Assessing lesion malignancy by scanning small-angle x-ray scattering of breast tissue with microcalcifications.

Scanning small-angle x-ray scattering (SAXS) measurements were performed on 36 formalin-fixed breast tissue biopsies obtained from two patients. All samples contained microcalcifications of type II, i.e. formed by hydroxyapatite. We demonstrate the feasibility of classifying breast lesions by scanning SAXS of tissues containing microcalcifications with a resolution of 35 [Formula: see text]m [Formula: see text] 30 [Formula: see text]m. We report a characteristic Bragg peak found around q = 1.725 nm-1 that occurs primarily for malignant lesions. Such a clear SAXS fingerprint is potentially linked to structural changes of breast tissue and corresponds to dimensions of about 3.7 nm. This material property could be used as an early indicator of malignancy development, as it is readily assessed by SAXS. If this fingerprint is combined with other known SAXS features, which also indicate the level of malignancy, such as lipid spacing and collagen periodicity, it could complement traditional pathology-based analyses. To confirm the SAXS-based classification, a histopathological workup and a gold standard histopathological diagnosis were conducted to determine the malignancy level of the lesions. Our aim is to report this SAXS fingerprint, which is clearly related to malignant breast lesions. However, any further conclusion based on our dataset is limited by the low number of patients and samples. Running a broad study to increase the number of samples and patients is of great importance and relevance for the breast-imaging community.

X-ray scanning microscopies of microcalcifications in abdominal aortic and popliteal artery aneurysms.

Abdominal aortic and popliteal artery aneurysms are vascular diseases which show massive degeneration, weakening of the vascular wall and loss of the vascular tissue functionality. They are driven by inflammatory, hemodynamical factors and biological alterations that may lead, in the case of an abdominal aortic aneurysm, to sudden and dangerous ruptures of the arteries. Here, human aortic and popliteal aneurysm tissues were obtained during surgical repair, and studied by synchrotron radiation X-ray scanning microdiffraction and small-angle scattering, to investigate the microcalcifications present in the tissues. Data collected during the experiments were transformed into quantitative microscopy images through the combination of statistical approaches and crystallographic methods. As a result of this multi-step analysis, microcalcifications, which are markers of the pathology, were classified in terms of chemical and structural content. This analysis helped to identify the presence of nanocrystalline hy-droxy-apatite and microcrystalline cholesterol, embedded in myofilament, and elastin-containing tissue with low collagen content in predominantly nanocrystalline areas. The generality of the approach allows it to be transferred to other types of tissue and other pathologies affected by microcalcifications, such as thyroid carcinoma, breast cancer, testicular microli-thia-sis or glioblastoma.

Model-free classification of X-ray scattering signals applied to image segmentation.

In most cases, the analysis of small-angle and wide-angle X-ray scattering (SAXS and WAXS, respectively) requires a theoretical model to describe the sample's scattering, complicating the interpretation of the scattering resulting from complex heterogeneous samples. This is the reason why, in general, the analysis of a large number of scattering patterns, such as are generated by time-resolved and scanning methods, remains challenging. Here, a model-free classification method to separate SAXS/WAXS signals on the basis of their inflection points is introduced and demonstrated. This article focuses on the segmentation of scanning SAXS/WAXS maps for which each pixel corresponds to an azimuthally integrated scattering curve. In such a way, the sample composition distribution can be segmented through signal classification without applying a model or previous sample knowledge. Dimensionality reduction and clustering algorithms are employed to classify SAXS/WAXS signals according to their similarity. The number of clusters, i.e. the main sample regions detected by SAXS/WAXS signal similarity, is automatically estimated. From each cluster, a main representative SAXS/WAXS signal is extracted to uncover the spatial distribution of the mixtures of phases that form the sample. As examples of applications, a mudrock sample and two breast tissue lesions are segmented.