Automated Detection of Sclerotic Metastases in the Thoracolumbar Spine at CT

Abstract

Purpose:To design and validate a computer system for automated detection and quantitative characterization of sclerotic metastases of the thoracolumbar spine on computed tomography (CT) images. Materials and Methods:This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. The data set consisted of CT examinations in 49 patients (14 female, 35 male patients; mean age, 57.0 years; range, 12-77 years), demonstrating a total of 532 sclerotic lesions of the spine of greater than 0.3 cm 3 in volume, and in 10 control case patients (four women, six men; mean age, 55.2 years; range, 19-70 years) without spinal lesions. CT examinations were divided into training and test sets, and images were analyzed according to prototypical fully-automated computer-aided detection (CAD) software. Free-response receiver operating characteristic analysis was performed. Results:Lesion detection sensitivity on images in the training set was 90%, relative to reference-standard marked lesions (95% confidence interval [CI]: 83%, 97%), at a false-positive rate (FPR) of 10.8 per patient (95% CI: 6.6, 15.0). For images in the testing set, sensitivity was 79% (95% CI: 74%, 84%), with an FPR of 10.9 per patient (95% CI: 8.5, 13.3). Conclusion:This CAD system successfully identified and segmented sclerotic lesions in the thoracolumbar spine.q RSNA, 2013

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications

“…It can be seen that the classification performance saturates quickly with increasing N . This means the run-time efficiency of our second Figure 7 compares the FROCs from the initial (first layer) CADe system [9] and illustrates the progression towards the proposed coarse-to-fine two tiered method in both training and testing datasets. This clearly demonstrates a marked improvement in performance.…”

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

“…Additionally, compared to prior CT-based detection systems designed for sclerotic lesion detection, 9,13,14 lytic lesion detection, 6,7 and sclerotic/lytic but not mixed type detection, 15,16 this system uses a triple classifier process to detect all three lesion types: lytic, sclerotic, and mixed. Additionally, compared to Ref.…”

“…The group later applied the technique to detect changes in lytic metastases from one cohort of patients taking bisphosphonates and one control cohort. 11 For sclerotic lesion CAD, Weise et al 12 and Burns et al 13 developed a sclerotic metastasis detection system on CT and examined the etiology of false negative and false positive detections. Roth et al 14 then proposed a deep convolutional neural network approach to reduce the number of false positives in the sclerotic metastasis CAD.…”

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT

“…challenging tasks of automatic detection and recognition of anatomical structures and pathological lesions in cross-sectional imaging remain part of intensive research. Burns et al recently demonstrated an automated algorithm for detection of bone metastasis [19]. Several prior studies have investigated techniques for automated labeling and segmentation of the spine [16][17][18][19][20][21][22][23].…”

“…To cope with the resulting large volume of data, an increasing number of software tools support the radiologist's work in routine clinical practice and accelerate the work processes, examples are lesion detection and tracking in oncology [16][17][18][19]. Nevertheless, challenging tasks like automatic detection and correct anatomical labeling of vertebral bodies are part of current research [16,[20][21][22].…”