The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter-and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper we demonstrate that change in volumetric measurements of lesion load alone is not a good method for performing this separation, even for highly performing segmentation methods. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on a second external dataset confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracy of 83 % in separating stable and progressive timepoints.Both lesion volume and lesion count have previously been shown to be, together with clinical covariates, strong predictors of disease course across a population. However, in this paper we demonstrate that for individual patients, changes in these measures are not an adequate means of establishing no evidence of disease activity. Meanwhile, directly detecting tissue which changes, with high confidence, from non-lesion to lesion is a feasible methodology for identifying radiologically active patients.
We hypothesized that machine learning analysis based on texture information from the preoperative MRI can predict IDH mutational status in newly diagnosed WHO grade II and III gliomas. This retrospective study included in total 79 consecutive patients with a newly diagnosed WHO grade II or III glioma. Local binary pattern texture features were generated from preoperative B0 and fractional anisotropy (FA) diffusion tensor imaging. Using a training set of 59 patients, a single hidden layer neural network was then trained on the texture features to predict IDH status. The model was validated based on the prediction accuracy calculated in a previously unseen set of 20 gliomas. Prediction accuracy of the generated model was 92% (54/59 cases; AUC = 0.921) in the training and 95% (19/20; AUC = 0.952) in the validation cohort. The ten most important features were comprised of tumor size and both B0 and FA texture information, underlining the joint contribution of imaging data to classification. Machine learning analysis of DTI texture information and tumor size reliably predicts IDH status in preoperative MRI of gliomas. Such information may increasingly support individualized surgical strategies, supplement pathological analysis and highlight the potential of radiogenomics.
Background: Administration of a gadolinium-based contrast material is widely considered obligatory for follow-up imaging of patients with multiple sclerosis (MS). However, advances in MRI have substantially improved the sensitivity for detecting new or enlarged lesions in MS. Purpose: To investigate whether the use of contrast material has an effect on the detection of new or enlarged MS lesions and, consequently, the assessment of interval progression. Materials and Methods: In this retrospective study based on a local prospective observational cohort, 507 follow-up MR images obtained in 359 patients with MS (mean age, 38.2 years 6 10.3; 246 women, 113 men) were evaluated. With use of subtraction maps, nonenhanced images (double inversion recovery [DIR], fluid-attenuated inversion recovery [FLAIR]) and contrast material-enhanced (gadoterate meglumine, 0.1 mmol/kg) T1-weighted images were separately assessed for new or enlarged lesions in independent readings by two readers blinded to each other's findings and to clinical information. Primary outcome was the percentage of new or enlarged lesions detected only on contrast-enhanced T1-weighted images and the assessment of interval progression. Interval progression was defined as at least one new or unequivocally enlarged lesion on follow-up MR images. Results: Of 507 follow-up images, 264 showed interval progression, with a total of 1992 new or enlarged and 207 contrastenhancing lesions. Four of these lesions (on three MR images) were retrospectively detected on only the nonenhanced images, corresponding to 1.9% (four of 207) of the enhancing and 0.2% (four of 1992) of all new or enlarged lesions. Nine enhancing lesions were not detected on FLAIR-based subtraction maps (nine of 1442, 0.6%). In none of the 507 images did the contrast-enhanced sequences reveal interval progression that was missed in the readouts of the nonenhanced sequences, with use of either DIR-or FLAIR-based subtraction maps. Interrater agreement was high for all three measures, with intraclass correlation coefficients of 0.91 with FLAIR, 0.94 with DIR, and 0.99 with contrast-enhanced T1-weighted imaging. Conclusion: At 3.0 T, use of a gadolinium-based contrast agent at follow-up MRI did not change the diagnosis of interval disease progression in patients with multiple sclerosis.
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