Literature DB >> 18067208

Current diagnostic approaches in tumor-induced bone disease.

S Sergieva1, G Kirova, A Dudov.   

Abstract

PURPOSE: To assess the role of the current imaging methods in the diagnosis, staging and post-therapeutic monitoring of cancer-induced bone disease. PATIENTS AND METHODS: 183 cancer patients underwent baseline whole body bone scintigraphy (WBBS) with 555- 740 MBq (99m)Tc-MDP. Computed tomography (CT) was carried out in 43 patients, and magnetic resonance imaging (MRI) in 26 patients with abnormal uptake on the bone scan in order to differentiate metastatic or degenerative skeletal lesions with similar scintigraphic appearance. Sixty-four patients with established tumor-induced bone disease were followed-up after their anticancer treatment.
RESULTS: WBBS was positive for metastatic disease in 54 patients and normal skeletal scan was obtained in 28 patients. Comparative analysis of the sensitivity and specificity of WBBS in relation to CT for diagnosis of metastatic spots in the examined group of 43 patients showed: for WBBS 93.3% (28/30) and 92.8% (13/14), respectively; for CT 90% (27/30) and 100% (13/13), respectively. Sensitivity and specificity of WBBS in relation to MRI in the examined group of 26 patients showed: for WBBS 86.6% (13/15) and 77.7% (7/9), respectively/ for MRI 100% (15/15) and 88% (8/9), respectively. Scintigraphic follow-up examinations of 64 patients after appropriate therapy showed partial response (PR) in 28 cases, stable disease (SD) in 12, progressive disease (PD) in 10, pathological fractures in 11 and "flare phenomenon) in 3 cases.
CONCLUSION: In most cases WBBS is suffi cient to find and locate osseous metastases. Because of the lower specificity of WBBS, patients with spots of increased mineral metabolism of unclear character and persisting pain in the spine necessitate target CT or MRI to evaluate the characteristics of the detected scintigraphic changes.

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Year:  2007        PMID: 18067208

Source DB:  PubMed          Journal:  J BUON        ISSN: 1107-0625            Impact factor:   2.533


  1 in total

1.  dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.

Authors:  Qiang Lin; Chuangui Cao; Tongtong Li; Zhengxing Man; Yongchun Cao; Haijun Wang
Journal:  BMC Med Imaging       Date:  2021-08-11       Impact factor: 1.930

  1 in total

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