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Background: Dynamic infrared (IR) imaging is an emerging functional imaging modality for the detection of breast cancer without evidence of optimal imaging and diagnostic application. Purpose: To evaluate dynamic IR imaging in breast cancer diagnostics by comparing a stepwise diagnostic scheme to digital mammography and postoperative histopathology. Material and Methods: Dynamic IR imaging of breasts was undertaken preoperatively with a long-wave quantum well (QWIP) and two mid-wave photovoltaic (PV) IR cameras in 10 cases (age 34–80 years) with breast cancer size 6–45 mm on mammography. Image stabilization, two-phase frequency analysis, and two image-processing algorithms were applied. Results: Combining image processing with frequency analysis proved advantageous in detecting breast cancer. The IR imaging process recognized the cancer area independently of tissue density, cancer size, and cancer appearance on mammography. Compared to histopathology, all cancers yielded abnormal analysis results, including one case of ductal carcinoma in situ. Evidence of lymphatic invasion in postoperative histopathology, imaging with PV camera, and image processing with the Wiener filtering combination correlated with highest confidence between normal and cancer tissue measured by the calculated superiority value. Conclusion: Dynamic IR imaging with image-processing-guided frequency analysis is a promising modality for breast cancer detection and may not have the tissue-dependent limitations of mammography. Our results encourage further work on medical IR imaging and comparison to established breast-imaging modalities.

Five combinations of image-processing algorithms were applied to dynamic infrared (IR) images of six breast cancer patients preoperatively to establish optimal enhancement of cancer tissue before frequency analysis. mid-wave photovoltaic (PV) IR cameras with 320x254 and 640x512 pixels were used. The signal-to-noise ratio and the specificity for breast cancer were evaluated with the image-processing combinations from the image series of each patient. Before image processing and frequency analysis the effect of patient movement was minimized with a stabilization program developed and tested in the study by stabilizing image slices using surface markers set as measurement points on the skin of the imaged breast. A mathematical equation for superiority value was developed for comparison of the key ratios of the image-processing combinations. The ability of each combination to locate the mammography finding of breast cancer in each patient was compared. Our results show that data collected with a 640x512-pixel mid-wave PV camera applying image-processing methods optimizing signal-to-noise ratio, morphological image processing and linear image restoration before frequency analysis possess the greatest superiority value, showing the cancer area most clearly also in the match centre of the mammography estimation.

In this novel study the breasts of 15 women with palpable breast cancer were preoperatively imaged with three technically different infrared (IR) cameras - micro bolometer (MB), quantum well (QWIP) and photo voltaic (PV) - to compare their ability to differentiate breast cancer from normal tissue. The IR images were processed, the data for frequency analysis were collected from dynamic IR images by pixel-based analysis and from each image selectively windowed regional analysis was carried out, based on angiogenesis and nitric oxide production of cancer tissue causing vasomotor and cardiogenic frequency differences compared to normal tissue. Our results show that the GaAs QWIP camera and the InSb PV camera demonstrate the frequency difference between normal and cancerous breast tissue; the PV camera more clearly. With selected image processing operations more detailed frequency analyses could be applied to the suspicious area. The MB camera was not suitable for tissue differentiation, as the difference between noise and effective signal was unsatisfactory.

To understand the genetic basis and clonal evolution underlying metastatic progression of human breast cancer in vivo, we analyzed the genetic composition of 29 primary breast carcinomas and their paired asynchronous metastases by comparative genomic hybridization and fluorescence in situ hybridization. The mean number of genetic changes by comparative genomic hybridization was 8.7 +/- 5.3 in primary tumors and 9.0 +/- 5.7 in their metastases. Although most of the genetic changes occurred equally often in the two groups, gains of the Xq12-q22 region were enriched in the metastases. According to a statistical analysis of shared genetic changes and breakpoints in paired specimens, 20 of the metastases (69%) showed a high degree of clonal relationship with the corresponding primary tumor, whereas the genetic composition of 9 metastases (31%) differed almost completely from that of the paired primary tumors. In both groups, however, chromosome X inactivation patterns suggested that the metastatic lesions originated from the same clone as the primary tumor. Fluorescence in situ hybridization analysis with probes specific to metastatic clones usually failed to find such cells in the primary tumor sample. In conclusion, detailed characterization of the in vivo progression pathways of metastatic breast cancer indicates that a linear progression model is unlikely to account for the progression of primary tumors to metastases. An early stem line clone apparently evolves independently in the primary tumor and its metastasis, eventually leading to multiple, genetically almost completely different, clones in the various tumor locations in a given patient. The resulting heterogeneity of metastatic breast cancer may underlie its poor responsiveness to therapy and explain why biomarkers of prognosis or therapy responsiveness measured exclusively from primary tumors give a restricted view of the biological properties of metastatic breast cancer.