TY - JOUR T1 - Automated batch dosimetry data processing software for improved clinical applications and workflow. JF - Journal of Nuclear Medicine JO - J Nucl Med SP - 1744 LP - 1744 VL - 59 IS - supplement 1 AU - Adam Kesner AU - Alexandre Chicheportiche AU - Nanette Freedman Y1 - 2018/05/01 UR - http://jnm.snmjournals.org/content/59/supplement_1/1744.abstract N2 - 1744Objectives: Personalized radionuclide dosimetry offers the potential of reducing risk of high dose to vulnerable normal organs while maximizing tumor dose. However, dosimetry entails significant time/efforts. Improving workflows to be more robust, dependable and faster may aid in bringing them into routine clinical operations. In this study, we created dose calculation workflow for Lu-177 DOTATATE dosimetry that integrates vendor image analysis software with in-house software for curve fitting, biodistribution modelling, and dosimetry calculations within an automated workflow and database architecture. Methods: We built in-house software (in IDL) entitled Dosimetry Data Processor (DDP), to support a nuclear medicine physics dose calculation workflow. The workflow has 2 main components: (1) organ uptake characterization via image analysis using vendor software and (2) automated dose processing including curve fitting, numerical integration, error checking, and absorbed dose calculation (using OLINDA/EXM v1.1 Dose Conversion Factors). In addition, all DDP input is automatically archived, enabling absorbed dose to be calculated (or recalculated) via batch data processing. To validate the system, dosimetry calculations were compared with standard OLINDA/EXM software (v1.1) in 5 test cases, and evaluated for speed and ease of use for sets of post-therapy images following 199 treatments. To demonstrate the ease/benefit and versatility of our batch reprocessing capacity, population dosimetry was calculated using both mono-exponential curve fitting and trapezoidal integration/physical half-life decay modelling assumptions. RESULTS: In 5 test cases, dose calculations using DDP yielded results identical to OLINDA/EXM. Median physicist time required to perform dosimetry using DDP in routine cases (i.e. after imaging/blood data available) was approximately 1 hr per patient/treatment cycle (~45 min image analysis, 10 min blood measurement verification and formatting, 5 min processing using DDP). Once a patient’s biodistribution details were logged into the database, dosimetry calculation required approximately 0.6 seconds and could be run automatically for an entire population. To demonstrate the ability to test the effect of our assumptions on the population dosimetry, we compared curve fitting methodologies in our population. We found that the dose to the kidneys, often the dose limiting organ, varied significantly between exponential curve fitting models and trapezoidal/physical half-life models, 0.74±0.32 mGy/MBq (avg ± SD) and 1.02 ± 0.45 mGy/MBq, respectively. The higher dose estimate for the latter model is attributable to use of physical half-life, higher than typical measured half-life for Lu-177 DOTATATE in kidneys, for the tail of the activity curve. CONCLUSIONS: As the use of dosimetry in radionuclide therapy expands, attention to process details should become more relevant. Traditional case-by-case processing/evaluation of dosimetry is useful, but can be problematic with increasing case load and may be prone to error. In this work, we built a clinical dosimetry protocol that utilizes software to record and automate as much of the dose calculation workflow as possible, and integrate it with database type batch processing. Advantages of using DDP included elimination of operator errors such as typos, the ability to reprocess population data under different dose calculation assumptions, and organized population data which supports easy review of data trends, identification of outlying results warranting review, and the comparison of calculations across different dosimetry assumptions, scanners, dose calibrators, and image analysis software operators. Further development of these types of software tools may improve workflows and standardization across the radionuclide dosimetry field. RESEARCH SUPPORT: We would like to acknowledge GE Healthcare for their partial support of this project. ER -