Non-Contrast MR T2-Weighted Imaging Is as Accurate as Contrast-Enhanced T1-Weighted Imaging in the Detection of Meningioma Growth.

To assess the use of contrast-enhanced T1-weighted images in comparison with short tau inversion recovery (STIR) images for the detection of vertebral bone marrow abnormalities. A total of 201 vertebral magnetic resonance (MR) examinations were included in a prospective trial. Examinations were performed on a 0.5-T MR scanner. The examination protocol included STIR, T2-weighted turbo spin-echo and T1-weighted spin-echo images before and after administration of gadopentetate dimeglumine. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of STIR images were calculated. In the case of abnormal STIR images the additional information from contrast-enhanced images was evaluated using Fisher's exact test. The value of the combined evaluation of STIR and contrast-enhanced T1-weighted images was compared with that of the combined assessment of T2-weighted and contrast-enhanced T1-weighted images. The PPV and the NPV of STIR images for detection of vertebral bone marrow abnormalities were 99.3 and 95.9%. In the case of normal STIR images no relevant additional information was found with contrast-enhanced T1-weighted images, while in the case of abnormal STIR images significant supplementary information was obtained. There was no difference in the diagnostic value when comparing combined assessment of STIR and contrast-enhanced T1-weighted images with combined evaluation of T2-weighted and contrast-enhanced T1-weighted images. Normal STIR images allow contrast-enhanced T1-weighted images for detection of bone marrow abnormalities to be omitted, whereas further imaging is needed in case of abnormal STIR images.

Noncontrast T2-Weighted Magnetic Resonance Imaging Sequences for Long-Term Monitoring of Asymptomatic Convexity Meningiomas

BackgroundThe 2010 Revisions to the McDonald Criteria have established that dissemination in time (DIT) of multiple sclerosis (MS) can be demonstrated by simultaneous presence of asymptomatic gadolinium-enhancing and nonenhancing lesions on a single magnetic resonance imaging (MRI). However, gadolinium-based contrast agents (GBCAs) have contraindications. Diffusion-weighted imaging (DWI) can detect diffusion alterations in active inflammatory lesions. The purpose of this study was to investigate if DWI can be an alternative to contrast-enhanced T1-weighted imaging (CE T1WI) for demonstrating DIT in MS.MethodsWe selected patients with clinically definite MS and evaluated their baseline brain MRI. Asymptomatic lesions were identified as either hyperintense or nonhyperintense on DWI and enhancing or nonenhancing on CE T1WI. Fisher’s exact test was performed to determine whether the hyperintensity on DWI was related to the enhancement on CE T1WI (P < 0.05). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the DWI to predict lesion enhancement were calculated.ResultsTwenty-two patients with 384 demyelinating lesions that were hyperintense on T2-weighted imaging and more than 3 mm in size were recruited. The diffusion hyperintensity and lesion enhancement were significantly correlated (P <0.001). The sensitivity, specificity, PPV, NPV and accuracy were 100%, 67.9%, 32.3%, 100% and 72.1%, respectively.ConclusionsA hyperintense DWI finding does not necessarily overlap with contrast enhancement. There are many false positives, possibly representing other stages of lesion development. Although DWI may not replace CE T1WI imaging to demonstrate DIT due to the low PPV, it may serve as a screening MRI sequence where the use of GBCAs is a concern.

Objective(s):The study objective was to assess the diagnostic performance of positron emission tomography (PET) for gliomas using the novel tracer 18F-fluciclovine (anti-[18F]FACBC) and to evaluate the safety of this tracer in patients with clinically suspected gliomas.Methods:Anti-[18F]FACBC was administered to 40 patients with clinically suspected high- or low-grade gliomas, followed by PET imaging. T1-weighted, contrast-enhanced T1-weighted, and fluid-attenuated inversion recovery (or T2-weighted) magnetic resonance imaging (MRI) scans were obtained to plan for the tissue collection. Tissues were collected from either “areas visualized using anti-[18F]FACBC PET imaging but not using contrast-enhanced T1-weighted imaging” or “areas visualized using both anti-[18F]FACBC-PET imaging and contrast-enhanced T1-weighted imaging” and were histopathologically examined to assess the diagnostic accuracy of anti-[18F]FACBC-PET for gliomas.Results:The positive predictive value of anti-[18F]FACBC-PET imaging for glioma in areas visualized using anti-[18F]FACBC-PET imaging, but not visualized using contrast-enhanced T1-weighted images, was 100.0% (26/26), and the value in areas visualized using both contrast-enhanced T1-weighted imaging and anti-[18F]FACBC-PET imaging was 87.5% (7/8). Twelve adverse events occurred in 7 (17.5%) of the 40 patients who received anti-[18F]FACBC. Five events in five patients were considered to be adverse drug reactions; however, none of the events were serious, and all except one resolved spontaneously without treatment.Conclusion:This Phase IIb trial showed that anti-[18F]FACBC-PET imaging was effective for the detection of gliomas in areas not visualized using contrast-enhanced T1-weighted MRI and the tracer was well tolerated.

BackgroundWhile early diagnosis of giant cell arteritis (GCA) based on clinical criteria and contrast-enhanced MRI findings can lead to early treatment and prevention of blindness and cerebrovascular accidents, previously reported diagnostic methods which utilize contrast-enhanced whole head images are cumbersome. Diagnostic delay is common as patients may not be aware of initial symptoms and their significance. To improve current diagnostic capabilities, new MRI-based diagnostic criteria need to be established. This study aimed to evaluate the “multifocal arcuate sign” on short tau inversion recovery (STIR) and contrast-enhanced T1-weighted (CE-T1W) images as a novel extracranial finding for the diagnosis of GCA.MethodsA total of 17 consecutive patients (including five with GCA) who underwent CE-T1W and whole-brain axial STIR imaging simultaneously between June 2010 and April 2020 were enrolled. We retrospectively reviewed their MR images. The “multifocal arcuate sign” was defined as “multiple distant arcuate areas with high signal intensity in extracranial soft tissues such as subcutaneous fat, muscles, and tendons.” Extracranial abnormal high-signal-intensity areas were classified as “None,” when no lesions were detected; “Monofocal,” when lesions were detected only in one place; and “Multifocal,” when lesions were detected in multiple places. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of “Multifocal” areas were calculated using cross tabulation. Fisher’s exact test was used to compare “Multifocal” areas in five patients with GCA and those with other diseases. In addition, mean Cohen’s kappa and Fleiss’ kappa statistics were used to compare inter-reader agreement.ResultsThe sensitivity, specificity, PPV, and NPV of the “multifocal arcuate sign” in patients with GCA were 60%, 92–100%, 75–100%, and 85–86%, respectively. Significantly more patients with GCA had “Multifocal” areas compared to those with other diseases (Fisher’s exact test, p = 0.008–0.027). Mean Cohen’s kappa and Fleiss’ kappa for inter-reader agreement with respect to the five GCA patients were 0.52 and 0.49, respectively, for both STIR and CE-T1W sequences.ConclusionsThe new radiologic finding of “multifocal arcuate sign” on STIR and CE-T1W images may be used as a radiologic criterion for the diagnosis of GCA, which can make plain MRI a promising diagnostic modality.

This study evaluates the performance of diffusion-weighted magnetic resonance imaging (DWI) for the detection of hepatocellular carcinoma (HCC) in pre-liver transplantation patients, compared and combined with contrast-enhanced T1-weighted imaging (CET1WI), using liver explant as the standard of reference. We included 52 patients with cirrhosis (40 men, 12 women; mean age, 56 years) who underwent DWI and CET1WI within 90 days of liver transplantation. Magnetic resonance images were analyzed for HCC detection in three separate sessions by two independent observers: DWI images (DW-set), CET1WI (CE-set), and all images together (All-set). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), per-patient accuracy, and per-lesion PPV were calculated for each image set. A total of 72 HCCs were present in 33 patients at explant (mean size, 1.5 cm [range, 0.3-6.2 cm]). Per-patient sensitivity and NPV of CE-set were significantly higher than those of DW-set when using pooled data between observers (P = 0.02 and 0.03, respectively), whereas specificity, PPV, and accuracy were equivalent. Per-lesion sensitivity was significantly higher for CE-set versus DW-set (59.0% versus 43.8%; P = 0.008, pooled data from two observers). When stratified by lesion size, the difference was significant only for lesions with a size between 1 and 2 cm (42.0% for DW-set versus 74.0% for CE-set; P = 0.001). The addition of DWI to CET1WI improved sensitivity for the more experienced observer. DWI is outperformed by CET1WI for detection of HCC, but represents a reasonable alternative to CET1WI for detection of HCC with a size above 2 cm. The addition of DWI to CET1WI slightly increases the detection rate.

To compare two MR sequences at 1.5 T-T2-weighted and contrast-enhanced T1-weighted images-by using macroscopic sections to determine which image type enables the most accurate assessment of cervical carcinoma. Forty consecutive patients (mean age, 39.2 years) with biopsy-proven cervical carcinoma were included. Each MR sequence was assessed for tumour localisations, tumour margins, and cancer extent with the consensus of two readers, and tumour margins were rated on a five-point scale. MR findings were correlated with histopathological findings. Contrast-to-noise ratios (CNRs) obtained with each image were compared using nonparametric tests. Thirty-one of 40 patients underwent hysterectomies and nine of 40 underwent trachelectomies. In 36 patients, lesions were identified on at least one sequence. The tumours at stage 1B or higher were detected in 94.7% on contrast-enhanced T1-weighted images and in 76.3% on T2-weighted images (P < 0.05). Tumour margins appeared significantly more distinct on contrast-enhanced T1-weighted images than on T2-weighted images (P < 0.001). The CNRs obtained using contrast-enhanced T1-weighted images were significantly higher (P < 0.001) than those obtained using T2-weighted images. Contrast-enhanced T1-weighted imaging is more useful for assessing cervical carcinoma than T2-weighted imaging.

The purpose of this study was to assess the utility of cerebral diffusion-weighted MR imaging in the diagnosis of multiple sclerosis (MS) in comparison with contrast-enhanced T1-weighted imaging. We reviewed T2-weighted spin-echo (SE), fluid-attenuated inversion-recovery (FLAIR), contrast-enhanced T1-weighted SE and echo-planar diffusion-weighted images (DWIs) obtained in ten patients with definite MS on 12 occasions. In total, 83 plaques were demonstrated on T2-weighted SE and/or FLAIR images. Thirteen of these plaques showed enhancement on contrast-enhanced T1-weighted images and hyperintensity on DWIs. Five non-enhancing plaques showed hyperintensity on DWIs. Diffusion-weighted imaging, which provides information based on pathophysiology different from contrast-enhanced imaging, is a potential supplementary technique for characterizing MS plaques.

Dural venous sinus abnormalities are clinically important but can potentially be overlooked using various MRI techniques. This study evaluates the diagnostic accuracy of spin-echo (SE) T1-weighted imaging, 3D gradient-recalled echo (GRE) T1-weighted imaging, and contrast-enhanced MR venography (MRV) for the detection of dural venous sinus thrombosis and transverse sinus (TS) stenosis. Seventy-three patients underwent MRI evaluation with unenhanced and contrast-enhanced axial SE T1-weighted imaging, contrast-enhanced sagittal 3D GRE T1-weighted imaging, and contrast-enhanced MRV sequences. Three neuroradiologists each evaluated these 219 total datasets in a randomized blinded fashion for the presence or absence of TS stenosis and for dural venous sinus thrombosis in each of 10 venous sinus segments (730 total segments). Diagnostic performance characteristics and kappa statistics were calculated for each technique. Thirteen patients (37 segments) had suspected dural venous sinus thrombosis by one or more readers; of those 13 patients, nine (23 segments) were thought to have definite thrombosis on contrast-enhanced MRV. Compared with contrast-enhanced MRV, the positive predictive value (PPV) and negative predictive value (NPV) for thrombosis were 60% and 97%, respectively, for both unenhanced and contrast-enhanced SE T1-weighted imaging and 100% and 98% for 3D GRE T1-weighted imaging. Kappa values calculated per venous segment were as follows: 0.41 for SE T1-weighted imaging, 0.72 for 3D GRE T1-weighted imaging, and 0.95 for contrast-enhanced MRV. Thirty patients (58 segments) had TS stenosis suspected by one or more readers; of those 30 patients, TS stenosis was deemed to be definite on contrast-enhanced MRV in 25 patients (50 segments). Compared with contrast-enhanced MRV, the PPV and NPV were 75% and 80%, respectively, for SE T1-weighted imaging and 91% and 92% for 3D GRE T1-weighted imaging for the detection of stenosis. Kappa values calculated per patient were -0.038 for SE T1-weighted imaging, 0.58 for 3D GRE T1-weighted imaging, and 0.98 for contrast-enhanced MRV. Contrast-enhanced 3D GRE T1-weighted imaging is superior to SE T1-weighted imaging for the detection of dural venous sinus thrombosis and TS stenosis but does not substitute for dedicated MRV. Hyperintensity on unenhanced SE T1-weighted imaging is unreliable for the detection of dural venous sinus thrombosis.

To compare contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) magnetic resonance (MR) images in depicting leptomeningeal metastases. Malignant lesions detected at cytologic examination of cerebrospinal fluid in 70 patients were reviewed. There were 58 studies in which both FLAIR and contrast-enhanced T1-weighted spin-echo MR images were available. A senior neuroradiologist reviewed the images from each sequence individually and separately for signs of leptomeningeal metastases and assigned a diagnostic rating of positive, indeterminate, or negative. Leptomeningeal metastases were depicted in 38 cases on contrast-enhanced T1-weighted spin-echo images and in 20 cases on FLAIR images. In three cases, leptomeningeal metastases were detected by using only FLAIR images. In 20 cases, leptomeningeal metastases were detected by using only contrast-enhanced T1-weighted spin-echo images. FLAIR imaging has a sensitivity of 34% for cytologically proved leptomeningeal metastases. Gadolinium-enhanced MR imaging has a sensitivity of 66%. Used alone, contrast-enhanced T1-weighted images are better than FLAIR images for detecting leptomeningeal metastases. This is particularly true for cases in which leptomeningeal metastases manifest primarily or solely as cranial nerve involvement.

To evaluate the diagnostic performance of MRI texture analysis (TA) for differentiation of pediatric craniofacial rhabdomyosarcoma (RMS) from infantile hemangioma (IH). This study included 15 patients with RMS and 42 patients with IH who underwent MRI before an invasive procedure. All patients had a solitary lesion. T2-weighted and fat-suppressed contrast-enhanced T1-weighted axial images were used for TA. Two readers delineated the tumor borders for TA independently and evaluated the qualitative MRI characteristics in consensus. The differences of the texture features' values between the groups were assessed and ROC curves were calculated. Logistic regression analysis was used to analyze the value of TA with and without the combination of the qualitative MRI characteristics. A p value < 0.05 was considered statistically significant. Thirty-eight texture features were calculated for each tumor. Eighteen features on T2-weighted images and 25 features on contrast-enhanced T1-weighted images were significantly different between the RMSs and IHs. On contrast-enhanced T1-weighted images, the short-zone emphasis (SZE), which was a gray-level zone length matrix (GLZLM) parameter, had the largest area under the curve: 0.899 (sensitivity 93%, specificity 87%). The independent predictor for the RMS among the qualitative MRI characteristics was heterogeneous contrast enhancement (p < 0.001). Using only a GLZLM_SZE value of lower than 0.72 was found to be the best diagnostic parameter in predicting RMS (p < 0.001; 95% CI, 8.770-992.4). MRI-based TA may contribute to differentiate RMS from IH without invasive procedures. • Texture analysis may help to distinguish between rhabdomyosarcoma and infantile hemangioma without invasive procedures. • The gray-level zone length matrix parameters, especially the short-zone emphasis, may be a potential predictor for rhabdomyosarcoma. • Using contrast-enhanced T1-weighted images may be superior to T2-weighted images to differentiate rhabdomyosarcoma from infantile hemangioma in texture analysis.

Forty-six patients with surgically proved disease (115 leiomyomas, 19 cases of adenomyosis, and 14 endometrial polyps) were studied to determine if gadopentetate dimeglumine-enhanced T1-weighted MR images improve the detection and characterization of benign tumors of the uterus. Lesion detection and characterization were assessed separately for each sequence (unenhanced T1-weighted, proton-density-weighted, and T2-weighted and contrast-enhanced T1-weighted images) and for combinations of sequences (unenhanced T1- and T2-weighted images, unenhanced and contrast-enhanced T1-weighted images, and unenhanced T1- and T2-weighted and contrast-enhanced T1-weighted images). In the evaluation of leiomyomas, analysis of all three sequences provided the best detection (92%) and characterization (92%), but the improvement, except when compared with unenhanced T1-weighted images alone, was not statistically significant. The use of contrast medium did not contribute to either tumor detection or characterization. In the evaluation of adenomyosis, T2-weighted images provided significantly better lesion detection and characterization than did either unenhanced or contrast-enhanced T1-weighted images. In the evaluation of endometrial polyps, however, contrast-enhanced T1-weighted images provided significantly better lesion detection and characterization than did unenhanced images. With contrast-enhanced images, the detection rate was 79%, compared with 36% for T2-weighted images and 7% for T1-weighted images. Lesion characterization was the best (73%) when all imaging sequences were analyzed. Our study shows that with conventional spin-echo sequences, the use of contrast-enhanced T1-weighted images does not improve the detection or characterization of uterine leiomyomas or adenomyosis but significantly improves the detection of endometrial polyps.

Diffusion-weighted MR imaging in multiple sclerosis: comparison with contrast-enhanced study

ObjectivesThe purpose of this research was to create and validate radiomic models based on machine learning that can effectively discriminate between primary non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in individuals with brain metastases (BMs) by utilizing high-dimensional radiomic characteristics derived from contrast-enhanced T1-weighted imaging (CE-T1WI).MethodsA cohort of 260 individuals were chosen as participants. Among them, 173 individuals had NSCLC with 228 BMs, while 87 patients were diagnosed with SCLC with 142 BMs. Patients were allocated to a training dataset with a total of 259 BMs and an independent test dataset with a total of 111 BMs. Tumor tissues in axial CE-T1WI were manually outlined to delineate regions of interest (ROIs). Radiomic features were obtained from the ROIs using PyRadiomics, which were then chosen through a multistep selection process, including least absolute shrinkage and selection operator (LASSO) regression. Ten machine learning models, including Light Gradient Boosting Machine (LightGBM), RandomForest, and eXtreme Gradient Boosting (XGBoost), were built using selected features. The models’ performance was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations, complemented by additional metrics such as accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).ResultsA total of 833 radiomic features were extracted from the ROIs. Through a multistep selection process, a refined subset of 15 optimal radiomic features was identified for model training. Ten classifier models were built based on features extracted from CE-T1WI. In the training dataset, the top-performing classifiers were the XGBoost, LightGBM, support vector machine (SVM) and random forest models, which achieved AUC of 0.963, 0.881, 0.876 and 0.855, respectively, with 5-fold cross-validation. Among the ten models tested, the LightGBM algorithm exhibited superior performance, with an AUC of 0.853 in the test cohort. This performance was superior to that of other models, such as RandomForest (AUC 0.843) and ExtraTrees (AUC 0.835). Radiomic features significantly contributed to the differentiation between NSCLC and SCLC.ConclusionMachine learning-based radiomics using CE-T1WI data is highly effective in distinguishing between NSCLC and SCLC in patients with BMs. The LightGBM model showed the best performance, suggesting that this approach shows promise as a supportive, non-invasive diagnostic tool, pending further validation in prospective clinical settings.

Patients with meningiomas require serial MRI for surveillance of tumor size and growth rate. The cost and resource requirements for contrast-enhanced MRI include intravenous cannulation, the contrast agent, risk of adverse reaction, and the time needed to acquire, review, and report the additional sequences. With repeated doses, gadolinium is known to accumulate in neural tissues. The authors compared the correlation and accuracy of axial T2-weighted imaging (T2WI) sequences alone for assessing tumor growth, dimensions, and dural venous sinus invasion compared with the current clinical practice of assessing both contrast-enhanced T1-weighted imaging (CE-T1WI) and T2WI sequences. The authors retrospectively identified 136 adult patients (65 patients with treated and 71 patients with untreated meningiomas) with two MRI scans obtained at least 6 months apart. For each patient, the two CE-T1WI sequences separated by time were paired, as were the two T2WI sequences, and assessed independently. The paired scans were assessed by a neuroradiologist and advanced radiology trainee blinded to clinical data. Tumor location, dimensions, growth, and venous invasion were evaluated. Peritumoral edema was assessed on T2WI only. Agreement between assessments on both CE-T1WI and T2WI sequences compared with T2WI alone was evaluated using Cohen's kappa (κ), the intraclass correlation coefficient (ICC), and Bland-Altman plots. Growth was detected in 36 tumors on T2WI compared with 39 when both CE-T1WI and T2WI were assessed. Growth assessed on T2WI alone showed near-perfect agreement with growth assessed on CE-T1WI and T2WI together (κ = 0.945). T2WI alone had an accuracy of 97.8%, specificity of 100%, and sensitivity of 92.3%. Interrater correlation between the radiologists for tumor dimensions was good to excellent (ICC > 0.843). Intrarater agreement between T2WI and CE-T1WI measurements of anteroposterior and transverse tumor dimensions was good (ICC > 0.883 for observer 1, > 0.767 for observer 2). There was substantial agreement between venous invasion on T2WI and both CE-T1WI and T2WI (κ = 0.771). Subgroup analysis for skull base (58.1%), treated (47.8%), and large (> 20-mm diameter; 38.2%) meningiomas did not show any significant difference in agreement between T2WI only and CE-T1WI and T2WI assessments of growth, venous invasion, or tumor dimension. In patients with treated and untreated meningiomas, unenhanced T2WI can assess tumor dimensions, detect growth, and detect venous invasion with comparable reliability and accuracy to the current clinical practice of using both CE-T1WI and T2WI.