Use Of Multiparametric Magnetic Resonance Imaging Research Articles

Multiparametric magnetic resonance imaging for hepatocellular carcinoma, part1 : Morphology and dynamic perfusion imaging in primary diagnostics and treatment monitoring

Radiology plays akey role in the diagnosis and monitoring of hepatocellular carcinoma (HCC). Ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) are used to identify HCC lesions. Multiparametric MRI provides detailed insights into the tumor biology through the analysis of morphology, perfusion and diffusion. In this way preoperative decisions can be optimized. The guidelines recommend using contrast-enhanced MRI or ultrasound for the diagnosis of HCC. The preferred method is MRI due to its superiority in the detection of small lesions The treatment response is evaluated using modified response evaluation criteria for solid tumors (RECIST) and the European Association for the Study of the Liver (EASL) criteria. The use of multiparametric MRI in conjunction with the liver imaging reporting and data system (LI-RADS) plays overall acentral role in the precise diagnosis and monitoring of the treatment of HCC.

Are there reliable multiparametric MRI criteria for differential diagnosis between intracranial meningiomas and solitary intracranial dural metastases?

Intracranial meningiomas are the most common tumors of the central nervous system (CNS). Meningiomas account for up to 36% of all brain tumors. The incidence of metastatic brain lesions has not been determined. Up to 30% of adult patients with cancer of one localization or another suffer from a secondary tumor lesion of the brain. The vast majority of meningiomas have meningeal localization; >90% are solitary. The incidence of intracranial dural metastases (IDM) is 8-9% of cases, while in 10% of cases, the brain is the only localization, and in 50% of cases the metastases are solitary. Typically, the task of distinguishing between meningioma and dural metastasis does not involve difficulties. Periodically, there is a situation when the differential diagnosis between these tumors is ambiguous, since meningiomas and solitary IDM may have similar characteristics, in particular, a cavity-less solid structure, limited diffusion of water molecules, the presence of extensive peritumoral edema, and an identical contrast pattern. The present study included 100 patients with newly diagnosed tumors of the CNS, who subsequently underwent examination and neurosurgical treatment at the Federal Center for Neurosurgery with histological verification between May 2019 and October 2022. Depending on the histological conclusion, two study groups of patients were distinguished: The first group consisted of patients diagnosed with intracranial meningiomas (n=50) and the second group of patients were diagnosed with IDM (n=50). The study was performed using a magnetic resonance imaging (MRI) General Electric Discovery W750 3T before and after contrast enhancement. The diagnostic value of this study was estimated using Receiver Operating Characteristic curve and area under the curve analysis. Based on the results of the study, it was found that the use of multiparametric MRI (mpMRI) in the differential diagnosis of intracranial meningiomas and IDM was limited by the similarity of the values of the measured diffusion coefficient. The assumption, previously put forward in the literature, regarding the presence of a statistically significant difference in the apparent diffusion coefficient values, which make it possible to differentiate tumors, was not confirmed. When analyzing perfusion data, IDM showed higher cerebral blood flow (CBF) values compared with intracranial meningiomas (P≤0.001). A threshold value of the CBF index was revealed, which was 217.9 ml/100 g/min, above which it is possible to predict IDM with a sensitivity and specificity of 80.0 and 86.0%, respectively. Diffusion-weighted images are not reliable criteria for differentiating intracranial meningiomas from IDM and should not influence the diagnosis suggested by imaging. The technique for assessing the perfusion of a meningeal lesion makes it possible to predict metastases with a sensitivity and specificity close to 80-90% and deserves attention when making a diagnosis. In the future, in order to reduce the number of false negative and false positive results, mpMRI would require additional criteria to be included in the protocol. Since IDM differs from intracranial meningiomas in the severity of neoangiogenesis and, accordingly, in greater vascular permeability, the technique for assessing vascular permeability (wash-in parameter with dynamic contrast enhancement) may serve as a refining criterion for distinguishing between dural lesions.

Search for Optimal MRI Protocol for the Diagnosis of Vaginal Tumor

Background. A wide variety of pulse sequences, the possibility of multiplanar imaging significantly increase the capabilities of magnetic resonance imaging (MRI) in diagnosing diseases of the female reproductive system. At the same time, the lack of a regulated scanning protocol, especially in patients who have undergone anticancer treatment, requires standardization of the technique and the search for an optimal set of pulse sequences that allows to visualize the vagina throughout its entire length and to perform differential diagnosis between continued tumor growth and post-radiation changes while maintaining an adequate examination time.Objective: to determine the optimal set of MRI pulse sequences for pelvic organs examination in patients with vaginal tumors and to form an original research protocol based on diagnostic information content.Material and methods. The study included 141 patients with vaginal tumors. A comparative analysis of four MRI protocols was carried out, built according to the principle “from simple to complex” (from native to multiparametric MRI).Results. A significant difference was obtained between all information content indicators of Protocol 4 compared to Protocol 1 (sensitivity p = 0.00006, specificity p = 0.00443, AUC p = 0.00000). Data analysis also showed a significant difference between sensitivity and AUC for Protocols 2 and 4 (p = 0.00150 and p = 0.00087, respectively), and Protocols 3 and 4 (p = 0.01333 and p = 0.01333, respectively).Conclusion. Significant increase in the information content of Protocol 4 compared to other protocols (sensitivity up to 93%, specificity up to 94%, accuracy up to 93%) indicates the expediency of the priority use of multiparametric MRI in the primary diagnosis of vaginal tumor lesions.

Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: an Updated Systematic Review

There has been growing utilisation of multiparametric magnetic resonance imaging (MPMRI) as a non-invasive tool to diagnose and localise clinically significant prostate cancer (CSPCa). This updated systematic review examines the use of MPMRI in patients with an elevated risk of CSPCa who have had a prior negative transrectal ultrasound systematic biopsy (TRUS-SB) and who were biopsy naïve. MEDLINE, EMBASE and the Cochrane Database of Systematic Reviews were searched for existing systematic reviews published up to September 2020. The literature search of the electronic databases combined disease-specific terms (prostate cancer, prostate carcinoma, etc.) and treatment-specific terms (magnetic resonance, etc.). Studies were included if they were randomised controlled trials (RCTs) comparing MPMRI to template transperineal mapping biopsy (TPMB) or to TRUS-SB. Thirty-six RCTs were eligible. For biopsy-naïve men, accuracy of diagnosis of CSPCa showed sensitivities from 87 to 96% and specificities ranging from 29 to 45%. Meta-analyses for CSPCa showed increased detection favouring MPMRI-targeted biopsy over TRUS-SB by 3% (95% confidence interval 0–7%, P = 0.03) and decreased detection of clinically insignificant prostate cancer (CISPCa) favouring MPMRI by 8% (95% confidence interval –11 to 5%, P < 0.00001). Accuracy of MPMRI for men with prior negative biopsy showed sensitivities of 78–100% and specificities of 30–100%. Meta-analyses comparing MPMRI to TRUS-SB showed increased detection of 5% (95% confidence interval 3–7%, P < 0.0001) with a reduction of CISPCa detection of 7% (95% confidence interval 4–9%, P < 0.00001). The growing acceptance of MPMRI utilisation internationally and the recent publication of several RCTs regarding MPMRI in reducing CISPCa detection rates, particularly in biopsy-naïve men, without loss of sensitivity for CSPCa necessitates the synthesis of updated evidence examining MPMRI in the diagnosis of CSPCa.

AUTHOR REPLY

Multiparametric magnetic resonance imaging (MP-MRI) provides essential information during the process of prostate cancer (PCa) management. However, a complete understanding of the association of the findings of MP-MRI with the histopathology of PCa should continue to be an area of active research. Indiscriminate use of MP-MRI, without appropriate Integration of other clinical variables, may cause harm to patients who may have been otherwise cured of PCa with surgery or radiation therapy. The goal should be to position MP-MRI within predictive models that include critical clinical variables to bank on the strengths of each while overcoming their limitations.

Application of machine learning algorithms for multiparametric MRI-based evaluation of murine colitis.

Magnetic resonance imaging (MRI) allows non-invasive evaluation of inflammatory bowel disease (IBD) by assessing pathologically altered gut. Besides morphological changes, relaxation times and diffusion capacity of involved bowel segments can be obtained by MRI. The aim of this study was to assess the use of multiparametric MRI in the diagnosis of experimentally induced colitis in mice, and evaluate the diagnostic benefit of parameter combinations using machine learning. This study relied on colitis induction by Dextran Sodium Sulfate (DSS) and investigated the colon of mice in vivo as well as ex vivo. Receiver Operating Characteristics were used to calculate sensitivity, specificity, positive- and negative-predictive values (PPV and NPV) of these single values in detecting DSS-treatment as a reference condition. A Model Averaged Neural Network (avNNet) was trained on the multiparametric combination of the measured values, and its predictive capacity was compared to those of the single parameters using exact binomial tests. Within the in vivo subgroup (n = 19), the avNNet featured a sensitivity of 91.3% (95% CI: 86.6–96.0%), specificity of 92.3% (95% CI: 85.1–99.6%), PPV of 96.9% (94.0–99.9%) and NPV of 80.0% (95% CI: 69.9–90.1%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.003) and performing significantly worse compared to none of the single values. Within the ex vivo subgroup (n = 30), the avNNet featured a sensitivity of 87.4% (95% CI: 82.6–92.2%), specificity of 82.9% (95% CI: 76.1–89.7%), PPV of 88.9% (84.3–93.5%) and NPV of 80.8% (95% CI: 73.8–87.9%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.015), exceeded by none of the single parameters. In experimental mouse colitis, multiparametric MRI and the combination of several single measured values to an avNNet can significantly increase diagnostic accuracy compared to the single parameters alone. This pilot study will provide new avenues for the development of an MR-derived colitis score for optimized diagnosis and surveillance of inflammatory bowel disease.

Multiparametric Magnetic Resonance Imaging in the Assessment of Primary Brain Tumors Through Radiomic Features: A Metric for Guided Radiation Treatment Planning.

PurposeThe definition of radiotherapy target volume is a critical step in treatment planning for all tumor sites. Conventional magnetic resonance imaging (MRI) pulse sequences are used for the definition of the gross target volume (GTV) and the contouring of glioblastoma multiforme (GBM) and meningioma. We propose the use of multiparametric MRI combined with radiomic features to improve the texture-based differentiation of tumor from edema for GTV definition and to differentiate vasogenic from tumor cell infiltration edema.MethodsTwenty-five patients with brain tumor and peritumoral edema (PTE) were assessed. Of the enrolled patients, 17 (63 ± 10 years old, six female and 11 male patients) were diagnosed with GBM and eight (64 ± 14 years old, five female and three male patients) with meningioma. A 3 Tesla (3T) MRI scanner was used to scan patients using a 3D multi-echo Gradient Echo (GRE) sequence. After the acquisition process, two experienced neuroradiologists independently used an in-house semiautomatic algorithm to conduct a segmentation of two regions of interest (ROI; edema and tumor) in all patients using functional MRI sequences, apparent diffusion coefficient (ADC), and dynamic contrast-enhanced MRI (DCE-MRI), as well as anatomical MRI sequences—T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR). Radiomic (computer-extracted texture) features were extracted from all ROIs through different approaches, including first-, second-, and higher-order statistics, both with and without normalization, leading to the calculation of around 300 different texture parameters for each ROI. Based on the extracted parameters, a least absolute shrinkage and selection operator (LASSO) analysis was used to isolate the parameters that best differentiated edema from tumors while irrelevant parameters were discarded.Results and conclusionsThe parameters chosen by LASSO were used to perform statistical analyses which allowed identification of the variables with the best discriminant ability in all scenarios. Receiver operating characteristic results showcase both the best single discriminator and the discriminant capacity of the model using all variables selected by LASSO. Excellent results were obtained for patients with GBM with all MRI sequences, with and without normalization; a T1-weighted sequence postcontrast (T1W+C) with normalization offered the best tumor classification (area under the curve, AUC > 0.97). For patients with meningioma, a good model of tumor classification was obtained through the T1-weighted sequence (T1W) without normalization (AUC > 0.71). However, there was no agreement between the results of both radiologists for some MRI sequences analyzed for patients with GBM and meningioma. In conclusion, a small subset of radiomic features showed an excellent ability to distinguish edema from tumor tissue through its most discriminating features.

Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy.

In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. “Radiomics”, as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.

Decision analysis model comparing cost of multiparametric magnetic resonance imaging vs. repeat biopsy for detection of prostate cancer in men with prior negative findings on biopsy

PurposeWe compared cost of multiparametric magnetic resonance imaging (MP-MRI) vs. repeat biopsy in detection of prostate cancer (PCa) in men with prior negative findings on biopsy. MethodsA decision tree model compared the strategy of office-based transrectal ultrasound–guided biopsy (TRUS) for men with prior negative findings on biopsy with a strategy of initial MP-MRI with TRUS performed only in cases of abnormal results on imaging. Study end points were cost, number of biopsies, and cancers detected. Cost was based on Medicare reimbursement. Cost of sepsis and minor complications were incorporated into analysis. Sensitivity analyses were performed by varying model assumptions. ResultsThe baseline model with 24% PCa found that the overall cost for 100 men was $90,400 and $87,700 for TRUS and MP-MRI arms, respectively. The MP-MRI arm resulted in 73 fewer biopsies per 100 men but detected 4 fewer cancers (16 vs. 20.4) than the TRUS arm did. A lower risk of PCa resulted in lower costs for the MP-MRI arm and a small difference in detected cancers. At lower cancer rates, MP-MRI is superior to TRUS over a wide range of sensitivity and specificity of MRI. A lower sensitivity of MP-MRI decreases the cost of the MP-MRI, as fewer biopsies are performed, but this also reduces the number of cancers detected. ConclusionsThe use of MP-MRI to select patients for repeat biopsy reduced the number of biopsies needed by 73% but resulted in a few cancers being missed at lower cost when compared with the TRUS arm. Further studies are required to determine whether cancers missed represent clinically significant tumors.

AB0771 The Use of Multi-Parametric MRI to Identify Synovitis in Patients with Osteoarthritis of the Knee without the Use of A Contrast Agent

BackgroundSynovitis is an important biomarker in arthritis and is visualised well using contrast enhanced magnetic resonance imaging (MRI). In osteoarthritis (OA) synovitis has been shown to correlate with histological change,...

The Impact of Multiparametric Pelvic Magnetic Resonance Imaging on Risk Stratification in Patients With Localized Prostate Cancer

To determine the impact of multiparametric magnetic resonance imaging (MP-MRI) of the prostate on established risk stratification criteria in patients with clinically localized adenocarcinoma of the prostate (ACP). The cohort included 71 patients who underwent MP-MRI of the prostate at a tertiary care referral center as part of their initial workup for ACP. Tumor characteristics comprising traditional risk stratification criteria (prostate-specific antigen, clinical T stage, and biopsy Gleason score) were recorded, and the initial National Comprehensive Cancer Network risk group was calculated. The National Comprehensive Cancer Network risk group was then recalculated incorporating MRI findings. The impact of MRI findings on changes in risk group classification was evaluated using the Stuart-Maxwell test. For patients undergoing radical prostatectomy, MRI findings were correlated with pathologic findings. The cohort included 11 (15.5%), 39 (54.9%), and 21 patients (29.6%) with low-, intermediate-, and high-risk disease, respectively. MRI findings led to risk group upstaging in 12 cases (16.9%). The highest yield was demonstrated in patients with intermediate-risk disease, in whom MRI led to upstaging in 25.6% of patients. There was a significant difference between pre-MRI and post-MRI risk group classifications (P<.01) for the entire cohort. Compared with radical prostatectomy specimens, the specificity of MRI for T3 disease was 92.9%. In our cohort of patients undergoing MP-MRI for previously untreated, clinically localized ACP, MRI findings led to changes in risk stratification in a substantial proportion of patients. Our findings support the use of MP-MRI in the workup of patients with localized ACP.