Project description:
Determining the presence or absence of metastases in freshly-excised lymph nodes of patients with proven primary cancer is crucial for prognosis and treatment planning. Histology is the gold-standard. However current histopathology standards may miss clinically significant micrometastases.
The long term goal of this multidisciplinary project is to develop and validate a 3D high-frequency quantitative ultrasound (QUS) method for reliably determining the presence or absence of metastases in freshly-excised lymph nodes.
To date, we have acquired and processed 3D high-frequency radio-frequency (RF) data from more than 300 lymph nodes from more than 170 patients. Parametric, 3D maps of acoustic parameters associated with tissue microstructure (QUS parameters) or associated with the envelope statistics have been constructed.
Digital pathology techniques will be applied to the corresponding histological images to classify local tissue composition (e.g. Adipose, stroma…) and automated analysis of histological data will be pursued using stochastic models issued from marked point processes applied to the nuclei.
We propose in this internship to explore the relationships between quantitative US parameters and quantitative parameters extracted from histologic data. Results will increase understanding of the manner in which acoustic propagation and scattering is influenced by tissue microstrucutre and will improve multiparametric analysis of US imaging data to non invasively indicate risk of metastases.
This project will be partly supported by NIH grant CA100183.
Required skills:
- Strong interest in biomedical image processing or applied mathematics.
- Matlab and C++.
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