An overlap‐volume‐histogram based method for rectal dose prediction and automated treatment planning in the external beam prostate radiotherapy following hydrogel injection

Abstract

The OVH metric can predict the rectal dose in the external beam prostate radiotherapy for patients with hydrogel injection. The predicted doses can be applied to the objectives of optimization in automated treatment planning to produce acceptable treatment plans.

A dosimetric evaluation of knowledge‐based VMAT planning with simultaneous integrated boosting for rectal cancer patients

“…1) and OAR (Fig. 2) metrics, the reduced degree of data dispersion in the RapidPlan group demonstrated its superior quality consistency and less variety than the original manual plans, which agreed with the RapidPlan rationale of alleviating the subjective dependency of individual planners 1 , 2 , 3 , 4 , 5 , 6 , 7 …”

“…As reported by many inhouse approaches, knowledge‐based radiotherapy (KBRT) treatment planning is deemed to reduce the interplanner varieties of plan quality 1 , 2 , 3 , 4 , 5 , 6 , 7 and expedite the planning process 8 , 9 , 10 , 11 . As a commercial KBRT optimization engine, RapidPlan (Varian Medical Systems, Palo Alto, CA) uses a pool of selected plans with consistent high quality as historical knowledge to train a DVH estimation model which predicts achievable DVH ranges and acceptable trade‐offs during the semi‐automatic plan optimization for the prospective patient.…”

A Multi-institutional Clinical Trial of Rectal Dose Reduction via Injected Polyethylene-Glycol Hydrogel During Intensity Modulated Radiation Therapy for Prostate Cancer: Analysis of Dosimetric Outcomes

“…Other metrics such as overlap volume histograms, which take into account three-dimensional geometric relationships between target volumes and organs at risk to determine achievable dose-volume histograms, may be useful in this regard(12). In a separate study using a subset of patients, we found that an overlap-volume histogram metric was more predictive of rectal sparing than volume of injected gel(13)…”

Evaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer

“…The same holds true for labor and computing resources, which can affect the implementation of new treatment planning techniques and treatment planning capacity. Various solutions are being investigated to improve planning consistency (3)(4)(5)(6)(7), including increased automation of planning by using knowledge-based approaches (8)(9)(10)(11)(12)(13). These approaches typically use libraries of existing patient plans to create models that predict the amount of organ-at-risk (OAR) sparing that can be achieved for a new patient, based, for example, on planning target volume (PTV)-OAR distance and overlap (14).…”