Diffusion‐weighted MRI treatment monitoring of primary hypofractionated proton and carbon ion prostate cancer irradiation using raster scan technique
Bibliographic record
Abstract
PURPOSE: To investigate parametric changes in the apparent diffusion coefficient (ADC) at multiple timepoints during and after completion of primary proton and carbon ion irradiation of prostate cancer (PCa) as compared with normal-appearing prostate parenchyma. MATERIALS AND METHODS: In all, 92 patients with histologically confirmed PCa received either proton or carbon ion hypofractionated radiotherapy (RT). All were prospectively evaluated with diffusion-weighted magnetic resonance imaging (DWI-MRI) at five timepoints: baseline, day 10 during therapy and 6 weeks, 6 months, and 18 months after treatment. Linear mixed models (LMM) were used to evaluate the effects of radiation, antihormonal therapy, time, and type of particle irradiation on manual ADC measurements. ADC differences related to prostate-specific antigen (PSA) relapse according to PSA thresholds and to Vancouver rules and Phoenix criteria were examined using LMM and unpaired Student's t-test. RESULTS: /s, week 6 / month 6 / month 18, P = 0.001/<0.001/<0.001) was found. ADC values of normal-appearing control tissue remained unchanged. Androgen deprivation (P ≥ 0.320), different PSA thresholds (P = 0.634), and PSA relapse criteria according to Vancouver rules (P ≥ 0.776) had no effect. A weak association between 18-month measurements and Phoenix criteria (P = 0.046) was found. CONCLUSION: ADC parametric changes were distinct in tumor tissue, highlighting the ability of diffusion MRI to evaluate different aspects of the microscopic pathophysiology. Although promising, their use as noninvasive imaging biomarkers requires further validation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:850-860.
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 | 0.000 | 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".