Accuracy of kidney dosimetry performed using simplified time activity curve modelling methods: a <sup>177</sup> Lu-DOTATATE patient study
Bibliographic record
Abstract
Lu-DOTATATE therapy has been shown to produce encouraging results in treatment of neuroendocrine tumours (NETs). Unfortunately, since dosimetry for radionuclide therapy is considered to be challenging, typically similar amount of radiopharmaceutical is administered to every patient. There is growing evidence that the efficacy of this therapy can be significantly improved by employing personalized protocols, based on the organ-at-risk dosimetry. The aim of this study is to propose a practical and accurate dosimetry protocol based on the simplified acquisition schedules. Data from fifty-three therapy cycles in thirty-nine NET patients were analyzed. Three SPECT/CT scans, acquired at 4 h (D0), 23 h (D1) and 70 h (D3) after injection, were performed. The kidney volume was determined using CT and the activity was determined from quantitative SPECT using an iterative thresholding method. For each dataset, four methods were used to model the time-activity-curves (TAC): M1-two trapezoid segments (0 to D0 and D0 to D1), followed by monoexponential fit to D1 + D3 data; M2-monoexponential fit to D0 + D1 + D3 data; M3 and M4-monoexponential fit to D0 + D3 and D1 + D3 data, respectively. Additionally, kidney doses obtained from single time point method using a monoexponential curve with the population mean effective half-life, normalized to activities at D0 or D1 or D3 points, were calculated. The accuracy of simplified dosimetry methods was assessed as the percentage difference relative to doses calculated from M1. The major contribution to the absorbed dose estimate comes from the area under the late time portion of the TAC (D1 to infinity). Therefore, information from the late scan (D3) is crucial for the determination of kidney absorbed doses. Single time point method using monoexponential TAC, with the population mean effective half-life normalized to the late data point (48-72 h for kidneys) produces <10% deviation in the absorbed dose estimation, thus is recommended for clinical use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".