title: Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?
abstract: We examined whether neural network clustering could support the characterization of diagnostically challenging breast lesions in dynamic magnetic resonance imaging (MRI). We examined 88 patients with 92 breast lesions (51 malignant, 41 benign). Lesions were detected by mammography and classified Breast Imaging and Reporting Data System (BIRADS) III (median diameter 14 mm). MRI was performed with a dynamic T1-weighted gradient echo sequence (one precontrast and five postcontrast series). Lesions with an initial contrast enhancement >or=50% were selected with semiautomatic segmentation. For conventional analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were divided into four clusters using minimal-free-energy vector quantization (VQ). With conventional analysis, maximum accuracy in detecting breast cancer was 71%. With VQ, a maximum accuracy of 75% was observed. The slight improvement using VQ was mainly achieved by an increase of sensitivity, especially in invasive lobular carcinoma and ductal carcinoma in situ (DCIS). For lesion size, a high correlation between different observers was found (R(2) = 0.98). VQ slightly improved the discrimination between malignant and benign indeterminate lesions (BIRADS III) in comparison with a standard evaluation method.
authors.affiliation: Institute for Clinical Radiology University of Munich, Ziemssenstr, 1 80336 Munich, Germany.
authors.forename: Gerda
authors.initials: G
authors.lastname: Leinsinger
authors.forename: Thomas
authors.initials: T
authors.lastname: Schlossbauer
authors.forename: Michael
authors.initials: M
authors.lastname: Scherr
authors.forename: Oliver
authors.initials: O
authors.lastname: Lange
authors.forename: Maximilian
authors.initials: M
authors.lastname: Reiser
authors.forename: Axel
authors.initials: A
authors.lastname: Wismüller
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000000981:diagnostic imaging* / Q000473:pathology; D018275:Carcinoma, Lobular / Q000000981:diagnostic imaging* / Q000473:pathology; D016000:Cluster Analysis; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008327:Mammography; D008875:Middle Aged; D015588:Observer Variation; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted