Validation of Region of Interest Measurements for the Objective Assessment of Post-Contrast Enhancement of Renal Lesions on MRI
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to validate the use of region of interest (ROI) measurements in MRI to objectively assess for enhancement in suspected solid renal masses and to determine a minimum threshold value for true enhancement. METHODS: Contrast-enhanced renal MRI studies performed between January 2015 and December 2017 for patients with a known renal mass who had subsequent biopsy, or partial/radical nephrectomy were included. Two body imaging fellows independently measured the mean ROI values of renal masses, normal renal parenchyma, the ipsilateral psoas muscle and external air on the pre- and post-contrast sequences. The absolute and percentage changes in the mean ROI values were calculated. The readers were blinded to the pathology results. RESULTS: 104 patients were included in this study (mean age of 65 years; 58 males and 46 females). 74 patients (71%) had a diagnosis of renal cell carcinoma (RCC). Pathology showed clear-cell RCC in 55%, papillary RCC in 22%, and other RCC subtypes in 23%. There were 30 non-RCC renal lesions (29%), including oncocytoma, renal papillary adenoma, and renal metastasis.The minimum percentage change in ROI values in the pre- versus post-contrast images for all pathology-proven RCCs was 23% (range: 23-437%, mean: 143%); this represents relative enhancement and was referred to as the Signal Intensity Index (SII). The percentage change for normal renal parenchyma ranged from 32-317%. The maximum percentage change in ROI values for pathology proven renal cysts was 13% (range: -5-13%, mean: 3.5%). There was excellent inter observer agreement between the two readers [Intra-class correlation coefficient (r) 0.81]. CONCLUSION: The percentage change in ROI values (SII) can be a helpful tool in the objective assessment of true enhancement of renal masses and can supplement subtraction images. The minimum threshold for enhancement in our study was 23%. ADVANCES IN KNOWLEDGE: Enhancement of a renal lesion can be determined using the objective tool of ROI measurements in the pre- and post-contrast MR images with a percentage change of 20% or above indicating enhancement. This is an additional objective tool, which in conjunction with the subtraction images may improve detection and appropriate diagnosis of renal lesions. It could also be helpful in cases where the subtraction images are degraded by motion artefact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".