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Updated: Apr 26, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
A quantitative method for the characterization of lytic metastases of the bone from radiographic images
Alicia Baltasar Sánchez1, Angel Gonzalez Sistal1
1Medical Imaging Research Laboratory, Department of Physiological Sciences II, Faculty of Medicine, University of Barcelona, C/ Feixa Llarga s/n Pavelló de Govern, Lab. 41.57, Hospitalet de Llobregat, 08907 Barcelona, Spain.
Abstract:
The aim of our study was to assess the diagnostic usefulness of the gray level parameters to distinguish osteolytic lesions using radiological images. Materials and Methods. A retrospective study was carried out. A total of 76 skeletal radiographs of osteolytic metastases and 67 radiographs of multiple myeloma were used. The cases were classified into nonflat (MM1 and OL1) and flat bones (MM2 and OL2). These radiological images were analyzed by using a computerized method. The parameters calculated were mean, standard deviation, and coefficient of variation (MGL, SDGL, and CVGL) based on gray level histogram analysis of a region-of-interest. Diagnostic utility was quantified by measurement of parameters on osteolytic metastases and multiple myeloma, yielding quantification of area under the receiver operating characteristic (ROC) curve (AUC). Results. Flat bone groups (MM2 and OL2) showed significant differences in mean values of MGL (P = 0.048) and SDGL (P = 0.003). Their corresponding values of AUC were 0.758 for MGL and 0.883 for SDGL in flat bones. In nonflat bones these gray level parameters do not show diagnostic ability. Conclusion. The gray level parameters MGL and SDGL show a good discriminatory diagnostic ability to distinguish between multiple myeloma and lytic metastases in flat bones.
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