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Asset Detail:
Predicting Von Hippel-Lindau mutation in kidney tumors using radiomic features
Asset Detail:
Predicting Von Hippel-Lindau mutation in kidney tumors using radiomic features
Overview
| ASSET LINK: | https://modac.cancer.gov/assetDetails?dme_data_id=NCI-DME-MS01-98736721 |
| PROGRAM NAME: | NCI Data Challenges |
| STUDY NAME: | NCI Cancer Research Data Commons (CRDC) Artificial Intelligence Data-Readiness (AIDR) Challenge |
| ASSET NAME: | Predicting Von Hippel-Lindau mutation in kidney tumors using radiomic features |
| ASSET PATH: | /NCI_DOE_Archive/challenges/crdc_aidr_challenge/jeff_van_oss_tier2_submission |
Asset Attributes
| ATTRIBUTE | VALUE |
|---|---|
| ASSET NAME | Predicting Von Hippel-Lindau mutation in kidney tumors using radiomic features |
| ASSET DESCRIPTION | This asset contains the submission from Jeff Van Oss and team of BAMF Health, the second place winner of the 2024 AI Data Readiness Challenge for the NCI Cancer Research Data Commons (CRDC) Tier 2: Training an AI/ML model with multi-modal data. In this tier, participants must train an AI/ML model utilizing data from more than one data class. Their submission focused on Category 3 (Diagnosis). General use case: Distinguish amongst different cancer subtypes. Specific use case: Use of radiological images from Imaging Data Commons and mutation data from The Cancer Genome Atlas to predict Von Hippel-Lindau (VHL) mutation status. |
| ASSET IDENTIFIER | jeff_van_oss_tier2_submission |
| ASSET TYPE | Model |
| MODEL DOMAIN | classification |
| MODEL FRAMEWORK | Scikit-learn |
| MODEL PLATFORM | None |
| PLATFORM VERSION | None |
| POC NAME | Jeff Van Oss |
| POC EMAIL | jeff.vanoss@bamfhealth.com |
| IS MODEL DEPLOYED | No |
| COLLECTION SIZE | 2.6 MB |
| CURATION STATUS | Unverified |
Asset Files
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