Bony Metastatic Disease Detection

Value Proposition

Accurate cancer staging is essential for decisions regarding timing and use of cancer treatments and for assessing effectiveness in clinical trials. Bones are important site of metastatic disease but are not focus of many types of scans. An algorithm that could detect bony lesions could improve the accuracy of radiologists staging cancer, which directly affects treatment decisions. Decreasing the error rate for detecting these lesions makes staging more accurate and leads to better treatment decisions for cancer patients.

Narrative(s)

A 65 year old patient with lung cancer who was not a surgical candidate was receiving a chemotherapy regimen for lung cancer. On a routine restaging chest CT, the thoracic radiologist described that the lung mass was stable and the mediastinal lymph nodes had decreased in size, but did not notice a new small lesion in the patient’s thoracic spine. This lesion was not discovered until 2 months later when the patient went to the ED with back pain and the bone lesion was much larger. Upon learning of the new lesion, the oncologist changed the chemotherapy regimen and the patient had a good response. Had this lesion been detected initially, this treatment change could have been made earlier, and may have prevented the ED visit and patient discomfort.

Workflow Description

Image obtained from modality and sent to PACS and the AI engine. Image analyzed by engine. System detects and describes skeletal metastases. This includes data on the size and classification of the metastases. An alert message is sent to PACS from the engine with the information and graphic highlighting the lesion(s).

Inputs

DICOM Study

Primary Outputs

Presence of Skeletal Metastasis

Segmentation of Skeletal Metastasis

Max width of skeletal metastasis

Disease Classification

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