Performance evaluation of a stacked classifier for predicting treatment response in unresectable colorectal liver metastases
Bibliographic record
Abstract
Colorectal cancer is the third most common cancer globally, with a high mortality rate due to metastatic progression, particularly in the liver. Early diagnosis and effective treatment are critical for improving survival rates. Surgical resection remains the cornerstone for curative treatment, but only a small subset of patients are eligible for surgery due to the already advanced stage of the disease at the time of the diagnosis. For patients with initially unresectable colorectal liver metastases (CRLM), neoadjuvant chemotherapy can downstage tumors, potentially making surgical resection feasible. Predicting treatment response is crucial for optimizing treatment strategies, as it can guide personalized approaches and avoid exposing patients to the toxicity of chemotherapy. In this study we explored quantitative image features employing radiomics, which were then analyzed using machine learning algorithms to predict treatment outcome, offering a non-invasive tool to assist clinical decision-making and tailor personalized treatment strategies for CRLM patients. The study involved 355 patients with initially unresectable CRLM. Our findings demonstrate that baseline computed tomography scans contain valuable prognostic information. Radiomic-based models achieved high predictive performance, with the area under the receiver operating characteristic curve values reaching 77% for the a priori prediction of treatment 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".