Abstract 4060: Use of small animal MRI and cone-beam CT for image-guided radiotherapy of orthotopic tumors in mice
Bibliographic record
Abstract
Abstract Introduction: Recent developments in image-guided radiation therapy (IGRT) of small animals aim to adapt the radiation planning and delivery protocols employed in the clinical setting to preclinical investigations. Accurate and targeted delivery of radiation to the disease volume of interest is necessary for evaluation of new treatment strategies and their timely translation to the clinic. This abstract reports the feasibility of employing a 1T small animal MR scanner and a cone-beam CT (CBCT)-equipped small animal irradiator to effectively visualize and delineate orthotopically implanted tumors on the treatment bed prior to targeted radiotherapy. Methods: Male nude mice bearing orthotopically implanted prostate tumor (DU145 and 22Rv1) and female SCID mice bearing orthotopically implanted cervical tumor (ME-180) were positioned on a transportable multimodality imaging and treatment bed (Minerve System, Bioscan) and scanned using a 1T MR scanner (M2, Aspect Imaging) and a small animal irradiator equipped with CBCT (X-rad 255Cx, Precision X-ray). The MR images were acquired using a fast spin echo sequence (TE/TR = 80/4800, NEX of 4 or 6, voxel size of 0.2 x 0.2 x 1.0 mm, 22 to 24 slices and imaging time of 6 to 8 minutes). The CBCT images were acquired at 80 and 100 kVp (for reduced streaking at the bone/soft tissue interface) and 0.5 mA (60 seconds acquisition) and reconstructed at 0.1 x 0.1 x 0.1 mm voxel size. Image analysis was conducted using Microview (GE Healthcare) by manually contouring of the tumor in selected slices and then linearly extrapolating across non-contoured slices. Results: The overall soft tissue contrast in the lower abdominal cavity was poor in the CBCT data set. There are cases where there is sufficient contrast between the bladder and the tumor, and between the tumor and selected segments of the intestinal tract. Nonetheless, it was challenging in most cases to confidently delineate the tumor margins using the CBCT images alone. Side-by-side consultation with the MR images during contouring allowed for the tumors to be more confidently delineated on the CBCT images (without co-registration with the MR data set) with a volumetric accuracy of 92.4 ± 12.3% (7 distinct tumors with volumes ranging from 181 to 1399 mm3) relative to the volumes contoured using the MR data set alone. Conclusion: Precise and accurate delivery of radiation to preclinical animal models must be supplemented by imaging techniques that allow for visualization and delineation of the disease volume in the treatment position. MR images acquired with the animal on a transportable multimodality imaging and treatment bed facilitated the delineation of volumetrically accurate treatment regions using a CBCT-equipped small animal irradiator. Acknowledgements: The authors would like to thank Coackley C, Zafarana G and Bristow RG for providing the prostate mouse models, and Chaudary N and McKee T for providing the cervical mouse model. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4060. doi:1538-7445.AM2012-4060
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".