Deep Learning for Tumor Progression in Glioblastoma: A Comprehensive Evaluation for Clinical Diagnosis
Bibliographic record
Abstract
The timely diagnosis of tumor progression is crucial to implementing treatment changes that can improve patient survival. In this study, we analyzed scans from 114 patients with glioblastoma multiforme to differentiate between pseudoprogression and true tumor progression. We used processed skull segmented and augmented data to perform transfer learning using a pre-trained, customized ResNet-18. An AUC of 0.71, an F1 score of 0.64, and a geometric mean of sensitivity and specificity of 0.67 were achieved. Although the saliency maps demonstrate that the model was able to identify the correct region of interest, the low scaled Brier score of 0.07 indicates that the model is just slightly better than random, underscoring the importance of outcome likelihood metrics. These results demonstrate that the achieved performance levels are not yet sufficient to support reliable clinical decision-making. To make the model clinically applicable, further investigation and refinement are required to enhance its accuracy, reliability, and overall clinical utility.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".