Safety and efficacy of histotripsy delivery through overlying gas-filled small bowel in an ex vivo swine model.

PURPOSE: To evaluate the safety and efficacy of performing histotripsy through overlying gas-filled bowel in an ex vivo swine model. METHODS: An ex vivo model was created to simulate histotripsy treatment of solid organs through gas-filled bowel. Spherical 2.5 cm histotripsy treatments were performed in agar phantoms for each of five treatment groups: 1) control with no overlying bowel (n = 6), 2) bowel 0 cm above phantom (n = 6), 3) bowel 1 cm above phantom (n = 6), 4) bowel 2 cm above phantom (n = 6), and 5) bowel 0 cm above the phantom with increased treatment amplitude (n = 6). Bowel was inspected for gross and microscopic damage, and treatment zones were measured. A ray-tracing simulation estimated the percentage of therapeutic beam path blockage by bowel in each scenario. RESULTS: All histotripsy treatments through partial blockage were successful (24/24). No visible or microscopic damage was observed to intervening bowel. Partial blockage resulted in a small increase in treatment volume compared to controls (p = 0.002 and p = 0.036 for groups with bowel 0 cm above the phantom, p > 0.3 for bowel 1 cm and 2 cm above the phantom). Gas-filled bowel was estimated to have blocked 49.6%, 35.0%, and 27.3% of the therapeutic beam at 0, 1, and 2 cm, respectively. CONCLUSION: Histotripsy has the potential to be applied through partial gas blockage of the therapeutic beam path, as shown by this ex vivo small bowel model. Further work in an in vivo survival model appears indicated.

Artificial Intelligence-Aided Selection of Needle Pathways: Proof-of-Concept in Percutaneous Lung Biopsies.

PURPOSE: To evaluate the concordance between lung biopsy puncture pathways determined by artificial intelligence (AI) and those determined by expert physicians. MATERIALS AND METHODS: An AI algorithm was created to choose optimal lung biopsy pathways based on segmented thoracic anatomy and emphysema in volumetric lung computed tomography (CT) scans combined with rules derived from the medical literature. The algorithm was validated using pathways generated from CT scans of randomly selected patients (n = 48) who had received percutaneous lung biopsies and had noncontrast CT scans of 1.25-mm thickness available in picture archiving and communication system (PACS) (n = 28, mean age, 68.4 years ± 9.2; 12 women, 16 men). The algorithm generated 5 potential pathways per scan, including the computer-selected best pathway and 4 random pathways (n = 140). Four experienced physicians rated each pathway on a 1-5 scale, where scores of 1-3 were considered safe and 4-5 were considered unsafe. Concordance between computer and physician ratings was assessed using Cohen's κ. RESULTS: The algorithm ratings were statistically equivalent to the physician ratings (safe vs unsafe: κ¯=0.73; ordinal scale: κ¯=0.62). The computer and physician ratings were identical in 57.9% (81/140) of cases and differed by a median of 0 points. All least-cost "best" pathways generated by the algorithm were considered safe by both computer and physicians (28/28) and were judged by physicians to be ideal or near ideal. CONCLUSIONS: AI-generated lung biopsy puncture paths were concordant with expert physician reviewers and considered safe. A prospective comparison between computer- and physician-selected puncture paths appears indicated in addition to expansion to other anatomic locations and procedures.