Prostate Cancer Detection with mpMRI According to PI-RADS v2 Compared with Systematic MRI/TRUS-Fusion Biopsy: A Prospective Study

BACKGROUND: mpMRI assesses prostate lesions through their PI-RADS score. The primary goal of this prospective study was to demonstrate the correlation of PI-RADS v2 score and the volume of a lesion with the presence and clinical significance of prostate cancer (PCa). The secondary goal was to determine the extent of additionally PCa in inconspicuous areas. METHODS: All 157 patients underwent a perineal MRI/TRUS-fusion prostate biopsy. Targeted biopsies as well as a systematic biopsy were performed. The presence of PCa in the probes was specified by the ISUP grading system. RESULTS: In total, 258 lesions were biopsied. Of the PI-RADS 3 lesions, 24% were neoplastic. This was also true for 36.9% of the PI-RADS 4 lesions and for 59.5% of the PI-RADS 5 lesions. Correlation between ISUP grades and lesion volume was significant (p < 0.01). In the non-suspicious mpMRI areas carcinoma was revealed in 19.7% of the patients. CONCLUSIONS: The study shows that the PI-RADS v2 score and the lesion volume correlate with the presence and clinical significance of PCa. However, there are two major points to consider: First, there is a high number of false positive findings. Second, inconspicuous mpMRI areas revealed PCa

The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading

Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine

The split apparent diffusion coefficient sign: A novel magnetic resonance imaging biomarker for cortical pathology with possible implications in autoimmune encephalitis

Introduction MRI is the imaging modality of choice for assessing patients with encephalopathy. In this context, we discuss a novel biomarker, the “split ADC sign,” where the cerebral cortex demonstrates restricted diffusion (high DWI signal and low ADC) and the underlying white matter demonstrates facilitated diffusion (high or low DWI signal and high ADC). We hypothesize that this sign can be used as a biomarker to suggest either acute encephalitis onset or to raise the possibility of an autoimmune etiology. Materials and Methods A full-text radiological information system search of radiological reports was performed for all entities known to produce restricted diffusion in the cortex excluding stroke between January 2012 and June 2022. Initial MRI studies performed upon onset of clinical symptoms were screened for the split ADC sign. Results 25 subjects were encountered with a positive split ADC sign (15 female; median age = 57 years, range 18–82). Diagnosis included six herpes simplex encephalitis, three peri-ictal MRI changes, eight PRES, two MELAS, and six autoimmune (3 anti-GABA$_{A}$R, two seronegative, and one anti-Ma2/Ta). Subjects were imaged at a mean 1.8 days after the onset of symptoms (range 0–8). Discussion We present a novel visual MRI biomarker, the split ADC sign, and highlight its potential usefulness in subjects with encephalopathy to suggest acute disease onset or to raise the possibility of an autoimmune etiology when location-based criteria are applied. When positive, the sign was present on the initial MRI and can therefore be used to help focus further clinical and laboratory workup

Comparison of clinically available dynamic susceptibility contrast post processing software to differentiate progression from pseudoprogression in post-treatment high grade glioma

INTRODUCTION: The purpose of this retrospective study was to compare two, widely available software packages for calculation of Dynamic Susceptibility Contrast (DSC) perfusion MRI normalized relative Cerebral Blood Volume (rCBV) values to differentiate tumor progression from pseudoprogression in treated high-grade glioma patients. MATERIAL AND METHODS: rCBV maps processed by Siemens Syngo.via (Siemens Healthineers) and Olea Sphere (Olea Medical) software packages were co-registered to contrast-enhanced T1 (T1-CE). Regions of interest based on T1-CE were transferred to the rCBV maps. rCBV was calculated using mean values and normalized using contralateral normal- appearing white matter. The Wilcoxon test was performed to assess for significant differences, and software-specific optimal rCBV cutoff values were determined using the Youden index. Interrater reliability was evaluated for two raters using the intraclass correlation coefficient. RESULTS: 41 patients (18 females; median age = 59 years; range 21-77 years) with 49 new or size-increasing post-treatment contrast-enhancing lesions were included (tumor progression = 40 lesions; pseudoprogression = 9 lesions). Optimal rCBV cutoffs of 1.31 (Syngo.via) and 2.40 (Olea) were significantly different, with an AUC of 0.74 and 0.78, respectively. Interrater reliability was 0.85. DISCUSSION: We demonstrate that different clinically available MRI DSC-perfusion software packages generate significantly different rCBV cutoff values for the differentiation of tumor progression from pseudoprogression in standard-of-care treated high grade gliomas. Physicians may want to determine the unique value of their perfusion software packages on an institutional level in order to maximize diagnostic accuracy when faced with this clinical challenge. Furthermore, combined with implementation of current DSC-perfusion recommendations, multi-center comparability will be improved

Sex and age dependencies of aqueductal cerebrospinal fluid dynamics parameters in healthy subjects

Objectives: To assess the influence of age and sex on 10 cerebrospinal fluid (CSF) flow dynamics parameters measured with an MR phase contrast (PC) sequence within the cerebral aqueduct at the level of the intercollicular sulcus.Materials and Methods: 128 healthy subjects (66 female subjects with a mean age of 52.9 years and 62 male subjects with a mean age of 51.8 years) with a normal Evans index, normal medial temporal atrophy (MTA) score, and without known disorders of the CSF circulation were included in the study. A PC MR sequence on a 3T MR scanner was used. Ten different flow parameters were analyzed using postprocessing software. Ordinal and linear regression models were calculated.Results: The parameters stroke volume (sex: p < 0.001, age: p = 0.003), forward flow volume (sex: p < 0.001, age: p = 0.002), backward flow volume (sex: p < 0.001, age: p = 0.018), absolute stroke volume (sex: p < 0.001, age: p = 0.005), mean flux (sex: p < 0.001, age: p = 0.001), peak velocity (sex: p = 0.009, age: p = 0.0016), and peak pressure gradient (sex: p = 0.029, age: p = 0.028) are significantly influenced by sex and age. The parameters regurgitant fraction, stroke distance, and mean velocity are not significantly influenced by sex and age.Conclusion: CSF flow dynamics parameters measured in the cerebral aqueduct are partly age and sex dependent. For establishment of reliable reference values for clinical use in future studies, the impact of sex and age should be considered and incorporated

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy

Evaluation of the clinical utility of maximum intensity projections of 3D contrast-enhanced, T1-weighted imaging for the detection of brain metastases

BACKGROUND To visualize and assess brain metastases on magnetic resonance imaging, radiologists face an ever-increasing pressure to perform faster and more efficiently. The usage of maximum intensity projections (MIPs) of contrast-enhanced T1-weighed (T1ce) magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) images proposes to increase reading efficiency by increasing lesion conspicuity while reducing in the number of images to be reviewed. AIM To assess if MIPs save reading time and achieve the same level of diagnostic accuracy as standard 1 mm T1ce images for the detection of brain metastases. METHODS Forty-four patients were included in this retrospective study. Axial reformations of T1ce MP-RAGE (TR/TE = 2300/2.25 ms, resolution = 1 mm$^{3}$ ) images were analyzed and post-processed into 5 and 10 mm MIPs. Two readers evaluated the randomly assorted images and recorded reading time. Reading time differences were analyzed using the Wilcoxon test, and inter-reader statistics were performed using Bland-Altman plots. RESULTS About 22.5 61.2 s/study and 43.8 ± 159.9 s/study were saved using 5 and 10 mm MIPs, respectively. Combined average sensitivity was 92.0% for 5 mm MIPs and 86.3% for 10 mm MIPs compared to standard 1 mm axial slices, with an average rate of 0.98 and 0.57 false positives per study, respectively CONCLUSION: While 5 mm and 10 mm T1ce MP-RAGE MIPs showed a clinical benefit in reducing reading times for evaluation of brain metastases, they should be used in conjunction with standard 1 mm images for best sensitivity and specificity, a practice which possibly annuls their benefit

Evaluation of 3D fat-navigator based retrospective motion correction in the clinical setting of patients with brain tumors

PURPOSE A 3D fat-navigator (3D FatNavs)-based retrospective motion correction is an elegant approach to correct for motion as it requires no additional hardware and can be acquired during existing 'dead-time' within common 3D protocols. The purpose of this study was to clinically evaluate 3D FatNavs in the work-up of brain tumors. METHODS An MRI-based fat-excitation motion navigator incorporated into a standard MPRAGE sequence was acquired in 40 consecutive patients with (or with suspected) brain tumors, pre and post-Gadolinium injection. Each case was categorized into key anatomical landmarks, the temporal lobes, the infra-tentorial region, the basal ganglia, the bifurcations of the middle cerebral artery, and the A2 segment of the anterior cerebral artery. First, the severity of motion in the non-corrected MPRAGE was assessed for each landmark, using a 5-point score from 0 (no artifacts) to 4 (non-diagnostic). Second, the improvement in image quality in each pair and for each landmark was assessed blindly using a 4-point score from 0 (identical) to 3 (strong correction). RESULTS The mean image improvement score throughout the datasets was 0.54. Uncorrected cases with light and no artifacts displayed scores of 0.50 and 0.13, respectively, while cases with moderate artifacts, severe artifacts, and non-diagnostic image quality revealed a mean score of 1.17, 2.25, and 1.38, respectively. CONCLUSION Fat-navigator-based retrospective motion correction significantly improved MPRAGE image quality in restless patients during MRI acquisition. There was no loss of image quality in patients with little or no motion, and improvements were consistent in patients who moved more

Evaluation of the clinical utility of maximum intensity projections of 3D contrast‐enhanced

BACKGROUND To visualize and assess brain metastases on magnetic resonance imaging, radiologists face an ever-increasing pressure to perform faster and more efficiently. The usage of maximum intensity projections (MIPs) of contrast-enhanced T1-weighed (T1ce) magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) images proposes to increase reading efficiency by increasing lesion conspicuity while reducing in the number of images to be reviewed. AIM To assess if MIPs save reading time and achieve the same level of diagnostic accuracy as standard 1 mm T1ce images for the detection of brain metastases. METHODS Forty-four patients were included in this retrospective study. Axial reformations of T1ce MP-RAGE (TR/TE = 2300/2.25 ms, resolution = 1 mm$^{3}$ ) images were analyzed and post-processed into 5 and 10 mm MIPs. Two readers evaluated the randomly assorted images and recorded reading time. Reading time differences were analyzed using the Wilcoxon test, and inter-reader statistics were performed using Bland-Altman plots. RESULTS About 22.5 61.2 s/study and 43.8 ± 159.9 s/study were saved using 5 and 10 mm MIPs, respectively. Combined average sensitivity was 92.0% for 5 mm MIPs and 86.3% for 10 mm MIPs compared to standard 1 mm axial slices, with an average rate of 0.98 and 0.57 false positives per study, respectively CONCLUSION: While 5 mm and 10 mm T1ce MP-RAGE MIPs showed a clinical benefit in reducing reading times for evaluation of brain metastases, they should be used in conjunction with standard 1 mm images for best sensitivity and specificity, a practice which possibly annuls their benefit