JOURNAL ARTICLE

High-frequency quantitative ultrasound approaches for cancer detection in freshly-excised lymph nodes

Jonathan MamouAlain CoronEmi Saegusa-BeecroftMasaki HataMichael L. OelzeEugene YanagiharaTadashi YamaguchiPascal LaugierJunji MachiErnest J. Feleppa

Year: 2013 Journal:   The Journal of the Acoustical Society of America Vol: 133 (5_Supplement)Pages: 3540-3540   Publisher: Acoustical Society of America

Abstract

Histology performed to assess lymph nodes excised during node-dissection surgeries from cancer patients suffers an unsatisfactory rate of false-negative determinations due to labor and time constraints. In this study, more than 300 lymph nodes were scanned in 3D using a 26-MHz high-frequency ultrasound transducer. Following scanning, individual nodes underwent a special histology procedure that involved step-sectioning each node at 50-µm intervals to guarantee that no significant cancer foci were missed. The 3D radio-frequency ultrasound dataset was analyzed using overlapping 3D regions-of-interests that were individually processed to yield 13 quantitative ultrasound (QUS) estimates associated with tissue microstructure and were hypothesized to show contrast between normal and cancerous regions in lymph nodes. Step-wise linear discriminant analyses were performed to yield an optimal QUS-based classifier. ROC curves and areas under the ROC curves (AUCs) were obtained to assess cancer-detection performance. The AUC for the linear combination of four QUS estimates was 0.83 for a dataset of 110 axillary nodes of breast-cancer patients. Similarly, using five QUS estimates, an AUC of 0.97 was obtained for a dataset of 180 nodes of gastrointestinal-cancer patients. These studies demonstrate that QUS methods may provide an effective tool to guide pathologist towards suspicious regions in lymph nodes.

Keywords:
Medicine Ultrasound Lymph node Breast cancer Lymph Radiology Cancer Linear discriminant analysis Pathology Mathematics Statistics Internal medicine

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Topics

Radiomics and Machine Learning in Medical Imaging
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Ultrasound Imaging and Elastography
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
AI in cancer detection
Physical Sciences →  Computer Science →  Artificial Intelligence

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