CANTO-RT: One of the Largest Prospective Multicenter Cohort of Early Breast Cancer Patients Treated with Radiotherapy including Full DICOM RT Data
Bibliographic record
Abstract
This article describes the methodology used and provides a characterization of the study population in CANTO-RT (CANcer TOxicities RadioTherapy). CANTO (NCT01993498) is a prospective clinical cohort study including patients with stage I-III BC from 26 French cancer centers. Patients matching all CANTO inclusion and exclusion criteria who received RT in one of the 10 top recruiting CANTO centers were selected. Individual full DICOM RT files were collected, pseudo-anonymized, structured and analyzed on the CANTO-RT/UNITRAD web platform. CANTO-RT included 3875 BC patients with a median follow-up of 64 months. Among the 3797 patients with unilateral RT, 3065 (80.4%) had breast-conserving surgery, and 2712 (71.5%) had sentinel node surgery. Tumor bed boost was delivered in 2658 patients (68.5%) and lymph node RT in 1356 patients (35%), including internal mammary chain in 844 patients (21.8%). Most patients (3691 (95.3%)) were treated with 3D conformal RT. Target volumes, organs at risk contours and dose/volume histograms were extracted after quality-control procedures. CANTO-RT is one of the largest early BC prospective cohorts with full individual clinical, biological, imaging and DICOM RT data available. It is a valuable resource for the identification and validation of clinical and dosimetric predictive factors of RT and multimodal treatment-related toxicities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".