Stereotactic body radiotherapy plus rucosopasem in locally advanced or borderline resectable pancreatic cancer: GRECO-2 phase II study design.

Ablative doses of stereotactic body radiotherapy (SBRT) may improve pancreatic cancer outcomes but may carry greater potential for gastrointestinal toxicity. Rucosopasem, an investigational selective dismutase mimetic that converts superoxide to hydrogen peroxide, can potentially increase tumor control of SBRT without compromising safety. GRECO-2 is a phase II, multicenter, randomized, double-blind, placebo-controlled trial of rucosopasem in combination with SBRT in locally advanced or borderline resectable pancreatic cancer. Patients will be randomized to rucosopasem 100 mg or placebo via intravenous infusion over 15 min, before each SBRT fraction (5 × 10 Gy). The primary end point is overall survival. Secondary end points include progression-free survival, locoregional control, time to metastasis, surgical resection rate, best overall response, in-field local response and acute and long-term toxicity.

The use of high doses of radiation delivered directly to tumors (stereotactic body radiation therapy [SBRT]) may improve survival compared with lower doses of radiation in patients with pancreatic cancer, but it may increase side effects. Rucosopasem, an investigational new drug being developed, can potentially improve the ability of SBRT to treat tumors without decreasing safety. In a previous study, median overall survival was improved when patients were treated with SBRT plus avasopasem, a drug that works the same way as rucosopasem. GRECO-2 is a clinical trial of rucosopasem used in combination with SBRT for treatment of localized pancreatic cancer. Patients will be randomly selected to receive either rucosopasem 100 mg or placebo via intravenous infusion over 15 min, before each SBRT treatment. The main result being studied is overall survival. Additional results include amount of time before tumors start to grow, how often patients get tumors surgically removed, best overall response and long-term safety. Clinical Trial Registration: NCT04698915 (ClinicalTrials.gov).

A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs.

Purpose: The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy. Methods and Materials: The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment. Expert segmentations were used as the independent standard for the prostate, seminal vesicles, bladder, rectum, and femoral heads for all 115 data sets. In addition, 3 independent radiation oncologists contoured the prostate, seminal vesicles, and rectum for a subset of patients such that the interobserver variability could be quantified. Consensus contours were then generated from these independent contours using a simultaneous truth and performance level estimation approach, and the deviation of Deep LOGISMOS + JEI contours to the consensus contours was evaluated and compared with the interobserver variability. Results: The absolute accuracy of Deep LOGISMOS + JEI generated contours was evaluated using median absolute surface-to-surface distance which ranged from a minimum of 0.20 mm for the bladder to a maximum of 0.93 mm for the prostate compared with the independent standard across all data sets. The median relative surface-to-surface distance was less than 0.17 mm for all organs, indicating that the Deep LOGISMOS + JEI algorithm did not exhibit a systematic under- or oversegmentation. Interobserver variability testing yielded a mean absolute surface-to-surface distance of 0.93, 1.04, and 0.81 mm for the prostate, seminal vesicles, and rectum, respectively. In comparison, the deviation of Deep LOGISMOS + JEI from consensus simultaneous truth and performance level estimation contours was 0.57, 0.64, and 0.55 mm for the same organs. On average, the Deep LOGISMOS algorithm took less than 26 seconds for contour segmentation. Conclusions: Deep LOGISMOS + JEI segmentation efficiently generated clinically acceptable prostate and normal tissue contours, potentially limiting the need for time intensive manual contouring with each fraction.