Correlating cone-beam CT and large-section histology image sets: Initial results using a surgical lumpectomy specimen

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Abstract

Radiographic signs indicating the presence of a malignancy are a result of the morphology and composition of the lesion. Assessment of the size, distribution, extent and location of disease are crucial in guiding patient management. Often mammographic estimates of size and extent are underestimated. Radiologic/pathologic correlation between features is often by indirect classification methods rather than a direct, whole-volume, one-to-one spatial correlation between radiologic and pathologic images. As an initial step toward understanding how tumour morphology and composition yields a mammographic sign, we have begun work on correlating whole-mount histology sections to cone-beam computed tomography (CBCT) images of the same specimen. Preliminary results for a lumpectomy sample containing a 3.5 cm invasive ductal carcinoma qualitatively show a remarkable correspondence between CBCT slices and histology sections. Ultimately, the 3D CBCT data could be used to predict mammographic features, which could then be correlated precisely to the anatomy of the tumour. © Springer-Verlag Berlin Heidelberg 2006.

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Mainprize, J. G., Okhai, S., Clarke, G. M., Kempston, M. P., Eidt, S., & Yaffe, M. J. (2006). Correlating cone-beam CT and large-section histology image sets: Initial results using a surgical lumpectomy specimen. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4046 LNCS, pp. 299–306). Springer Verlag. https://doi.org/10.1007/11783237_41

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