Association of Apical Spacing with Bowel Quality-of-Life: A Secondary Analysis of the Hyaluronic Acid Randomized Trial

PURPOSE/OBJECTIVE(S)

Recently, a randomized trial (NCT04189913) reported that a hyaluronic acid (HA) rectal spacer reduced acute grade 2+ gastrointestinal (GI) toxicity for hypofractionated radiation therapy (RT). However, 26.5% of patients who received the spacer experienced a minimally clinically important difference (MCID; 5 points) in EPIC bowel quality-of-life (QOL). We evaluated whether characteristics of the spacer implant, particularly apical separation, were associated with change in bowel QOL at 3-months and acute grade 1+ GI toxicity.

MATERIALS/METHODS

We conducted a secondary analysis of 136 patients randomized to receive the HA spacer. The post-spacer implant structure sets and treatment plans were analyzed. The mid-plane (MP) was defined as the prostate center-of-mass. Four horizontal planes were defined at the superior (MP +1 cm), mid-gland, inferior (MP - 1 cm), and apex of the prostate. Separations between the prostate and anterior rectal wall at midline were computed at each plane in a custom Python programming environment. Implant symmetry was computed based on a previously published method (Fischer-Valuck, PRO, 2017). The volume of rectum receiving > = 30 Gy (rV30) was extracted from DVHs; rV30 has been associated with bowel frequency, fecal incontinence, and rectal pain for hypofractionated RT (Wilkins, IJROBP, 2020). First, we evaluated whether any of the 4 separation or symmetry variables were associated with rV30. Then, we evaluated whether significant spacing variables, rV30, and baseline bowel QOL were predictive of the change in bowel QOL at 3-months using multivariate linear regression. Finally, we evaluated whether significant spacing variables and rV30 were predictive of acute grade 1+ GI toxicity (21 events) within 3-months, utilizing multivariate logistic regression.

RESULTS

The mean (standard deviation) superior, mid-gland, inferior, and apex separations were 15.6 (SD 6.0), 12.7 (3.7), 11.2 (3.7), and 9.7 (4.0), respectively. 130 of 136 (95.6%) had a symmetry score of 1 (symmetric). Apical separation was the only variable significantly associated with rV30 (r = -0.32; p < 0.01). On multivariate analysis, apical separation (0.41/mm; p = 0.01) was significantly associated with the change in bowel QOL, after adjusting for baseline bowel score (p = 0.0002) and rectum V30 (p = 0.50). Mean (SD) changes in bowel QOL were 0.01 (5.9) and -3.7 (8.1) for apical separations > = 10 vs <10 mm, respectively. Respective percentages of patients with a bowel MCID were 14.8% and 36.6% (p = 0.006). However, apical separation was not associated with increased odds of experiencing grade 1+ GI toxicity (p = 0.98), when adjusted for rectum V30 (odds ratio 1.04; p = 0.04).

CONCLUSION

Increased apical separation may be associated with improved EPIC bowel QOL at 3-months for patients who received a HA rectal spacer prior to hypofractionated RT. This finding is clinically important, because HA can be deliberately injected into the perirectal space at the level of the prostate apex.

A Hybrid Intelligence (HI) System for Segmenting Rectoprostatic Spacer Gel and Key OARs on CT Images for Prostate Cancer Radiation Therapy Planning

PURPOSE/OBJECTIVE(S)

Our hybrid intelligence (HI) system, combining natural and artificial intelligence, is effective for auto-contouring H&N and thorax organs at risk (OARs) for radiation therapy (RT) planning with FDA 510(k) clearance. The purpose of this study is to test the HI system to segment a commercially available retroprostatic hyaluronic acid spacer gel (RSG) and pelvic OARs in planning CT images for prostate cancer RT.

HYPOTHESIS

HI can achieve clinically acceptable auto segmentation for tissue-equivalent RSG in this domain.

MATERIALS/METHODS

RSG is injected in the peri-rectal space in men with prostate cancer prior to RT to minimize rectal toxicity. 190 patients with prostate cancer were included in this post-hoc image analysis from a multi-center, prospective, randomized trial, with 136 in the spacer arm. The HI system has 3 steps: rough recognition from fuzzy model (FM) based automatic anatomy recognition (AAR-R), deep learning-based recognition (DL-R) refinement, and deep learning-based delineation (DL-D) to contour objects guided by the recognition results. FM encodes high level 3D anatomy knowledge of object shape and its relationship with other OARs; DL-R and DL-D focus on pixel-level details. The 190 studies are divided into disjoint training (100) and testing (90) subsets. 100 samples are used in DL-R and DL-D training, with 45 to build the FM for AAR-R. RSG and 4 other OARs (pelvic skin, prostate, bladder, rectum) are contoured. Location error (LE) is used to evaluate recognition; Dice coefficient (DC) and Hausdorff distance (HD) are employed to evaluate delineation. Acceptability scores (AS) (range 1-5, 1 for poor quality, 5 for best quality) from an observer study are recorded for HI-output and ground truth masks of RSG for assessing segmentation quality.

RESULTS

The HI system achieves highest DC (0.94±0.07) and lowest HD (1.96±1.61 mm) for bladder, for rectum and prostate similar DC (0.82±0.08) and HD (2.62±1.65mm), for RSG, the most challenging object, a good DC close to 0.7 (0.67±0.10) and excellent HD (2.66±1.44mm). AS for auto-segmentations (3.86±0.85) were significantly better than those for ground truth segmentations (3.45±1.00) (p = 0.02, paired t-test). Table 1 summarizes results.

CONCLUSION

The HI system achieves clinically acceptable segmentations for pelvic OARs and significantly better acceptability of segmentation of RSG compared to clinically performed ground truth segmentations. This has implications in improving efficiency and accuracy of CT-based RT planning in patients with prostate cancer.