Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures
| Autor: | Wendy A. Wells, Brian W. Pogue, Rebecca A. Zuurbier, Brady Hunt, Keith D. Paulsen, Samuel S. Streeter |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Optical image
medicine.medical_specialty Optical Phenomena Science Breast Neoplasms Cancer detection In Vitro Techniques Mastectomy Segmental Malignancy Multimodal Imaging Article Breast cancer Humans Medicine Breast lumpectomy Stochastic Processes Multidisciplinary business.industry Optical Imaging Imaging and sensing Margins of Excision X-Ray Microtomography Translational research medicine.disease Domain imaging Adipose Tissue Feature (computer vision) Diagnostic assessment Female Radiology business Biomedical engineering |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | High positive margin rates in oncologic breast-conserving surgery are a pressing clinical problem. Volumetric X-ray scanning is emerging as a powerful ex vivo specimen imaging technique for analyzing resection margins, but X-rays lack contrast between non-malignant and malignant fibrous tissues. In this study, combined micro-CT and wide-field optical image radiomics were developed to classify malignancy of breast cancer tissues, demonstrating that X-ray/optical radiomics improve malignancy classification. Ninety-two standardized features were extracted from co-registered micro-CT and optical spatial frequency domain imaging samples extracted from 54 breast tumors exhibiting seven tissue subtypes confirmed by microscopic histological analysis. Multimodal feature sets improved classification performance versus micro-CT alone when adipose samples were included (AUC = 0.88 vs. 0.90; p-value = 3.65e−11) and excluded, focusing the classification task on exclusively non-malignant fibrous versus malignant tissues (AUC = 0.78 vs. 0.85; p-value = 9.33e−14). Extending the radiomics approach to high-dimensional optical data—termed “optomics” in this study—offers a promising optical image analysis technique for cancer detection. Radiomic feature data and classification source code are publicly available. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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