Click here to

  1. Home
  2. Meeting Program
  3. All Sessions
  4. Abstracts
Session: Imaging for Treatment Guidance [Return to Session]

Predicting Bladder Dose Constraints Using Combined Overlap Volume Histogram and Relative Pose Method: Quantitative Characterization of the Reference Bladder for Setup

Y Lao1*, M Cao1, S Tenn1, J Neylon1, K Sheng1, (1) UCLA School of Medicine, Los Angeles, CA

Presentations

TU-D1000-IePD-F5-2 (Tuesday, 7/12/2022) 10:00 AM - 10:30 AM [Eastern Time (GMT-4)]

Exhibit Hall | Forum 5

Purpose: To introduce a novel overlap volume histogram and relative pose (OVH-RP) combined method for bladder dose constraint prediction to guide clinical patient setup in prostate cancer (PC) radiotherapy.

Methods: Clinical planning CTs and dose distributions for 69 PC patients were retrospectively solicited. Overlap volume histogram (OVH) - targeting the patient-specific relationship between the bladder and PTV, and the relative pose (RP) - describing the relative position of the bladder to the population mean were used to characterize the bladder geometry based on binary segmentation. Specifically, OVH was calculated by isotropically dilating the PTV. Dilation distances corresponding to 10-50% OVH, namely DDOVH-10%, DDOVH-20%, DDOVH-30%, DDOVH-40%, DDOVH-50%, were extracted. RP parameters were obtained through surface-based Procrustes alignment between a subject bladder to the population-averaged bladder, predominately matched through constrained harmonic registration. The Procrustes alignment resulted in 1 scale, 3 rotation, and 3 translation parameters. Using multiple linear regression, we correlated the combined 12 OVH-RP descriptors with 50% to 100% percent dose (D50-D100) derived from clinical plans. The population was randomly divided into 70% (49) for training and 30% (20) for testing. As a comparison, the same analysis was conducted using 5 OVH parameters only.

Results: In the testing set, prediction of the OVH-RP model resulted in an averaged mean absolute error (MAE) of 4.20% over the entire range (Dentire), with MAEs of D50 and D100 being 5.94% and 3.09%, respectively. In contrast, MAEs of Dentire, D50, and D100 predicted using OVH parameters only were 4.45%, 6.26%, 3.11%, respectively.

Conclusion: We developed a novel OVH-RP descriptor of the bladder CT geometry for planning dose prediction. The model outperformed the OVH only method in dose constraint prediction. The OVH-RP method can be used to quickly determine the quality of patient setup and the readiness of bladder volume to meet planning dose constraints.

Keywords

Setup Verification, Shape Analysis

Taxonomy

IM- CT: Quantitative imaging/analysis

Contact Email

Share:

Additional Links