Radiomics‐based kidney lesion classification: Mitigating batch effect with nested combat harmonization
Bibliographic record
Abstract
BACKGROUND: The increased use of CT imaging has elevated the incidental detection of renal masses, necessitating accurate differentiation between benign and malignant nodules. Radiomics offers potential for improved diagnostics; however, it is limited by variability in imaging parameters such as slice thickness, highlighting the need for effective harmonization techniques. PURPOSE: The purpose of this study is to conduct a comprehensive radiomics analysis, evaluating the impact of slice thickness in distinguishing between kidney cysts and tumors using machine learning techniques, thus contributing to more precise and effective patient management strategies. METHODS: We utilized a publicly available dataset, KITS23, and extracted radiomic features from contrast-enhanced computed tomography (CT) scans using the PyRadiomics library. The dataset consists of 599 cases, which were divided into training (60%) and testing (40%) cohorts to develop and validate predictive models. Six feature selection methods and ten machine learning classifiers were employed. Additionally, the Nested Combat harmonization technique was applied to address variations in imaging protocols across institutions. RESULTS: We observed improvements in AUC values across various feature selection methods and classifiers after harmonization, with the highest AUC reaching 0.95. This represents significant enhancements in model performance, with mean AUC improvements ranging from 0.7% to 7.7% across different feature selection methods, bringing our results in line with, and in some cases surpassing, the AUCs reported in the literature. CONCLUSIONS: These findings underscore the potential of radiomics-based machine learning models to enchance diagnostic accuracy and patient management in clinical practice. The use of harmonization techniques, such as, Nested Combat is crucial in achieving reliable and generalizable predictive models for renal oncology.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".