Artificial Intelligence in Bone Metastases: An MRI and CT Imaging Review

Abstract

(1) Background: The purpose of this review is to study the role of radiomics as a supporting tool in predicting bone disease status, differentiating benign from malignant bone lesions, and characterizing malignant bone lesions. (2) Methods: Two reviewers conducted the literature search independently. Thirteen articles on radiomics as a decision support tool for bone lesions were selected. The quality of the methodology was evaluated according to the radiomics quality score (RQS). (3) Results: All studies were published between 2018 and 2021 and were retrospective in design. Eleven (85%) studies were MRI-based, and two (15%) were CT-based. The sample size was <200 patients for all studies. There is significant heterogeneity in the literature, as evidenced by the relatively low RQS value (average score = 22.6%). There is not a homogeneous protocol used for MRI sequences among the different studies, although the highest predictive ability was always obtained in T2W-FS. Six articles (46%) reported on the potential application of the model in a clinical setting with a decision curve analysis (DCA). (4) Conclusions: Despite the variability in the radiomics method application, the similarity of results and conclusions observed is encouraging. Substantial limits were found; prospective and multicentric studies are needed to affirm the role of radiomics as a supporting tool.

Radiomics in Lung Metastases: A Systematic Review

“…This is due to several factors, including a lack of harmonization of imaging protocols, clinical validation issues, and an overall poor scientific quality of the studies in the field [ 43 , 45 , 103 , 104 ]. In line with this scenario, a RQS of 27.8% (range 22.2–38.9%) was calculated for the 8 articles retrieved in this review ( Table 2 ), consistent with other reviews focused on radiomics [ 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 ].…”

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

“…Radiomics is a relatively new branch of machine learning that involves converting medical images containing important information related to tumour features into measurable and quantifiable data [ 55 ]. This information can then aid clinicians in the assessment of tumours by providing additional data about tumour behaviour and pathophysiology beyond that of current subjective visual interpretation (inferable by human eyes) [ 56 , 57 ], such as tumour subtyping and grading [ 58 ]. Combined with clinical and qualitative imaging data, radiomics has been shown to guide and improve medical decision making [ 59 ], and can be used to aid disease prediction, provide prognostic information, along with treatment response assessment [ 58 ].…”

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

“…The evaluation of minor bone changes, such as tumour mineralization, bone cortex changes, and periosteal reaction, are also better depicted on CT scans [ 16 ]. In addition, CT provides simultaneous evaluation for both bone and soft tissue lesions in cases of suspected malignancy (systemic staging), which reduces the burden of imaging for patients [ 17 , 18 ]. However, CT is deficient in evaluating the soft tissue extent of bone lesions, as well as the degree of medullary involvement [ 19 , 20 ].…”