MP50-05 HOW WELL DOES RENAL MASS SIZE MEASURED BY ULTRASOUND CORRELATE WITH MEASUREMENTS OBTAINED BY CT, MRI OR PATHOLOGY? ANALYSIS FROM THE CANADIAN KIDNEY CANCER INFORMATION SYSTEM
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Active surveillance is a well-established treatment modality for small renal masses. Despite its widespread adoption, it is not clear which imaging modality is ideal. Given the risks of ionizing radiation with cross-sectional imaging, the use of US is gaining popularity. There is a paucity of data comparing correlation of US imaging findings with CT, MRI, or pathological size. The goal of our study was to identify the correlation of US to CT/MRI and pathological size and US to CT/MRI using a multi-institutional, prospectively maintained kidney cancer database, the Canadian Kidney Cancer Information System (CKCis). METHODS: Between January 2011 and October 2019 we identified a cohort of patients who had a pre-operative US and cross-sectional imaging (CT or MRI) within 8 weeks of each other and within 6 months of surgery. A scatter plot of the largest tumor diameters using US to CT/MRI and to pathological size were analyzed. A Bland-Altman plot of largest tumor diameters for all imaging modalities and a sensitivity plot for imaging modalities were created using the images closest to the date of the surgery. RESULTS: 1380 patients from the CKCis were identified. The mean age was 60.0 years (±12.1) and BMI was 29.4 (±6.04). The mean size of the masses as measured by pathology was 5.59 cm. Pearson correlation between CT/MRI or US to pathological size was 0.93 and 0.89 respectively (p<0.0001). Correlation between US to CT/MRI was 0.91 (p<0.0001). In 794 patients (57.5%) US and CT/MRI measurements were within 0.5 cm, with 113 (8.2%) revealing a discordance greater than 2cm (Table 1). Bland-Altman plots demonstrated a greater agreement for smaller renal masses (Figure 1). CONCLUSIONS: There is a very strong correlation between US and CT or MRI and pathological size when measuring small renal masses. This study provides support for utilization of US in active surveillance to help minimize the risk of ionizing radiation in patients.Source of Funding: None
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".