Interpretable deep learning model for distinguishing tumor pseudoprogression from true progression using MRI imaging of glioblastoma patients
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) are extremely invasive cancers. The treatment of GBM involves microsurgical resection followed by radiochemotherapy and chemotherapy. As a response to radiation treatment, in many cases, a new or a progressing lesion is observed in imaging studies which resolves without additional treatment. This phenomenon is called pseudoprogression (PsP). In contrast to PsP, a True Progression (TP) represents an enlarging lesion that requires a change in the treatment. Distinguishing between PsP and TP is thus central to treatment choice and clinical management. However, both types of progression present themselves with overlapping characteristics in imaging as assessed by radiologists. An automated machine learning method that can learn to discover distinctive markers to reliably differentiate between the two situations will thus be an effective prognostic tool in the clinical management of GBM. In this paper we present a 3D convolutional neural network (CNN) trained on 3D MRI images from 114 GBM patients to classify PsP and TP. Using a 5-fold cross validation strategy, we report multiple metrics by evaluating the model performance on left-out MRI image volumes not used during training. Specifically, our trained model performs with: AUCROC: 0.74, Peak geometric mean of specificity and sensitivity: 0.69, Brier Score: 0.22, Scaled Brier Score: 0.04. We also present decision curve analysis for our model. Prior works on this topic have reported only AUCROC. For model interpretability, we have used the technique GradCAM to discover and visualize the most salient regions in the MRI volume that are used by the CNN for making the decision. Our results show the neural network paying more attention to the lesion and peri-tumoral regions. These findings suggest further investigation of deep learning models trained on larger imaging datasets to build more robust and generalizable models for distinguishing between PsP and TP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".