MétaCan
Menu
Back to cohort
Record W2957364020 · doi:10.1088/1361-6560/ab32a1

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

2019· article· en· W2957364020 on OpenAlexafffund
Xinchi Hou, Wei Zhao, Jean‐Mathieu Beauregard, A. Ćeller

Bibliographic record

VenuePhysics in Medicine and Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversité LavalCentre hospitalier de l'Université LavalCentre hospitalier universitaire de QuébecUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsDosimetryRadionuclide therapyNuclear medicineMedicineKidneyNeuroendocrine tumorsRadiologyInternal medicine

Abstract

fetched live from OpenAlex

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 &lt; 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.402
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicNeuroendocrine Tumor Research AdvancesFrench-language works237,207