For many types of cancer, early detection and treatment improves mortality. In the case of prostate cancer (PCa), however, the morbidity/mortality of definitive therapy for non-aggressive PCa may exceed the morbidity/mortality associated with the natural course of the disease. Therefore, it is paramount to focus on detection of the more aggressive PCa cases and implement a cost-effective screening and treatment program.
In the past few years, multiparametric MRI (mp-MRI) has become the standard of care for diagnosis of aggressive PCa. Although mp-MRI guided biopsy preferentially detects more aggressive PCa relative to traditional systematic biopsy, several studies have demonstrated that mp-MRI does miss aggressive PCa. Moreover, with about 60% false positive detections, mp-MRI causes many biopsies which are in retrospect unnecessary. Together with the high cost of MRI, the geographic variability in the availability of MRI systems, and the poor reproducibility of the results in low-volume clinical centers, the current situation underlines a strong clinical need for a more cost-effective, accurate, and accessible alternative to detect aggressive PCa.
This project brings together basic scientists, clinicians and industry ultrasound engineers to develop multiparametric-ultrasound (mp-US) as such an alternative technique. The main component of mp-US will be microbubble-based contrast enhanced ultrasound imaging (CEUS), based on novel sub-harmonic imaging techniques as well as contrast ultrasound dispersion imaging (CUDI). In addition, tissue viscoelasticity will be assessed by ultrasound elastography. Altogether, the extracted ultrasound parameters will provide a powerful set of the key imaging markers of PCa. These parameters will be optimally combined in a multiparametric fashion by machine learning in order to achieve accurate PCa diagnosis. While 2D ultrasound has inherent limitations in accuracy and clinical workflow, 3D ultrasound is nowadays emerging as a valuable technology for PCa diagnostics.
Our ultimate goal is to develop 3D mp-US techniques that can selectively identify aggressive PCa and prove that the accuracy of mp-US is non-inferior to mp-MRI. Thus, at the end of this project our academic industrial partnership will deliver an accurate, 3D mp-US system ready for clinical deployment. As our mp-US technology is based on ultrasound imaging, it will be cost effective for clinicians as well as for patients and, therefore, perfect for use in low resource neighborhoods or even underdeveloped countries.
With this general aim, this specific PhD position will focus on further developments of the CUDI technology and the design of a machine-learning framework combining complementary ultrasound parameters that reflect those changes in the microvascular architecture and the mechanical properties of tissue which are related to cancer.
This project is financed by the National Institute of Health (NIH) in the USA. The position is available within the BM/d research lab, part of the Signal Processing Systems (SPS) group (Electrical Engineering department, TU/e), and it involves tight collaborations with the University Medical Center in Amsterdam (location AMC), Thomas Jefferson University (USA), and 3 key industrial partners, namely, GE, Eigen, and Angiogenesis Analytics.
Biomedical diagnostic (BM/d) research lab at TU/e
The BM/d lab is devoted to model-based quantitative analysis of medical images and bio-signals, with the goal of improving patient care and management. The lab, which counts over 30 PhD students both technical and clinical, has a long tradition in ultrasound imaging and 15-year experience in prostate cancer diagnostics. This has led to the development of CUDI and the foundation of the startup company Angiogenesis Analytics.
We are seeking a highly motivated master graduate with a strong background and interest in the analysis and interpretation of ultrasound images, meet the following requirements:
For more information about this position contact prof.dr. Massimo Mischi, e-mail: m.mischi[at]tue.nl.
For information about terms of employment click here contact HRServices.flux[at]tue.nl.
Please visit www.tue.nl/jobs to find out more about working at TU/e!
The application should consist of the following parts:
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The selection process with start directly and will continue until the position gets filled. The position is fully funded and immediately available. The successful candidate is expected to start ASAP.