A Dataset of Anatomical Environments for Medical Robots: Modeling Respiratory Deformation

Anatomical models of a medical robot's environment can significantly help guide design and development of a new robotic system. These models can be used for benchmarking motion planning algorithms, evaluating controllers, optimizing mechanical design choices, simulating procedures, and even as resources for data generation. Currently, the time-consuming task of generating these environments is repeatedly performed by individual research groups and rarely shared broadly. This not only leads to redundant efforts, but also makes it challenging to compare systems and algorithms accurately. In this work, we present a collection of clinically-relevant anatomical environments for medical robots operating in the lungs. Since anatomical deformation is a fundamental challenge for medical robots operating in the lungs, we describe a way to model respiratory deformation in these environments using patient-derived data. We share the environments and deformation data publicly by adding them to the Medical Robotics Anatomical Dataset (Med-RAD), our public dataset of anatomical environments for medical robots

Autonomous Medical Needle Steering In Vivo

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.Comment: 22 pages, 6 figure

High Expression of HIF1a Is a Predictor of Clinical Outcome in Patients with Pancreatic Ductal Adenocarcinomas and Correlated to PDGFA, VEGF, and bFGF

PURPOSE: Pancreatic cancer still has one of the worst prognoses in gastrointestinal cancers with a 5-year survival rate of 5%, making it necessary to find markers or gene sets that would further classify patients into different risk categories and thus allow more individually adapted multimodality treatment regimens. In this study, we investigated the prognostic values of HIF1a, bFGF, VEGF, and PDGFA gene expressions as well as their interrelationships. EXPERIMENTAL DESIGN: Formalin-fixed paraffin-embedded tissue samples were obtained from 41 patients with pancreatic adenocarcinoma (age, 65; range, 34–85 years). After laser capture microdissection, direct quantitative real-time reverse transcription-polymerase chain reaction assays were performed in triplicates to determine HIF1a, PDGFA, VEGF, and bFGF gene expression levels. Multivariate Cox proportional hazards regression analysis was used to assess the impact of HIF1a gene expression on prognosis. RESULTS:HIF1a was significantly correlated to every gene we tested: bFGF (P = .04), VEGF (P = .02), and PDGFA (P = .03). Tumor size, P = .04, and high HIF1a mRNA expression (cutoff, 75th percentile) had a significant impact on survival, P = .009 (overall model fit, P = .02). High HIF1a expression had a sensitivity of 87.1% and a specificity of 55.6% for the diagnosis short (<6 months) versus long (6–60 months) survival. CONCLUSIONS: Measuring PDGFA, bFGF, and HIF1a expression may contribute to a better understanding of the prognosis of patients with pancreatic cancer and may even play a crucial role for the distribution of patients to multimodal therapeutic regimens. Larger studies including patients treated with actual chemotherapeutics seem to be warranted