This study proposes a computational framework of quantification of cancerous nodules in soft tissue. The proposed framework is capable of quantifying a tumor nodule in soft tissue without a priori information about its geometry, thus presenting great promise in clinical palpation diagnosis for a wide variety of solid tumors including breast and prostate cancer.
The methodology is first demonstrated using computational models and then validated using tissue-mimicking gelatin phantoms, where the depth and volume of the tumor nodule is estimated with good accuracy. The profile of force feedback results is then compared with the benchmark in silico models to estimate the size and depth of the cancerous nodule. The methodology, using prostate tissue as an exemplar, is based on instrumented palpation performed at positions with various indentation depths over the surface of the relevant structure (in this case, the prostate gland). This study proposes a computational framework of identification and quantification of cancerous nodules in soft tissue without a priori knowledge of its geometry, size, and depth. However, to develop such methods as instrumented palpation, there remain challenges in using the mechanical response during palpation to quantify tumor load. Variation in mechanical properties is a useful marker for cancer in soft tissue and has been used in clinical diagnosis for centuries. However, best practice guidelines, the future regulatory landscape, and health economic considerations need to be addressed before this synergy of new technologies is ready for the mainstream. This has proven useful in NS-RARP for preoperative planning, simulation and patient engagement. Despite the nascency of the field, 3D printed models are emerging in the uro-oncological literature as a useful tool in visualising complex anatomy. Two publications (25%) utilised 3D printed prostate models for simulation and training, and two publications (25%) used the models for patient engagement. Of these articles, five publications (62.5%) reported on the utility of 3D printed models for NS-RARP planning. There were five prospective single centre studies, one case series, one technical report and one letter to the editor. Eight articles were included six were identified via database searches, to which a further two articles were located via a snowballing approach. A literature search of PubMed and OVID Medline databases was performed using the terms “3D Printing”, “Robot Assisted Radical Prostatectomy” and related index terms as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
This is the first systematic review to critically assess the potential of 3D printed patient-specific prostate cancer models in improving visualisation and the practice of NS-RARP. Complementary to this, 3D printing has proven its utility in improving the visualisation of complex anatomy. Precise knowledge of each patient’s index cancer and surrounding anatomy is required for nerve-sparing robot-assisted radical prostatectomy (NS-RARP).