Computer aided automatic detection of malignant lesions in diffuse optical mammography - Busch, David R.; Guo, Wensheng; Choe, Regine; Durduran, Turgut; Feldman, Michael D.; Mies, Carolyn; Rosen, Mark A.; Schnall, Mitchell D.; Czerniecki, Brian J.; Tchou, Julia; DeMichele, Angela; Putt, Mary E.; et al. (Medical physics (Lancaster), 2010)
Purpose: Computer aided detection (CAD) data analysis procedures are introduced and applied to derive composite diffuse optical tomography (DOT) signatures of malignancy in human breast tissue. In contrast to previous optical mammography analysis schemes, the new statistical approach utilizes optical property distributions across multiple subjects and across the many voxels of each subject. The methodology is tested in a population of 35 biopsy-confirmed malignant lesions. Methods: DOT CAD employs multiparameter, multivoxel, multisubject measurements to derive a simple function that transforms DOT images of tissue chromophores and scattering into a probability of malignancy tomogram. The formalism incorporates both intrasubject spatial heterogeneity and intersubject distributions of physiological properties derived from a population of cancer-containing breasts (the training set). A weighted combination of physiological parameters from the training set define a malignancy parameter ( M ) , with the weighting factors optimized by logistic regression to separate training-set cancer voxels from training-set healthy voxels. The utility of M is examined, employing 3D DOT images from an additional subjects (the test set). Results: Initial results confirm that the automated technique can produce tomograms that distinguish healthy from malignant tissue. When compared to a gold standard tissue segmentation, this protocol produced an average true positive rate (sensitivity) of 89% and a true negative rate (specificity) of 94% using an empirically chosen probability threshold. Conclusions: This study suggests that the automated multisubject, multivoxel, multiparameter statistical analysis of diffuse optical data is potentially quite useful, producing tomograms that distinguish healthy from malignant tissue. This type of data analysis may also prove useful for suppression of image artifacts.