Evaluation of automated pre-treatment and transit in-vivo dosimetry in radiotherapy using empirically determined parameters

Abstract

Background and purpose: First reports on clinical use of commercially automated systems for Electronic Portal Imaging Device (EPID)-based dosimetry in radiotherapy showed the capability to detect important changes in patient setup, anatomy and external device position. For this study, results for more than 3000 patients, for both pre-treatment verification and in-vivo transit dosimetry were analyzed.

Materials and methods: For all Volumetric Modulated Arc Therapy (VMAT) plans, pre-treatment quality assurance (QA) with EPID images was performed. In-vivo dosimetry using transit EPID images was analyzed, including causes and actions for failed fractions for all patients receiving photon treatment (2018-2019). In total 3136 and 32,632 fractions were analyzed with pre-treatment and transit images respectively. Parameters for gamma analysis were empirically determined, balancing the rate between detection of clinically relevant problems and the number of false positive results.

Results: Pre-treatment and in-vivo results depended on machine type. Causes for failed in-vivo analysis included deviations in patient positioning (32%) and anatomy change (28%). In addition, errors in planning, imaging, treatment delivery, simulation, breath hold and with immobilization devices were detected. Actions for failed fractions were mostly to repeat the measurement while taking extra care in positioning (54%) and to intensify imaging procedures (14%). Four percent initiated plan adjustments, showing the potential of the system as a basis for adaptive planning.

Conclusions: EPID-based pre-treatment and in-vivo transit dosimetry using a commercially available automated system efficiently revealed a wide variety of deviations and showed potential to serve as a basis for adaptive planning.

Keywords: In-vivo; Transit dosimetry.

Fig. 1

Pre-treatment QA results: a) All results, b) Only old generation machine type results, c) Only new generation machine type results.

Fig. 2

Analysis of measured in-vivo fractions, classified per treatment site and/or technique, indicating the number of passed fractions, the number of false positives and the number of failed fractions due to planning problems, deviations in patient positioning and changes in patient anatomy.

Fig. 3

Causes and actions for failed fractions in the second analysis: a) Causes: software problems including software bugs and irradiation on another (matched) machine with a different imager causing a deviation when comparing to a baseline of another machine; wrong imager position; problems with imager calibration; interrupted beams causing missing dose in the image; planning problems; deviations in patient position; changes in patient anatomy. b) Actions: taking a new measurement; adding extra pre-treatment imaging; plan adjustment; taking measures regarding patient preparation (e.g. bladder and rectum protocol); taking no action; adjusting the used tolerances.

Fig. 4

Analysis of the actions for failed fractions in the second analysis classified per treatment site and/or technique: taking a new measurement; adding extra pre-treatment imaging; plan adjustment; taking measures regarding patient preparation (e.g. bladder and rectum protocol); taking no action; adjusting used tolerances.

Fig. A1

Decision charts for when a measurement is out of tolerance. The first decision chart is meant for physicists including detecting false positives, the second decision chart is meant for physicians to take actions for patient related errors. Results are always compared with the available imaging. Taking Cone Beam Computed Tomography (CBCT) is often one of the first actions.

Fig. A1

Decision charts for when a measurement is out of tolerance. The first decision chart is meant for physicists including detecting false positives, the second decision chart is meant for physicians to take actions for patient related errors. Results are always compared with the available imaging. Taking Cone Beam Computed Tomography (CBCT) is often one of the first actions.

Fig. A2

Deviation in breast position. On the left, in-vivo software results. On the right, offline review images where the integrated images could be matched with the Digitally Reconstructed Radiograph (DRR).

Fig. A3

Breast swelling. On the left, in-vivo software results. On the right, offline review images where the integrated images could be matched with the DRR.

Fig. A4

Pneumonia. On the left, in-vivo software results. On the right, transversal views of planning CT and CBCT’s with the structures projected from the planning CT.

Fig. A5

Planning problem: skin flash tool not well used. On the left, in-vivo software results. On the right, Beam’s Eye View (BEV) of the planned dose in the planning system.

Fig. A6

Wrong table shift. On the left, in-vivo software results. On the right, offline review results showing a table shift of almost 7 cm before the last field.

Fig. A7

Tumor reduction. On the left, in-vivo software results. On the right, frontal and transversal views of the CBCT’s, with the structures projected from the planning CT.

Fig. A8

Tumor reduction and weight loss. On the top left, in-vivo software results. On the top right, sagittal and transversal views of the original plan. On the bottom left, sagittal view of the CBCT and transversal view of the original plan on the evaluation CT. On the bottom right, the change in Dose Volume Histogram for several structures (squares representing the original plan on the original CT and triangles the original plan on the evaluation CT).

Fig. A9

Planning problem not avoiding hip implant. On the top, transversal and frontal view of the plan on the planning CT. On the bottom, in-vivo software results.

Fig. A10

Problem where slow breathing induced artefacts masking large tumor movement. On the top left, in-vivo software results. On the bottom right, sagittal and frontal view of the planning CT.

Fig. A11

Simulated with the arms down, but planned with the arms up without notification. On the left, in-vivo software results. On the right, offline review images of setup fields and transversal view of the plan on the planning CT.

Fig. A12

Patient swallowed during simulation. On the left, in-vivo software results. On the right, offline review images of the CBCT, with the structures projected from the planning CT.

Fig. A13

Air in the rectum. On the left, in-vivo software results. On the right, transversal view of the CBCT, with the structures projected from the planning CT.

Fig. A14

Large table shift not applied after online match. On the left, in-vivo software results. On the right, offline review images of setup fields.

Fig. A15

Weight loss at first fraction. On the left, in-vivo software results. On the right, structures of the planning CT projected on a transversal view of the CBCT.

Fig. A16

Bad breathing technique. On the left, in-vivo software results. On the right, offline review setup image matched with the DRR.

Fig. A17

Belly board problem. On the left, in-vivo software results. On the top right, structures of the planning CT projected on a sagittal view of the CBCT.