Prospective Pilot Trial to Evaluate a High Resolution Diffusion-Weighted MRI in Prostate Cancer Patients.
OBJECTIVE
High-resolution prostate imaging may allow for detection of subtle changes in tumor size, decrease the reliance on biopsies, and help define tumor boundaries during ablation. This pilot clinical trial evaluates a novel high-resolution prostate MRI for detection of small, biopsy-proven prostate tumors.


METHODS
Our team developed a software that can be loaded on any modern MRI to generate high resolution diffusion-weighted imaging sequences (HR-DWI), which were compared to standard diffusion-weighted imaging sequence (S-DWI) in a prospective pilot trial in active surveillance patients. HR-DWI captures the entire volume of the prostate rather than sections, reducing streaking artifacts and geometric distortions. Multiple shots, rather than single shots, are used to differentiate signal and noise, enhancing resolution. All images were read by two radiologists. The primary outcome was the percent of biopsy-proven zones seen in 17 patients. The trial was powered to detect discordant proportions of 0.04 and 0.40 at one-sided alpha=0.05.


RESULTS
The resolution was defined using standard phantoms. HR-DWI produced a 5-fold improvement in spatial resolution when compared to S-DWI. Multiparametric (MP)-MRI incorporating S-DWI was useful for predicting biopsy results (AUC 0.72, Fisher's exact p<0.001); however, using HR-DWI allowed MP-MRI to be more highly predictive of biopsy results (AUC 0.88, Fisher's exact p<0.001). AUC for MP-MRI incorporating HR-DWI was significantly larger than MP-MRI incorporating S-DWI (p=0.002). MP-MRI with HR-DWI had a sensitivity of 95.7% and identified tumor in 22 of 23 zones proven to have cancer on biopsy. In contrast, MP-MRI with S-DWI had a sensitivity of 60.9% and only identified 14 of 23 biopsy-positive zones (p=0.004).


CONCLUSIONS
We developed a novel DWI and evaluated its improved resolution in a clinical setting. This technology has many potential applications and should be evaluated in future clinical trials as a patient management tool.
Division of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Ali-Reza
AR
Sharif-Afshar
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Christopher
C
Nguyen
Division of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Tom S
TS
Feng
Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Lucas
L
Payor
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Zhaoyang
Z
Fan
Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Rola
R
Saouaf
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States.
Debiao
D
Li
Division of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Hyung L
HL
Kim
D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010865:Pilot Projects; D011159:Population Surveillance; D011446:Prospective Studies; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D012984:Software
Diffusion weighted imaging; MRI; Prostate cancer