Quantitative Diffusion-weighted Imaging Research Articles

Plasma proteomic biomarkers as mediators or moderators for the association between poor cardiovascular health and white matter microstructural integrity: The UK Biobank study.

The plasma proteome's mediating or moderating roles in the association between poor cardiovascular health (CVH) and brain white matter (WM) microstructural integrity are largely unknown. Data from 3953 UK Biobank participants were used (40-70 years, 2006-2010), with a neuroimaging visit between 2014 and 2021. Poor CVH was determined using Life's Essential 8 (LE8) and reversing standardized z-scores (LE8z _rev). The plasma proteome was examined as a potential mediator or moderator of LE8z _rev's effects on quantitative diffusion-weighted magnetic resonance imaging (dMRI) metrics. LE8z_rev was significantly associated with deteriorated WM microstructural integrity, as reflected by lower tract-averaged fractional anisotropy (dMRI-FAmean), (β±standared error (SE): -0.00152±0.0003, p<0.001) and higher tract-averaged orientation dispersion (dMRI-ODmean), (β±SE:+0.00081±0.00017, p<0.001). Ten strongly mediating plasma proteins of 1463 were identified, with leptin as the principal driver. Poor CVH is linked to poor WM microstructural integrity measures (lower FAmean and higher ODmean), mostly mediated through leptin. Up to 3953 UK Biobank participants were selected for this study. Poor cardiovascular health (CVH) was determined using Life's Essential 8. The plasma proteome was examined as a potential mediator or moderator of poor CVH's effect on dMRI metrics. Ten plasma proteins were identified with strong mediating effects, with leptin being the principal driver.

Quality control of quantitative diffusion-weighted magnetic resonance imaging: metrological problems

Quality control of quantitative diffusion-weighted magnetic resonance imaging: metrological problems

Differentiation between high-grade gliomas and solitary brain metastases based on multidiffusion MRI model quantitative analysis.

Differentiating high-grade gliomas (HGGs) from solitary brain metastases (SBMs) using conventional magnetic resonance imaging (MRI) remains challenging due to their similar imaging features. This study aimed to evaluate the diagnostic performance of advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator magnetic resonance imaging (MAP-MRI), incomparison to traditional techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) for distinguishing HGGs from SBMs. In total, 17 patients with HGGs and 26 patients with SBMs were prospectively recruited based on the established inclusion and exclusion criteria. Structural MRI sequences and diffusion spectrum imaging (DSI) were utilized to assess quantitative parameter models, including NODDI, MAP-MRI, DWI, DTI, and DKI. Quantitative parameters were measured for both the tumor parenchymal area and the peritumoral edema area. The quantitative parameters of the two patient groups were compared using either the independent Student's t-test or the Mann-Whitney U test. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Finally, the DeLong test was employed to compare the diagnostic performance of each model through pairwise comparisons of ROC curves. Isotropic volume fraction (Viso) based on NODDI; mean squared displacement (MSD) and the return to plane probabilities (RTPP) based on MAP-MRI; radial diffusivity (RDk) and mean diffusivity (MDk) based on DKI; and axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) based on DTI of the peritumoral edema tumor were significantly different between HGGs and SBMs (p < 0.05). The optimal single discriminant parameters for each model are NODDI_Viso, MAP-MRI_MSD, DKI_MDk, and DTI_AD. Among these, the AUC of Viso (0.809) exceeds that of MSD (0.733), MDk (0.718), and AD (0.779). The combined model, which incorporates DTI_AD, DKI_RD, and NODDI_Viso, demonstrated superior diagnostic performance (0.897). Advanced diffusion MRI quantitative parameters derived from NODDI, such as Viso, have the potential to enhance the differentiation between HGGs and SBMs. The integrated utilization of these models is anticipated to enhance diagnostic accuracy and refine MRI protocols for brain tumor assessment.

Quality control of quantitative diffusion-weighted magnetic resonance imaging: metrological problems

Quantitative magnetic resonance imaging is a modern method for detecting pathological changes in the patient’s tissues. However, images with quantitative characteristics are not widely used due to the limitation of the accuracy and reproducibility of the measured values. The purpose of this work is to formulate the metrological problem of quantitative magnetic resonance imaging and to ensure the reliability of research based on the analysis of practical approaches to quality control of diffusion-weighted magnetic resonance imaging. As part of the work performed, an analysis was carried out of the use of phantoms as means to ensure quality control of certain parameters of quantitative magnetic resonance imaging. The importance of validation was noted, the metrics used to control the quality of quantitative magnetic resonance imaging were highlighted, an overview of examples of clinical studies using diffusion-weighted magnetic resonance imaging was presented. It was found that accurate calibration and testing of magnetic resonance imaging scanners, as well as verification of image analysis tools, are necessary for the use of quantitative magnetic resonance imaging data in clinical practice.

Diffusion-Weighted Imaging for Skin Pathologies of the Breast-A Feasibility Study.

Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget's disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.

Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.

Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes. This study aimed to evaluate the performance of machine learning models in accurately distinguishing malignant orbital tumors from benign ones using multiparametric 3 T magnetic resonance imaging (MRI) data. In this single-center prospective study, patients with orbital masses underwent presurgery 3 T MRI scans between December 2015 and May 2021. The MRI protocol comprised multiparametric imaging including dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), as well as morphological imaging acquisitions. A repeated nested cross-validation strategy using random forest classifiers was used for model training and evaluation, considering 8 combinations of explanatory features. Shapley additive explanations (SHAP) values were used to assess feature contributions, and the model performance was evaluated using multiple metrics. One hundred thirteen patients were analyzed (57/113 [50.4%] were women; average age was 51.5 ± 17.5 years, range: 19-88 years). Among the 8 combinations of explanatory features assessed, the performance on predicting malignancy when using the most comprehensive model, which is the most exhaustive one incorporating all 46 explanatory features-including morphology, DWI, DCE, and IVIM, achieved an area under the curve of 0.9 [0.73-0.99]. When using the streamlined "10-feature signature" model, performance reached an area under the curve of 0.88 [0.71-0.99]. Random forest feature importance graphs measured by the mean of SHAP values pinpointed the 10 most impactful features, which comprised 3 quantitative IVIM features, 4 quantitative DCE features, 1 quantitative DWI feature, 1 qualitative DWI feature, and age. Our findings demonstrate that a machine learning approach, integrating multiparametric MRI data such as DCE, DWI, IVIM, and morphological imaging, offers high-performing models for differentiating malignant from benign orbital tumors. The streamlined 10-feature signature, with a performance close to the comprehensive model, may be more suitable for clinical application.

El análisis cuantitativo de las imágenes potenciadas en difusión (DWI) mejora la precisión diagnóstica del sistema de puntuación O-RADS RM en lesiones anexiales indeterminadas

IntroductionDespite the overall high accuracy of the O-RADS MRI scoring system for characterization of indeterminate adnexal lesions, a non-negligible percentage of adnexal lesions remains indeterminate. Given this, the main objective of this study was to evaluate the value of adding quantitative diffusion-weighted imaging (DWI) to Ovarian-Adnexal Reporting and Data System (O-RADS) MRI to improve the diagnostic accuracy of the scoring system in a cohort of indeterminate adnexal lesions according to International Ovarian Tumor Analysis Group Simple Rules (IOTA-SR). Methods and materialSeventy-nine women with 81 pelvic lesions classified as indeterminate according to IOTA-SR underwent 3-Tesla MRI with a conventional multiparametric protocol. DWI was quantitatively analyzed. Lesions were surgically removed or followed-up, according to a local protocol. Sensitivity, specificity, positive and negative predictive values and accuracy were determined for conventional multiparametric MRI and quantitative DWI-derived data. ResultsTwenty masses in 72 patients (27.8%) were malignant. An apparent diffusion coefficient (ADC) cut-off value of 1.30×10-3mm2/s had 89% sensitivity and 80% specificity for malignancy. Overall, adding quantitative DWI to O-RADS MRI increased the specificity (98.08%, P<.001), positive predictive value (94.12%, P<.001), and accuracy (93.06%, P=.05). In the specific O-RADS MRI score 4 subgroup, an ADC cut-off value of 1.22×10-3 mm2/s had 86% sensitivity and 67% specificity for distinguishing benign from malignant lesions. ConclusionIn IOTA-SR indeterminate adnexal lesions, quantitative DWI significantly improves the diagnostic performance of conventional multiparametric MRI in all O-RADS MRI score groups.

A multicenter study of cervical cancer using quantitative diffusion-weighted imaging.

Parameters from diffusion-weighted imaging (DWI) have been increasingly used as imaging biomarkers for the diagnosis and monitoring of treatment responses in cancer. The consistency of DWI measurements across different centers remains uncertain, which limits the widespread use of quantitative DWI in clinical settings. To investigate the consistency of quantitative metrics derived from DWI between different scanners in a multicenter clinical setting. A total of 193 patients with cervical cancer from four scanners (MRI1, MRI2, MRI3, and MRI4) at three centers were included in this retrospective study. DWI data were processed using the mono-exponential and intravoxel incoherent motion (IVIM) model, yielding the following parameters: apparent diffusion coefficient (ADC); true diffusion coefficient (D); pseudo-diffusion coefficient (D*); perfusion fraction (f); and the product of f and D* (fD*). Various parameters of cervical cancer obtained from different scanners were compared. The parameters D and ADC derived from MRI1 and MRI2 were significantly different from those derived from MRI3 or MRI4 (P <0.01 for all comparisons). However, there was no significant difference in cervical cancer perfusion parameters (D* and fD*) between the different scanners (P >0.05). The P values of comparisons of all DWI parameters (D, D*, fD*, and ADC) between MRI3 and MRI4 (same vendor in different centers) for cervical cancer were all >0.05, except for f (P = 0.05). Scanners of the same model by the same vendor can yield close measurements of the ADC and IVIM parameters. The perfusion parameters showed higher consistency among the different scanners.

Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm

PurposeOur study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis.MethodsEighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation (DWISD and ADCSD), DWI and ADC signal intensity ratio (DWISIR and ADCSIR), mean ADC and minimum ADC value (ADCmean and ADCmin) and ADC value standard deviation (ADCVSD). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result.ResultsTwo independent predictors of malignant nodules were identified by multivariate analysis: DWISIR (P = 0.007) and ADCmin (P < 0.001). The AUCs for multivariate prediction model, combined DWISIR and ADCmin thresholds model, combined DWISIR and ADCSIR thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined DWISIR and ADCmin threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS.ConclusionQuantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined DWISIR and ADCmin thresholds model significantly reduced the unnecessary biopsy rate.

Papel de la resonancia magnética de difusión en el diagnóstico inicial de los tumores de partes blandas

BackgroundSoft tissue tumours (STT) constitute a heterogeneous group of lesions frequently studied by Magnetic Resonance Imaging (MRI). It has not yet been clearly established whether the inclusion of apparent diffusion coefficient (ADC) diffusion-weighted imaging (DWI) maps would better determine tumour aggressiveness. PurposeTo assess the diagnostic value of quantitative ADC DWI maps in the initial diagnosis of STT; and to determine whether the inclusion of DWI provides more valuable information than conventional sequences alone. Material and methodsRetrospective study of patients with histologically proven STT. Conventional morphological MRI sequences and the DWI sequence were analysed. The ADC was quantified using a region of interest (ROI) that covered the largest sectional area (global ADC) and another that selected the area of greatest restriction (selected ADC). Differences in ADC values were analysed between both benign and malignant lesions and high and low-grade sarcomas. A multivariate analysis was performed to determine the ability of ADC to adequately diagnose the nature of STTs when associated with other morphological characteristics. Results84 patients with STT, of which 40 were benign and 44 malignant. The malignant group included 10 low-grade sarcomas, 23 high-grade sarcomas, 4 non-sarcomatous neoplasms and 7 sarcomas with no histological grading. The ADC values were significantly higher in benign lesions for the selected ADC. Significantly higher selected ADC values were also obtained in low-grade sarcomas. In the multivariate analysis, the highest diagnostic precision values were obtained when morphological features and ADC were included, with a sensitivity, specificity, and area under the curve (AUC) of 84, 75 and 91%, respectively. ConclusionThe inclusion of ADC DWI values improves the diagnostic accuracy of MRI for STTs, especially when used in combination with conventional MRI sequences.

Role of diffusion-weighted MR imaging in the initial diagnosis of soft tissue tumours

BackgroundSoft tissue tumours (STT) constitute a heterogeneous group of lesions frequently studied by Magnetic Resonance Imaging (MRI). It has not yet been clearly established whether the inclusion of apparent diffusion coefficient (ADC) diffusion-weighted imaging (DWI) maps would better determine tumour aggressiveness. PurposeTo assess the diagnostic value of quantitative ADC DWI maps in the initial diagnosis of STT; and to determine whether the inclusion of DWI provides more valuable information than conventional sequences alone. Material and methodsRetrospective study of patients with histologically proven STT. Conventional morphological MRI sequences and the DWI sequence were analysed. The ADC was quantified using a region of interest (ROI) that covered the largest sectional area (global ADC) and another that selected the area of ​​greatest restriction (selected ADC). Differences in ADC values ​​were analysed between both benign and malignant lesions and high and low-grade sarcomas. A multivariate analysis was performed to determine the ability of ADC ​​to adequately diagnose the nature of STTs when associated with other morphological characteristics. Results84 patients with STT, of which 40 were benign and 44 malignant. The malignant group included 10 low-grade sarcomas, 23 high-grade sarcomas, 4 non-sarcomatous neoplasms and 7 sarcomas with no histological grading. The ADC values ​​were significantly higher in benign lesions for the selected ADC. Significantly higher selected ADC values ​​were also obtained in low-grade sarcomas. In the multivariate analysis, the highest diagnostic precision values were obtained when morphological features and ADC were included, with a sensitivity, specificity, and area under the curve (AUC) of 84, 75 and 91%, respectively. ConclusionThe inclusion of ADC DWI values ​​improves the diagnostic accuracy of MRI for STTs, especially when used in combination with conventional MRI sequences.

Neuroimaging in infants with congenital cytomegalovirus infection and its correlation with outcome: emphasis on white matter abnormalities

ObjectiveTo evaluate the association between neuroimaging and outcome in infants with congenital cytomegalovirus (cCMV), focusing on qualitative MRI and quantitative diffusion-weighted imaging of white matter abnormalities (WMAs).MethodsMulticentre retrospective cohort study...

Feasibility of quantitative diffusion-weighted imaging during intra-procedural MRI-guided brachytherapy of locally advanced cervical and vaginal cancers

PurposeTo determine the feasibility of quantitative apparent diffusion coefficient (ADC) acquisition during magnetic resonance imaging-guided brachytherapy (MRgBT) using reduced field-of-view (rFOV) diffusion-weighted imaging (DWI). Methods and MaterialsT2-weighted (T2w) MR and full-FOV single-shot echo planar (ssEPI) DWI were acquired in 7 patients with cervical or vaginal malignancy at baseline and prior to brachytherapy, while rFOV-DWI was acquired during MRgBT following brachytherapy applicator placement. The gross target volume (GTV) was contoured on the T2w images and registered to the ADC map. Voxels at the GTV's maximum Maurer distance comprised a central sub-volume (GTVcenter). Contour ADC mean and standard deviation were compared between timepoints using repeated measures ANOVA. ResultsssEPI-DWI mean ADC increased between baseline and prebrachytherapy from 1.03 ± 0.18 10−3 mm2/s to 1.34 ± 0.28 10−3 mm2/s for the GTV (p = 0.06) and from 0.84 ± 0.13 10−3 mm2/s to 1.26 ± 0.25 10−3 mm2/s at the level of the GTVcenter (p = 0.03), consistent with early treatment response. rFOV-DWI during MRgBT demonstrated mean ADC values of 1.28 ± 0.14 10−3 mm2/s and 1.28 ± 0.19 10−3 mm2/s for the GTV and GTVcenter, respectively (p = 0.02 and p = 0.03 relative to baseline). No significant differences were observed between ssEPI-DWI and rFOV-DWI ADC measurements. ConclusionsQuantitative ADC measurement in the setting of MRI guided brachytherapy implant placement for cervical and vaginal cancers is feasible using rFOV-DWI, with comparable mean ADC comparable to prebrachytherapy ssEPI-DWI, and may enable MRI-guided radiotherapy targeting of low ADC, radiation resistant sub-volumes of tumor.

Endometrioid adenocarcinoma: combined multiparametric MRI and tumour marker HE4 to evaluate tumour grade and lymphovascular space invasion

To assess the value of semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and quantitative diffusion-weighted imaging parameters combined with human epididymis protein 4 (HE4) in predicting the pathological grade and lymphovascular space invasion (LVSI) of endometrioid adenocarcinoma (EAC). Between October 2018 and December 2021, 60 women (mean age, 55 [range, 32-77] years) with EAC underwent preoperative pelvic MRI and HE4 level measurements. The positive enhancement integral (PEI), time to peak, maximum slope of increase (MSI), and maximum slope of decrease were measured by manually drawing a region of interest on the neoplastic tissue. The receiver operating characteristic curve was used to calculate the diagnostic efficiency of the single parameter and combined factors. Lower apparent diffusion coefficients (ADCs) were observed in high-grade tumours (G3) than in low-grade tumours (G1/G2). PEI, MSI, and HE4 levels were higher in the high-grade tumours than in the low-grade tumours (p<0.05). The area under the curve (AUC) for G3 diagnosis using multiparametric MRI combined with HE4 was 0.929. ADC values were significantly lower in the EAC with LVSI than in those without LVSI. Tumours with LVSI showed higher PEI and HE4 levels than those without LVSI (p<0.05). The AUC for LVSI-positive diagnosis using multiparametric MRI combined with HE4 was 0.814. Semi-quantitative DCE-MRI, ADC values, and serum HE4 levels can be used to predict tumour grade and LVSI, and the prediction efficiency of multiparametric MRI combined with serum HE4 is better than that of any single factor.

Quantitative Diffusion-Weighted Imaging Analyses to Predict Response to Neoadjuvant Immunotherapy in Patients with Locally Advanced Head and Neck Carcinoma.

Neoadjuvant immune checkpoint blockade (ICB) prior to surgery may induce early pathological responses in head and neck squamous cell carcinoma (HNSCC) patients. Routine imaging parameters fail to diagnose these responses early on. Magnetic resonance (MR) diffusion-weighted imaging (DWI) has proven to be useful for detecting HNSCC tumor mass after (chemo)radiation therapy. 32 patients with stage II-IV, resectable HNSCC, treated at a phase Ib/IIa IMCISION trial (NCT03003637), were retrospectively analyzed using MR-imaging before and after two doses of single agent nivolumab (anti-PD-1) (n = 6) or nivolumab with ipilimumab (anti-CTLA-4) ICB (n = 26). The primary tumors were delineated pre- and post-treatment. A total of 32 features were derived from the delineation and correlated with the tumor regression percentage in the surgical specimen. MR-DWI data was available for 24 of 32 patients. Smaller baseline tumor diameter (p = 0.01-0.04) and higher sphericity (p = 0.03) were predictive of having a good pathological response to ICB. Post-treatment skewness and the change in skewness between MRIs were negatively correlated with the tumor's regression (p = 0.04, p = 0.02). Pre-treatment DWI tumor diameter and sphericity may be quantitative biomarkers for the prediction of an early pathological response to ICB. Furthermore, our data indicate that ADC skewness could be a marker for individual response evaluation.

Diffusion-weighted MRI of total hip arthroplasty for classification of synovial reactions: A pilot study

BackgroundConventional quantitative diffusion-weighted imaging (DWI) is sensitive to changes in tissue microstructure, but its application to evaluating patients with orthopaedic hardware has generally been limited due to metallic susceptibility artifacts. The apparent diffusion coefficient (ADC) and T2-values from a multi-spectral imaging (MSI) DWI combined with 2D multi-spectral imaging with a 2D periodically rotated overlapping parallel lines with enhanced reconstruction (2D-MSI PROPELLER DWI) based sequence and a MAVRIC based T2 mapping sequence, respectively, may mitigate the artifact and provide additional quantitative information on synovial reactions in individuals with total hip arthroplasty (THA). The aim of this pilot study is to utilize a 2D-MSI PROPELLER DWI and a MAVRIC-based T2 mapping to evaluate ADC and T2-values of synovial reactions in patients with THA. MethodsCoronal morphologic MRIs from THA patients underwent evaluation of the synovium and were assigned a synovial classification of ‘normal’, or ‘grouped abnormal’ (consisting of sub-groups ‘infection’, ‘polymeric’, ‘metallosis’, ‘adverse local tissue reaction’ [ALTR], or ‘non-specific’) and type of synovial reaction present (fluid-like, solid-like, or mixed). Regions of interest (ROIs) were placed in synovial reactions for measurement of ADC and T2-values, obtained from the 2D-MSI PROPELLER DWI and T2-MAVRIC sequences, respectively. A one-way analysis of variance (ANOVA) and Kruskal-Wallis rank sum tests were used to compare the differences in ADC and T2-values across the different synovial reaction classifications. A Kruskal-Wallis test was used to compare the ROI areas for the ADC and T2-values. A principal component analysis (PCA) was performed to evaluate the possible effects of ADC values, size of the ADC ROI, T2-values, and size of the T2 ROI with respect to synovial reaction classification. ResultsDifferences of ADC and T2 among the individual synovial reactions were not found. A difference of ADC between ‘normal’ and ‘grouped abnormal’ synovial reactions was also not detected even as the ADC area of ‘grouped abnormal’ synovial reactions were significantly larger (p = 0.02). The ‘grouped abnormal’ synovial reactions had significantly shorter T2-values than ‘normal’ synovial reactions (p = 0.02), and that the T2 area of ‘grouped abnormal’ synovial reactions were significantly larger (p = 0.01). A larger ROI area on the T2-maps was observed in the mixed synovial reaction type as compared to the fluid-like reaction type area (p = 0.01). Heterogeneity was noted in calculated ADC and T2 maps. PCA analysis revealed obvious clustering by the ‘normal’ and ‘grouped abnormal’ classifications. Conclusions2D-MSI PROPELLER DWI and MAVRIC-T2 generate quantitative images of periprosthetic tissues within clinically feasible scan times. The combination of derived ADC and T2-values with area of synovial reaction may aid in differentiating normal from abnormal synovial reactions between types of synovial reactions in patients with THA.

Quantitative Assessment of Restriction Spectrum MR Imaging for the Diagnosis of Breast Cancer and Association With Prognostic Factors.

Restriction spectrum imaging (RSI) is an advanced quantitative diffusion-weighted magnetic resonance imaging (DWI) technique to assess breast cancer. To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer. Retrospective. Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions. 3-T; RSI-based DWI sequence with echo-planar imaging technique. The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f1 , hindered diffusion f2 , free diffusion f3 , and signal fractions f1 f2 ) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer. Mann-Whitney U test or Student's t-test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P-value <0.05 was considered statistically significant. Malignant breast lesions showed significantly lower ADC and f3 values, and significantly higher f1 and f1 f2 values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI-derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC=0.973). The f3 value was significantly differed between estrogen receptor (ER)-positive and ER-negative tumors. The ADC, f1 , f3 , and f1 f2 values were significantly different progesterone receptor (PR)-positive and PR-negative status. The RSI-derived parameters (f1 , f3 , and f1 f2 ) may facilitate the differential diagnosis between benign and malignant breast lesions. 4 TECHNICAL EFFICACY: Stage 2.

Association Between Quantitative Diffusion-Weighted Magnetic Resonance Neuroimaging and Outcome After Pediatric Cardiac Arrest.

Diffusion MRI can quantify the extent of hypoxic-ischemic brain injury after cardiac arrest. Our objective was to determine the association between the adult-derived threshold of apparent diffusion coefficient (ADC) <650 × 10-6 mm2/s in >10% of brain tissue and an unfavorable outcome after pediatric cardiac arrest. Since ADC decreases exponentially as a function of increasing age, we determined the association between (1) having >10% of brain tissue below a novel age-dependent ADC threshold, and (2) age-normalized whole-brain mean ADC and unfavorable outcome. This was a retrospective study of patients aged ≤18 years who had cardiac arrest and a clinically obtained brain MRI within 7 days. The primary outcome was unfavorable neurologic status at hospital discharge based on the Pediatric Cerebral Performance Category score. ADC images were extracted from 3-direction diffusion imaging. We determined whether each patient had >10% of voxels with an ADC below prespecified thresholds. We computed the whole-brain mean ADC for each patient. One hundred thirty-four patients were analyzed. Patients with ADC <650 × 10-6 mm2/s in >10% of voxels had 15 times higher odds (95% CI 5-65) of an unfavorable outcome compared with patients with ADC <650 × 10-6 mm2/s (area under the receiver operating characteristic curve [AUROC] 0.72 [95% CI 0.63-0.80]). These ADC criteria had a sensitivity and specificity of 0.49 and 0.94, respectively, and positive and negative predictive values of 0.93 and 0.52, respectively, for an unfavorable outcome. The age-dependent ADC threshold that yielded optimal sensitivity and specificity for unfavorable outcomes was <300 × 10-6 mm2/s below each patient's predicted whole-brain mean ADC. The sensitivity, specificity, and positive and negative predictive values for this ADC threshold were 0.53, 0.96, 0.96, and 0.54, respectively (odds ratio [OR] 26.4 [95% CI 7.5-168.3]; AUROC 0.74 [95% CI 0.66-0.83]). Lower age-normalized whole-brain mean ADC was also associated with an unfavorable outcome (OR 0.42 [0.24-0.64], AUROC 0.76 [95% CI 0.66-0.82]). Quantitative diffusion thresholds on MRI within 7 days after cardiac arrest were associated with an unfavorable outcome in children. The age-independent ADC threshold was highly specific for predicting an unfavorable outcome. However, the specificity and sensitivity increased when using age-dependent ADC thresholds. Age-dependent ADC thresholds may improve prognostic accuracy and require further investigation in larger cohorts. This study provides Class III evidence that quantitative diffusion-weighted imaging within 7 days postarrest can predict an unfavorable clinical outcome in children.

Preoperative prediction of extramural venous invasion in rectal cancer by dynamic contrast-enhanced and diffusion weighted MRI: a preliminary study

BackgroundTo explore the value of the quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in assessing preoperative extramural venous invasion (EMVI) in rectal cancer.MethodsEighty-two rectal adenocarcinoma patients who had underwent MRI preoperatively were enrolled in this study. The differences in quantitative DCE-MRI and DWI parameters including Krans, Kep and ADC values were analyzed between MR-detected EMVI (mrEMVI)-positive and -negative groups. Multivariate logistic regression analysis was performed to build the combined prediction model for pathologic EMVI (pEMVI) with statistically significant quantitative parameters. The performance of the model for predicting pEMVI was evaluated using receiver operating characteristic (ROC) curve.ResultsOf the 82 patients, 24 were mrEMVI-positive and 58 were -negative. In the mrEMVI positive group, the Ktrans and Kep values were significantly higher than those in the mrEMVI negative group (P < 0.01), but the ADC values were significantly lower (P < 0.01). A negative correlation was observed between the Ktrans vs ADC values and Kep vs ADC values in patients with rectal cancer. Among the four quantitative parameters, Ktrans and ADC value were independently associated with mrEMVI by multivariate logistic regression analysis. ROC analysis showed that combined prediction model based on quantitative DCE parameters and ADC values had a good prediction efficiency for pEMVI in rectal cancer.ConclusionThe quantitative DCE-MRI parameters, Krans, Kep and ADC values play important role in predicting EMVI of rectal cancer, with Ktrans and ADC value being independent predictors of EMVI in rectal cancer.

Abstract P3-03-03: Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

Abstract Background: Early and accurate prediction of response to neoadjuvant therapy (NAT) would empower personalization of breast cancer treatment regimens based on expected response. Noninvasive, quantitative dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), when performed during the course of NAT, can accurately predict the ultimate pathological response. While these techniques have been incorporated into clinical trials in the academic setting, they have not yet been translated to the community setting, where most cancer patients receive their care. Implementation of quantitative MRI in the community setting widely expands the potential impact it can provide by 1) allowing community settings to participate in clinical trials that require quantitative MRI, and 2) advancing quantitative MRI towards standard-of-care for prediction of response in breast cancer. Methods: Women with locally advanced breast cancer (N = 28) were imaged four times during the course of NAT: 1) prior to the start of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after MRI #3. Imaging data was acquired on 3T Siemens Skyra scanners equipped with breast coils and sited in a community hospital and radiology clinic, respectively. DW-MRI and DCE-MRI were acquired over 10 slices of 5 mm thickness. DW-MRI was acquired with diagonal monopolar diffusion-encoding gradients with b-values of 0, 200, and 800 s/mm2 in a total scan time of 1 minute 39 seconds. Voxel wise tumor cellularity was quantified using the apparent diffusion coefficient (ADC). For DCE-MRI, a gadolinium-based contrast agent was administered intravenously at 2 mL/sec after the acquisition of baseline scans. DCE-MRI data was acquired dynamically with a temporal resolution of 7.27 seconds for a total acquisition time of 8 minutes. The volume transfer constant Ktrans was calculated using Patlak analysis of DCE-MRI data to characterize the tumor vasculature. The tumor was semi-automatically segmented using a manually drawn region of interest followed by fuzzy c-means clustering of DCE-MRI data to identify a functional tumor volume. Measurements of tumor volume were combined with both ADC and Ktrans to yield metrics of tumor cellularity and bulk tumor flow, respectively. Results: Women who achieved pathological complete response at the time of surgery (pCR; n=8) displayed significantly different treatment-induced changes in MRI-derived tumor parameters versus women who did not achieve pCR (non-pCR, n=20). After 1 cycle of NAT, women who achieved pCR had smaller functional tumor volume and lower cellularity (p &amp;lt; 0.05) than non-pCR study participants. At the third and fourth MRI, tumor volume, ADC, Ktrans, cellularity, and bulk tumor flow were all significantly different between the pCR and non-pCR cohorts (p &amp;lt; 0.05). Of note, longest tumor diameter was not predictive of pCR at any time point in this study. Conclusions: This study demonstrates that quantitative DCE- and DW-MRI can be implemented successfully in community care facilities within standard-of-care settings for imaging locally advanced breast cancer. Metrics extracted from the change in DW-MRI (ADC) and DCE-MRI (Ktrans) can accurately predict pathological complete response to neoadjuvant therapy and may be more sensitive to tumor response than the RECIST criteria. Furthermore, incorporating quantitative metrics with tumor volume further increases the ability to predict pathological response to NAT in locally advanced breast cancer. While there are still challenges to address to effectively implement these quantitative metrics into the clinical workflow, this study is first in its kind to transition a decade’s worth of quantitative MRI advancements from academic settings into standard-of-care. Citation Format: John Virostko, Anna G Sorace, Kalina P Slavkova, Anum S Kazerouni, Angela M Jarrett, Julie C DiCarlo, Stefanie Woodard, Sarah Avery, Boone W Goodgame, Debra Patt, Thomas E Yankeelov. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-03.