In prostate cancer (PCa), the presence and amount of residual tumor at the surface of the excised prostate is the only prognostic factor that is affected by surgical technique, vs. other factors, which are fixed and non-modifiable. Yet, positive surgical margins (PSM), defined as the presence of tumor cells at the inked surface of the removed specimen, are common, especially in advanced stage cancers, and are a strong independent risk factor for clinical progression and secondary treatment. In addition, elevated post-operative PSA, which is understood to be related to tumor left behind in the patient, is also associated with high risk of progression and secondary treatment. Thus, complete tumor removal is important to achieve to improve pa- tient outcomes and reduce overtreatment, yet there is a delicate balance between resection radicality and min- imizing damage to the neurovascular bundles to preserve post-operative function. The NeuroSAFE trial demonstrated that real-time detection and correction of PSMs by comprehensive histology of the prostate cir- cumference results in improved patient outcomes, yet there are no widely adoptable methods to achieve this. In an effort to address this technical gap, we have developed video-rate structured illumination microscopy (VR-SIM) and demonstrated that it enables accurate diagnosis of PCa in the biopsy setting, and that it can rap- idly deliver gigapixel microscopic images of the entire prostate surface for detection of PSMs. VR-SIM delivers the surface area coverage and resolution needed to detect PSMs, yet it is also fast and relatively simple and inexpensive, opening the possibility for widespread adoption. In this project, our interdisciplinary team of engi- neers and clinicians with significant prostate expertise will further advance this technology towards clinical translation, by completing critical technology development steps and by prospectively validating it in a large patient series. Specifically, we will increase the mechanical automation speed of the device, and will develop a fully automated system for handling of the removed prostate, to enable gigapixel panoramas of the entire pros- tate circumferential surface to be delivered within 10 minutes of removal and with minimal tissue processing and user intervention. We will then leverage our recently developed dual-color fluorescent stain that replicates standard H&E with high specificity, to develop expert-validated clinical image atlases using biopsies and ca- daveric specimens to enhance image interpretation of VR-SIM prostate panoramas. These improvements will be combined to test the system in a prospective 250-patient clinical study, to determine the accuracy of the device for intra-operative detection of PSMs and prediction of post-operative PSA based on measurement of PSM extent. Finally, we will measure and model expert reviewer behavior using a novel web-enabled visual observer tracking method, which could be used in future computer-assisted search algorithms to expedite in- tra-operative image review. Successful completion will set the stage for multi-center clinical trials to validate the clinical utility of VR-SIM for real-time detection and correction of PSMs, and improvement of patient outcomes.

Public Health Relevance

Achieving complete tumor removal in prostate cancer is an important goal to achieve because leaving tumor behind in the patient contributes to increased risk of cancer progression and need for additional harmful treatment. Yet, it is difficult to determine in real-time if surgery is successful due to the lack of available technologies for intra-operative guidance. This project will develop a rapid scanner that will automatically scan the entire prostate surface for residual tumor within 10 minutes of removal, providing images which are readily interpreted by trained pathologists and enabling real-time guidance of nerve-sparing prostate cancer surgery.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA222831-01A1
Application #
9604121
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Baker, Houston
Project Start
2018-06-25
Project End
2022-05-31
Budget Start
2018-06-25
Budget End
2019-05-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Tulane University
Department
Biomedical Engineering
Type
Schools of Arts and Sciences
DUNS #
053785812
City
New Orleans
State
LA
Country
United States
Zip Code
70118