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Record W1964317792 · doi:10.1118/1.2977762

Feasibility of small animal cranial irradiation with the microRT system

2008· article· en· W1964317792 on OpenAlexaff
Erich L. Kiehl, Strahinja Stojadinović, K Malinowski, David D. Limbrick, Sarah C. Jost, Joel R. Garbow, Joshua B. Rubin, Joseph O. Deasy, D Khullar, Enrique W. Izaguirre, Parag J. Parikh, Daniel A. Low, Andrew Hope

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer InstituteNational Institutes of HealthVarian Medical Systems
KeywordsMedical imagingMedical physicsNuclear medicineMedicineRadiology

Abstract

fetched live from OpenAlex

PURPOSE: To develop and validate methods for small-animal CNS radiotherapy using the microRT system. MATERIALS AND METHODS: A custom head immobilizer was designed and built to integrate with a pre-existing microRT animal couch. The Delrin couch-immobilizer assembly, compatible with multiple imaging modalities (CT, microCT, microMR, microPET, microSPECT, optical), was first imaged via CT in order to verify the safety and reproducibility of the immobilization method. Once verified, the subject animals were CT-scanned while positioned within the couch-immobilizer assembly for treatment planning purposes. The resultant images were then imported into CERR, an in-house-developed research treatment planning system, and registered to the microRTP treatment planning space using rigid registration. The targeted brain was then contoured and conformal radiotherapy plans were constructed for two separate studies: (1) a whole-brain irradiation comprised of two lateral beams at the 90 degree and 270 degree microRT treatment positions and (2) a hemispheric (left-brain) irradiation comprised of a single A-P vertex beam at the 0 degree microRT treatment position. During treatment, subject animals (n=48) were positioned to the CERR-generated treatment coordinates using the three-axis microRT motor positioning system and were irradiated using a clinical Ir-192 high-dose-rate remote after-loading system. The radiation treatment course consisted of 5 Gy fractions, 3 days per week. 90% of the subjects received a total dose of 30 Gy and 10% received a dose of 60 Gy. RESULTS: Image analysis verified the safety and reproducibility of the immobilizer. CT scans generated from repeated reloading and repositioning of the same subject animal in the couch-immobilizer assembly were fused to a baseline CT. The resultant analysis revealed a 0.09 mm average, center-of-mass translocation and negligible volumetric error in the contoured, murine brain. The experimental use of the head immobilizer added 0.1 mm to microRT spatial uncertainty along each axis. Overall, the total spatial uncertainty for the prescribed treatments was +/-0.3 mm in all three axes, a 0.2 mm functional improvement over the original version of microRT. Subject tolerance was good, with minimal observed side effects and a low procedure-induced mortality rate. Throughput was high, with average treatment times of 7.72 and 3.13 min/animal for the whole-brain and hemispheric plans, respectively (dependent on source strength). CONCLUSIONS: The method described exhibits conformality more in line with the size differential between human and animal patients than provided by previous prevalent approaches. Using pretreatment imaging and microRT-specific treatment planning, our method can deliver an accurate, conformal dose distribution to the targeted murine brain (or a subregion of the brain) while minimizing excess dose to the surrounding tissue. Thus, preclinical animal studies assessing the radiotherapeutic response of both normal and malignant CNS tissue to complex dose distributions, which closer resemble human-type radiotherapy, are better enabled. The procedural and mechanistic framework for this method logically provides for future adaptation into other murine target organs or regions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.262
Teacher spread0.244 · 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

Citations34
Published2008
Admission routes1
Has abstractyes

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