Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

“…Multiple computer-aided detection (CADe) software systems have been developed for the automated detection of spinal metastases. 8,[23][24][25][26][27][28][29][30] There are many systems for detecting a single type of lesion along the radiodensity spectrum, such as lytic [26][27][28] or sclerotic, [23][24][25] although some detect both types 8,29 as well as mixed lesions. 30 Among systems for the identification of osteolytic spinal lesions, the CADe algorithm developed by O'Connor et al identifies lytic lesions on CT of the chest, abdomen, and/or pelvis performed for other clinical indications.…”

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

“…(15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection. (15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection.…”

“…In addition to detection, the system determined Genant classification of the fractures (26,58) (Fig. (27) This system is a first step toward the quantitative analysis of metastatic spine disease for determination of tumor burden, assessment of lesion change over time, and inclusion of bone lesions into treatment response criteria such as RECIST. In a set of CT scans with 210 fractured vertebrae, the sensitivity for detection of vertebrae with compression fractures was 95.7%, with a false-positive rate of 0.29 per patient.…”

“…Examples of this work include automated detection of traumatic and compression fractures, degenerative changes, epidural masses, bone metastases, and bone mineral density of the spine on CT utilizing both second-and third-generation techniques. (15,16,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) The typical strategy for these applications involves vertebral level labeling and segmentation, followed by feature-or learning-based automated measurement or detection. (39)(40)(41)(42) Magnetic resonance imaging Because MRI is widely used for assessing spinal degenerative changes in the setting of neck and back pain, significant efforts have been made toward automated delineation of spinal anatomy on MRI images.…”

“…(15,16,(59)(60)(61) In one such system, the sensitivity (and false-positive rate per patient) was 81% (2.1), 81% (1.3), and 76% (2.1) for sclerotic, lytic, and mixed lesions of the spine, respectively, using SVM classifiers. (27) This system is a first step toward the quantitative analysis of metastatic spine disease for determination of tumor burden, assessment of lesion change over time, and inclusion of bone lesions into treatment response criteria such as RECIST. In other work, 185 sclerotic lesions were identified in patient ribs, and a system with 75.4% sensitivity at an average of 5.6 false-positives per case was designed and tested using an SVM classifier.…”