Dose painting by numbers in a standard treatment planning system using inverted dose prescription maps

Abstract

background. Dose painting by numbers (DPBN) is a method to deliver an inhomogeneous tumor dose voxel-by-voxel with a prescription based on biological medical images. However, planning of DPBN is not supported by commercial treatment planning systems (TPS) today. Here, a straightforward method for DPBN with a standard TPS is presented. Material and methods. DPBN tumor dose prescription maps were generated from 18 F-FDG-PET images applying a linear relationship between image voxel value and dose. An inverted DPBN prescription map was created and imported into a standard TPS where it was defined as a mock pre-treated dose. Using inverse optimization for the summed dose, a planned DPBN dose distribution was created. The procedure was tested in standard TPS for three different tumor cases; cervix, lung and head and neck. The treatment plans were compared to the prescribed DPBN dose distribution by three-dimensional (3D) gamma analysis and quality factors (QFs). Delivery of the DPBN plans was assessed with portal dosimetry (PD). results. Maximum tumor doses of 149%, 140% and 151% relative to the minimum tumor dose were prescribed for the cervix, lung and head and neck case, respectively. DPBN distributions were well achieved within the tumor whilst normal tissue doses were within constraints. Generally, high gamma pass rates ( 89% at 2%/2 mm) and low QFs ( 2.6%) were found. PD showed that all DPBN plans could be successfully delivered. conclusions. The presented methodology enables the use of currently available TPSs for DPBN planning and delivery and may therefore pave the way for clinical implementation.

Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients

“…Such tailored treatment will be easier to implement for a continuous tumor region than for more scattered clusters [20]. Voxel clusters related to locoregional relapse would be most relevant to use for dose painting, as such clusters would indicate radiotherapy-resistant areas in the tumor [19].…”

Progress towards Patient-Specific, Spatially-Continuous Radiobiological Dose Prescription and Planning in Prostate Cancer IMRT: An Overview

“…DPbN essentially establishes a mathematical link between imaging parameters and dose prescriptions that optimise chosen clinical endpoints [4,5]. The majority of DPbN studies assumed a linear relationship between image intensity and the required boost dose [24][25][26][27][28]. The linear function usually extends from a minimum dose, typically set to the current clinical dose prescription, and a maximum dose, set to a value that is considered "safe" for the target (Figure 2).…”

“…A major drawback of DPbN studies is the use of arbitrarily-chosen functions that have not been validated against clinical outcomes data [24,25,29,[31][32][33][34]. They are likely to be an oversimplification of the complex tumour dose-response.…”

Robust maximization of tumor control probability for radicality constrained radiotherapy dose painting by numbers of head and neck cancer

“…For head and neck cancer it has in several studies been shown that increasing standardized uptake values (SUV) from 18 Ffluorodeoxyglucose positron emission tomography ( 18 FDG-PET) correlate with an increased recurrence risk after RT [2][3][4][5][6][7]. As noted in a review by Bentzen and Grégoire [8], the simplest image based dose prescription is an ad hoc linear mapping of image data into doses within suitable dose ranges, as used in several planning studies [9][10][11][12][13][14][15][16][17][18][19]. However, the same reviewers stated that the dose prescription ideally should be based upon empirical observations of pre-RT functional image data with post-RT dose-responses.…”