be significantly influenced by the DECT time point chosen for the extrapolation of the VIC values.

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions.

Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review.

Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

Automatic Prediction and Assessment of Treatment Response in Patients with Hodgkin's Lymphoma Using a Whole-Body DW-MRI Based Approach.

The lack of validation and standardization represents the main drawback for a clear role of whole-body diffusion weighted imaging (WB-DWI) for prediction and assessment of treatment response in Hodgkin's lymphoma (HL). We explored the reliability of an automatic approach based on the WB-DWI technique for prediction and assessment of response to treatment in patients with HL. The study included 20 HL patients, who had whole-body positron emission tomography (PET)/ magnetic resonance Imaging (MRI) performed before, during and after chemotherapy. Using the syngo.via MR Total Tumor Load tool, we automatically extracted values of diffusion volume (DV) and its associated histogram features by WB-DWI images, and evaluated their utility in predicting and assessing interim and end-of-treatment (EOT) response. The Mann-Whitney test followed by receiver operator characteristic (ROC) analysis was performed between features and their inter-time point percentage differences for patients having a complete or partial treatment response, revealing that several WB-DWI associated features allowed for prediction of interim response and both prediction and assessment of EOT response. Our proposed method offers huge advantages in terms of saving time and work, enabling clinicians to draw conclusions relating to HL treatment response in a fully automatic way, and encloses, also, all DWI advantages compared to PET/ computed tomography (CT).