MP41-03 IS A RENAL TUMOUR BENIGN OR MALIGNANT? A PREDICTION TOOL INCLUDING PATIENTS MANAGED WITH SURGERY, ABLATION, OR SURVEILLANCE
Bibliographic record
Abstract
You have accessJournal of UrologyCME1 Apr 2023MP41-03 IS A RENAL TUMOUR BENIGN OR MALIGNANT? A PREDICTION TOOL INCLUDING PATIENTS MANAGED WITH SURGERY, ABLATION, OR SURVEILLANCE Ameeta Nayak, Luke Lavallee, Ranjeeta Mallick, Simon Tanguay, Frederic Pouliot, Antonio Finelli, Anil Kapoor, Ricardo Rendon, Alan So, Darrel Drachenberg, Bimal Bhindi, Jean-Baptiste Lattouf, Lucas Dean, Aly-Khan Lalani, Lori Wood, Daniel Heng, and Rodney Breau Ameeta NayakAmeeta Nayak More articles by this author , Luke LavalleeLuke Lavallee More articles by this author , Ranjeeta MallickRanjeeta Mallick More articles by this author , Simon TanguaySimon Tanguay More articles by this author , Frederic PouliotFrederic Pouliot More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Anil KapoorAnil Kapoor More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , Alan SoAlan So More articles by this author , Darrel DrachenbergDarrel Drachenberg More articles by this author , Bimal BhindiBimal Bhindi More articles by this author , Jean-Baptiste LattoufJean-Baptiste Lattouf More articles by this author , Lucas DeanLucas Dean More articles by this author , Aly-Khan LalaniAly-Khan Lalani More articles by this author , Lori WoodLori Wood More articles by this author , Daniel HengDaniel Heng More articles by this author , and Rodney BreauRodney Breau More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003279.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Many kidney tumors are benign and some malignant tumors behave in an indolent fashion. We sought to develop predictive models of kidney tumor malignancy and high-grade malignancy. METHODS: Patients diagnosed with solitary renal masses were identified from the Canadian Kidney Cancer information system (CKCis). Specifically, we identified patients with clinical stage T1 and T2 disease. Demographic, clinical, and imaging data were compared to the pathologic diagnosis from surgery or biopsy. Tumors were categorized as malignant or benign, and aggressive (high-grade malignant) or indolent (low-grade malignant and benign). Logistic regression models were constructed to identify predictors of each category. Nomograms were created using statistically significant risk factors and were internally validated using bootstrap methods. RESULTS: Of 5,517 CKCis patients with a solitary tumor between January 2011 and October 2022, 5,054 (92%) had malignant histology and 1,953 (40%) had high-grade disease. Factors associated with malignancy and high-grade malignancy were male sex (Odds Ratio [OR] 1.45; 95% confidence interval [CI] 1.19-1.77; OR 1.65, 95%CI 1.44-1.89, respectively) and tumor size (OR 1.27, 95%CI 1.20-1.33; OR 1.29, 95%CI 1.25-1.31, per increase in 1cm, respectively). An interaction between age and sex was identified for malignancy; odds of malignancy statistically decrease with older age in men and increase (though not significantly) with age in women (OR 0.89, 95%CI 0.84-0.95; OR 1.01, 95%CI 0.95-1.07, respectively). Older age was predictive of high-grade malignancy (OR 1.06, 95%CI 1.03-1.09, per increase in 5 years). The nomograms for malignant/benign tumors had moderate discrimination and excellent calibration (optimism corrected area under the curve [AUC]= 0.69, root mean square error [RSME]=0.02, calibration slope=0.99). The nomogram for aggressive/indolent tumors had good discrimination and excellent calibration (AUC=0.75, RSME=0.01, calibration slope=1.00). CONCLUSIONS: Patient and tumor characteristics are independently associated with cancer risk and high grade-cancer risk. The CKCis nomograms presented should be externally validated. These prediction tools can be used by physicians and patients with kidney tumors to help determine an optimal management plan. Source of Funding: No direct role or influence by sponsors. The Canadian Kidney Cancer information system (CKCis) is funded by the Kidney Cancer Research Network of Canada which receives funding support from industry sponsors. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e555 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ameeta Nayak More articles by this author Luke Lavallee More articles by this author Ranjeeta Mallick More articles by this author Simon Tanguay More articles by this author Frederic Pouliot More articles by this author Antonio Finelli More articles by this author Anil Kapoor More articles by this author Ricardo Rendon More articles by this author Alan So More articles by this author Darrel Drachenberg More articles by this author Bimal Bhindi More articles by this author Jean-Baptiste Lattouf More articles by this author Lucas Dean More articles by this author Aly-Khan Lalani More articles by this author Lori Wood More articles by this author Daniel Heng More articles by this author Rodney Breau More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.012 |
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".