https://www.ahajournals.org/doi/pdf/10.1161/STR.0000000000000375

2021 Guideline for the Prevention of Stroke in Patients With Stroke and Transient Ischemic Attack: A Guideline From the American Heart Association/American Stroke Association

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

https://journals.sagepub.com/doi/pdf/10.1177/17562872221109020

Radiomics in prostate cancer: an up-to-date review

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Background and purpose Although inflammation has been proposed to be a candidate risk factor for cerebral small vessel disease (CSVD), previous findings remain largely inconclusive and vary according to disease status and study designs. The present study aimed to investigate possible associations between inflammatory biomarkers and MRI markers of CSVD. Methods A group of 15 serum inflammatory biomarkers representing a variety of those putatively involved in the inflammatory cascade was grouped and assessed in a cross-sectional study involving 960 stroke-free subjects. The biomarker panel was grouped as follows: systemic inflammation (high-sensitivity C reactive protein (hsCRP), interleukin 6 and tumour necrosis factor α), endothelial-related inflammation (E-selectin, P-selectin, intercellular adhesion molecule 1, vascular cell adhesion molecule 1 (VCAM-1), CD40 ligand, lipoprotein-associated phospholipase A2, chitinase-3-like-1 protein and total homocysteine (tHCY)) and media-related inflammation (matrix metalloproteinases 2, 3 and 9, and osteopontin). The association(s) between different inflammatory groups and white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMBs), enlarged perivascular space (PVS) and the number of deep medullary veins (DMVs) were investigated. Results High levels of serum endothelial-related inflammatory biomarkers were associated with both increased WMH volume (R2=0.435, p=0.015) and the presence of lacunes (R2=0.254, p=0.027). Backward stepwise elimination of individual inflammatory biomarkers for endothelial-related biomarkers revealed that VCAM-1 was significant for WMH (β=0.063, p=0.005) and tHCY was significant for lacunes (β=0.069, p<0.001). There was no association between any group of inflammatory biomarkers and CMBs or PVS. Systemic inflammatory biomarkers were associated with fewer DMVs (R2=0.032, p=0.006), and backward stepwise elimination of individual systemic-related inflammatory biomarkers revealed that hsCRP (β=−0.162, p=0.007) was significant. Conclusion WMH and lacunes were associated with endothelial-related inflammatory biomarkers, and fewer DMVs were associated with systemic inflammation, thus suggesting different underlying inflammatory processes and mechanisms.

/pdf/inflammatory-biomarkers-and-cerebral-small-vessel-disease-a-3cvd7hu9.pdf

Inflammatory biomarkers and cerebral small vessel disease: a community-based cohort study

Purpose To compare the performance of biparametric magnetic resonance imaging (bpMRI) to that of multiparametric MRI (mpMRI) in combination with prostate-specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa) in patients with PSA serum levels of 4∼10 ng/mL. Materials and methods A total of 123 men (mean age, 66.3 ± 8.9 [SD]; range: 42–83 years) with PSA serum levels of 4∼10 ng/mL with suspected csPCa were included. All patients underwent mpMRI at 3 Tesla and transrectal ultrasound-guided prostate biopsy in their clinical workup and were followed-up for >1 year when no csPCa was found at initial biopsy. The mpMRI images were reinterpreted according to the Prostate Imaging Reporting and Data System (PI-RADS, v2.1) twice in two different sessions using either mpMRI sequences or bpMRI sequences. The patients were divided into 2 groups according to whether csPCa was detected. The PI-RADS (mpMRI or bpMRI) categories and PSAD were used in combination to detect csPCa. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to compare the efficacy of the different models (mpMRI, bpMRI, PSAD, mpMRI + PSAD and bpMRI + PSAD). Results Thirty-seven patients (30.1%, 37/123) had csPCa. ROC analysis showed that bpMRI (AUC = 0.884 [95% confidence interval (CI): 0.814–0.935]) outperformed mpMRI (AUC = 0.867 [95% CI: 0.794–0.921]) (P = 0.035) and that bpMRI and mpMRI performed better than PSAD (0.682 [95% CI: 0.592–0.763]) in detecting csPCa; bpMRI + PSAD (AUC = 0.907 [95% CI: 0.841–0.952]) performed similarly to mpMRI + PSAD (AUC = 0.896 [95% CI: 0.828–0.944]) (P = 0.151) and bpMRI (P = 0.224). The sensitivity and specificity were 81.1% (95% CI: 64.8–92.0%) and 88.4% (95% CI: 79.7–94.3%), respectively for bpMRI, and 83.8% (95% CI: 68.0–93.8%) and 80.2% (95% CI: 70.2–88.0%), respectively for mpMRI (P > 0.999 for sensitivity and P = 0.016 for specificity). Among the 5 decision models, the decision curve analysis showed that all models (except for PSAD) achieved a high net benefit. Conclusion In patients with PSA serum levels of 4∼10 ng/mL, bpMRI and bpMRI combined with PSAD achieve better performance than mpMRI in detecting csPCa; bpMRI has a higher specificity than mpMRI, which could decrease unnecessary biopsy, and may serve as a potential alternative to mpMRI to optimize clinical workup.

MRI combined with PSA density in detecting clinically significant prostate cancer in patients with PSA serum levels of 4∼10ng/mL: Biparametric versus multiparametric MRI.

Background and Purpose— Intracranial arterial dolichoectasia (IADE) is a poorly understood arteriopathy compared with intracranial atherosclerotic stenosis (ICAS). We aimed to investigate the risk factors of IADE and ICAS and their relationship with neuroimaging markers of cerebral small vessel disease in a population-based study. Methods— This study comprised 1237 participants (aged 57.2±9.4 years, 37.6% men) who underwent brain magnetic resonance imaging and magnetic resonance angiography. IADE was assessed based on basilar artery dolichoectasia (diameter, height of bifurcation, and laterality of basilar artery) and dilation of basilar artery and internal carotid artery (intracranial volume-adjusted diameter ≥2 SD). ICAS was defined as any degree of stenosis in at least 1 intracranial artery. The neuroimaging markers of cerebral small vessel disease, including lacunes, white matter hyperintensities, microbleeds, dilated perivascular spaces, and brain atrophy, were evaluated. Results— Basilar arterial dolichoectasia was observed in 3.6% (45/1237); intracranial arterial dilation in 5.9% (67/1142); and ICAS in 15.7% (194/1237). Older age, higher systolic blood pressure, diabetes mellitus, higher LDL-C (low-density lipoprotein cholesterol) and lower HDL-C (high-density lipoprotein cholesterol) were associated with the presence of ICAS (all P P P P P =0.01) and, to a lesser extent, associated with lacunes and microbleeds. Conclusions— IADE and ICAS had different risk factor profiles and associated with different imaging phenotypes of cerebral small vessel disease, suggesting different underlying mechanisms.

Intracranial Arterial Dolichoectasia and Stenosis: Risk Factors and Relation to Cerebral Small Vessel Disease

BACKGROUND Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging. PURPOSE To develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies. STUDY TYPE Retrospective. SUBJECTS In all, 199 patients with PSA levels of 4-10 ng/mL. FIELD STRENGTH/SEQUENCE 3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI. ASSESSMENT Lesion regions of interest (ROIs) from T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical-radiological risk factors. STATISTICAL TESTS For continuous variables, variance equality was assessed by Levene's test and Student's t-test, and Welch's t-test was used to assess between-group differences. For categorical variables, Pearson's chi-square test, Fisher's exact test, or the approximate chi-square test was used to assess between-group differences. P < 0.05 was considered statistically significant. RESULTS The combined model incorporating the multi-imaging fusion model, age, PSA density (PSAD), and the PI-RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical-radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical-radiological model by 18.4%. DATA CONCLUSION The combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4-10 ng/mL and might aid in treatment decision-making and reduce unnecessary biopsies. LEVEL OF EVIDENCE 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890-1899.

Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies.

We aimed to assess the associations of large artery stenosis (LAS) and cerebral small vessel disease (CSVD) with the risk of ischemic stroke and to investigate their respective and combined contributions. In the prospective population-based Shunyi Study, 1,082 stroke-free participants aged 55.9±9.1 years were included. Participants were followed for incident stroke throughout the study period (2013-2019). Total small vessel disease score was used to measure CSVD burden. Cervico-cerebral large artery stenosis was evaluated via brain magnetic resonance angiography and carotid ultrasound. We estimated the risk of ischemic stroke in relation to LAS and CSVD with Cox regression models. During a mean follow-up of 4.2 years, 34 participants (3.1%) experienced at least one ischemic stroke. Severe LAS (≥50% stenosis versus no stenosis: HR=3.27 (95% CI: 1.31-8.18)) and high CSVD burden (total small vessel disease score 2-4 versus 0 point: HR=12.73 (4.83-33.53)) were associated with increased stroke risk independently. In multivariate models, CSVD burden (7.72%) explained a larger portion of the variation in stroke risk than severity of LAS (3.49%). Our findings identified that both LAS and CSVD were associated with future ischemic stroke in asymptomatic subjects, while those with high CSVD burden deserve more attention in primary prevention of stroke.

Association between large artery stenosis, cerebral small vessel disease and risk of ischemic stroke.

Background: Precise and reliable brain morphometry analysis is critical for clinical and research purposes. The magnetization-prepared rapid gradient echo (MPRAGE), multi-echo MPRAGE (MEMPRAGE) and magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequences have all been used to acquire brain structural images, but it is unclear which of these sequences is the most suitable for brain morphometry and whether the number of coil channels (20 or 32) affects scan precision. This study aimed to assess the impact of T1-weighted image acquisition variables (sequence and head coil) on the repeatability of resultant automated volumetric measurements.
 Methods: Twenty-four healthy volunteers underwent back-to-back scanning protocols with three sequences and two different coils (i.e., six scanning conditions in total) presented in a randomized order in a single session. MorphoBox prototype and FreeSurfer were used for brain segmentation. Brain structures were divided into cortical and subcortical regions for more precise analysis. The acquired volume and thickness values were used to calculate test-retest variability (TRV) values. TRV values from the six different combinations were compared for total brain structures, total cortical structures, total subcortical structures, and every single structure.
 Results: The median TRV value for all brain regions was 1.23% with MorphoBox and 3.14% with FreeSurfer. When using FreeSurfer results to compare the six combinations, for total brain structures volume and total cortical structures volume and thickness, the MEMPRAGE-32 channel combination showed significantly lower TRV values than the others (P Conclusions: TRV values were generally low, indicating generally high reliability for every region. The MEMPRAGE sequence was the most reliable of the three sequences for total brain structures and cortical structures. However, MP2RAGE was the most reliable for subcortical structures. The 32-channel coil showed better repeatability results than the 20-channel coil.

/pdf/test-retest-variability-of-brain-morphometry-analysis-an-1n5a3sglo3.pdf

Test-retest variability of brain morphometry analysis: an investigation of sequence and coil effects.

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Papers

Intracranial Arterial Dolichoectasia and Stenosis: Risk Factors and Relation to Cerebral Small Vessel Disease

Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies.

Association between large artery stenosis, cerebral small vessel disease and risk of ischemic stroke.

Test-retest variability of brain morphometry analysis: an investigation of sequence and coil effects.