Personalized kidney dosimetry in <sup>177</sup> Lu-octreotate treatment of neuroendocrine tumours: a comparison of kidney dosimetry estimates based on a whole organ and small volume segmentations
Bibliographic record
Abstract
Abstract Peptide receptor radionuclide therapy (PRRT) with 177 Lu- radiolabeled octreotate is an effective treatment method for inoperable neuroendocrine tumours (NETs). There is growing evidence that estimates of the organ-at-risks (OARs) doses are necessary for the optimization of personalized PRRT (P-PRRT). Dosimetry, however, requires a complicated and time-consuming procedure, which hinders its implementation in the clinic. The aim of this study is to develop a practical and automatic technique to simplify personalized dosimetry of kidney, the major OAR in 177 Lu P-PRRT. The data from 30 NETs patients undergoing 44 personalized 177 Lu-DOTA-TATE therapy cycles were analyzed. To determine the biokinetics of the radiopharmaceutical in the kidneys, for each patient three SPECT/CT scans were acquired, at about 4 h, 24 h and 70 h after injection. The kidneys doses were evaluated using three different approaches: (1) a traditional approach based on whole kidney (WK) segmentation; (2) a small volume (SV) manual approach (M-SV) with observer-defined SV location; and (3) a software based SV-approach that automatically defines SV location (A-SV). Four different methods of automatic SV location selections were investigated. The SV kidney doses estimated using M-SV and A-SV approaches was evaluated and the accuracy of these two approaches were compared to the WK dosimetry. The kidney bio-kinetics, in terms of effective half-lives, obtained from both of the A-SV and M-SV approaches agreed to within 10% with those obtained from the WK segmentation. The average ratios of SV doses to WK doses were mostly about 1.8 ± 0.2 for both A-SV and M-SV approaches. The linear correlation coefficients between SV doses (both A-SV and M-SV) and WK doses were up to 0.9 with p < 0.001. The differences between A-SV and M-SV were minor. By comparing different methods of SV location selections, independently selecting SV in images from each of the acquisitions was proved the most appropriate and accurate approach. An automatic, observer-independent method for selecting the location of the small volume in kidneys was developed. The accuracy of this dose estimation approach has been demonstrated by comparing it with the manual SV dosimetry, as well as the WK dosimetry. The proposed automatic approach can potentially be considered as a practical and simple method for dose estimation in the future clinical studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".