Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0
Bibliographic record
Abstract
Artificial intelligence (AI) has the potential to enhance medical diagnostics, but its clinical application is often limited by issues related to interpretability. This study bridges the gap by associating standardized quantitative imaging features, known as radiomics features ($\mathbf{R F}$), derived from medical images with established clinical frameworks such as PI-RADS, ensuring that AI models are both interpretable and aligned with clinical practice. A team comprising two medical physicists, a physician, a radiologist, and two MDs collaboratively created a radiological/biological dictionary that connects the visual semantic features of PI-RADS with radiomics features, promoting a unified understanding among medical and AI professionals. In this study, six interpretable and seven complex classifiers, paired with nine feature selection algorithms focused on risk factors, were applied to segmented lesions in multiparametric prostate MRI sequences (T2-weighted (T2WI), diffusion-weighted (DWI), and apparent diffusion coefficient (ADC) imaging) to predict UCLA scores. The dictionary was then used to interpret the most predictive models. By combining T2WI, DWI, and ADC with FSAs such as ANOVA F-test, Correlation Coefficient, and Fisher Score, and using logistic regression, key features were identified: the 90th percentile from T2WI, indicating hypointensity linked to prostate cancer risk; variance from T2WI, representing lesion heterogeneity; shape metrics like Least Axis Length and Surface Area to Volume ratio from ADC, describing lesion shape and compactness; and Run Entropy from ADC, which reflects texture consistency. This approach achieved an average accuracy of$0.78 \pm 0.01$, significantly surpassing single-sequence methods ($\mathbf{p}$-value$<0.05$). The developed Prostate-MRI dictionary (PM1.0) provides a shared framework, fostering collaboration between clinicians and AI developers to create trustworthy, interpretable AI solutions that support reliable clinical decision-making.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".