Quality Assurance Considerations in Radiopharmaceutical Therapy Dosimetry Using PLANETDose: An International Atomic Energy Agency Study

Abstract

Implementation of radiopharmaceutical therapy dosimetry varies depending on the clinical application, dosimetry protocol, software, and ultimately the operator. Assessing clinical dosimetry accuracy and precision is therefore a challenging task. This work emphasizes some pitfalls encountered during a structured analysis, performed on a single-patient dataset consisting of SPECT/CT images by various participants using a standard protocol and clinically approved commercial software. Methods: The clinical dataset consisted of the dosimetric study of a patient administered with [¹⁷⁷Lu]Lu-DOTATATE at Tygerberg Hospital, South Africa, as a part of International Atomic Energy Agency-coordinated research project E23005. SPECT/CT images were acquired at 5 time points postinjection. Patient and calibration images were reconstructed on a workstation, and a calibration factor of 122.6 Bq/count was derived independently and provided to the participants. A standard dosimetric protocol was defined, and PLANETDose (version 3.1.1) software was installed at 9 centers to perform the dosimetry of 3 treatment cycles. The protocol included rigid image registration, segmentation (semimanual for organs, activity threshold for tumors), and dose voxel kernel convolution of activity followed by absorbed dose (AD) rate integration to obtain the ADs. Iterations of the protocol were performed by participants individually and within collective training, the results of which were analyzed for dosimetric variability, as well as for quality assurance and error analysis. Intermediary checkpoints were developed to understand possible sources of variation and to differentiate user error from legitimate user variability. Results: Initial dosimetric results for organs (liver and kidneys) and lesions showed considerable interoperator variability. Not only was the generation of intermediate checkpoints such as total counts, volumes, and activity required, but also activity-to-count ratio, activity concentration, and AD rate-to-activity concentration ratio to determine the source of variability. Conclusion: When the same patient dataset was analyzed using the same dosimetry procedure and software, significant disparities were observed in the results despite multiple sessions of training and feedback. Variations due to human error could be minimized or avoided by performing intensive training sessions, establishing intermediate checkpoints, conducting sanity checks, and cross-validating results across physicists or with standardized datasets. This finding promotes the development of quality assurance in clinical dosimetry.

Keywords: PLANETDose; SPECT/CT; clinical dosimetry; quality assurance; variability assessment.

Graphical abstract

FIGURE 1.

Workflow used for analyzing data and computing final results and recommendations. SOP = standard operating procedure.

FIGURE 2.

(A) Anatomic segmentation (for whole liver and kidneys). (B) Functional segmentation for lesions. (C) Normal liver from Boolean subtraction.

FIGURE 3.

Organ and lesion volumes in first treatment cycle for 5 time points (1–5). Whole liver signifies anatomic liver, whereas normal liver represents healthy liver (whole liver − 4 lesions).

FIGURE 4.

Total counts (A), activity (B), and activity-to-count ratio (C) for each VOI in third cycle for time points 1–5. Whole liver signifies anatomic liver, whereas normal liver represents healthy liver (whole liver − 4 lesions).

FIGURE 5.

ADR (A), AC (B), and ADR-to-AC ratio (C) for each VOI in third cycle for time points 1–5. Whole liver signifies anatomic liver, whereas normal liver represents healthy liver (whole liver − 4 lesions).

FIGURE 6.

AD (in Gy) for each VOI (organs and lesions) in first cycle. Whole liver signifies anatomic liver, whereas normal liver represents healthy liver (whole liver − 4 lesions).

FIGURE 7.

Typical error illustrating selection of small bounding box (in red) for anterior lesion while performing threshold-based segmentation. Blue VOI represents correct segmentation. Bounding box refers to predetermined subregion or selection in which thresholding is performed. By performing single click in area of high uptake, it is possible to generate tumor contour, thus preventing any potential correlation with uptake regions that are near tumor (e.g., organ, another tumor).