Multiparametric Magnetic Resonance Imaging Parameters Research Articles

Early therapy evaluation of intra-arterial trastuzumab injection in a human breast cancer xenograft model using multiparametric MR imaging.

To investigate the treatment efficacy of intra-arterial (IA) trastuzumab treatment using multiparametric magnetic resonance imaging (MRI) in a human breast cancer xenograft model. Human breast cancer cells (BT474) were stereotaxically injected into the brains of nude mice to obtain a xenograft model. The mice were divided into four groups and subjected to different treatments (IA treatment [IA-T], intravenous treatment [IV-T], IA saline injection [IA-S], and the sham control group). MRI was performed before and at 7 and 14 d after treatment to assess the efficacy of the treatment. The tumor volume, apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) MRI parameters (Ktrans, Kep, Ve, and Vp) were measured. Tumor volumes in the IA-T group at 14 d after treatment were significantly lower than those in the IV-T group (13.1 mm3 [interquartile range 8.48-16.05] vs. 25.69 mm3 [IQR 20.39-30.29], p = 0.005), control group (IA-S, 33.83 mm3 [IQR 32.00-36.30], p<0.01), and sham control (39.71 mm3 [IQR 26.60-48.26], p <0.001). The ADC value in the IA-T group was higher than that in the control groups (IA-T, 7.62 [IQR 7.23-8.20] vs. IA-S, 6.77 [IQR 6.48-6.87], p = 0.044 and vs. sham control, 6.89 [IQR 4.93-7.48], p = 0.004). Ktrans was significantly decreased following the treatment compared to that in the control groups (p = 0.002 and p<0.001 for vs. IA-S and sham control, respectively). Tumor growth was decreased in the IV-T group compared to that in the sham control group (25.69 mm3 [IQR 20.39-30.29] vs. 39.71 mm3 [IQR 26.60-48.26], p = 0.27); there was no significant change in the MRI parameters. IA treatment with trastuzumab potentially affects the early response to treatment, including decreased tumor growth and decrease of Ktrans, in a preclinical brain tumor model.

Investigation of lacrimal gland and extraocular muscle in thyroid eye disease patients with severe subjective dry eye disease: a multiparametric magnetic resonance imaging study.

To analyze the radiological features of the lacrimal gland (LG) and extraocular muscle (EOM) in thyroid eye disease (TED) patients with severe subjective dry eye disease (DED) using magnetic resonance imaging (MRI) measurements. In this cross-sectional study, mechanical ocular exposure, dry eye assessment and MRI data were collected. Patients were classified into non-severe subjective DED group with ocular surface disease index (OSDI) < 33 and severe subjective DED group with OSDI ≥ 33. Linear regression model was applied for comparing the OSDI < 33 and OSDI ≥ 33 group in TED patients. The predictive performance of MRI parameters and models was assessed by receiver operating characteristic curve (ROC) analysis. Consecutive 88 TED patients (176 eyes) were included in this study. In the OSDI < 33 group, 52 TED patients (104 eyes) with a mean clinical activity score (CAS) of 0.63 ± 0.75. In the OSDI ≥ 33 group, there are 36 TED patients (72 eyes), with a mean CAS of 1.50 ± 1.54. The age and sex of the patients were matched between the two groups. The OSDI ≥ 33 group had shorter tear break-up time, larger levator palpebrae superioris / superior rectus (LPS/SR), inferior rectus and lateral rectus, smaller LG, more inflammatory LPS/SR and inferior rectus than OSDI < 33 DED group (P < 0.05). In the linear regression analysis, compare to the OSDI < 33 DED group, the OSDI ≥ 33 group had larger medial rectus cross-sectional area (β = 0.06, 95%CI: (0.02, 0.10), P = 0.008), larger inferior rectus cross-sectional area (β = 0.06, 95%CI: (0.00, 0.12), P = 0.048), smaller LG cross-sectional area (β = -0.14, 95%CI: (-0.25, -0.04), P = 0.008). In the ROC analysis, the area under curve of medial rectus, inferior rectus, LG, and combined model are 0.625, 0.640, 0.661 and 0.716, respectively. Multiparametric MRI parameters of the LG and EOM in TED patients with severe subjective DED were significantly altered. Novel models combining the cross-sectional area of LG, medial rectus and inferior rectus showed good predictive performance in TED patients with severe subjective DED.

Prediction of immunocyte infiltration and prognosis in postoperative hepatitis B virus-related hepatocellular carcinoma patients using magnetic resonance imaging.

The immune microenvironment (IME) is closely associated with prognosis and therapeutic response of hepatitis B virus-related hepatocellular carcinoma (HBV-HCC). Multi-parametric magnetic resonance imaging (MRI) enables non-invasive assessment of IME and predicts prognosis in HBV-HCC. We aimed to construct an MRI prediction model of the immunocyte-infiltration subtypes and explore its prognostic significance. HBV-HCC patients at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) with radical surgery (between 1 October and 30 December 2021) were prospectively enrolled. Patients with pathologically proven HCC (between 1 December 2013 and 30 October 2019) were retrospectively enrolled. Pearson correlation analysis was used to examine the relationship between the immunocyte-infiltration counts and MRI parameters. An MRI prediction model of immunocyte-infiltration subtypes was constructed in prospective cohort. Kaplan-Meier survival analysis was used to analyse its prognostic significance in the retrospective cohort. Twenty-four patients were prospectively enrolled to construct the MRI prediction model. Eighty-nine patients were retrospectively enrolled to determine its prognostic significance. MRI parameters (relative enhancement, ratio of the apparent diffusion coefficient value of tumoral region to peritumoral region [rADC], T1 value) correlated significantly with the immunocyte-infiltration counts (leukocytes, T help cells, PD1+Tc cells, B lymphocytes). rADC differed significantly between high and low immunocyte-infiltration groups (1.47 ± 0.36 vs 1.09 ± 0.25, P = 0.009). The area under the curve of the MRI model was 0.787 (95% confidence interval 0.587-0.987). Based on the MRI model, the recurrence-free time was longer in the high immunocyte-infiltration group than in the low immunocyte-infiltration group (P = 0.026). MRI is a non-invasive method for assessing the IME and immunocyte-infiltration subtypes, and predicting prognosis in post-operative HBV-HCC patients.

The value of multiparametric MRI combined with clinical prognostic parameters in predicting the 5-year survival of stage IIIC1 cervical squamous cell carcinoma

ObjectivesTo explore the value of multiparametric magnetic resonance imaging（MRI）in predicting the 5-year progression-free survival (PFS) and overall survival (OS) of cervical squamous cell carcinoma (CSCC) in 2018 FIGO stage IIIC1. MethodsThis retrospective study collected156 patients with CSCC from Dec. 2014 to Jul. 2018. Sixty-one patients underwent radical hysterectomy (RH), and 95 patients underwent concurrent chemoradiotherapy (CCRT). Clinical and MR parameters of primary tumours were analysed. A 1:1 ratio propensity score matching (PSM) was performed for the RH group and CCRT group according to T stage. The Cox proportional hazard model was used to evaluate the associations between imaging or clinical variables and PFS and OS. ResultsThe 5-year PFS and OS rates were 72.6% and 78.3%, respectively. The analysis results show that the treatment method, ADCmin < 0.604 × 10−3 mm2/s, and Ktrans < 0.699 min−1 correlated with worse PFS, while SCC-Ag > 6.7 ng/L, ADCmin < 0.604 × 10−3 mm2/s, and Ktrans < 0.699 min−1 correlated with worse OS. After PSM, we confirmed that the treatment methods did not affect the long-term survival of patients with stage IIIC1 disease, and a low Ktrans value was an independent poor prognostic factor. ConclusionFunctional MRI parameters and SCC-Ag have potential predictive value for the 5-year survival of 2018 FIGOIIIC1 CSCC. There were no significant differences in survival between CCRT and RH + adjuvant therapy for IIIC1 stage CSCC if the T stage was earlier.

The application of a novel platform of multiparametric magnetic resonance imaging in a bioenvironmental toxic carbon tetrachloride-induced mouse model of liver fibrosis

The use of multiparametric magnetic resonance imaging (MRI) to distinguish complex histopathological changes in liver fibrosis has not yet been systematically established. The purpose of this study is to gauge the efficacy of a cutting-edge MRI platform for evaluating ecotoxicologically hazardous carbon tetrachloride (CCl4) induced liver fibrosis, while also scrutinizing the relationship between MRI and its histopathological features. Thirty-six mice were randomly divided into 6 groups, each with 6 mice. Control mice received an intraperitoneal injection of olive oil, while the experimental mice received different doses of intraperitoneal injection of CCl4. Both sets underwent this process twice per week over a duration of 5 weeks. MRI measurements encompassed T1WI, T2WI, T1 mapping, T2 mapping, T2* mapping. Liver fibrosis and inflammation were assessed and classified using Metavir and activity scoring systems. CCl4 successfully induced liver fibrosis in mice, showing an increasing extent of liver fibrosis and liver function damage with the increasing dosage of CCl4. Compared with the control group, T1, ΔT1, and T2 in the experimental group were considerably elevated (P < 0.05) than those in the control group. Spearman's correlation showed that the correlation of Native T1 and △T1 with fibrosis (r = 0.712, 0.678) was better than with inflammation (r = 0.688, 0.536). T2 correlation with inflammation (r = 0.803) was superior to fibrosis (r = 0.568). ROC analysis showed that the AUC of Native T1 was highest (0.906), followed by ΔT1 (0.852), while the AUC increased to 0.945 when all relevant MRI parameters were combined. T1 is the most potent MRI parameter for evaluating CCl4-induced liver fibrosis, followed by ΔT1. Meanwhile, T2 may not be suitable for evaluating liver fibrosis but is more suitable for evaluating liver inflammation.

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.

To construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB). The preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models. A total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871-1.000) in the training cohort and 0.751 (95% CI: 0.587-0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898-1.000) in the training cohort and 0.849 (95% CI: 0.695-1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong's test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work. The combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.

Multiparametric MR imaging with diffusion-weighted, intravoxel incoherent motion, diffusion tensor, and dynamic contrast-enhanced perfusion sequences to assess gallbladder wall thickening: a prospective study based on surgical histopathology.

To investigate the diagnostic performance of a multiparametric magnetic resonance imaging (MRI) protocol comprising quantitative MRI (diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) perfusionMRI) and conventional MRI in the characterization of gallbladder wall thickening (GWT). This prospective study comprised consecutive adults with GWT who underwent multiparametric MRI between July 2020 and April 2022. Two radiologistsevaluated the MRI independently. The final diagnosis was based on surgical histopathology. The association of MRI parameters with malignant GWT was evaluated. The area under the curve (AUC) for the quantitative MRI parameters and diagnostic performance of conventional, and multiparametric MRI were compared. The interobserver agreementbetween two radiologists was calculated. Thirty-five patients (mean age, 56years, 23 females) with GWT (25 benign and ten malignant) were evaluated. The quantitative MRI parameters significantly associated with malignant GWT were apparent diffusion coefficient on DWI (p = 0.007) and mean diffusivity (MD) on DTI (p = 0.013), perfusion fraction (f) on IVIM (p = 0.033), time to peak enhancement (TTP, p = 0.008), and wash in rate (p = 0.049) on DCE-MRI. TTP had the highest AUC of 0.790, followed by MD (0.782) and f (0.742) (p = 0.213) for predicting malignant GWT. Multiparametric MRI had significantly higher sensitivity (90% vs. 80%, p = 0.045) than conventional MRI for diagnosing malignant GWT. The two radiologists' reading had substantial to near-perfect agreement (kappa = 0.639-1) and moderate to strong correlation (interclass correlation coefficient = 0.5-0.88). Multiparametric protocol incorporating advanced sequences improved the diagnostic performance of MRI for differentiating benign and malignant GWT. • Multiparametric MRI had 90% sensitivity and 88% specificity for diagnosing malignant GWT, compared to 80% sensitivity and 88% specificity for conventional CE-MRI. • Among the quantitative MRI parameters, TTP (perfusion-MRI) had the highest AUC of 0.790, followed by MD (0.782) and IVIM-f (0.742). • For most quantitative MRI parameters, there was moderate to strong agreement (ICC = 0.5-0.88).

Diagnostic Efficacy of Multiparametric Magnetic Resonance Imaging Parameters in Differentiating Common Subtypes of Renal Cortical Tumors

Diagnostic Efficacy of Multiparametric Magnetic Resonance Imaging Parameters in Differentiating Common Subtypes of Renal Cortical Tumors

Noninvasive Assessment of the Renal Function, Oxford Classification and Prognostic Risk Stratification of IgAN by Using Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Blood Oxygenation Level-Dependent MRI.

Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. Oxford classification including mesangial hypercellularity (M), endothelial hypercellularity (E), segmental sclerosis (S), interstitial fibrosis/tubular atrophy (T), and crescent (C) were recommended to predict the prognosis of IgAN. To explore whether multiparametric magnetic resonance imaging (MRI) can be applied to assess the renal function, Oxford classification, and risk of progression to end-stage kidney disease within 5 years of IgAN. Prospective. A total of 46 patients with pathologically confirmed IgAN and 20 healthy volunteers. A 3-T, blood oxygenation level-dependent (BOLD)-MRI, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). Two radiologists measured the cortex and medulla T2*, apparent diffusion coefficient (ADC), true diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (fp). All participants were divided into three groups: group 1, healthy volunteers; group 2, patients with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 ; group 3, patients with eGFR <60 mL/min/1.73 m2 . Or two groups: group A, 5-year risk scores ≤10% and group B, 5-year risk scores >10%. Intraclass correlation coefficient, one-way analysis of variance, least-significant difference, Student's t-test, Pearson product-moment correlation, Spearman's rank correlation, and receiver operating characteristics (ROC) with the area under the curve (AUC). A P value <0.05 was considered statistically significant. Except for cortical Dp, all other MRI parameters showed significant differences between group 1 and group 2. None of the MRI parameters showed a significant correlation with M, E, or C scores. Cortical T2*, Dt, fp, and medullary Dt and fp showed low-to-moderate significant correlations with S scores. Except for cortical and medullary Dp, all other MRI parameters were significantly correlated with T scores. Cortical Dt showed the largest AUC for differentiating group A from group B (AUC=0.927) and T0 from T1/T2 (AUC=0.963). Imaging by IVIM-DWI and BOLD-MRI could facilitate noninvasive assessment of the renal function, Oxford classification, and prognostic risk of IgAN patients. 2. Stage 3.

An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer

Objective. Multi-parametric magnetic resonance imaging (MP-MRI) has played an important role in prostate cancer diagnosis. Nevertheless, in the clinical routine, these sequences are principally analyzed from expert observations, which introduces an intrinsic variability in the diagnosis. Even worse, the isolated study of these MRI sequences trends to false positive detection due to other diseases that share similar radiological findings. Hence, the main objective of this study was to design, propose and validate a deep multimodal learning framework to support MRI-based prostate cancer diagnosis using cross-correlation modules that fuse MRI regions, coded from independent MRI parameter branches. Approach. This work introduces a multimodal scheme that integrates MP-MRI sequences and allows to characterize prostate lesions related to cancer disease. For doing so, potential 3D regions were extracted around expert annotations over different prostate zones. Then, a convolutional representation was obtained from each evaluated sequence, allowing a rich and hierarchical deep representation. Each convolutional branch representation was integrated following a special inception-like module. This module allows a redundant non-linear integration that preserves textural spatial lesion features and could obtain higher levels of representation. Main results. This strategy enhances micro-circulation, morphological, and cellular density features, which thereafter are integrated according to an inception late fusion strategy, leading to a better differentiation of prostate cancer lesions. The proposed strategy achieved a ROC-AUC of 0.82 over the PROSTATEx dataset by fusing regions of K trans and apparent diffusion coefficient (ADC) maps coded from DWI-MRI. Significance. This study conducted an evaluation about how MP-MRI parameters can be fused, through a deep learning representation, exploiting spatial correlations among multiple lesion observations. The strategy, from a multimodal representation, learns branches representations to exploit radio-logical findings from ADC and K trans. Besides, the proposed strategy is very compact (151 630 trainable parameters). Hence, the methodology is very fast in training (3 s for an epoch of 320 samples), being potentially applicable in clinical scenarios.

Prediction of response to radiotherapy in locally advanced carcinoma cervix using multiparametric MRI: A prospective, single-center, longitudinal study.

To evaluate the possible role of a multiparametric magnetic resonance imaging (MRI) and semiquantitative fusion map for the prediction of response to radiotherapy in carcinoma cervix. This was a prospective, single-center, longitudinal observational study performed on patients with locally advanced carcinoma cervix. Relative blood flow (rBF), relative blood volume (rBV), and apparent diffusion coefficient (ADC) values were obtained before and after the onset of radiotherapy. rBV, rBF, and ADC values were used to generate a semiquantitative pharmacokinetic model map to identify any hypoxic component of the tumor. The subjects were retrospectively classified as responders and nonresponders based on response to treatment. Prospective prediction of response status was done using pretreatment multiparametric MRI parameters (rBF, rBV, and ADC) and semiquantitative parametric map. In 32 patients (29 with parametrial involvement and 15 with lymphadenopathy), pretreatment ADC of the primary tumor was the most accurate measure for predicting response to treatment as well as for treatment-induced fractional volume reduction. Although rBV and rBF were insignificant metrics in isolation for predicting response status, a combination with ADC in the form of parametric map had a sensitivity of 86.4% and 77.2%, specificity of 70% and 70%, positive predictive value of 86.4% and 85%, and negative predictive value 70% and 59% respectively by two independent observers. ADC is the most accurate measure for predicting the response to treatment. A manual parametric map generated by an off-line fusion of the above map with those generated by pharmacokinetic modeling of perfusion-weighted MRI may be a useful tool for the prediction of response to radiotherapy.

Re: Risk Stratification of Patients Candidate to Radical Prostatectomy Based on Clinical and Multiparametric Magnetic Resonance Imaging Parameters: Development and External Validation of Novel Risk Groups

Re: Risk Stratification of Patients Candidate to Radical Prostatectomy Based on Clinical and Multiparametric Magnetic Resonance Imaging Parameters: Development and External Validation of Novel Risk Groups

Common Subtype of Small Renal Mass MR Imaging Characterisation: A Medical Center Experience in Taiwan

PurposeMany studies have shown that multiparametric magnetic resonance imaging (MRI) may be helpful for differentiating malignant renal cell carcinomas (RCCs) from benign lesions. However, the key imaging characteristics that differ between malignant and benign tumors still require further discussion.MethodsWe analyzed 60 adult patients diagnosed with 72 small renal masses (SRMs) who received preoperative MRI from 2014 to 2019 at a hospital in Taiwan. The MRI features included conventional MRI parameters, diffusion-weighted imaging (DWI) data, and dynamic contrast-enhanced (DCE) patterns, which were documented and compared among the four common subtypes: clear cell RCC (ccRCC), papillary RCC (pRCC), angiomyolipoma (AML) and other types of RCC. The apparent diffusion coefficient (ADC) values of high- and low-grade RCCs were also analyzed.ResultsThe results show that ccRCC had higher T2-weighted signal intensity than the other three subgroups, a higher arterial wash-in index (AWI) and ADC value than AML and pRCC, and manifested a plateau (n = 9, 25%) or washout (n = 27, 75%) enhancement pattern. AMLs exhibited more intravoxel fat than the other three subtype groups, and half of the AMLs (6 in 12) contained bulk fat. pRCC demonstrated a more progressive (n = 3, 60%) enhancement pattern than the other three subgroups. The ADC value of high-grade RCCs was significantly lower than that of low-grade RCCs.ConclusionThese findings may indicate that multiparametric MRI is useful in differentiating among four common pathological types of SRMs, and the ADC value may be helpful in evaluating the histological grade of malignancy.

Application of Multiparametric Magnetic Resonance Imaging to Monitor the Early Antitumor Effect of CuS@GOD Nanoparticles in a 4 T1 Breast Cancer Xenograft Model.

We have developed hybrid nanoparticles (NPs) by co-loading copper sulfide (CuS) NPs and glucose oxidase (GOD) (CuS@GOD NPs) to explore their antitumor properties. To investigate the feasibility of using multiparametric magnetic resonance imaging (MRI) including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and R2 * mapping to quantitatively assess the early antitumor effect of CuS@GOD NPs. Prospective. The orthotopic BALB/c mice 4 T1 breast cancer model. The 4 T1 xenografts in group 1 mice received normal saline, group 2 received CuS@GOD NPs, group 3 received CuS NPs plus laser, and group 4 received CuS@GOD NPs plus laser (n=28 for each group). A 3.0 T/IVIM-DWI MRI single-shot echo-planar imaging, R2 * mapping spoiled gradient recalled echo (SPGR) sequence, T2-weighted images (T2WI) and T1-weighted images (T1WI) fast spin echo (FSE) sequence. The IVIM-DWI and R2 * mapping were performed before and after treatment at 0 hour, 0.5 hour, 1 hour, 2 hours, 4 hours, and 24 hours in four groups and the MRI parameters were obtained. Correlation analysis between the MRI parameters and histological analyses was conducted. One-way ANOVA, Pearson's correlation analysis, two independent samples t test, intraclass correlation coefficient. P < 0.05 was considered to be statistically significant. In group 4, the tumoral D value was significantly higher than that of group 2 at 24 hours (0.541 ± 0.065 vs. 0.492 ± 0.051). The f value of group 4 was significantly lower than that of groups 1 and 2 at 2 hours (10.83 ± 2.16 vs. 14.28 ± 1.86, 16.67 ± 3.53, respectively). The R2 * value was significantly increased at 0 hour in group 4 compared to that of groups 1 and 2 (64.552 ± 4.663 vs. 42.441 ± 1.516, 43.165 ± 1.709, respectively). D, f, and R2 * were correlated with the histological staining results (r=0.695-0.970). The IVIM-DWI-derived D and f and R2 * mapping-derived R2 * could monitor early response to CuS@GOD NPs treatment in vivo. 2 TECHNICAL EFFICACY: Stage 2.

Risk Stratification of Patients Candidate to Radical Prostatectomy Based on Clinical and Multiparametric Magnetic Resonance Imaging Parameters: Development and External Validation of Novel Risk Groups

BackgroundDespite the key importance of magnetic resonance imaging (MRI) parameters, risk classification systems for biochemical recurrence (BCR) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) are still based on clinical variables alone. ObjectiveWe aimed at developing and validating a novel classification integrating clinical and radiological parameters. Design, setting, and participantsA retrospective multicenter cohort study was conducted between 2014 and 2020 across seven academic international referral centers. A total of 2565 patients treated with RP for PCa were identified. Outcome measurements and statistical analysisEarly BCR was defined as two prostate-specific antigen (PSA) values of ≥0.2 ng/ml within 3 yr after RP. Kaplan-Meier and Cox regressions tested time and predictors of BCR. Development and validation cohorts were generated from the overall patient sample. A model predicting early BCR based on Cox-derived coefficients represented the basis for a nomogram that was validated externally. Predictors consisted of PSA, biopsy grade group, MRI stage, and the maximum diameter of lesion at MRI. Novel risk categories were then identified. The Harrel’s concordance index (c-index) compared the accuracy of our risk stratification with the European Association of Urology (EAU), Cancer of the Prostate Risk Assessment (CAPRA), and International Staging Collaboration for Cancer of the Prostate (STAR-CAP) risk groups in predicting early BCR. Results and limitationsOverall, 200 (8%), 1834 (71%), and 531 (21%) had low-, intermediate-, and high-risk disease according to the EAU risk groups. The 3-yr overall BCR-free survival rate was 84%. No differences were observed in the 3-yr BCR-free survival between EAU low- and intermediate-risk groups (88% vs 87%; p = 0.1). The novel nomogram depicted optimal discrimination at external validation (c-index 78%). Four new risk categories were identified based on the predictors included in the Cox-based nomogram. This new risk classification had higher accuracy in predicting early BCR (c-index 70%) than the EAU, CAPRA, and STAR-CAP risk classifications (c-index 64%, 63%, and 67%, respectively). ConclusionsWe developed and externally validated four novel categories based on clinical and radiological parameters to predict early BCR. This novel classification exhibited higher accuracy than the available tools. Patient summaryOur novel and straightforward risk classification outperformed currently available preoperative risk tools and should, therefore, assist physicians in preoperative counseling of men candidate to radical treatment for prostate cancer.

Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?

We aimed to explore whether multiparametric magnetic resonance imaging (MRI)-based radiomics combined with selected blood inflammatory markers could effectively predict the grade and proliferation in glioma patients. This retrospective study included 152 patients histopathologically diagnosed with glioma. Stratified sampling was used to divide all patients into a training cohort (n=107) and a validation cohort (n=45) according to a ratio of 7:3, and five-fold repeat cross-validation was adopted in the training cohort. Multiparametric MRI and clinical parameters, including age, the neutrophil-lymphocyte ratio and red cell distribution width, were assessed. During image processing, image registration and gray normalization were conducted. A radiomics analysis was performed by extracting 1584 multiparametric MRI-based features, and the least absolute shrinkage and selection operator (LASSO) was applied to generate a radiomics signature for predicting grade and Ki-67 index in both training and validation cohorts. Statistical analysis included analysis of variance, Pearson correlation, intraclass correlation coefficient, multivariate logistic regression, Hosmer-Lemeshow test, and receiver operating characteristic (ROC) curve. The radiomics signature demonstrated good performance in both the training and validation cohorts, with areas under the ROC curve (AUCs) of 0.92, 0.91, and 0.94 and 0.94, 0.75, and 0.82 for differentiating between low and high grade gliomas, grade III and grade IV gliomas, and low Ki-67 and high Ki-67, respectively, and was better than the clinical model; the AUCs of the combined model were 0.93, 0.91, and 0.95 and 0.94, 0.76, and 0.80, respectively. Both the radiomics signature and combined model showed high diagnostic efficacy and outperformed the clinical model. The clinical factors did not provide additional improvement in the prediction of the grade and proliferation index in glioma patients, but the stability was improved.

Neurovascular multiparametric MRI defines epileptogenic and seizure propagation regions in experimental mesiotemporal lobe epilepsy.

Improving the identification of the epileptogenic zone and associated seizure-spreading regions represents a significant challenge. Innovative brain-imaging modalities tracking neurovascular dynamics during seizures may provide new disease biomarkers. With use of a multi-parametric magnetic resonance imaging (MRI) analysis at 9.4 Tesla, we examined, elaborated, and combined multiple cellular and cerebrovascular MRI read-outs as imaging biomarkers of the epileptogenic and seizure-propagating regions. Analyses were performed in an experimental model of mesial temporal lobe epilepsy (MTLE) generated by unilateral intra-hippocampal injection of kainic acid (KA). In the ipsilateral epileptogenic hippocampi, tissue T1 and blood-brain barrier (BBB) permeability to gadolinium were increased 48-72hours post-KA, as compared to sham and contralateral hippocampi. BBB permeability endured during spontaneous focal seizures (4-6weeks), along with a significant increase of apparent diffusion coefficient (ADC) and blood volume fraction (BVf). Simultaneously, ADC and BVf were augmented in the contralateral hippocampus, a region characterized by electroencephalographic seizure spreading, discrete histological neurovascular cell modifications, and no tissue sclerosis. We next asked whether combining all the acquired MRI parameters could deliver criteria to classify the epileptogenic from the seizure-spreading and sham hippocampi in these experimental conditions and over time. To differentiate sham from epileptogenic areas, the automatic multi-parametric classification provided a maximum accuracy of 97.5% (32 regions) 48-72hours post-KA and of 100% (60 regions) at spontaneous seizures stage. To differentiate sham, epileptogenic, and seizure-spreading areas, the accuracies of the automatic classification were 93.1% (42 regions) 48-72hours post-KA and 95% (80 regions) at spontaneous seizure stage. Combining multi-parametric MRI acquisition and machine-learning analyses delivers specific imaging identifiers to segregate the epileptogenic from the contralateral seizure-spreading hippocampi in experimental MTLE. The potential clinical value of our findings is critically discussed.

The role of multi-parametric magnetic resonance imaging (MRI) in early prediction of malignant transformation of low-grade Gliomas (A Systematic review)

Introduction: Low-grade gliomas is the most common primary brain tumour, although the presentation may take up to two decades, there is high tendency of early malignant transformation which raise a growing concern. Multi-parametric MRI studies have the potential for predicting the early malignant transformation. Methods: A comprehensive electronic search of various databases was conducted together with forward tracking of the reference list to retrieve relevant qualitative primary studies. Moreover, hand search for journal that was not available electronically was also conducted. Through assessment of the relevant studies was ensured and the included studies were carefully selected. The relevant data was extracted by data extraction form recommended by Cochrane collaborations. Results: The search yielded 1158 which was narrowed down to eight (8) studies that satisfied the inclusion criteria. These studies are assessing the role of different MRI parameters in predicting the early malignant transformation of Low-grade gliomas. The risk of bias and the applicability concern of the included studies are low. Conclusion: Based on the findings of this review; Multi-parametric MRI studies have the potential of predicting the early malignant transformation of low-grade gliomas. There is need for high quality large scale, prospective studies on the role of multi-parametric MRI studies in early prediction of malignant transformation of LGGs and meta-analysis of these studies is highly recommended.

The Role of Multiparametric Magnetic Resonance Imaging in the Study of Primary Tumor and Pelvic Lymph Node Metastasis in Stage IB1-IIA1 Cervical Cancer.

The aim of this study was to investigate the value of multiparametric magnetic resonance imaging (MRI) in demonstrating the metastatic potential of primary tumor and differentiating metastatic lymph nodes (MLNs) from nonmetastatic lymph nodes (non-MLNs) in stage IB1-IIA1 cervical cancer. Fifty-seven stage IB1-IIA1 subjects were included. The apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced MRI (DCE-MRI) parameters of primary tumors and lymph nodes and the conventional imaging features of the lymph nodes were measured and analyzed. Mann-Whitney test and χ test were used to analyze statistically significant parameters, logistic regression was used for multivariate analysis, and receiver operating characteristic analysis was used to compare the diagnostic performance of the MLNs. Nineteen subjects had lymph node metastasis. A total of 94 lymph nodes were evaluated, including 30 MLNs and 64 non-MLNs. There were no significant difference in ADC and DCE-MRI parameters between metastatic and nonmetastatic primary tumors. The heterogeneous signal was more commonly seen in MLNs than in non-MLNs (P = 0.001). The values of ADCmean, ADCmin, and ADCmax of MLNs were lower than those of non-MLNs (P < 0.001). The values of short-axis diameter, K, Kep, and Ve of MLNs were higher than those of non-MLNs (P < 0.05). Compared with individual MRI parameters, the combined evaluation of short-axis diameter, ADCmean, and K showed the highest area under the curve of 0.930. Diffusion-weighted imaging and DCE-MRI could not demonstrate the metastatic potential of primary tumor in stage IB1-IIA1 cervical cancer. Compared with individual MRI parameters, the combination of multiparametric MRI could improve the diagnostic performance of lymph node metastasis.

Prediction of early response to concurrent chemoradiotherapy in cervical cancer: Value of multi-parameter MRI combined with clinical prognostic factors

PurposeThis study aimed to investigate the prediction of early response to concurrent chemoradiotherapy (CCRT) through a combination of pretreatment multi-parametric magnetic resonance imaging (MRI) with clinical prognostic factors (CPF) in cervical cancer patients. MethodsEighty-five patients with pathologically confirmed cervical cancer underwent conventional MRI, intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) before CCRT. The patients were divided into non- and residual tumor groups according to post-treatment MRI. Univariable and multivariable analyses were performed to pretreatment MRI parameters and CPF between the two groups, and optimal thresholds and predictive performance for post-treatment residual tumor occurrence were estimated by drawing the receiver operating characteristic (ROC) curve. ResultsThere were 52 patients in non- and 33 in residual group. The residual group showed a lower perfusion fraction (f) value and volume transfer constant (Ktrans) value, a higher apparent diffusion coefficient (ADC) value, diffusion coefficient (D) value and volume fraction of extravascular extracellular space (Ve) value, and a higher stage than the non-residual tumor group (all P < .05). D, Ktrans, Ve and stage were independent prognostic factors. The combination of D, Ktrans and Ve improved the diagnostic performance compared with individual MRI parameters. A further combination of these three MRI parameters with stage exhibited the highest predictive performance. ConclusionsPretreatment D, Ktrans, Ve and stage were independent prognostic factors for cervical cancer. The predictive capacity of multi-parametric MRI was superior to individual MRI parameters. The combination of multi-parametric MRI with CPF further improved the predictive performance.