Magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) hyperthermia for primary rectal cancer: A virtual feasibility analysis.
Bibliographic record
Abstract
77 Background: MR-HIFU Hyperthermia (HT) is a non-invasive treatment modality with real-time thermometry that ensures accurate and precise heating of a target with minimal effect on adjacent tissue. This energy deposition within a tumour can produce local bioeffects resulting in thermal chemo- and radiosensitization. MR-HIFU has been shown to be safe and feasible in a companion phase I study for recurrent rectal cancer. The purpose of this study is to determine the feasibility of MR-HIFU in treating primary rectal tumours. Methods: With ethics approval, the anatomic characteristics and surrounding structures of rectal tumours diagnosed at Sunnybrook from 2014-2019 were retrospectively analyzed. Three orthogonal views of MR images were used to determine the potential ultrasound (US) beam path and organs at risk (OAR). In part 2 of the study, the gross tumour volume was delineated for 30 rectal tumours (10 low, mid &high). Image datasets were imported into the Sonalleve MR-HIFU workstation for virtual treatment simulation and planning to determine tumour targetability, coverage, optimal patient set-up, and transducer positioning. Results: Of the 105 tumours analyzed, 36, 52, and 17 were low, mid, and high, respectively. The average width of the acoustic window (sciatic notch) for the US beam path was 5.8 ± 1.4cm, average tumour length was 5.24 ± 2.0cm, and average beam path (skin to tumour edge) was 7.3 ± 1.9cm. Eighty one percent of tumours were ≤ 0.3cm from an OAR. Of the 24 virtually simulated tumours to date, 6/8 lower, 6/8 mid, and 1/8 upper rectal tumours were targetable by MR-HIFU. Conclusions: This is the first virtual analysis to evaluate MR-HIFU HT targetability in primary rectal cancer. Results from this study will support MR-HIFU HT as an option to enhance the treatment of primary rectal cancer. Acknowledgments: This study has been funded by the Canadian Cancer Society. Patient & tumour characteristics. [Table: see text]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".