Partial volume effects and varying metabolic activity (dependent on tumor type) seem to represent the most significant limitations for the routine diagnostic application of PET. The number of invasive procedures is therefore unlikely to be significantly reduced by PET imaging in patients presenting with abnormal mammography. However, the high positive-predictive value, resulting from the increased metabolic activity of malignant tissue, may be used with carefully selected subsets of patients as well as to determine the extent of disease or to assess therapy response.

This study demonstrates that in patients with advanced breast cancer undergoing primary chemotherapy, FDG-PET differentiates responders from nonresponders early in the course of therapy. This may help improve patient management by avoiding ineffective chemotherapy and supporting the decision to continue dose-intensive preoperative chemotherapy in responding patients.

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To optimize the sensitivity and specificity of gray-scale imaging and color Doppler in breast tumor diagnosis, alone and in combination, 89 women with palpable breast masses were scanned preoperatively and standard parameters were determined in both modes. Parameters significant for differentiation of benign and malignant tumors identified using univariate analysis were combined and weighted using multivariate analysis (multiple logistic regression). Histologically 59 tumors were malignant and 30 benign. Gray-scale sonography alone achieved a sensitivity of 88% and a specificity of 96% using the parameters of wall structure and posterior acoustic attenuation. Color Doppler achieved a sensitivity of 85% and a specificity of 79% using resistance index and pulsatility index as parameters. Combination of both methods yielded an accurate diagnosis in 84/87 patients (sonographic lesion correlates were absent in two cases), equivalent to a sensitivity of 97% and a specificity of 96%. Thus the individual diagnostic performance of gray-scale imaging and color Doppler sonography in palpable breast disease is further enhanced using multiple logistic regression to combine independently significant parameters.

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