May Sadik, Iman Hamadeh, Pierre Nordblom, Madis Suurkula, Peter Höglund, Mattias Ohlsson and Lars Edenbrandt
Computer-Assisted Interpretation of planar whole-body bone scans
The Journal of Nuclear Medicine 49, 1958-1965 (2008)
Abstract:
The purpose is to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases.
Methods: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the study. Whole-body images, anterior and posterior, were obtained after injection of Technetium-99m methylene diphosphonate. The image-processing techniques included algorithms for automatic segmentation of the skeleton, automatic detection and feature extraction of hot spots. Two sets of artificial neural networks were used to classify the images, one classifying each hot spot separately and the other classifying the whole bone scan. A test group of 59 patients with breast or prostate cancer was used to evaluate the CAD system. The patients in the test group were selected to reflect the spectrum of pathology found in everyday clinical work. As the gold standard for the test group we used the final clinical assessment of each case. This assessment was based on follow-up scans and other clinical data including the results of laboratory tests, and available diagnostic images, such as magnetic resonance imaging, computed tomography, and X-rays from a mean follow-up period of 4.8 years.
Results: The CAD system correctly identified 19 of the 21 patients with metastases in the test group, showing a sensitivity of 90%. False-positive classification of metastases was made in four of the 38 patients not classified as having metastases by the gold standard, resulting in a specificity of 89%.
Conclusion: A completely automated CAD system can be used to detect metastases in bone scans. Application of the method as a clinical decision support tool appears to have significant potential.
The purpose is to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases.
Methods: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the study. Whole-body images, anterior and posterior, were obtained after injection of Technetium-99m methylene diphosphonate. The image-processing techniques included algorithms for automatic segmentation of the skeleton, automatic detection and feature extraction of hot spots. Two sets of artificial neural networks were used to classify the images, one classifying each hot spot separately and the other classifying the whole bone scan. A test group of 59 patients with breast or prostate cancer was used to evaluate the CAD system. The patients in the test group were selected to reflect the spectrum of pathology found in everyday clinical work. As the gold standard for the test group we used the final clinical assessment of each case. This assessment was based on follow-up scans and other clinical data including the results of laboratory tests, and available diagnostic images, such as magnetic resonance imaging, computed tomography, and X-rays from a mean follow-up period of 4.8 years.
Results: The CAD system correctly identified 19 of the 21 patients with metastases in the test group, showing a sensitivity of 90%. False-positive classification of metastases was made in four of the 38 patients not classified as having metastases by the gold standard, resulting in a specificity of 89%.
Conclusion: A completely automated CAD system can be used to detect metastases in bone scans. Application of the method as a clinical decision support tool appears to have significant potential.
LU TP 08-15