3D Slicer

• Using baseline MRI radiomic features to predict the efficacy of repetitive transcranial magnetic stimulation in Alzheimer's patients
• From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas
• Impact of desmopressin on hematoma expansion in patients presenting to the emergency department on antiplatelet therapy: Don't expand study
• Correlation Between Meckel's Cave Size and lohexol Dosage in Percutaneous Microballoon Compression for Trigeminal Neuralgia of Aged Patients
• MRI evaluation of lumbar foraminal stenosis: correlation between a new quantitative evaluation and the qualitative Lee's classification
• Association of middle cerebral artery aneurysms and variation of the A1 segment
• Management of cervical carotid pseudoaneurysms: Integrating clinical practice with computational fluid dynamics insights
• Radiological progression of medulloblastoma from non-existence to symptomatic mass in 42 days: Case report, kinetic analysis, and literature review

3D Slicer (Slicer) is a free and open-source software package for image analysis and scientific visualization. Slicer is used in a variety of medical applications, including autism, multiple sclerosis, systemic lupus erythematosus, prostate cancer, schizophrenia, orthopedic biomechanics, COPD, cardiovascular disease and neurosurgery.

https://www.slicer.org/

Pre-operative 3D FLAIR images of 23 Low-grade gliomas LGGs were segmented three times in the software 3D Slicer. Tumor volumes were calculated, together with the absolute and relative difference between the segmentations. To quantify the intra-rater variability, they used the Jaccard coefficient comparing both two (J2) and three (J3) segmentations as well as the Hausdorff Distance (HD). The variability measured with J2 improved significantly between the two last segmentations compared to the two first, going from 0.87 to 0.90 (p = 0.04). Between the last two segmentations, larger tumors showed a tendency towards smaller relative volume difference (p = 0.07), while tumors with well-defined borders had significantly less variability measured with both J2 (p = 0.04) and HD (p < 0.01).

They found no significant relationship between variability and histological sub-types or Apparent Diffusion Coefficients (ADC). They found that the intra-rater variability can be considerable in serial LGG-segmentation, but the variability seems to decrease with experience and higher grade of border conspicuity. Thos findings highlight that some criteria defining tumor borders and progression in 3D volumetric segmentation is needed, if moving from 2D to 3D assessment of size and growth of LGGs ¹⁾.