Change in diffusion weighted MRI during liver cancer radiotherapy: Preliminary observations
Bibliographic record
Abstract
PURPOSE: To evaluate diffusion weighted magnetic resonance imaging (DWI) in liver and liver cancers during and following conformal radiotherapy (RT). To determine the feasibility of using changes in apparent diffusion coefficients (ADC) as a potential surrogate for tumour control or normal tissue injury. MATERIALS AND METHODS: Patients on a six-fraction conformal liver RT protocol underwent DW-MRI at the time of treatment planning, during RT (week one and two) and one month following RT. Diffusion weighted MR images were acquired in exhale breath hold, using b-values of 0 and 600. Regions of interest (ROIs) corresponding to maximal tumour dose, high-dose peri-tumour liver, irradiated normal liver, non-irradiated liver, and spleen were analyzed on ADC maps. RESULTS: Eleven patients (four hepatocellular carcinoma, five liver metastases, two cholangiocarcinoma) were evaluated. The baseline median tumour ADC of 1.56 x 10(-3)mm(2)/sec increased to 1.89 x 10(-3)mm(2)/sec at RT week one, to 1.91 x 10(-3)mm(2)/sec during week two and to 2.01 x 10(-3)mm(2)/sec at one month following treatment (p < 0.0001). Early increases in mean ADC were correlated with higher dose and sustained tumour response, whereas RECIST and volume changes on T2 images were not. Peri-tumour mean ADC also increased, from 1.40 x 10(-3)mm(2)/sec (baseline) to 1.55 x 10(-3)mm(2)/sec (RT week 2) and 1.64 x 10(-3)mm(2)/sec (follow-up). Small ADC changes were seen in the irradiated liver, and no significant changes were seen in the un-irradiated liver. CONCLUSIONS: Changes in tumour ADC were seen during RT. Larger increases were correlated with higher doses and increased likelihood of response.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".