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Purpose: Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with prostate cancer. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous patient groups. Because there is more than one model available for prediction of most outcomes, model comparisons are necessary for selection of the best model.We describe the criteria based on which to judge predictive tools, describe the limitations of current predictive tools, and compare the different predictive methodologies that have been used in the prostate cancer literature. Experimental Design: Using MEDLINE, a literature search was done on prostate cancer decision aids from January 1966 to July 2007. Results: The decision aids consist of nomograms, risk groupings, artificial neural networks, probability tables, and classification and regression tree analyses. The following considerations need to be applied when the qualities of predictive models are assessed: predictive accuracy (internal or ideally external validation), calibration (i.e., performance according to risk level or in specific patient subgroups), generalizability (reproducibility and transportability), and level of complexity relative to established models, to assess whether the new model offers advantages relative to available alternatives. Studies comparing decision aids have shown that nomograms outperform the other methodologies. Conclusions: Nomograms provide superior individualized disease-related risk estimations that facilitate management-related decisions. Of currently available prediction tools, the nomograms have the highest accuracy and the best discriminating characteristics for predicting outcomes in prostate cancer patients.

HoLEP and TURP were equally effective for relieving obstruction and lower urinary tract symptoms. HoLEP was associated with shorter catheterization time and hospital stay. At 1 year of followup complications were similar in the 2 groups.

Purpose To assess the association of lymphovascular invasion (LVI) with cancer recurrence and survival in a large international series of patients treated with radical nephroureterectomy (RNU) for upper urinary tract urothelial carcinoma (UTUC). Patients and Methods Data were collected on 1,453 patients treated with RNU at 13 academic centers and combined into a relational database. Pathologic slides were rereviewed by genitourinary pathologists according to strict criteria. LVI was defined as presence of tumor cells within an endothelium-lined space. Results LVI was observed in 349 patients (24%). Proportion of LVI increased with advancing tumor stage, high tumor grade, presence of tumor necrosis, sessile tumor architecture, and presence of lymph node metastasis (all P < .001). LVI was an independent predictor of disease recurrence and survival (P < .001 for both). Addition of LVI to the base model (comprising pathologic stage, grade, and lymph node status) marginally improved its predictive accuracy for both disease recurrence and survival (1.1%, P = .03; and 1.7%, P < .001, respectively). In patients with negative lymph nodes and those in whom a lymphadenectomy was not performed (n = 1,313), addition of LVI to the base model improved the predictive accuracy of the base model for both disease recurrence and survival by 3% (P < .001 for both). In contrast, LVI was not associated with disease recurrence or survival in node-positive patients (n = 140). Conclusion LVI was an independent predictor of clinical outcomes in nonmetastatic patients who underwent RNU for UTUC. Assessment of LVI may help identify patients who could benefit from multimodal therapy after RNU. After confirmation, LVI should be included in staging of UTUC.

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The beneficial impact of aRT on survival in patients with pN1 prostate cancer is highly influenced by tumor characteristics. Men with low-volume nodal disease (≤ two PLNs) in the presence of intermediate- to high-grade, non-specimen-confined disease and those with intermediate-volume nodal disease (three to four PLNs) represent the ideal candidates for aRT after surgery.