Neoadjuvant chemotherapy evaluation by MRI volumetry in rectal cancer followed by chemoradiation and total mesorectal excision: Initial experience
Bibliographic record
Abstract
PURPOSE: To evaluate rectal cancer volumetry in predicting initial neoadjuvant chemotherapy response. MATERIALS AND METHODS: Sixteen consecutive patients who underwent neoadjuvant chemotherapy (CX) before chemoradiotherapy (CRT) and surgery were enrolled in this retrospective study. Tumor volume was evaluated at the first magnetic resonance imaging (MRI), after CX and after CRT. Tumor volume regression (TVR) and downstaging were compared with histological results according to Tumor Regression Grade (TRG) to assess CX and CRT response, respectively. RESULTS: The mean tumor volume was 132 cm(3) ± 166 before and 56 cm(3) ± 71 after CX. TVR after CX was significantly different between patients with poor histologic response (TRG1/2) and those with good histologic response (TRG3/4) (P = 0.001). An optimal cutoff of TVR >68% (area under the curve [AUC]: 0.9, 95% confidence interval [CI]: 0.65-0.98, P = 0.0001) to predict good histology response after CX was assessed by receiver operating characteristic curve. According to previous data and this study, we defined 70% as the best cutoff values according to sensitivity (86%), specificity (100%) of TVR for predicting good histology response. In contradistinction, MRI downstaging was associated with TRG only after CRT (P = 0.04). CONCLUSION: Our pilot study showed that MRI volumetry can predict early histological response after CX and before CRT. MRI volumetry could help the clinician to distinguish early responders in order to aid appropriate individually tailored therapies.
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 | 0.002 |
| 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) | 0.000 | 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".