Chondral Bone Lesion Characterization


Provide a probability of a chondral bone lesion having benign vs malignant potential





Define-AI ID



 Martin Torriani

Panel Chair

 Jay Patti

Panel Reviewers

 Musculoskeletal Panel


 Creative Commons 4.0

Status  Published

Clinical Implementation

Value Proposition

Bone tumors containing cartilage (enchondromas, low-grade chondrosarcomas) can have similar radiographic appearances. This struggle in differentiating an enchondroma (benign lesion) from a low-grade chondrosarcoma (malignant lesion) is a long-held diagnostic dilemma for which no satisfactory diagnostic support is available. A dedicated algorithm trained to differentiate between these two entities may detect features that are not readily noticeable to human readers. This diagnostic dilemma can be particularly burdensome to nonspecialists (radiologists, pathologists, and orthopedists), who would benefit from an algorithm providing a probability distribution for benign versus malignant lesions. AI meeting this use case would help to reduce the false negative rate, patient risk, and the medical legal risk for the radiologists.


An adult patient presents with a lytic lesion suggestive of enchondroma on an x-ray (incidental or not). The algorithm evaluates the image and provides a probability of enchondroma, chondrosarcoma, or unknown. The radiologist is informed of this information at the time of interpretation.

Workflow Description

An image is obtained from the modality and sent to PACS and the AI engine. The image is analyzed by the engine. The system segments the lesion and analyzes in comparison with a model trained from known cases of each class. A message is sent to PACS from the engine with the classification information. If the location of the lesion is also identified, location information can also be sent to PACS to highlight the region the engine identified.

Consideration for future work would be to provide a combined probability score derived from the assessment of multiple modalities. Therefore, this system may perform best if x-ray and CT data are provided, rather than x-ray alone. In this scenario, the system would need to be trained for multiple modalities.

Considerations for Dataset Development

Procedures(s): {X-ray, Lower/Upper Extremity; X-ray Pelvis; CT; MRI}

View(s): Various obliquities of patient on the images


Sex at Birth: {Male, Female}


Age: [18,90]


Data Source: {Referral Centers; Community Clinics}


Location of tumor: {Hand, Humerus, Femur, Tibia, Ribs, Pelvis}


Differential Diagnoses: {bone infarcts, fibrous lesions, bone cysts, degenerative cysts}


Anatomic Location: {Hand, Humerus, Femur, Tibia, Ribs, Pelvis}


Diagnosis (Radiology/Pathology): Preference for referral centers. Use data that tracks patients over time to verify enchondroma. Ideally, data comes from bone pathologists with experience in sarcoma imaging.


Other: {Fracture, Pain}

Technical Specifications




X-ray, Lower/Upper Extremity; X-ray Pelvis

Data Type





Primary Outputs

Chondrosarcoma Probability Score

RadElement ID



Calculates the probability of a given lesion to be low-grade chondrosarcoma. Values closer to 1 indicate chondrosarcoma. Values closer to 0 indicate enchondroma.

Data Type


Value Set




Related Datasets

No known related public datasets at this time,  please alert us if you know of any.