Ultra-high b-value DWI in rectal cancer: image quality assessment and regional lymph node prediction based on radiomics.

OBJECTIVES: This study aims to evaluate image quality and regional lymph node metastasis (LNM) in patients with rectal cancer (RC) on multi-b-value diffusion-weighted imaging (DWI). METHODS: This retrospective study included 199 patients with RC who had undergone multi-b-value DWI. Subjective (five-point Likert scale) and objective assessments of quality images were performed on DWIb1000, DWIb2000, and DWIb3000. Patients were randomly divided into a training (n = 140) or validation cohort (n = 59). Radiomics features were extracted within the whole volume tumor on ADC maps (b = 0, 1000 s/mm2), DWIb1000, DWIb2000, and DWIb3000, respectively. Five prediction models based on selected features were developed using logistic regression analysis. The performance of radiomics models was evaluated with a receiver operating characteristic curve, calibration, and decision curve analysis (DCA). RESULTS: The mean signal intensity of the tumor (SItumor), signal-to-noise ratio (SNR), and artifact and anatomic differentiability score gradually were decreased as the b-value increased. However, the contrast-to-noise (CNR) on DWIb2000 was superior to those of DWIb1000 and DWIb3000 (4.58 ± 0.86, 3.82 ± 0.77, 4.18 ± 0.84, p < 0.001, respectively). The overall image quality score of DWIb2000 was higher than that of DWIb3000 (p < 0.001) and showed no significant difference between DWIb1000 and DWIb2000 (p = 0.059). The area under curve (AUC) value of the radiomics model based on DWIb2000 (0.728) was higher than conventional ADC maps (0.690), DWIb1000 (0.699), and DWIb3000 (0.707), but inferior to multi-b-value DWI (0.739) in predicting LNM. CONCLUSION: DWIb2000 provides better lesion conspicuity and LNM prediction than DWIb1000 and DWIb3000 in RC. CLINICAL RELEVANCE STATEMENT: DWIb2000 offers satisfactory visualization of lesions. Radiomics features based on DWIb2000 can be applied for preoperatively predicting regional lymph node metastasis in rectal cancer, thereby benefiting the stratified treatment strategy. KEY POINTS: Lymph node staging is required to determine the best treatment plan for rectal cancer. DWIb2000 provides superior contrast-to-noise ratio and lesion conspicuity and its derived radiomics best predict lymph node metastasis. DWIb2000 may be recommended as the optimal b-value in rectal MRI protocol.

Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions.

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Combining multimodal diffusion-weighted imaging and morphological parameters for detecting lymph node metastasis in cervical cancer.

BACKGROUND: Accurate detection of lymph node metastasis (LNM) is crucial for determining the tumor stage, selecting optimal treatment, and estimating the prognosis for cervical cancer. This study aimed to assess the diagnostic efficacy of multimodal diffusion-weighted imaging (DWI) and morphological parameters alone or in combination, for detecting LNM in cervical cancer. METHODS: In this prospective study, we enrolled consecutive cervical cancer patients who received multimodal DWI (conventional DWI, intravoxel incoherent motion DWI, and diffusion kurtosis imaging) before treatment from June 2022 to June 2023. The largest lymph node (LN) observed on each side on imaging was matched with that detected on pathology to improve the accuracy of LN matching. Comparison of the diffusion and morphological parameters of LNs and the primary tumor between the positive and negative LN groups. A combined diagnostic model was constructed using multivariate logistic regression, and the diagnostic performance was evaluated using receiver operating characteristic curves. RESULTS: A total of 93 cervical cancer patients were enrolled: 35 with LNM (48 positive LNs were collected), and 58 without LNM (116 negative LNs were collected). The area under the curve (AUC) values for the apparent diffusion coefficient, diffusion coefficient, mean diffusivity, mean kurtosis, long-axis diameter, short-axis diameter of LNs, and the largest primary tumor diameter were 0.716, 0.720, 0.716, 0.723, 0.726, 0.798, and 0.744, respectively. Independent risk factors included the diffusion coefficient, mean kurtosis, short-axis diameter of LNs, and the largest primary tumor diameter. The AUC value of the combined model based on the independent risk factors was 0.920, superior to the AUC values of all the parameters mentioned above. CONCLUSION: Combining multimodal DWI and morphological parameters improved the diagnostic efficacy for detecting cervical cancer LNM than using either alone.

Whole-body MRI in oncology: acquisition protocols, current guidelines, and beyond.

Acknowledging the increasing use of whole-body magnetic resonance imaging (WB-MRI) in the oncological setting, we conducted a narrative review focusing on practical aspects of the examination and providing a synthesis of various acquisition protocols described in the literature. Firstly, we addressed the topic of patient preparation, emphasizing methods to enhance examination acceptance. This included strategies for reducing anxiety and patient distress, improving staff-patient interactions, and increasing overall patient comfort. Secondly, we analysed WB-MRI acquisition protocols recommended in existing imaging guidelines, such as MET-RADS-P, MY-RADS, and ONCO-RADS, and provided an overview of acquisition protocols reported in the literature regarding other expanding applications of WB-MRI in oncology, in patients with breast cancer, ovarian cancer, melanoma, colorectal and lung cancer, lymphoma, and cancers of unknown primary. Finally, we suggested possible acquisition parameters for whole-body images across MR systems from three different vendors.

Diffusion-Weighted MR Neurography with Unidirectional Motion-Probing Gradient to Evaluate Lumbar Nerve Roots at 1.5T MR.

Purpose: Recent studies have demonstrated the usefulness of diffusion-weighted MR neurography (DW MRN) for assessing nerve roots. This study aimed to evaluate the utility of DW MRN with a unidirectional motion-probing gradient (MPG) for the lumbar nerve roots at 1.5T MR. Materials and Methods: Sixty-four lumbar spine MRI scans with DW MRN using anteroposterior unidirectional MPG were retrospectively analyzed. Any changes in the 512 lumbar spinal nerve roots from L3 to S1 were evaluated using T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE T1WI), and DW MRN, with agreement and correlation analysis. Results: T2WI revealed compression of 78 nerve roots, and CE T1WI revealed 52 instances of nerve root enhancement. Sixty-seven nerve roots showed swelling and hyperintensity on DW MRN. A total of 42 nerve roots showed changes in the CE T1WI and DW MRN sequences. Moderate to substantial agreement and moderate positive correlation were observed between DW MRN and CE T1WI, as well as DW MRN and T2WI (κ = 0.59-0.65, ρ = 0.600-0.653). Conclusion: DW MRN with unidirectional anteroposterior MPG can help evaluate neuritis-related changes in spinal nerve roots and could serve as a sequence capable of complementing or substituting gadolinium CE imaging.

Intimate partner violence perpetration among veterans: associations with neuropsychiatric symptoms and limbic microstructure.

Background: Intimate partner violence (IPV) perpetration is highly prevalent among veterans. Suggested risk factors of IPV perpetration include combat exposure, post-traumatic stress disorder (PTSD), depression, alcohol use, and mild traumatic brain injury (mTBI). While the underlying brain pathophysiological characteristics associated with IPV perpetration remain largely unknown, previous studies have linked aggression and violence to alterations of the limbic system. Here, we investigate whether IPV perpetration is associated with limbic microstructural abnormalities in military veterans. Further, we test the effect of potential risk factors (i.e., PTSD, depression, substance use disorder, mTBI, and war zone-related stress) on the prevalence of IPV perpetration. Methods: Structural and diffusion-weighted magnetic resonance imaging (dMRI) data were acquired from 49 male veterans of the Iraq and Afghanistan wars (Operation Enduring Freedom/Operation Iraqi Freedom; OEF/OIF) of the Translational Research Center for TBI and Stress Disorders (TRACTS) study. IPV perpetration was assessed using the psychological aggression and physical assault sub-scales of the Revised Conflict Tactics Scales (CTS2). Odds ratios were calculated to assess the likelihood of IPV perpetration in veterans with either of the following diagnoses: PTSD, depression, substance use disorder, or mTBI. Fractional anisotropy tissue (FA) measures were calculated for limbic gray matter structures (amygdala-hippocampus complex, cingulate, parahippocampal gyrus, entorhinal cortex). Partial correlations were calculated between IPV perpetration, neuropsychiatric symptoms, and FA. Results: Veterans with a diagnosis of PTSD, depression, substance use disorder, or mTBI had higher odds of perpetrating IPV. Greater war zone-related stress, and symptom severity of PTSD, depression, and mTBI were significantly associated with IPV perpetration. CTS2 (psychological aggression), a measure of IPV perpetration, was associated with higher FA in the right amygdala-hippocampus complex (r = 0.400, p = 0.005). Conclusion: Veterans with psychiatric disorders and/or mTBI exhibit higher odds of engaging in IPV perpetration. Further, the more severe the symptoms of PTSD, depression, or TBI, and the greater the war zone-related stress, the greater the frequency of IPV perpetration. Moreover, we report a significant association between psychological aggression against an intimate partner and microstructural alterations in the right amygdala-hippocampus complex. These findings suggest the possibility of a structural brain correlate underlying IPV perpetration that requires further research.

Microstructural white matter damage on MRI is associated with disease severity in Dutch-type cerebral amyloid angiopathy.

Peak width of skeletonized mean diffusivity (PSMD) is an emerging diffusion-MRI based marker to study subtle early alterations to white matter microstructure. We assessed PSMD over the clinical continuum in Dutch-type hereditary CAA (D-CAA) and its association with other CAA-related MRI-markers and cognitive symptoms. We included (pre)symptomatic D-CAA mutation-carriers and calculated PSMD from diffusion-MRI data. Associations between PSMD-levels, cognitive performance and CAA-related MRI-markers were assessed with linear regression models. We included 59 participants (25/34 presymptomatic/symptomatic; mean age 39/58 y). PSMD-levels increased with disease severity and were higher in symptomatic D-CAA mutation-carriers (median [range] 4.90 [2.77-9.50]mm2/s × 10-4) compared with presymptomatic mutation-carriers (2.62 [1.96-3.43]mm2/s × 10-4) p = <0.001. PSMD was positively correlated with age, CAA-SVD burden on MRI (adj.B [confidence interval] = 0.42 [0.16-0.67], p = 0.002), with number of cerebral microbleeds (adj.B = 0.30 [0.08-0.53], p = 0.009), and with both deep (adj.B = 0.46 [0.22-0.69], p = <0.001) and periventricular (adj.B = 0.38 [0.13-0.62], p = 0.004) white matter hyperintensities. Increasing PSMD was associated with decreasing Trail Making Test (TMT)-A performance (B = -0.42 [-0.69-0.14], p = 0.04. In D-CAA mutation-carriers microstructural white matter damage is associated with disease phase, CAA burden on MRI and cognitive impairment as reflected by a decrease in information processing speed. PSMD, as a global measure of alterations to the white matter microstructure, may be a useful tool to monitor disease progression in CAA.

Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images.

BACKGROUND AND PURPOSE: Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. METHODS: Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. RESULTS: In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%-60.7% and 73.7%-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen's kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. CONCLUSION: Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.

Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction.

INTRODUCTION: Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs. METHODS AND RESULTS: We examined 1335 stroke-free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free-water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance. DISCUSSION: Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF-associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free-water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF-related cognitive decline.

Tractometry of Human Visual White Matter Pathways in Health and Disease.

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

R2* mapping and reduced field-of-view diffusion-weighted imaging for preoperative assessment of nonenlarged lymph node metastasis in rectal cancer.

The aim of the current study is to investigate the diagnostic value of R2* mapping versus reduced field-of-view diffusion-weighted imaging (rDWI) of the primary lesion of rectal cancer for preoperative prediction of nonenlarged lymph node metastasis (NLNM). Eighty-one patients with pathologically confirmed rectal cancer underwent preoperative R2* mapping and rDWI sequences before total mesorectal excisions and accompanying regional lymph node dissections. Two radiologists independently performed whole-tumor measurements of R2* and apparent diffusion coefficient (ADC) parameters on primary lesions of rectal cancer. Patients were divided into positive (NLNM+) and negative (NLNM-) groups based on their pathological analysis. The tumor location, maximum diameter of the tumor, and maximum short diameter of the lymph node were assessed. R2* and ADC, pT stage, tumor grade, status of mesorectal fascia, and extramural vascular invasion were also studied for their potential relationships with NLNM using multivariate logistic regression analysis. The NLNM+ group had significantly higher R2* (43.56 ± 8.43 vs. 33.87 ± 9.57, p < 0.001) and lower ADC (1.00 ± 0.13 vs. 1.06 ± 0.22, p = 0.036) than the NLNM- group. R2* and ADC were correlated to lymph node metastasis (r = 0.510, p < 0.001 for R2*; r = -0.235, p = 0.035 for ADC). R2* and ADC showed good and moderate diagnostic abilities in the assessment of NLNM status with corresponding area-under-the-curve values of 0.795 and 0.636. R2* provided a significantly better diagnostic performance compared with ADC for the prediction of NLNM status (z = 1.962, p = 0.0498). The multivariate logistic regression analysis demonstrated that R2* was a compelling factor of lymph node metastasis (odds ratio = 56.485, 95% confidence interval: 5.759-554.013; p = 0.001). R2* mapping had significantly higher diagnostic performance than rDWI from the primary tumor of rectal cancer in the prediction of NLNM status.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Background: Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. Methods: In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. Results: A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). Conclusions: DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Assessment of whole-body muscle MRI for the early diagnosis of Amyotrophic Lateral Sclerosis.

OBJECTIVES: To evaluate muscle signal abnormalities on whole-body muscle MRI with T2 and diffusion-weighted imaging in early ALS stages. METHODS: 101 muscles were analyzed in newly diagnosed ALS patients and healthy controls on a whole-body MRI protocol including four-point T2-Dixon imaging and diffusion-weighted imaging (b0 and b800). Sensitivity and inter-observer agreement were assessed. RESULTS: 15 patients (mean age, 64 +/- 12 [SD], 9 men) who met the Awaji-Shima criteria for definite, probable or possible ALS and 9 healthy controls were assessed (mean age, 53 +/- 13 [SD], 2 men). 61 % of the muscles assessed in ALS patients (62/101) showed signal hyperintensities on T2-weighted imaging, mainly in the upper and lower extremities (legs, hands and feet). ALS patients had a significantly higher number of involved muscles compared to healthy controls (p = 0,006). Diffusion-weighted imaging allowed for the detection of additional involvement in 22 muscles, thus improving the sensitivity of whole-body MRI from 60 % (using T2-weighted imaging only) up to 80 % (with the combination of T2-weighted and diffusion-weighted imaging). CONCLUSIONS: ALS patients exhibited significant muscle signal abnormalities on T2-weighted and diffusion-weighted imaging in early disease stages. Whole-body MRI could be used for pre-EMG mapping of muscle involvement in order to choose suitable targets, thus improving early diagnosis.

Expanding the Treatable Imaging Profile in Patients With Large Ischemic Stroke: Subanalysis From a Randomized Clinical Trial.

BACKGROUND: We aimed to examine the boundary of the ischemic core volume in patients undergoing endovascular thrombectomy (EVT) versus those receiving medical management to determine the minimum optimal size for favorable treatment outcomes. METHODS: This is a prespecified substudy of the RESCUE-Japan LIMIT (Recovery by Endovascular Salvage for Cerebral Ultra-Acute Embolism-Japan Large Ischemic Core Trial). Patients with large vessel occlusion were enrolled between November 2018 and September 2021 with a National Institutes of Health Stroke Scale score of at least 6 on admission and an Alberta Stroke Program Early Computed Tomography Score value of 3 to 5. We investigated the correlation between optimal quantified ischemic core volume, assessed solely using magnetic resonance diffusion-weighted imaging, and functional outcomes (modified Rankin Scale score, 0-3) at 90 days by predictive marginal plots. Final infarct volume and safety outcomes (symptomatic intracerebral hemorrhage and mortality) were also assessed. RESULTS: Of the 203 cases, 168 patients (85 in the EVT group versus 83 in the medical management group) were included. The median (interquartile range) core volume was 94 (65-160) mL in patients with EVT and 115 (71-141) mL in the medical management group (P=0.72). The predictive marginal probabilities of the 2 groups intersected at 128 mL for estimating functional outcomes. Symptomatic intracerebral hemorrhage and mortality within 90 days had overlay margins through all core volumes in both groups. The median final infarct volume (interquartile range) was smaller in the EVT group (142 [80-223] mL versus 211 [123-289] mL in the medical management group; P<0.001). CONCLUSIONS: In this prespecified analysis of a randomized clinical trial involving patients with large ischemic strokes, patients with an estimated core volume of up to 128 mL on diffusion-weighted imaging benefit from EVT. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03702413.

Background Breast Parenchymal Signal During Menstrual Cycle on Diffusion-Weighted MRI: A Prospective Study in Healthy Premenopausal Women.

OBJECTIVE: To prospectively investigate the influence of the menstrual cycle on the background parenchymal signal (BPS) and apparent diffusion coefficient (ADC) of the breast on diffusion-weighted MRI (DW-MRI) in healthy premenopausal women. MATERIALS AND METHODS: Seven healthy premenopausal women (median age, 37 years; range, 33-49 years) with regular menstrual cycles participated in this study. DW-MRI was performed during each of the four phases of the menstrual cycle (four examinations in total). Three radiologists independently assessed the BPS visual grade on images with b-values of 800 sec/mm² (b800), 1200 sec/mm² (b1200), and a synthetic 1500 sec/mm² (sb1500). Additionally, one radiologist conducted a quantitative analysis to measure the BPS volume (%) and ADC values of the BPS (ADCBPS) and fibroglandular tissue (ADCFGT). Changes in the visual grade, BPS volume (%), ADCBPS, and ADCFGT during the menstrual cycle were descriptively analyzed. RESULTS: The visual grade of BPS in seven women varied from mild to marked on b800 and from minimal to moderate on b1200 and sb1500. As the b-value increased, the visual grade of BPS decreased. On b800 and sb1500, two of the seven volunteers showed the highest visual grade in the early follicular phase (EFP). On b1200, three of the seven volunteers showed the highest visual grades in EFP. The BPS volume (%) on b800 and b1200 showed the highest value in three of the six volunteers with dense breasts in EFP. Three of the seven volunteers showed the lowest ADCBPS in the EFP. Four of the seven volunteers showed the highest ADCBPS in the early luteal phase (ELP) and the lowest ADCFGT in the late follicular phase (LFP). CONCLUSION: Most volunteers did not exhibit specific BPS patterns during their menstrual cycles. However, the highest BPS and lowest ADCBPS were more frequently observed in EFP than in the other menstrual cycle phases, whereas the highest ADCBPS was more common in ELP. The lowest ADCFGT was more frequent in LFP.

Impact of the Alberta Stroke Program CT Score subregions on long-term functional outcomes in acute ischemic stroke: Results from two multicenter studies in China.

Background and Objectives: The Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions' involvement in the long-term functional outcomes of acute ischemic stroke (AIS). Materials and Methods: Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included. The ASPECTS score and subregion involvement for each patient was assessed using posttreatment magnetic resonance diffusion-weighted imaging. Univariate and multivariable regression analyses were conducted to identify subregions related to 3-month poor functional outcome (modified Rankin Scale scores, 3-6) in the reperfusion and medical therapy cohorts, respectively. In addition, prognostic efficiency between the region-based ASPECTS and ASPECTS score methods were compared using receiver operating characteristic curves and DeLong's test. Results: A total of 365 patients (median age, 64 years; 70% men) were included, of whom 169 had poor outcomes. In the reperfusion therapy cohort, multivariable regression analyses revealed that the involvement of the left M4 cortical region in left-hemisphere stroke (adjusted odds ratio [aOR] 5.39, 95% confidence interval [CI] 1.53-19.02) and the involvement of the right M3 cortical region in right-hemisphere stroke (aOR 4.21, 95% CI 1.05-16.78) were independently associated with poor functional outcomes. In the medical therapy cohort, left-hemisphere stroke with left M5 cortical region (aOR 2.87, 95% CI 1.08-7.59) and caudate nucleus (aOR 3.14, 95% CI 1.00-9.85) involved and right-hemisphere stroke with right M3 cortical region (aOR 4.15, 95% CI 1.29-8.18) and internal capsule (aOR 3.94, 95% CI 1.22-12.78) affected were related to the increased risks of poststroke disability. In addition, region-based ASPECTS significantly improved the prognostic efficiency compared with the conventional ASPECTS score method. Conclusion: The involvement of specific ASPECTS subregions depending on the affected hemisphere was associated with worse functional outcomes 3 months after stroke, and the critical subregion distribution varied by clinical management. Therefore, region-based ASPECTS could provide additional value in guiding individual decision making and neurological recovery in patients with AIS.

Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma.

OBJECTIVES: To investigate associations between tissue diffusion, stiffness, and different tumor microenvironment features in resected hepatocellular carcinoma (HCC). METHODS: Seventy-two patients were prospectively included for preoperative magnetic resonance (MR) diffusion-weighted imaging and MR elastography examination. The mean apparent diffusion coefficient (ADC) and stiffness value were measured on the central three slices of the tumor and peri-tumor area. Cell density, tumor-stroma ratio (TSR), lymphocyte-rich HCC (LR-HCC), and CD8 + T cell infiltration were estimated in resected tumors. The interobserver agreement of MRI measurements and subjective pathological evaluation was assessed. Variables influencing ADC and stiffness were screened with univariate analyses, and then identified with multivariable linear regression. The potential relationship between explored imaging biomarkers and histopathological features was assessed with linear regression after adjustment for other influencing factors. RESULTS: Seventy-two patients (male/female: 59/13, mean age: 56 ± 10.2 years) were included for analysis. Inter-reader agreement was good or excellent regarding MRI measurements and histopathological evaluation. No correlation between tumor ADC and tumor stiffness was found. Multivariable linear regression confirmed that cell density was the only factor associated with tumor ADC (Estimate = -0.03, p = 0.006), and tumor-stroma ratio was the only factor associated with tumor stiffness (Estimate = -0.18, p = 0.03). After adjustment for fibrosis stage (Estimate = 0.43, p < 0.001) and age (Estimate = 0.04, p < 0.001) in the multivariate linear regression, intra-tumoral CD8 + T cell infiltration remained a significant factor associated with peri-tumor stiffness (Estimate = 0.63, p = 0.02). CONCLUSIONS: Tumor ADC surpasses tumor stiffness as a biomarker of cellularity. Tumor stiffness is associated with tumor-stroma ratio and peri-tumor stiffness might be an imaging biomarker of intra-tumoral immune microenvironment. CLINICAL RELEVANCE STATEMENT: Tissue stiffness could potentially serve as an imaging biomarker of the intra-tumoral immune microenvironment of hepatocellular carcinoma and aid in patient selection for immunotherapy. KEY POINTS: Apparent diffusion coefficient reflects cellularity of hepatocellular carcinoma. Tumor stiffness reflects tumor-stroma ratio of hepatocellular carcinoma and is associated with tumor-infiltrating lymphocytes. Tumor and peri-tumor stiffness might serve as imaging biomarkers of intra-tumoral immune microenvironment.

Inter- and Intra-Patient Repeatability of Radiomic Features from Multiparametric Whole-Body MRI in Patients with Metastatic Prostate Cancer.

(1) Background: We assessed the test-re-test repeatability of radiomics in metastatic castration-resistant prostate cancer (mCPRC) bone disease on whole-body diffusion-weighted (DWI) and T1-weighted Dixon MRI. (2) Methods: In 10 mCRPC patients, 1.5 T MRI, including DWI and T1-weighted gradient-echo Dixon sequences, was performed twice on the same day. Apparent diffusion coefficient (ADC) and relative fat-fraction-percentage (rFF%) maps were calculated. Per study, up to 10 target bone metastases were manually delineated on DWI and Dixon images. All 106 radiomic features included in the Pyradiomics toolbox were derived for each target volume from the ADC and rFF% maps. To account for inter- and intra-patient measurement repeatability, the log-transformed individual target measurements were fitted to a hierarchical model, represented as a Bayesian network. Repeatability measurements, including the intraclass correlation coefficient (ICC), were derived. Feature ICCs were compared with mean ADC and rFF ICCs. (3) Results: A total of 65 DWI and 47 rFF% targets were analysed. There was no significant bias for any features. Pairwise correlation revealed fifteen ADC and fourteen rFF% feature sub-groups, without specific patterns between feature classes. The median intra-patient ICC was generally higher than the inter-patient ICC. Features that describe extremes in voxel values (minimum, maximum, range, skewness, and kurtosis) showed generally lower ICCs. Several mostly shape-based texture features were identified, which showed high inter- and intra-patient ICCs when compared with the mean ADC or mean rFF%, respectively. (4) Conclusions: Pyradiomics texture features of mCRPC bone metastases varied greatly in inter- and intra-patient repeatability. Several features demonstrated good repeatability, allowing for further exploration as diagnostic parameters in mCRPC bone disease.

Utility of abbreviated MRI in the post-treatment evaluation of rectal cancer.

BACKGROUND: Post-treatment evaluation of patients with rectal cancer (RC) using magnetic resonance imaging (MRI) burdens medical resources, necessitating an exploration of abbreviated protocols. PURPOSE: To evaluate the diagnostic performance of abbreviated MRI (A-MRI) for the post-treatment evaluation of RC patients. MATERIAL AND METHODS: This retrospective study included RC patients who underwent non-contrast rectal MRI and standard liver MRI, as well as abdominal contrast-enhanced computed tomography (CECT) for post-treatment evaluation. A-MRI comprised diffusion-weighted imaging (DWI) and T2-weighted imaging of the upper abdomen and the pelvic cavity. Three radiologists independently reviewed A-MRI, CECT, and standard liver MRI in the detection of viable disease. The diagnostic performances were compared using a reference standard considering all available information, including pathology, FDG-PET, endoscopic results, and clinical follow-up. RESULTS: We included 78 patients (50 men, 28 women; mean age=60.9 ± 10.2 years) and observed viable disease in 34 (43.6%). On a per-patient-basis analysis, A-MRI showed significantly higher sensitivity (95% vs. 81%, P = 0.04) and higher accuracy (93% vs. 82%, P < 0.01), compared to those of CECT, while A-MRI showed comparable sensitivity (91% vs. 91%, P = 0.42) and accuracy (97% vs. 98%, P = 0.06) to that of standard liver MRI. On a per-lesion-based analysis, A-MRI exhibited significantly superior lesion detectability than that of CECT (figure of merit 0.91 vs. 0.77, P < 0.01) and comparable to that of standard liver MRI (figure of merit 0.91 vs. 0.92, P = 0.75). CONCLUSION: A-MRI exhibited higher sensitivity and diagnostic accuracy than those of CECT in the post-treatment evaluation of RC, while it showed comparable performances with standard liver MRI. A-MRI provides diagnostic added value in the follow-up of RC patients.

Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study.

Background: The intravoxel Incoherent Motion (IVIM) model extracts perfusion map and diffusion coefficient map using diffusion-weighted imaging. The main limitation of this model is inaccuracy in the presence of noise. Objective: This study aims to improve the accuracy of IVIM output parameters. Material and Methods: In this simulated and analytical study, the Kalman filter is applied to reject artifact and measurement noise. The proposed method purifies the diffusion coefficient from blood motion and noise, and then an artificial neural network is deployed in estimating perfusion parameters. Results: Based on the T-test results, however, the estimated parameters of the conventional method were significantly different from actual values, those of the proposed method were not substantially different from actual. The accuracy of f and D* also was improved by using Artificial Neural Network (ANN) and their bias was minimized to 4% and 12%, respectively. Conclusion: The proposed method outperforms the conventional method and is a promising technique, leading to reproducible and valid maps of D, f, and D*.