-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
With the development of imaging techniques, MRI prediction of medulloblastoma (MB) molecular subsets provides a noninvasive preoperative pathway
Although most rare in MB, WNT mutations have the best prognosis and are therefore most likely to benefit
Machine learning can mine high-dimensional image features, aiding in precise analysis of quantitative features
Recently, a study published in the journal Radiology formed a large pediatric MB cohort in 12 major centers in the United States, Canada and the United Kingdom, and developed an IBSI-based radiogenomics strategy to achieve early and accurate identification and diagnosis of four clinically significant SUBgroups of MB molecules
This retrospective study evaluated consecutive pediatric patients with new diagnoses of MB on MRI at 12 international paediatric sites
The study cohort included 263 patients (mean age at diagnosis± SD, 87 months±60; 166 boys
This study proposes an MRI-based machine learning decision-making pathway and predicts four clinically relevant subgroups
Original source:
Michael Zhang,Samuel W Wong,Jason N Wright,et al.
