OBJECTIVE: To evaluate whether assessing anatomical characteristics of renal masses increases predictive accuracy for tumor pathology of small renal masses (SRMs).
PATIENTS AND METHODS: We retrospectively reviewed 1,129 consecutive patients who underwent extirpative surgeries for a clinical T1 renal mass, for which the PADUA (preoperative aspects and dimensions used for an anatomical) classification system was available. Multivariate logistic regression analyses of demographic and anatomical characteristics were done. Nomograms to predict malignancy and high-grade pathology were constructed using a basic model (age, sex, and tumor size), and an extended model (anatomical characteristics incorporated into the basic model), and the area under the curve (AUC) between models was compared.
RESULTS: Age, sex and tumor size were significantly associated with malignancy and high-grade pathology in T1 and T1a category (except sex for high-grade pathology in T1a). Exophytic rate (T1 and T1a), and renal sinus or urinary collecting system involvement (only T1a) were also significant predictors for high-grade pathology. Nomograms using the extended model for malignancy showed an insignificant AUC increase compared with those using the basic model (T1, from 0.771 to 0.780, p=0.149, and T1a, from 0.803 to 0.819, p=0.055). For high-grade pathology, the extended model achieved a significant AUC increase (from 0.595 to 0.643, p=0.014) in T1a category, but the AUC for both T1 and T1a showed merely modest competence (0.654 and 0.643, respectively).
CONCLUSION: Age, sex, and tumor size are the primary predictors for tumor pathology of SRMs, and incorporating other anatomical characteristics has only a limited positive effect on the predictive accuracy of pathologic outcomes.
Written by:
Shin TY, Kim J, Koo KC, Lim SK, Kim DW, Kang MW, Rha KH, Choi YD, Ham WS. Are you the author?
Departments of Urology, Hallym University College of Medicine, Chuncheon, Korea.
Reference: BJU Int. 2013 Sep 5. Epub ahead of print.
doi: 10.1111/bju.12446
PubMed Abstract
PMID: 24053790
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