Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

O-RADS MRI SCORE: An Essential First-Step Tool for the Characterization of Adnexal Masses

The ovarian-adnexal reporting and data system on magnetic resonance imaging (O-RADS MRI) score is now a well-established tool to characterize pelvic gynecological masses based on their likelihood of malignancy. The main added value of O-RADS MRI over O-RADS US is to correctly reclassify lesions that were considered suspicious on US as benign on MRI. The crucial issue when characterizing an adnexal mass is to determine the presence/absence of solid tissue and thus need to perform gadolinium injection. O-RADS MR score was built on a multivariate analysis and must be applied as a step-by-step analysis: 1) Is the mass an adnexal mass? 2) Is there an associated peritoneal carcinomatosis? 3) Is there any significant amount of fatty content? 4) Is there any wall enhancement? 5) Is there any internal enhancement? 6) When an internal enhancement is detected, does the internal enhancement correspond to solid tissue or not? 7) Is the solid tissue malignant? With its high value to distinguish benign from malignant adnexal masses and its high reproducibility, the O-RADS MRI score could be a valuable tool for timely referral of a patient to an expert center for the treatment of ovarian cancers. Finally, to make a precise diagnosis allowing optimal personalized treatment, the radiologist in gynecological imaging will combine the O-RADS MRI score with many other clinical, biological, and other MR criteria to suggest a pathological hypothesis. Level of Evidence: 5. Technical Efficacy Stage: 3

ESUR recommendations for MR imaging of the sonographically indeterminate adnexal mass: an update

An update of the 2010 published ESUR recommendations of MRI of the sonographically indeterminate adnexal mass integrating functional techniques is provided. An algorithmic approach using sagittal T2 and a set of transaxial T1 and T2WI allows categorization of adnexal masses in one of the following three types according to its predominant signal characteristics. T1 'bright' masses due to fat or blood content can be simply and effectively determined using a combination of T1W, T2W and FST1W imaging. When there is concern for a solid component within such a mass, it requires additional assessment as for a complex cystic or cystic-solid mass. For low T2 solid adnexal masses, DWI is now recommended. Such masses with low DWI signal on high b value image (e.g. > b 1000 s/mm2 ) can be regarded as benign. Any other solid adnexal mass, displaying intermediate or high DWI signal, requires further assessment by contrast-enhanced (CE)T1W imaging, ideally with DCE MR, where a type 3 curve is highly predictive of malignancy. For complex cystic or cystic-solid masses, both DWI and CET1W—preferably DCE MRI—is recommended. Characteristic enhancement curves of solid components can discriminate between lesions that are highly likely malignant and highly likely benign

Assessing Prognosis from Nonrandomized Studies: An Example from Brain Arteriovenous Malformations

Two recent publications from Helsinki and Toronto that investigated the natural history of brain AVMs are the background topic for reviewing some principles and pitfalls of prognostic studies. Multivariable prognostic research involves 3 steps: developing the prognostic model, validating its performance in other individuals, and assessing its clinical impact on patients' outcomes. Unfortunately, the predictive ability of the model can be poor when it is applied to a new population, and clinical impact studies are rarely performed. Models that have not been validated should not be used to inform clinical decisions. Unfortunately, for rare outcomes in rare diseases, clinical data are limited. Although the 2 studies on brain AVMs may represent the best data currently available, they still included few patients with events and there are several methodologic concerns undermining the reliability of results. The estimates of risk of rupture per year are uncertain. Multiplying those uncertain numbers by the life expectancy of individuals can inflate error beyond control. Hence relying on these estimates to make clinical decisions may be dangerous

Impact of DWI and ADC values in ovarian-adnexal reporting and data system (O-RADS) MRI score

Purpose: Introduce DWI and quantitative ADC evaluation in O-RADS MRI system and observe how diagnostic performance changes. Assess its validity and reproducibility between readers with different experience in female pelvic imaging. Finally, evaluate any correlation between ADC value and histotype in malignant lesions. Materials and methods: In total, 173 patients with 213 indeterminate adnexal masses (AMs) on ultrasound were subjected to MRI examination, from which 140 patients with 172 AMs were included in the final analysis. Standardised MRI sequences were used, including DWI and DCE sequences. Two readers, blinded to histopathological data, retrospectively classified AMs according to the O-RADS MRI scoring system. A quantitative analysis method was applied by placing a ROI on the ADC maps obtained from single-exponential DWI sequences. AMs considered benign (O-RADS MRI score 2) were excluded from the ADC analysis. Results: Excellent inter-reader agreement was found in the classification of lesions according to the O-RADS MRI score (K = 0.936; 95% CI). Two ROC curves were created to determine the optimal cut-off value for the ADC variable between O-RADS MRI categories 3-4 and 4-5, respectively, 1.411 × 10-3 mm2/sec and 0.849 × 10-3 mm2/sec. Based on these ADC values, 3/45 and 22/62 AMs were upgraded, respectively, to score 4 and 5, while 4/62 AMs were downgraded to score 3. ADC values correlated significantly with the ovarian carcinoma histotype (p value < 0.001). Conclusion: Our study demonstrates the prognostic potential of DWI and ADC values in the O-RADS MRI classification for better radiological standardisation and characterisation of AMs

Current Update on the Randomized Controlled Trials of Intracranial Aneurysms

Endovascular coiling has become the primary treatment modality for the treatment of intracranial ruptured aneurysms in many centers. A multicenter randomized controlled trial (RCT), ISAT study, has demonstrated that endovascular coiling of ruptured intracranial aneurysms has benefits over surgical clipping in those patients suitable for either treatment. Because RCT comparing conservative management with surgical clipping and with endovascular coiling have not been performed to date for unruptured intracranial aneurysms, the best management for unruptured aneurysm remains unclear. A RCT is ongoing to answer the question whether active treatment can improve the outcome of patients with unruptured intracranial aneurysms as compared with observation

European society of urogenital radiology (ESUR) guidelines: MR imaging of pelvic endometriosis

Endometriosis is a common gynaecological condition of unknown aetiology that primarily affects women of reproductive age. The accepted first-line imaging modality is pelvic ultrasound. However, magnetic resonance imaging (MRI) is increasingly performed as an additional investigation in complex cases and for surgical planning. There is currently no international consensus regarding patient preparation, MRI protocols or reporting criteria. Our aim was to develop clinical guidelines for MRI evaluation of pelvic endometriosis based on literature evidence and consensus expert opinion. This work was performed by a group of radiologists from the European Society of Urogenital Radiology (ESUR), experts in gynaecological imaging and a gynaecologist expert in methodology. The group discussed indications for MRI, technical requirements, patient preparation, MRI protocols and criteria for the diagnosis of pelvic endometriosis on MRI. The expert panel proposed a final recommendation for each criterion using Oxford Centre for Evidence Based Medicine (OCEBM) 2011 levels of evidence.info:eu-repo/semantics/publishedVersio