Artificial intelligence in skeletal metastasis imaging

Abstract

In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.

Keywords: Artificial intelligence; Bone metastasis; Deep learning; Medical imaging.

Fig. 1

An example of automatic bone metastasis segmentation on CT images by YOLOv5 ; the green box was outlined by a nuclear medicine physician, and the red box was the segmented lesion as predicted by the algorithm. (a): the predicted lesion was smaller than the actual one; (b): the algorithm falsely segmented the lesion-free cross-section.

Fig. 2

An example of automatic bone metastasis segmentation on MRI images by YOLOv5; the green box was outlined by a nuclear medicine physician, and the red box was the segmented lesion as predicted by the algorithm. The upper right zoomed area in (a): the predicted lesion matched the actual outline; The lower right zoomed corner in (a) and the zoomed box in Fig. 2(b): the algorithm falsely segmented those lesions as metastatic.

Fig. 3

An example of automatic bone metastasis detection on PET images by YOLOv5; the green box was outlined by a nuclear medicine physician, and the red box was the detected lesion as predicted by the algorithm. (a): the predicted lesion matched the actual outline; (b): the algorithm falsely detected the upper high uptake lesion as metastatic.