T17: Predicting lesion size measurements¶
Objective:
Develop a model to extract and predict standardized lesion size measurements from free-text radiology reports. Each report describes one or more lesions and includes a defined prediction objective (e.g., longest diameter, short axis), which is provided as a prefix in the report text.
Patient Population:
Patients with radiology reports from three real-world diagnostic cohorts including:
- Pulmonary nodules
- RECIST target lesions
- Pancreatic ductal adenocarcinomas (PDAC)
Imaging Data:
Not applicable. The task is based solely on textual data — radiology reports written in Dutch.
Test Data:
Each report is provided in a JSON file along with the expected prediction objective. Participants must extract a numerical lesion size in millimeters (mm) according to the provided objective.
Reference Standard:
Lesion size annotations were manually created by trained investigators or radiologists. Ground truth sizes vary by cohort:
- Pulmonary nodules: 1–30 mm
- RECIST lesions: 4–166 mm
- PDAC lesions: 6–130 mm
Evaluation Metrics:
Performance is measured using Robust Symmetric Mean Absolute Percentage Error (RSMAPE) with a 4 mm tolerance, to account for clinical relevance and measurement variability.
Relation to Existing Challenges:¶
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Task 17 in the UNICORN challenge is a combination of the following tasks from the DRAGON challenge:
- T22 (Pancreatic ductal adenocarcinoma size measurement)
- T23 (Pulmonary nodule size measurement)
- T24 (RECIST lesion size measurement)
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Unlike DRAGON, each UNICORN test case includes a prediction objective as a prefix to guide the model, supporting a few-shot learning setup conducted entirely on-platform.