Soft-Tissue Tumor Bed Size Change
| Purpose | Detect and quantify volumetric changes of a soft-tissue tumor |
| Tag(s) |
|
| Panel | Musculoskeletal |
| Define-AI ID | 18050013 |
| Originator | Jay Patti |
| Panel Chair | Jay Patti |
| Panel Reviewers | Musculoskeletal Panel |
| License | |
| Status | Published |
Clinical Implementation
Value Proposition
Sarcomas and other soft-tissue tumors are sometimes treated with local excision (removal while preserving adjacent anatomy) or radiation/chemotherapy (intravenous medical or high-energy radiation treatment without surgery, in which the intent is to shrink the tumor) or a combination of both of these techniques. In such cases, imaging is used to follow the tumor and assess the response to the chosen therapy. When the tumor decreases in volume, this is usually an indication that the current therapy is effective. When the tumor increases in volume, this is an indication that the current therapy is not effective. Changes are then made to the chosen therapy to hopefully achieve an effective response.Narrative(s)
A 63-year-old patient presents to the oncologist after local resection of a low-grade sarcoma. MRI imaging is obtained after the surgery but before the initiation of chemotherapy. The postsurgical area is then imaged every year with MRI for the next 5 years. Each time the patient is imaged, the AI tool, with the help of a radiologist, identifies the postsurgical area and defines it in 3-dimensional space. Each year, the new area is compared with the prior year's imaging, and AI-defined fiduciary markers are compared, which the AI tool uses to determine if the postsurgical area has increased or decreased in volume. Areas where the AI believes there has been an enlargement are highlighted for the radiologist.Workflow Description
Volumetric DICOM images are obtained from the modality and sent to PACS and the AI engine. The radiologist interacts with the images to identify or verify the center of the postsurgical area. The AI engine identifies the borders of the postsurgical area and calculates the volume of the postsurgical area or mass. The location and size data are sent to PACS to highlight the region that the engine identified.Considerations for Dataset Development
Procedures(s): {MRI, any body region with soft-tissue mass}
View(s): Volumetric dataset
Sex at Birth: {Male, Female}
Age: [0,90]
Soft Tissue Tumor Types: {Angiosarcoma, Dermatofibrosarcoma, Epithelioid sarcoma, Ewing sarcoma, Fibrosarcoma, Gastrointestinal stromal tumors, Kaposi sarcoma, Leiomyosarcoma, Liposarcoma, Malignant fibrous histiocytoma, Neurofibrosarcoma, Rhabdomyosarcoma, Synovial sarcoma}
Technical Specifications
Inputs
DICOM Study
| Procedure | MRI |
| Views | Volumetric dataset |
| Data Type | DICOM |
| Modality | MRI |
Primary Outputs
Tumor Bed Size Change
| RadElement ID | |
| Definition | Status of the tumor bed |
| Data Type | Categorical |
| Value Set | 0-No change in size 1-Increase in size 2-Decrease in size |
| Units | N/A |
Future Development Ideas
Segment the tumor bed and represent the change in size from prior imaging.Related Datasets
No known related public datasets at this time, please us alert us if you know of any.