Hippocampal T1WI radiomics- and clinical feature-based models for predicting early mild cognitive impairment in secondary hydrocephalus
Bibliographic record
Abstract
Introduction: Mild cognitive impairment (MCI) represents the initial stage of dementia, and early diagnosis is crucial in clinical practice. This study aimed to investigate the predictive performance of three models based on clinical features, radiomics features of hippocampal T1-weighted imaging, and a combination of these features for identifying MCI in patients with secondary hydrocephalus. Methods: = 75). The samples were randomly stratified into a training set (34 MCI and 52 non-MCI patients) and a validation set (15 MCI and 23 non-MCI patients). Radiomic features from the bilateral hippocampi were extracted based on the region of interest, and the optimal parameters were selected through dimensionality reduction. Predictive models were constructed using clinical data, radiomic data, and a combination of both, with the radiomic score being utilized. The performance of each model was then assessed in both training and validation sets. Additionally, the diagnostic performance of the optimal model was compared with that of the Montreal Cognitive Assessment (MoCA) Scale. Results: In the clinical model, the disease course, serum uric acid, serum cystatin C, and the lateral ventricular temporal horn ratio emerged as independent risk factors for MCI following hydrocephalus. In the radiomics model, four optimal hippocampal features were identified. The AUC values for the clinical, radiomics, and combined models in the training/validation sets were 0.827 (0.736 ~ 0.919)/0.812 (0.666 ~ 0.957), 0.864 (0.790 ~ 0.937)/0.849 (0.724 ~ 0.974), and 0.937 (0.889 ~ 0.985)/0.907 (0.804 ~ 1.000), respectively. The combined model exhibited higher AUC values than the MoCA scale in both datasets. There was a significant difference in the training set, and while the validation set showed a consistent trend, it did not achieve statistical significance. Conclusion: The combined model achieved optimal performance and demonstrated superior predictive capabilities for MCI in the patients with secondary hydrocephalus outperforming other models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".