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Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures.
Streeter, Samuel S; Hunt, Brady; Zuurbier, Rebecca A; Wells, Wendy A; Paulsen, Keith D; Pogue, Brian W.
Afiliação
  • Streeter SS; Thayer School of Engineering, Dartmouth College, 14 Engineering Dr., Hanover, NH, 03755, USA. Samuel.S.Streeter.TH@Dartmouth.edu.
  • Hunt B; Thayer School of Engineering, Dartmouth College, 14 Engineering Dr., Hanover, NH, 03755, USA.
  • Zuurbier RA; Department of Radiology, Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr., Lebanon, NH, 03756, USA.
  • Wells WA; Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr., Lebanon, NH, 03756, USA.
  • Paulsen KD; Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr., Lebanon, NH, 03756, USA.
  • Pogue BW; Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr., Lebanon, NH, 03756, USA.
Sci Rep ; 11(1): 21832, 2021 11 08.
Article em En | MEDLINE | ID: mdl-34750471
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mastectomia Segmentar / Microtomografia por Raio-X / Imagem Óptica Tipo de estudo: Diagnostic_studies Limite: Female / Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Mastectomia Segmentar / Microtomografia por Raio-X / Imagem Óptica Tipo de estudo: Diagnostic_studies Limite: Female / Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article