MRI tumor volume was more predictive of RFS than tumor diameter, suggesting that volumetric changes measured using MRI may provide a more sensitive assessment of treatment efficacy.

Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.

The computerized analysis of mammograms suggests that mammographic patterns in carriers of BRCA1 and BRCA2 mutations differ from those of women at low risk for breast cancer. Our computer-extracted features may be useful as radiographic markers for identifying women at high risk for breast cancer.

Specialist radiologists detect more cancers and more early-stage cancers, recommend more biopsies, and have lower recall rates than general radiologists.

No abstract

Spiculation is a primary sign of malignancy for masses detected by mammography. In this study, we developed a technique that analyzes patterns and quantifies the degree of spiculation present. Our current approach involves (1) automatic lesion extraction using region growing and (2) feature extraction using radial edge-gradient analysis. Two spiculation measures are obtained from an analysis of radial edge gradients. These measures are evaluated in four different neighborhoods about the extracted mammographic mass. The performance of each of the two measures of spiculation was tested on a database of 95 mammographic masses using ROC analysis that evaluates their individual ability to determine the likelihood of malignancy of a mass. The dependence of the performance of these measures on the choice of neighborhood was analyzed. We have found that it is only necessary to accurately extract an approximate outline of a mass lesion for the purposes of this analysis since the choice of a neighborhood that accommodates the thin spicules at the margin allows for the assessment of margin spiculation with the radial edge-gradient analysis technique. The two measures performed at their highest level when the surrounding periphery of the extracted region is used for feature extraction, yielding Az values of 0.83 and 0.85, respectively, for the determination of malignancy. These are similar to that achieved when a radiologist's ratings of spiculation (Az = 0.85) are used alone. The maximum value of one of the two spiculation measures (FWHM) from the four neighborhoods yielded an Az of 0.88 in the classification of mammographic mass lesions.

Fibroepithelial lesions with cellular stroma (FELCS) in breast core needle biopsy (CNB) specimens may result in either fibroadenoma or phyllodes tumor at excision. We evaluated histologic features, proliferation indices (by Ki-67 and topoisomerase II a immunostaining) and p53 expression in 29 cases of FELCS in CNB specimens and correlated these with excision findings in a blinded manner. On excision, 16 patients had fibroadenomas and 12 had phyllodes tumors. All CNB specimens with mildly increased stromal cellularity were fibroadenomas on excision (n=4), and all with markedly cellular stroma were phyllodes tumors (n=4). Among CNB specimens with moderate cellularity (12 fibroadenomas and 8 phyllodes tumors), only stromal mitoses were discriminatory histologically. Stromal proliferation indices were significantly higher in CNB that were phyllodes tumors vs fibroadenomas. Assessment of stromal cellularity, mitoses, and proliferation indices might help determine the probability of phyllodes tumor occurring and guide management of these cases.

The heterogeneous presentation of DCIS on MRI reflects underlying histopathologic differences.