Spontaneous intracranial hypotension: Exploring the viability of non-contrast FLAIR as a substitute for contrast-enhanced T1WI in assessing pachymeningeal thickening

PURPOSE

To avoid contrast administration in spontaneous intracranial hypotension (SIH), some studies suggest accepting diffuse pachymeningeal hyperintensity (DPMH) on non-contrast fluid-attenuated inversion recovery (FLAIR) as an equivalent sign to diffuse pachymeningeal enhancement (DPME) on contrast-enhanced T1WI (T1ce), despite lacking thorough performance metrics. This study aimed to comprehensively explore its feasibility.

METHODS

In this single-center retrospective study, between April 2021 and November 2023, brain MRI examinations of 43 patients clinically diagnosed with SIH were assessed using 1.5 and 3.0 Tesla MRI scanners. Two radiologists independently assessed the presence or absence of DPMH on FLAIR and DPME on T1ce, with T1ce serving as a gold-standard for pachymeningeal thickening. The contribution of the subdural fluid collections to DPMH was investigated with quantitative measurements. Using Cohen's kappa statistics, interobserver agreement was assessed.

RESULTS

In 39 out of 43 patients (90.7%), pachymeningeal thickening was observed on T1ce. FLAIR sequence produced an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 72.1%, 71.8%, 75.0%, 96.6%, and 21.4% respectively, for determining pachymeningeal thickening. FLAIR identified pachymeningeal thickening in 28 cases; however, among these, 21 cases (75%) revealed that the pachymeningeal hyperintense signal was influenced by subdural fluid collections. False-negative rate for FLAIR was 28.2% (11/39).

CONCLUSION

The lack of complete correlation between FLAIR and T1ce in identifying pachymeningeal thickening highlights the need for caution in removing contrast agent administration from the MRI protocol of SIH patients, as it reveals a major criterion (i.e., pachymeningeal enhancement) of Bern score.

Atezolizumab-induced cerebellar ataxia in a patient with metastatic small cell lung cancer: A case report and literature review

INTRODUCTION

The use of immune checkpoint inhibitors, which have an important role in the treatment of malignant tumors, is increasing. Although rarely observed, neurological immune-related adverse events associated with immune checkpoint inhibitors result in high morbidity and mortality. Small cell lung cancer is a common cause of neurological paraneoplastic syndromes. The differentiation between paraneoplastic syndromes and neurological immune-related adverse events is important in patients using immune checkpoint inhibitors. Cerebellar ataxia caused by atezolizumab is a rare immune-related adverse event.

CASE REPORT

In this context, we present a 66-year-old man with small cell lung cancer who developed immune-mediated cerebellar ataxia after three cycles of atezolizumab, a programmed cell death ligand-1 inhibitor. The admission of brain and spinal gadolinium-based contrast-enhanced magnetic resonance imaging supported the preliminary diagnosis and indicated leptomeningeal involvement. However, the blood tests and a lumbar puncture did not reveal any structural, biochemical, paraneoplastic, or infectious cause.

MANAGEMENT AND OUTCOME

High-dose steroid treatment resulted in an improvement in the radiological involvement, as evidenced both clinically and on follow-up whole spine magnetic resonance imaging. Therefore, the immunotherapy was discontinued. The patient was discharged on day 20 without neurological sequelae.

DISCUSSION

In light of this, we present this case to emphasize the differential diagnosis of neurological immune-related adverse events originating from immune checkpoint inhibitors, which require rapid diagnosis and treatment, and clinically similar paraneoplastic syndromes and radiologically similar leptomeningeal involvement, in a case of small cell lung cancer.

Multiple sclerosis versus cerebral small vessel disease in MRI: a practical approach using qualitative and quantitative signal intensity differences in white matter lesions

BACKGROUND

Multiple sclerosis (MS) and cerebral small vessel disease (CSVD) are relatively common radiological entities that occasionally necessitate differential diagnosis.

PURPOSE

To investigate the differences in magnetic resonance imaging (MRI) signal intensity (SI) between MS and CSVD related white matter lesions.

MATERIAL AND METHODS

On 1.5-T and 3-T MRI scanners, 50 patients with MS (380 lesions) and 50 patients with CSVD (395 lesions) were retrospectively evaluated. Visual inspection was used to conduct qualitative analysis on diffusion-weighted imaging (DWI)_b1000 to determine relative signal intensity. The thalamus served as the reference for quantitative analysis based on SI ratio (SIR). The statistical analysis utilized univariable and multivariable methods. There were analyses of patient and lesion datasets. On a dataset restricted by age (30-50 years), additional evaluations, including unsupervised fuzzy c-means clustering, were performed.

RESULTS

Using both quantitative and qualitative features, the optimal model achieved a 100% accuracy, sensitivity, and specificity with an area under the curve (AUC) of 1 in patient-wise analysis. With an AUC of 0.984, the best model achieved a 94% accuracy, sensitivity, and specificity when using only quantitative features. The model's accuracy, sensitivity, and specificity were 91.9%, 84.6%, and 95.8%, respectively, when using the age-restricted dataset. Independent predictors were T2_SIR_max (optimal cutoff=2.1) and DWI_b1000_SIR_mean (optimal cutoff=1.1). Clustering also performed well with an accuracy, sensitivity, and specificity of 86.5%, 70.6%, and 100%, respectively, in the age-restricted dataset.

CONCLUSION

SI characteristics derived from DWI_b1000 and T2-weighted-based MRI demonstrate excellent performance in differentiating white matter lesions caused by MS and CSVD.

Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine

RATIONALE AND OBJECTIVES

Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the radiology and nuclear medicine papers on AI in terms of transparency and open science.

MATERIALS AND METHODS

A systematic literature search was performed in PubMed to identify original research studies on AI. The search was restricted to studies published in Q1 and Q2 journals that are also indexed on the Web of Science. A random sampling of the literature was performed. Besides six baseline study characteristics, a total of five availability items were evaluated. Two groups of independent readers including eight readers participated in the study. Inter-rater agreement was analyzed. Disagreements were resolved with consensus.

RESULTS

Following eligibility criteria, we included a final set of 194 papers. The raw data was available in about one-fifth of the papers (34/194; 18%). However, the authors made their private data available only in one paper (1/161; 1%). About one-tenth of the papers made their pre-modeling (25/194; 13%), modeling (28/194; 14%), or post-modeling files (15/194; 8%) available. Most of the papers (189/194; 97%) did not attempt to create a ready-to-use system for real-world usage. Data origin, use of deep learning, and external validation had statistically significantly different distributions. The use of private data alone was negatively associated with the availability of at least one item (p<0.001).

CONCLUSION

Overall rates of availability for items were poor, leaving room for substantial improvement.

Publications poorly report the essential RadiOmics ParametERs (PROPER): A meta-research on quality of reporting

PURPOSE

To investigate the quality of reporting in radiomics research, with a focus on the most basic technical parameters.

METHODS

A PubMed literature search was conducted to identify original studies on radiomics (last search: January 2, 2023). Following a sample size calculation with an a priori power analysis, a random sample of the radiomic literature was collected. In addition to baseline characteristics, the key aspects of radiomic software, resampling, and discretization were evaluated. Agreement between raters was analyzed. Disagreements were resolved through consensus.

RESULTS

A sample of 87 publications was evaluated. Most publications (89%; 77/87) were retrospective. They were conducted predominantly with private data (87%; 76/87) at a single institution (77%; 67/87) without external validation (90%; 78/87). 69% (60/87) of the papers reported the radiomic software used (p < 0.001), with nearly half (43%; 26/60) omitting the version. 37% (32/87) reported the resampling size (p = 0.018), while 22% (7/32) did not report using iso-voxel resampling. 34% (30/87) reported the discretization parameters (p < 0.01), but more than three-quarters (77%; 23/30) did not experiment with different discretization parameters. A wide range of discretization parameter values were reported. Most papers (79%; 69/87) failed to report all three essential items simultaneously (p < 0.001).

CONCLUSION

Even the essential radiomic parameters that are usually displayed on the user interface of radiomic software tools were poorly reported in radiomics-related publications. This issue of transparency may require additional action from researchers, editors, and reviewers in the form of adopting more stringent reporting standards (e.g., checklists, guidelines).

REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): A word of caution

OBJECTIVE

To evaluate the reliability of consensus-based segmentation in terms of reproducibility of radiomic features.

METHODS

In this retrospective study, three tumor data sets were investigated: breast cancer (n = 30), renal cell carcinoma (n = 30), and pituitary macroadenoma (n = 30). MRI was utilized for breast and pituitary data sets, while CT was used for renal data set. 12 readers participated in the segmentation process. Consensus segmentation was created by making corrections on a previous region or volume of interest. Four experiments were designed to evaluate the reproducibility of radiomic features. Reliability was assessed with intraclass correlation coefficient (ICC) with two cut-off values: 0.75 and 0.9.

RESULTS

Considering the lower bound of the 95% confidence interval and the ICC threshold of 0.90, at least 61% of the radiomic features were not reproducible in the inter-consensus analysis. In the susceptibility experiment, at least half (54%) became non-reproducible when the first reader is replaced with a different reader. In the intra-consensus analysis, at least about one-third (32%) were non-reproducible when the same second reader segmented the image over the same first reader two weeks later. Compared to inter-reader analysis based on independent single readers, the inter-consensus analysis did not statistically significantly improve the rates of reproducible features in all data sets and analyses.

CONCLUSIONS

Despite the positive connotation of the word "consensus", it is essential to REMIND that consensus-based segmentation has significant reproducibility issues. Therefore, the usage of consensus-based segmentation alone should be avoided unless a reliability analysis is performed, even if it is not practical in clinical settings.

Predicting IDH Mutation Status of Grade 2-4 Gliomas with DTI Parameters Derived from Model-based DTI and Model-free Q-Sampling Imaging Reconstructions

OBJECTIVE

To determine if diffusion tensor imaging (DTI) parameters acquired with model-based DTI and model-free Q-sampling imaging (GQI) reconstructions may noninvasively predict isocitrate dehydrogenase (IDH) mutational status in patients with grade 2-4 gliomas.

METHODS

Forty patients with known IDH genotype (28 IDH wild-type; 12 IDH mutant) who underwent preoperative DTI evaluation on a 3 Tesla MRI scanner were retrospectively analyzed. Absolute values obtained from model-based and model-free reconstructions were compared. Using the intraclass correlation coefficient, interobserver agreement was assessed for various sampling techniques. Variables having statistically significant distributions between IDH groups were subjected to a receiver operating characteristic (ROC) analysis. Using multivariable logistic regression analysis, independent predictors, if present, were identified and a model was developed.

RESULTS

Six imaging parameters (3 from model-based DTI and 3 from model-free GQI reconstructions) showed statistically significant differences between groups (p <0.001, power >0.97), with very high correlation to each other (p <0.001). Age difference between the groups was statistically significant (p <0.001). Optimal logistic regression model comprised a GQI-based parameter and age, which were independent predictors as well, producing an area under the ROC curve, accuracy, sensitivity, and specificity of 0.926, 85%, 75%, and 89%, respectively. Using the GQI reconstruction feature alone with a cut-off of 1.60, an 85% of accuracy was also achieved with ROC analysis.

CONCLUSIONS

The imaging parameters acquired from model-based DTI and model-free GQI reconstructions, combined with the clinical variable age, may have the ability to non-invasively predict the IDH genotype in gliomas, either alone or in particular combinations.

Use of Thalamus L-Sign to Differentiate Periventricular Leukomalacia From Neurometabolic Disorders

PURPOSE

To assess the diagnostic value of the thalamus L-sign on magnetic resonance imaging (MRI) in distinguishing between periventricular leukomalacia and neurometabolic disorders in pediatric patients.

METHODS

In this retrospective study, clinical and imaging information was collected from 50 children with periventricular leukomalacia and 52 children with neurometabolic disorders. MRI was used to evaluate the L-sign of the thalamus (ie, injury to the posterolateral thalamus) and the lobar distribution of signal intensity changes. Age, sex, gestational age, and level of Gross Motor Function Classification System (only for periventricular leukomalacia) constituted the clinical parameters. Statistical evaluation of group differences for imaging and clinical variables were conducted using univariable statistical methods. The intra- and inter-observer agreement was evaluated using Cohen's kappa. Univariable or multivariable logistic regression was employed for selection of variables, determining independent predictors, and modeling.

RESULTS

The thalamus L-sign was observed in 70% (35/50) of patients in the periventricular leukomalacia group, but in none of the patients with neurometabolic disorder (P < .001). The gestational age between groups varied significantly (P < .001). Involvement of frontal, parietal, and occipital lobes differed significantly between groups (P < .001). In the logistic regression, the best model included negative thalamus L-sign and gestational age, yielding an area under the curve, accuracy, sensitivity, specificity, and precision values of 0.995, 96.1%, 96%, 96.2%, and 96%, respectively. Both the lack of thalamus L-sign and gestational age were independent predictors (P < .001).

CONCLUSIONS

The thalamus L-sign and gestational age may be useful in distinguishing between periventricular leukomalacia and neurometabolic disorders.