The reproducibility, biologic background, and prognostic value of syntactic structure analysis were studied in a group of 94 patients with invasive primary breast cancer. Using an interactive digitizing video overlay system, the centers of malignant breast cancer nuclei were marked in the five (subjectively) most cellular fields of vision for each case at a final magnification of X1,200, and the corresponding minimum spanning tree was composed for each field. From each minimum spanning tree, 10 syntactic structure features were derived; subsequently, the mean, standard deviation, minimum, and maximum values of the five fields analyzed were calculated for further statistical analysis. Forty statistics thus were available for each case. The reproducibility of repeatedly measuring the same field (independent of nuclearity) and the same patient twice by the same or different observers was good for most of the syntactic structure features. When comparing the statistics of the syntactic structure features with other established prognosticators, correlations were found with (in this order) standard deviation and mean nuclear area (expressing nuclear differentiation), volume percentage epithelium (expressing architectural differentiation), and mitotic activity index. In univariate survival analysis several syntactic structure statistics yielded prognostic significance, the best being the maximum of the number of nuclei with two neighbors (P = .002; Mantel-Cox test, 12.4). Multivariate analysis revealed that syntactic structure features did not provide additional prognostic values with regard to each other. However, they did show additional prognostic values with regard to the multivariate prognostic index (currently one of the best prognosticators in breast cancer), which combines the mitotic activity index, lymph node status, and tumor size. Measurement of syntactic structure features could therefore contribute to an improved prediction of prognosis of breast cancer patients. In conclusion, syntactic structure analysis is a simple, fast, and highly reproducible technique that shows prognostic value in invasive breast cancer and also has important prognostic value with regard to the well-established and prognostically strong morphometric features. It seems to be a promising new method of analyzing tissue architecture in breast cancer and perhaps in many other tumors.