Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches
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Bibliographic record
Abstract
BACKGROUND: Radiomics may be useful in rectal cancer management. The aim of this study was to assess and compare different radiomics approaches over qualitative evaluation to predict disease-free survival (DFS) in patients with locally advanced rectal cancer treated with neoadjuvant therapy. METHODS: Patients from a phase II, multicentre, randomized study (GRECCAR4; NCT01333709) were included retrospectively as a training set. An independent cohort of patients comprised the independent test set. For both time points and both sets, radiomic features were extracted from two-dimensional manual segmentation (MS), three-dimensional (3D) MS, and from bounding boxes. Radiomics predictive models of DFS were built using a hyperparameters-tuned random forests classifier. Additionally, radiomics models were compared with qualitative parameters, including sphincter invasion, extramural vascular invasion as determined by MRI (mrEMVI) at baseline, and tumour regression grade evaluated by MRI (mrTRG) after chemoradiotherapy (CRT). RESULTS: In the training cohort of 98 patients, all three models showed good performance with mean(s.d.) area under the curve (AUC) values ranging from 0.77(0.09) to 0.89(0.09) for prediction of DFS. The 3D radiomics model outperformed qualitative analysis based on mrEMVI and sphincter invasion at baseline (P = 0.038 and P = 0.027 respectively), and mrTRG after CRT (P = 0.017). In the independent test cohort of 48 patients, at baseline and after CRT the AUC ranged from 0.67(0.09) to 0.76(0.06). All three models showed no difference compared with qualitative analysis in the independent set. CONCLUSION: Radiomics models can predict DFS in patients with locally advanced rectal cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it