Dynamic Contrast-enhanced Sequences Research Articles

Multi-parametric magnetic resonance imaging of prostate cancer: correlation between Ktrans, Gleason score, and PI-RADS score

PurposeTo measure Ktrans and correlate it with Gleason score (GS) and PI-RADS score in patients with prostate cancer.MethodsThis retrospective study included patients with pathologically proven prostate cancer who had undergone clinically indicated 1.5 Tesla multi-parametric magnetic resonance imaging (MRI) examination between February and December 2020. T2-weighted (T2w) images, diffusion-weighted images (DWI), and dynamic contrast-enhanced (DCE) sequences were obtained. PI-RADS score was calculated for all tumor lesions. From DCE-MRI dataset, Ktrans was computed and compared between patients with clinically insignificant (GS ≤ 6) and clinically significant (GS ≥ 7) prostate cancer. Spearman rank-order correlation coefficient (ρ) was used to assess the correlation strength between Ktrans and GS and between Ktrans and PI-RADS score.ResultsTwenty-one patients (age: 67 ± 12 years; BMI: 26.63 ± 4.04 kg/m2) with a PSA of 7.91 ± 3.01 were included in the study. Seven patients (33.3%) had clinically insignificant prostate cancer, while 14 patients (66.7%) were diagnosed with clinically significant prostate cancer. Mean Ktrans value was 0.42 ± 0.20 min−1 (range: 0.15–0.75). Ktrans was significantly higher (0.50 ± 0.17 min−1) in clinically significant prostate cancer compared to clinical insignificant prostate cancer (0.23 ± 0.15 min−1; P = 0.001). Ktrans showed moderate significant correlation with GS (ρ = 0.575, P = 0.006), but showed no significant correlation with PI-RADS (ρ = 0.386, P = 0.069).ConclusionKtrans may discriminate between clinically insignificant and significant prostate cancer and shows moderate correlation with GS. Thus, MP-MRI may serve as an imaging biomarker in prostate cancer.

Relationship between kinetic parameters of ultrafast dynamic contrast-enhanced (DCE) MRI and tumor-infiltrating lymphocytes (TILs) in breast cancer.

To evaluate the relationship between kinetic parameters of ultrafast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and tumor-infiltrating lymphocytes (TILs) in breast cancer. This retrospective study was approved by an institutional review board and included 76 women (median age: 60) with 76 surgically proven breast cancers who underwent DCE MRI including ultrafast sequence. Based on the TILs level, we classified the patients into the low-TILs (< 10%) group and the high-TILs (≥ 10%) group. Maximum slope (MS) and time to enhancement (TTE) derived from ultrafast DCE sequence were correlated in each TILs group. The percentages of six kinetic patterns (fast, medium, and slow from the early phase, washout, plateau, and persistent from the delayed phase) derived from the conventional DCE sequence were also correlated in each TILs group. Of the 76 breast cancers, 57 were in the low-TILs group and 19 comprised the high-TILs group. The median MS in the high-TILs group (32.4%/sec) was significantly higher than that in the low-TILs group (23.68%/s) (p = 0.037). In a receiver-operating characteristic (ROC) analysis, the area under the curve (AUC) for differentiating between the high- and low-TILs group was 0.661. The TTE in the high-TILs group was significantly shorter than that in the low-TILs group (p = 0.012). In the ROC analysis, the AUC was 0.685. There were no significant differences between the percentages of the six kinetic patterns from the conventional DCE sequence and the TILs level (p = 0.075-0.876). Compared to the low-TILs group, the high-TILs group had higher MS and shorter TTE.

Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study.

Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches. To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer. Retrospective. A total of 506 females (range: 24-83 years) with cT1-T2 stage breast cancer from two institutions, forming the training (N = 340), internal validation (N = 85), and external validation cohorts (N = 81), respectively. This study used 1.5-T, axial fat-suppressed T2-weighted turbo spin-echo sequence and axial three-dimensional dynamic contrast-enhanced fat-suppressed T1-weighted gradient echo sequence. Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k-Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best-performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease-free survival (DFS) were examined. Chi-squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer-Lemeshow test, log-rank, Cox regression analyses, and intraclass correlation coefficient were performed. A P-value <0.05 was considered statistically significant. The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059-15.207). The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1-T2 stage breast cancer patients. 3 TECHNICAL EFFICACY: Stage 2.

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. Retrospective. One hundred ninety-one patients (median age, 65 years; age range, 54-73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. Student's t-tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan-Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792-0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749-0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. 3 TECHNICAL EFFICACY: Stage 3.

Intracranial meningioma: A review of recent and emerging data on the utility of preoperative imaging for management.

Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.

Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques.

Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear. To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation. Systematic review and meta-analysis. Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488). Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T. Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs. Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant. HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08). ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible. Stage 2.

AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer

PurposeTo develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI. MethodsThis study was approved by the institutional review board and requirement for study-specific informed consent was waived. A mobile app was developed to integrate AI-based image quality analysis into clinical workflow. An expert radiologist provided reference decisions. Diagnostic performance parameters (sensitivity and specificity) were calculated and inter-reader agreement was evaluated. ResultsFully automated evaluation was possible in 87% of cases, with the application reaching a sensitivity of 80% and a specificity of 100% in selecting patients for multiparametric MRI. In 2% of patients, the application falsely decided on omitting DCE. With a technician reaching a sensitivity of 29% and specificity of 98%, and resident radiologists reaching sensitivity of 29% and specificity of 93%, the use of the application allowed a significant increase in sensitivity. ConclusionThe presented AI application accurately decides on a patient-specific MRI protocol based on image quality analysis, potentially allowing omission of DCE in the diagnostic workup of patients with suspected prostate cancer. This could streamline workflow and optimize time utilization of healthcare professionals.

Added Value of [18F]PSMA-1007 PET/CT and PET/MRI in Patients With Biochemically Recurrent Prostate Cancer: Impact on Detection Rates and Clinical Management.

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) can change management in a large fraction of patients with biochemically recurrent prostate cancer (BCR). To investigate the added value of PET to MRI and CT for this patient group, and to explore whether the choice of the PET paired modality (PET/MRI vs. PET/CT) impacts detection rates and clinical management. Retrospective. 41 patients with BCR (median age [range]: 68 [55-78]). 3T, including T1-weighted gradient echo (GRE), T2-weighted turbo spin echo (TSE) and dynamic contrast-enhanced GRE sequences, diffusion-weighted echo-planar imaging, and a T1-weighted TSE spine sequence. In addition to MRI, [18F]PSMA-1007 PET and low-dose CT were acquired on the same day. Images were reported using a five-point Likert scale by two teams each consisting of a radiologist and a nuclear medicine physician. The radiologist performed a reading using CT and MRI data and a joint reading between radiologist and nuclear medicine physician was performed using MRI, CT, and PET from either PET/MRI or PET/CT. Findings were presented to an oncologist to create intended treatment plans. Intrareader and interreader agreement analysis was performed. McNemar test, Cohen's κ, and intraclass correlation coefficients. A P-value <0.05 was considered significant. 7 patients had positive findings on MRI and CT, 22 patients on joint reading with PET/CT, and 18 patients joint reading with PET/MRI. For overall positivity, interreader agreement was poor for MR and CT (κ = 0.36) and almost perfect with addition of PET (PET/CT κ = 0.85, PET/MRI κ = 0.85). The addition of PET from PET/CT and PET/MRI changed intended treatment in 20 and 18 patients, respectively. Between joint readings, intended treatment was different for eight patients. The addition of [18F]PSMA-1007 PET/MRI or PET/CT to MRI and CT may increase detection rates, could reduce interreader variability, and may change intended treatment in half of patients with BCR. 3 TECHNICAL EFFICACY: Stage 3.

Validating the diagnostic accuracy of an MRI-based scoring system for differentiating benign uterine leiomyomas from leiomyosarcomas

ObjectiveUterine leiomyomas are the most common benign uterine tumors. They are difficult to distinguish from their malignant counterparts—smooth muscle tumors of unknown malignant potential (STUMP) and leiomyosarcoma. The purpose of...

Practical Guide to VI-RADS: MRI Protocols, Lesion Characterization, and Pitfalls.

Urothelial carcinoma is the most common type of bladder cancer (BC), accounting for approximately 90% of all cases. Evaluating the depth of tumor invasion in the bladder wall (tumor staging) is essential for determining the treatment and prognosis in patients with BC. Neoadjuvant therapy followed by radical cystectomy is the most common treatment of localized muscle-invasive BC (MIBC). Therefore, it is vital to differentiate non-MIBC from MIBC. Transurethral resection of bladder tumor (TURBT) is the reference standard to determine the extent of tumor invasion into the bladder wall through tissue sampling. However, this diagnostic and therapeutic method may not adequately sample the muscularis propria, leading to a higher risk of residual disease, early recurrence, and tumor understaging in approximately 50% of patients during the initial TURBT. Multiparametric MRI can overcome some of the limitations of TURBT when evaluating BC, particularly regarding tumor staging. In this context, the Vesical Imaging Reporting and Data System (VI-RADS) classification was developed to establish standards for bladder multiparametric MRI and interpretation. It uses a 5-point scale to assess the likelihood of detrusor muscle invasion. T2-weighted MR images are particularly useful as an initial guide, especially for categories 1-3, while the presence of muscular invasion is determined with diffusion-weighted and dynamic contrast-enhanced sequences. Diffusion-weighted imaging takes precedence as the dominant method when optimal image quality is achieved. The presence of a stalk or a thickened inner layer and no evidence of interruption of the signal intensity of the muscular layer are central for predicting a low likelihood of muscle invasion. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Hoegger in this issue.

Three-Dimensional Multifrequency MR Elastography for Microvascular Invasion and Prognosis Assessment in Hepatocellular Carcinoma.

Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies. To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE). Prospective. 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up. 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences. Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors. Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant. Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance. Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established. 2 TECHNICAL EFFICACY: Stage 2.

Understanding Spatial Correlation Between Multiparametric MRI Performance and Prostate Cancer.

Multiparametric MRI (mpMRI) has shown a substantial impact on prostate cancer (PCa) diagnosis. However, the understanding of the spatial correlation between mpMRI performance and PCa location is still limited. To investigate the association between mpMRI performance and tumor spatial location within the prostate using a prostate sector map, described by Prostate Imaging Reporting and Data System (PI-RADS) v2.1. Retrospective. One thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022. 3.0 T. T2-weighted turbo spin-echo, a single-shot spin-echo EPI sequence for diffusion-weighted imaging, and a gradient echo sequence for dynamic contrast-enhanced MRI sequences. Integrated relative cancer prevalence (rCP), detection rate (DR), and positive predictive value (PPV) maps corresponding to the prostate sector map for PCa lesions were created. The relationship between tumor location and its detection/missing by radiologists on mpMRI compared to WMHP as a reference standard was investigated. A weighted chi-square test was performed to examine the statistical differences for rCP, DR, and PPV of the aggregated sectors within the zone, anterior/posterior, left/right prostate, and different levels of the prostate with a statistically significant level of 0.05. A total of 1665 PCa lesions were identified in 1143 patients, and from those 1060 lesions were clinically significant (cs)PCa tumors (any Gleason score [GS] ≥7). Our sector-based analysis utilizing weighted chi-square tests suggested that the left posterior part of PZ had a high likelihood of missing csPCa lesions at a DR of 67.0%. Aggregated sector analysis indicated that the anterior or apex locations in PZ had the significantly lowest csPCa detection at 67.3% and 71.5%, respectively. Spatial characteristics of the per-lesion-based mpMRI performance for diagnosis of PCa were studied. Our results demonstrated that there is a spatial correlation between mpMRI performance and locations of PCa on the prostate. 4 TECHNICAL EFFICACY: Stage 2.

Improved diagnostic confidence and tumor type prediction in adult-type diffuse glioma by multimodal imaging including DCE perfusion and diffusion kurtosis mapping – A standardized multicenter study

Background and purposeTo evaluate the feasibility of a multimodal approach involving dynamic contrast-enhanced (DCE) perfusion imaging and diffusion kurtosis imaging (DKI) in the preoperative imaging of brain tumors in a multicenter setting, and to evaluate the effect on diagnostic confidence and accuracy for tumor grade and type prediction. Materials and MethodsOne hundred and thirty-three patients with brain tumors were imaged in six hospitals with a standardized multimodal protocol. Standard imaging and six parameter maps derived from DCE and DKI sequences were reviewed off-site by two independent readers. Image quality and diagnostic confidence were evaluated in qualitative analyses. Quantitative analyses were performed to assess diagnostic accuracy and the performance of DKI and DCE parameters for tumor grade differentiation and molecular tumor type determination. ResultsStandardized acquisition of DCE and DKI maps was feasible with excellent image quality. Diagnostic confidence was significantly improved from 85 % to 96 % (p = 0.0005) by additional review of the DCE and DKI maps. The combination of mean kurtosis and CBV was particularly advantageous for differentiating low-grade and high-grade glioma, oligodendroglial vs. astrocytic, and IDH1/2 wild type vs. mutated tumors. ConclusionA multimodal imaging approach with DCE and DKI improves diagnostic confidence and yields higher diagnostic accuracy for predicting tumor grade and type in adult-type glioma.

Utility of Prostate Health Index Density for Biopsy Strategy in Biopsy-Naïve Patients With PI-RADS v2.1 Category 3 Lesions.

Category 3 lesions in PI-RADSv2.1 pose diagnostic challenges, complicating biopsy decisions. Recent biomarkers like prostate health index (PHI) have shown higher specificity in detecting clinically significant prostate cancer (csPCa) than prostate-specific antigen (PSA). Yet their integration with MRI remains understudied. To evaluate the utility of PSA and PHI with its derivatives for detecting csPCa in biopsy-naïve patients with category 3 lesion on initial prostate MRI scan. Retrospective. One hundred ninety-three biopsy-naïve patients who underwent MRI, PSA, and PHI testing, followed by both targeted and systematic biopsies. Turbo spin-echo T2-weighted imaging, diffusion-weighted single-shot echo-planar imaging, and dynamic contrast-enhanced T1-weighted fast field echo sequence imaging in 3 T. PHI density (PHID) and PSA density (PSAD) derived by dividing serum PHI and PSA with prostate volume (MRI based methodology suggested by PI-RADSv2.1). Risk-stratified models to evaluate the utility of markers in triaging patients for biopsy, including low-, intermediate-, and high-risk groups. Independent t-test, Mann-Whitney U test, Mantel-Haenszel test, generalized estimating equation, and receiver operating characteristic (ROC) curve analysis were used. Statistical significance defined as P < 0.05. CsPCa was found in 16.6% (32/193) of patients. PHID had the highest area under the ROC curve (AUROC) of 0.793, followed by PHI of 0.752, PSAD of 0.750, and PSA of 0.654. PHID with two cut-off points (0.88/mL and 1.82/mL) showed the highest potential biopsy avoidance of 47.7% (92/193) with 5% missing csPCa, and the lowest intermediate-risk group (borderline decision group) at 38.9% (75/193), compared to PSA and PHI. PHID demonstrated better potential in triaging patients with category 3 lesions, possibly aiding more selective and confident biopsy decisions for csPCa detection, than traditional markers. 4 TECHNICAL EFFICACY: Stage 5.

Diagnostic Utility of Integration of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MR Perfusion Employing Split Bolus Technique in Differentiating High-Grade Glioma.

Background : Despite documented correlation between glioma grades and dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion-derived parameters, and its inherent advantages over dynamic susceptibility contrast (DSC) perfusion, the former remains underutilized in clinical practice. Given the inherent spatial heterogeneity in high-grade diffuse glioma (HGG) and assessment of different perfusion parameters by DCE (extravascular extracellular space volume [Ve] and volume transfer constant in unit time [k-trans]) and DSC (rCBV), integration of the two into a protocol could provide a holistic assessment. Considering therapeutic and prognostic implications of differentiating WHO grade 3 from 4, we analyzed the two grades based on a combined DCE and DSC perfusion. Methods : Perfusion sequences were performed on 3-T MR. Cumulative dose of 0.1 mmol/kg of gadodiamide, split into two equal boluses, was administered with an interval of 6 minutes between the DCE and DSC sequences. DCE data were analyzed utilizing commercially available GenIQ software. Results : Of the 41 cases of diffuse gliomas analyzed, 24 were WHO grade III and 17 grade IV gliomas (2016 WHO classification). To differentiate grade III and IV gliomas, Ve cut-off value of 0.178 provided the best combination of sensitivity (88.24%) and specificity (87.50%; AUC: 0.920; p < 0.001). A relative cerebral blood volume (rCBV) of value 3.64 yielded a sensitivity of 70.59% and specificity of 62.50% ( p = 0.018). The k-trans value, although higher in grade III than in grade IV gliomas, did not reach statistical significance ( p = 0.108). Conclusion : Uniqueness of employed combined perfusion technique, treatment naïve patients at imaging, user-friendly postprocessing software utilization, and ability of Ve and rCBV to differentiate between grade III and IV gliomas ( p < 0.05) are the strengths of the present study, contributing to the existing literature and moving a step closer to achieving accurate MR perfusion-based glioma grading.

Comparative Analysis of FSE T2 Weighted, Chemical Shift and Dynamic Contrast-Enhanced MR Imaging in the Characterization of Adrenal Masses Based on Qualitative and Quantitative Parameters.

The purpose of our study was to investigate the role of different magnetic resonance imaging (MRI) parameters in the characterization of adrenal masses. A total of 150 patients who presented with 186 adrenal tumors were retrospectively evaluated in this study. Final patient cohort consisted of 17 pheochromocytomas, 3 adrenocortical carcinomas, 24 metastases, 31 lipid-poor adenomas and 111 lipid-rich adenomas. We carried out a visual assessment on FSE (Fast spin echo)T2 weighted images and also calculated T2 signal intensity ratio of all adrenal masses and also performed a qualitative assessment on chemical shift imaging (CSI) together with quantitative calculation using Adrenal to spleen signal intensity (si) ratio and Adrenal si index formulas. On dynamic contrast-enhanced sequences, visual assessment based on enhancement patterns on late-arterial phase images was performed and also mean signal intensity measurements were carried out. All examinations were interpreted by two abdominal radiologists in consensus who were blinded to the clinical and pathological findings. Statistical analysis was performed. On FSE T2 weighted imaging, isointense to liver and slightly hyperintense than liver was found higher in benign cases, however, in malignant cases moderately and strikingly hyperintense than liver was higher than in benign cases (p=0.001, p<0.01). There was a statistically significant difference between the T2 signal intensity ratio values of adrenal tumor groups (p=0.001, p<0.01). In lipid-rich and lipid-poor adenoma groups, T2 signal intensity ratio values was significantly lower than in pheochromocytoma and metastasis cases. In malignant group, T2 signal intensity ratio values were found statistically significantly higher than in the benign group (p=0.001, p<0.01). There was a statistically significant difference between CSI visual assessment of adrenal tumor groups (p=0.001, p<0.01). Although moderate and significant signal intensity loss was usually detected in lipid-rich adenoma group, never detected in other tumor groups. There was also a statistically significant difference between benign and malignant adrenal tumor groups (p=0.001, p<0.01). In the malignant group, Adrenal to spleen si ratio values were found significantly higher whereas, Adrenal si index values were significantly lower compared to benign tumors (p=0.001, p<0.01). Based on malignancy, there was a statistically significant difference between adrenal tumor groups (p=0.001, p<0.01). Although capillary blush and homogenous type enhancement were more common in benign cases than in malignant ones, peripheral-patchy and strikingly capillary blush type enhancement was more frequent in malignant tumors. Based on malignancy, mean arterial signal intensity values of malignant tumors were statistically higher than benign tumors (p=0.001; p<0.01). Dynamic contrast-enhanced MRI protocol including CSI aids in the characterization of indeterminate adrenal masses. Herein, the combined use of qualitative and quantitative parameters enables more tumors to be recognized that otherwise would be indeterminate.

The value of dynamic contrast-enhanced MRI and diffusion-weighted sequence in the evaluation of endometrial lesions

Background/Aim: Endometrial abnormalities represent a diagnostic challenge due to overlapping imaging features with normal endometrium. Aim of this study was to assess accuracy of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging (MRI) in evaluation of endometrial lesions in comparison with T2 and to assess local staging validity and degree of myometrial invasion in malignancy. Methods: Forty patients with abnormal vaginal bleeding or sonographic thickened endometrial were recruited. MRI examination of pelvis was per-formed using 1.5 T scanner with a pelvic array coil. Conventional T1-and T2, dynamic contrast-enhanced (DCE) sequences and diffusion-weighted image (DWI) were performed. Results: Mean age of patients was 53.2 years and 60 % of patients COM-plained of post-menopausal bleeding. Irregular margin, type III enhancement curve, a high signal in T2WI and DWI and low signal of apparent diffusion coefficient (ADC) were significantly associated with malignancy. The optimum ADC threshold value for distinguishing benign from malignant endometrial lesions was 0.905 × 10 -3 mm 2 /S, with 95.5 % sensitivity and 92.9 % specificity. DWI was most sensitive to malignant endometrial lesions, followed by DCE (89.6 %, 98.4 %) and T2 (86.7 %, 91.4 %). DWI and DCE staging correlated with FIGO staging (p = 0.0001 and p = 0.019, respectively). DWI had the best sensitivity for myometrial invasion (95.6 %), followed by DCE (91.9 %) and T2WI (90.1 %). All three sequences had 89.7 % specificity. Conclusion: DWI and DCE MRI were superior to conventional MRI at distinguishing malignant from benign endometrial lesions and can improve myometrial invasion depth evaluation and therapy planning when COM-bined with morphological T2WI. ADC cutoff at a high b value improved MRI diagnostic sensitivity and specificity.

Feature-enhanced multi-sequence MRI-based fusion mechanism for breast tumor segmentation

Multi-sequence MRI plays a crucial role in the effective segmentation of breast tumors, contributing to accurate clinical diagnosis and treatment. However, the problem of missing certain sequence images may occur in clinical practice, leading to a potential impact on network performance. In medical imaging tasks, researchers are prone to ignore the correlation between different sequences, resulting in lack of expression of the extracted features. To address these problems, we propose the Feature-enhanced Multi-sequence Feature Fusion Network (FMFF-Net), which fuses information from dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) sequences to improve the accuracy of breast tumor segmentation. Utilizing an attention mechanism and adversarial learning, FMFF-Net identifies potential mappings and relationships between sequences, generating features for missing sequences and enhancing feature representation. The network incorporates a High-Frequency Edge Attention Block (HFAB) to accentuate tumor edge details, leading to more precise segmentation. Tested on a dataset of 98 high-risk breast cancer MRI images, FMFF-Net demonstrated superior performance, with a Dice Similarity Coefficient (DSC) of 83.6%, Intersection over Union (IoU) of 73.4%, F1 score of 88.1%, and Sensitivity of 74.1%. Comparative analysis against mainstream segmentation methods revealed that our proposed FMFF-Net exhibits a competitive edge in the field of breast tumor segmentation.

Inter-Rater Variability of Prostate Lesion Segmentation on Multiparametric Prostate MRI.

External radiotherapy is a major treatment for localized prostate cancer (PCa). Dose escalation to the whole prostate gland increases biochemical relapse-free survival but also acute and late toxicities. Dose escalation to the dominant index lesion (DIL) only is of growing interest. It requires a robust delineation of the DIL. In this context, we aimed to evaluate the inter-observer variability of DIL delineation. Two junior radiologists and a senior radiation oncologist delineated DILs on 64 mpMRIs of patients with histologically confirmed PCa. For each mpMRI and each reader, eight individual DIL segmentations were delineated. These delineations were blindly performed from one another and resulted from the individual analysis of the T2, apparent diffusion coefficient (ADC), b2000, and dynamic contrast enhanced (DCE) sequences, as well as the analysis of combined sequences (T2ADC, T2ADCb2000, T2ADCDCE, and T2ADCb2000DCE). Delineation variability was assessed using the DICE coefficient, Jaccard index, Hausdorff distance measure, and mean distance to agreement. T2, ADC, T2ADC, b2000, T2 + ADC + b2000, T2 + ADC + DCE, and T2 + ADC + b2000 + DCE sequences obtained DICE coefficients of 0.51, 0.50, 0.54, 0.52, 0.54, 0.55, 0.53, respectively, which are significantly higher than the perfusion sequence alone (0.35, p < 0.001). The analysis of other similarity metrics lead to similar results. The tumor volume and PI-RADS classification were positively correlated with the DICE scores. Our study showed that the contours of prostatic lesions were more reproducible on certain sequences but confirmed the great variability of prostatic contours with a maximum DICE coefficient calculated at 0.55 (joint analysis of T2, ADC, and perfusion sequences).

Radiomics nomogram for predicting disease-free survival after partial resection or radical cystectomy in patients with bladder cancer.

To create a MRI-derived radiomics nomogram that combined clinicopathological factors and radiomics signature (Rad-score) for predicting disease-free survival (DFS) in patients with bladder cancer (BCa) following partial resection (PR) or radical cystectomy (RC), including lymphadenectomy (LAE). Finally, 80 patients with BCa after PR or RC with LAE were enrolled. Patients were randomly split into training (n = 56) and internal validation (n = 24) cohorts. Radiomic features were extracted from T2-weighted, dynamic contrast-enhanced, diffusion-weighted imaging, and apparent diffusion coefficient sequence. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was applied to choose the valuable features and construct the Rad-score. The DFS prediction model was built using the Cox proportional hazards model. The relationship between the Rad-score and DFS was assessed using Kaplan-Meier analysis. A radiomics nomogram that combined the Rad-score and clinicopathological factors was created for individualized DFS estimation. In both the training and validation cohorts, the Rad-score was positively correlated with DFS (P < .001). In the validation cohort, the radiomics nomogram combining the Rad-score, tumour pathologic stage (pT stage), and lymphovascular invasion (LVI) achieved better performance in DFS prediction (C-index, 0.807; 95% CI, 0.713-0.901) than either the clinicopathological (C-index, 0.654; 95% CI, 0.467-0.841) or Rad-score-only model (C-index, 0.770; 95% CI, 0.702-0.837). The Rad-score was an independent predictor of DFS for patients with BCa after PR or RC with LAE, and the radiomics nomogram that combined the Rad-score, pT stage, and LVI achieved better performance in individual DFS prediction. This study provided a non-invasive and simple method for personalized and accurate prediction of DFS in BCa patients after PR or RC.