Clinical iterative model development improves knowledge-based plan quality for high-risk prostate cancer with four integrated dose levels

Bladder Cancer
08/10/2020

Acta Oncol. 2020 Oct 8:1-8. doi: 10.1080/0284186X.2020.1828619. Online ahead of print.

ABSTRACT

BACKGROUND: Manual volumetric modulated arc therapy (VMAT) treatment planning for high-risk prostate cancer receiving whole pelvic radiotherapy (WPRT) with four integrated dose levels is complex and time consuming. We have investigated if the radiotherapy planning process and plan quality can be improved using a well-tuned model developed through a commercial system for knowledge-based planning (KBP).

MATERIAL AND METHODS: Treatment plans from 69 patients treated for high-risk prostate cancer with manually planned VMAT were used to develop an initial KBP model (RapidPlan, RP). Prescribed doses were 50, 60, 67.5, and 72.5 Gy in 25 fractions to the pelvic lymph nodes, prostate and seminal vesicles, prostate gland, and prostate tumour(s), respectively. This RP model was in clinical use from July 2019 to February 2020, producing another set of 69 clinically delivered treatment plans for a new patient group, which were used to develop a second RP model. Both models were validated on an independent group of 40 patients. Plan quality was compared by D 98% and the Paddick conformity index for targets, mean dose (D mean) and generalised equivalent uniform dose (gEUD) for bladder, bowel bag and rectum, and number of monitor units (MU).

RESULTS: Target coverage and conformity was similar between manually created and RP treatment plans. Compared to the manually created treatment plans, the final RP model reduced average D mean and gEUD with 2.7 Gy and 1.3 Gy for bladder, 1.2 Gy and 0.9 Gy for bowel bag, and 2.7 Gy and 0.8 Gy for rectum, respectively (p < .05). For rectum, the interpatient variation (i.e., 95% confidence interval) of DVHs was reduced by 23%.

CONCLUSION: KBP improved plan quality and consistency among treatment plans for high-risk prostate cancer. Model tuning using KBP-based clinical plans further improved model outcome.