Radiomics feature based benign vs. malignant characterization of solid renal masses on MRI
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is well suited for Solid renal masses (SRMs) characterization (e.g., benign vs. malignant) due to its superior soft tissue contrast. Though renal mass detection and characterization using deep-learning (DL) methods have been extensively studied for computed tomography (CT) images, those same tasks are yet to be investigated on MRI images. SRMs need active surveillance as they consist of biologically diverse heterogeneous groups of benign or malignant masses. Among them, malignant clear cell renal carcinoma (ccRCC) is frequently aggressive. There are inter-observer and intra-observer differences in the assessment of SRMs by expert clinicians because of their experience and expertise. Therefore, it is essential to develop a machine learningbased noninvasive imaging diagnosis to distinguish SRMs as benign and malignant. Our retrospective study consisted of malignant (renal cell carcinoma- clear cell, papillary, and chromophobe) and benign (fat-poor angiomyolipoma-fpAML, oncocytomas) SRMs. We extracted first and second-order radiomics features from SRMs on T2W and T1W-CM MRI to train different machine learning (ML) models using the 5-fold cross-validation for benign vs malignant classification. The support vector machine (SVM) algorithm generated benign vs malignant classification accuracy of 90.00% with ROC-AUC of 76.19% on T2W MRI and the custom-designed multilayer perceptron model (MLP) model produced accuracy of 80.00% with ROC-AUC of 75.47% on T1W-CM MRI. Thus, ML-based radiomics features classification of SRMs extracted on MRI may be an alternative to biopsy using a non-invasive assessment of SRMs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".