The efficacy of 99mTc-MIBI scintimammography in the evaluation of breast lesions and axillary involvement: a comparison with X-rays mammography, ultrasonography and magnetic resonance imaging

This study was performed because the efficacy of technetium-99m methoxyisobutyl isonitrile scintimammography (Tc-99m-MIBI-SM) in the evaluation of the breast lesions and axillary lymph node involvement in comparison with X-rays mammography (XRM), ultrasonography (US) and magnetic resonance imaging (MRI) has not been fully investigated. Forty six female patients were included in this study, with suspicious lesions detected in their breasts by palpation, by imaging modalities or clinically. All patients underwent Tc-99m-MIBI-SM, US and MRI for the evaluation of breast lesions. All patients according to clinical situation and imaging studies underwent fine needle aspiration, mass extirpation, core biopsy, modified radical mastectomy or partial lumpectomy in order to confirm the nature of the lesions. Our results showed that Tc-99m-MIBI-SM detected 15 of 16, US 11 of 16, XRM 13 of 16 and MRI 13 of 16 malignant lesions. Sensitivities were 93%, 68%, 81% and 81%, respectively. Among these cases there were 4 of 15 false positive (FP) results on SM whereas 4 of 11 on US, 11 of 13 on XRM and 8 of 13 on MRI. The specificities of the above modalities were 86%, 87%, 63% and 73%, respectively. The sensitivities of the above imaging modalities for the detection of axillary lymph node metastases were 55%, 55%, 11% and 77% for SM, US, XRM and MRI, respectively. The sensitivity and specificity of SM for the palpable lesions (n=31) were 100% and 84% and for the nonpalpable lesions (n=15) were 75% and 90%, respectively. In conclusion, although the number of patients studied was small it is the opinion of the authors that Tc-99m-MIBI-SM has a much better sensitivity and less FP results in detecting malignant breast lesions than the other 3 modalities and also better sensitivity than XRM and MRI. Thus Tc-99m-MIBI-SM can be included in the diagnostic algorithms for detecting malignancy in breast tumors.