Tumor Volume Predicts for Pathologic Complete Response in Rectal Cancer Patients Treated With Neoadjuvant Chemoradiation
Bibliographic record
Abstract
OBJECTIVES: Nonoperative management (NOM) of locally advanced rectal cancer is an emerging approach allowing patients to preserve their anal sphincter. Identifying clinical factors associated with pathologic complete response (pCR) is essential for physicians and patients considering NOM. MATERIALS AND METHODS: In total, 412 locally advanced rectal cancer patients were included in this retrospective analysis. Tumor volumes were derived from pretreatment MRI. Clinical parameters such as tumor volume, stage, and location were analyzed by univariate and multivariate analysis, against pCR. A receiver operator characteristic curve was generated to identify a tumor volume cut-off with the highest clinically relevant Youden index for predicting pCR. RESULTS: Seventy-five of 412 patients (18%) achieved pCR. A tumor volume threshold of 37.3 cm 3 was identified as predictive for pCR. On regression analysis, a tumor volume >37.3 cm 3 was associated with a greater than 78% probability of not achieving pCR. On multivariate analysis, a GTV <37.3 cm 3 [odds ratio (OR)=3.7, P <0.0001] was significantly associated with an increased pCR rate, whereas tumor length > 4.85 cm was associated with pCR on univariate (OR=3.03, P <0.01) but not on multivariate analysis (OR=1.45, P =0.261). Other clinical parameters did not impact pCR rates. CONCLUSIONS: A tumor volume threshold of 37.3 cm 3 was identified as predictive for pCR in locally advanced rectal cancer patients receiving neoadjuvant chemoradiation. Tumors above this volume threshold corresponded to a greater than 78% probability of not achieving pCR. This information will be helpful at diagnosis for clinicians who are considering potential candidates for NOM.
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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 | 0.001 | 0.004 |
| 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".