Data-Driven Dose-Volume Histogram Prediction

Abstract

Purpose: To evaluate dose-volume histogram (DVH) prediction from prior radiation therapy data.

Methods and materials: An Oncospace radiation therapy database was constructed including images, structures, and dose distributions for patients with advanced lung cancer. DVH data was queried for total lungs, esophagus, heart, and external body contours. Each query returned DVH data for the N-most similar organs at risk (OARs) based on OAR-to-planning-target-volume (PTV) geometry via the overlap volume histogram (OVH). The DVHs for 5, 20, and 50 of the most similar OVHs were returned for each OAR for each patient. The OVH(0cm) is the relative volume of the OAR overlapping with the PTV, and the OVH(2cm) is the relative volume of the OAR 2 cm away from the PTV. The OVH(cm) and DVH(%) queried from the database were separated into interquartile ranges (IQRs), nonoutlier ranges (NORs) (equal to 3 × IQR), and the average database DVH (DVH-DB) computed from the NOR data. The ability to predict the clinically delivered DVH was evaluated based on percentiles and differences between the DVH-DB and the clinical DVH (DVH-CL) for a varying number of returned patient DVHs for a subset of patients.

Results: The ability to predict the clinically delivered DVH was excellent in the lungs and body; the IQR and NOR were <4% and <16%, respectively, in the lungs and <1% and <5%, respectively, in the body at all distances less than 2 cm from the PTV. For 21/23 patients considered, the differences in lung DVH-DB and DVH-CL were <4.6% and in 14/23 cases, <3%. In esophagus and heart, the ability to predict DVH-CL was weaker, with mean DVH differences >10% for 12/23 esophagi and 10/23 hearts. In esophagus and heart queries, the NOR was often 10% to 100% volume in dose ranges between 0% and 50% of prescription, independent of the number of patients queried.

Conclusions: Using prior data to predict clinical dosimetry is increasingly of interest, but model- and data-driven methods have limitations if based on limited data sets. This study's results showed that prediction may be reasonable in organs containing tumors with known overlap, but for nonoverlapped OARs, planning preference and plan design may dominate the clinical dose.

Fig. 1

The variation in overlap volume histogram (OVH) returned from the Oncospace database, as a function of increasing number of similar patients returned. The esophagus OVH (top left) had the most variation, with interquartile ranges of 25% to 75% exceeding 5% volume at distances greater than 1 cm from the planning target volume (PTV). The external OVH is the most consistent, which reflects similar patient and PTV sizes in the database.

Fig. 2

The clinically delivered dose-volume histogram (DVH) (red) is compared with the predicted database DVH (DVH-DB) for 3 patients (1 per row) and 4 organs at risk per patient. The top row shows a clinical plan superior to the average DVH-DB, the middle row shows a clinical plan inferior to the average DVH-DB, and the bottom row shows a clinical DVH approximately equivalent to the DVH-DB. However, when considering the nonoutlier range of the data from the database, it is clear that a significant reduction in the heart and esophagus dose may be possible.

Fig. 3

The clinical dose-volume histogram (DVH) is compared with the database-derived DVH for a single patient. The columns increase the number of patients included in the query of similar patient data from 5 (left column) to 50 (right column). In this case, the clinical DVH is worse (or higher) than the average database DVH in the heart and esophagus.

Fig. 4

The clinical dose-volume histogram (DVH) is compared with the database derived DVH for a single patient. The columns increase the number of patients included in the query of similar patient data from 5 (left column) to 50 (right column). In this case, the clinical DVH is better (or lower) than the average database DVH in the heart and esophagus.