LUMBER: A feasible MRI-based 3D printed mould platform for ex vivo sampling of prostate cancer

Accurate, reliable means to obtain fresh viable clinically localised prostate cancer tissue do not exist. We developed a method in which bespoke 3D-printed moulds can be created for any prostate and allow for ex vivo sampling of magnetic resonance imaging (MRI)-visible, biopsy-proven cancer lesions. We sought to demonstrate the ability of our platform to obtain fresh viable prostate cancer tissue after robot-assisted radical prostatectomy (RARP).

Inclusion criteria were a patient that underwent MR-fusion biopsy (UroNav, Philips) with a resulting biopsy proven Gleason Grade (GG) ≥ 2 target. STL files for prostate boundary and target regions of interest (ROI), created as part of fusion biopsy, were exported from the UroNav and imported into SolidWorks (Dassault), a solid modelling computer-aided design and engineering application. A macro within SolidWorks was then applied to create a material-optimised mould around the prostate with needle guides to allow for targeted sampling. The 3D mould was exported as an STL file and then 3D-printed on a Stratasys Fortus 250 MC 3D Printer. During RARP, the specimen is extracted, the seminal vesicles detached and the prostate is placed in the mould for biopsy. The biopsy cores are sent to pathology for analysis and compared to specimens from the initial biopsy.

Twelve patients with MRI-visible lesions and biopsy proven GGG ≥ 2 cancer underwent RARP. In 12 out of 12 patients, ex vivo biopsies performed with the 3D-printed mould yielded prostate tissue with cancer.

Our 3D-printed mould platform allows for ex vivo sampling of MRI identified and previously biopsied prostate cancer at the time of RARP. The native, cancerous tissue may then be used to advance further research. The potential applications for a platform that can reliably sample living prostate cancer tissue are numerous, including the ability to advance future cancer research as well as other solid-organ malignancies with targetable lesions.