X-ray Diffraction Spectral Imaging for Breast Cancer Assessment

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Spencer, James Rodney


Kapadia, Anuj J

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Breast cancer surgical treatment options prove effective at treating breast cancer and reducing breast cancer death rates, prompting women to elect to surgically excise the tumor via a lumpectomy procedure. Despite women choosing lumpectomy over a mastectomy in 60% of cases, and despite the general effectiveness of the lumpectomy procedures, patient recall rates due to missed cancerous tissue are unfavorably high and variable at approximately 25% nationally. In addition, drawn-out processing times due to pathology assessment contribute to sub-optimal patient care and overly onerous costs and workload for hospitals. Therefore, it is the focus of this work to develop, evaluate, and refine a novel imaging modality to aid pathologists and pathologists’ assistants in assessing breast cancer via a more quantified means that would eventually lower the recall rates in breast cancer surgery.

Through previous work, we established a Coded Aperture Coherent Scatter Spectral Imaging (CACSSI) system, characterized several facets of the imaging setup, and evaluated its utility in breast cancer applications. Using Monte Carlo simulations, anthropomorphic breast phantoms, and human breast tissue specimens, we previously validated CACSSI’s utility in differentiating breast tissue types in a clinically relevant manner, which makes the system a promising candidate to act as a supplementary tool to implement in the pathology workflow. This work continues the previous research by applying and implementing the tissue classification ability within a short, clinically feasible timeframe (5-30 minutes) and demonstrating utility in a broader population of 12 patient-derived lumpectomy specimens. The work presented herein is broken into three subprojects: (1) Assessing various characterizations of the system (i.e. the background signal effects, the detector temperature-dependent response, the precision in consecutive scans, and the effect of formalin-fixation) to demonstrate its feasibility for the cancer detection/classification tasks; (2) Evaluating the accuracy of the system in a population of 12 excised breast tissue specimens while establishing and implementing the scan room procedures across multiples specimens; and (3) Utilizing a concurrently-developed classification scheme to more thoroughly compare the system’s fidelity and robustness against pathology-assessed outcomes, which currently serve as the clinical gold standard for breast cancer judgments.

The typical workflow included Surgical Pathology preparing the surgically excised specimens and indicating via palpation the location of the tumor. The specimen, with the preliminary tumor location marked, was then scanned in our imaging system, and spectral scatter signatures were obtained at multiple locations within the tissue. The resulting form factor spectra were then compared with reference spectra to classify the tissue as cancerous or non-cancerous (healthy). The tissue classification mapping was compared against the indicated tumor area or against pathology-stained microslides for verification of tumor diagnosis.

Formalin-fixation was found inconsequential for tissue classification, with fresh-to-formalin-fixed spectra correlations of 0.9782 and 0.9881 over 10 spot scans each for healthy and cancer tissue, respectively. The spatial resolution of the system was found to be 1.5 mm in the lateral direction and 5 mm along the beam path. Our CACSSI system was able to distinguish between cancerous and healthy areas in the tissue slices in a consistent manner, and the system was, on average, 82.93% accurate for the initial classification scheme and 83.70% accurate using a more quantitative classification scheme. Furthermore, we were able to achieve these results in a clinically relevant timeframe on the order of 30 minutes, integrating into the pathology workflow with minimal interruption. Aggregating these results CACSSI will continue to be developed for use as a clinical imaging tool in breast cancer assessment and other diagnostic purposes.





Spencer, James Rodney (2017). X-ray Diffraction Spectral Imaging for Breast Cancer Assessment. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/15287.


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