Document type

Article

Version

Published version

Publication date

Publication license

cc-by (c) Baltasar Sánchez, Alicia et al., 2014
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/65630

A Quantitative method for the characterization of lytic metastases of the bone from radiographic images

Journal Title

Director/Tutor

Journal ISSN

Volume Title

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 bymeasurement of parameters on osteolyticmetastases andmultiplemyeloma, 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 parametersMGL and SDGL show a good discriminatory diagnostic ability to distinguish between multiple myeloma and lytic metastases in flat bones.

Citation

Citation

BALTASAR SÁNCHEZ, Alicia and GONZÁLEZ SISTAL, Ángel. A Quantitative method for the characterization of lytic metastases of the bone from radiographic images. Scientific World Journal. 2014. Vol. 2014, num. 2014, pags. 1-5. ISSN 1537-744X. [consulted: 16 of June of 2026]. Available at: https://hdl.handle.net/2445/65630

Export metadata

JSON - METS

Share record