Postoperative systems models more accurately predict risk of significant disease progression than standard risk groups and a 10‐year postoperative nomogram: potential impact on the receipt of adjuvant therapy after surgery

• Published in: BJU international Vol. 109; no. 1; pp. 40 - 45
• Main Authors: Donovan, Michael J., Khan, Faisal M., Powell, Douglas, Bayer‐Zubek, Valentina, Cordon‐Cardo, Carlos, Costa, Jose, Eastham, James, Scardino, Peter
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01-01-2012; Wiley-Blackwell; Wiley Subscription Services, Inc
• Subjects: Biological and medical sciences; Combined Modality Therapy; Disease Progression; Disease-Free Survival; Gynecology. Andrology. Obstetrics; Humans; Incidence; Male; Male genital diseases; Medical sciences; Neoplasm Recurrence, Local - epidemiology; Neoplasm Recurrence, Local - prevention & control; Neoplasm Staging; Nephrology. Urinary tract diseases; nomogram; Nomograms; Patients; Postoperative Period; Predictive Value of Tests; Prognosis; prostate cancer; Prostatectomy; Prostatic Neoplasms - pathology; Prostatic Neoplasms - therapy; Risk assessment; Risk Assessment - methods; Risk Factors; Survival Rate - trends; systems‐based models; Tumors; Tumors of the urinary system; United States - epidemiology; Urinary tract. Prostate gland; United States; Postoperative; Nephrology; Urinary system disease; Prostate disease; Disease; Nomogram; Evolutivity; Adjuvant treatment; Standard; Malignant tumor; Standards; Urology; systems-based models; Surgery; Nomograms; Risk factor; Models; Predictive factor; Male genital diseases; Prostate cancer; Cancer
• ISSN: 1464-4096; 1464-410X
• DOI: 10.1111/j.1464-410X.2011.10398.x

Study Type – Prognostic (individual cohort)
Level of Evidence 2b
What's known on the subject? and What does the study add?
Physicians are becoming increasingly aware of the need to better understand adverse pathology (e.g. margin positive or extracapsular extension) post‐surgery. Systems models provide the necessary risk discriminatory tools to guide patient monitoring and decision‐making.
OBJECTIVE
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To compare the performance of a systems‐based risk assessment tool with standard defined risk groups and the 10‐year postoperative nomogram for predicting disease progression, including biochemical relapse and clinical (systemic) failure.
PATIENTS AND METHODS
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Clinical variables, biometric profiles and outcome results from a training cohort comprising 373 patients in a published postoperative systems‐based prognostic model were obtained.
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Patients were stratified according to D'Amico standard risk groups, Kattan 10‐year postoperative nomogram and prognostic scores from the postoperative tissue model.
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The association of pathological variables and calculated risk groups with biochemical recurrence and clinical (systemic) failure was assessed using the concordance index (C‐index) and hazard ratio (HR).
RESULTS
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Systems‐based post‐prostatectomy models to predict significant disease progression (post‐treatment clinical failure) were more accurate than the D'Amico defined risk groups and the Kattan 10‐year postoperative nomogram (systems model: C‐index, 0.84; HR, 17.46; P < 0.001 vs D'Amico: C‐index, 0.73; HR, 11; P= 0.001; 10‐year nomogram: C‐index, 0.79; HR, 5.06; P < 0.001).
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The systems models were also more accurate than standard risk groups for predicting prostate‐specific antigen recurrence (systems model: C‐index, 0.76; HR, 8.94; P < 0.001 vs D'Amico C‐ index, 0.70; HR, 4.67; P < 0.001) and showed incremental improvement over the 10‐year postoperative nomogram (C‐index, 0.75; HR, 5.83; P < 0.001).
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The postoperative tissue model provided additional risk discrimination over surgical margin status and extracapsular extension for predicting disease outcome, and was most significant for the clinical (systemic) failure endpoint (surgical margin: C‐index, 0.58; HR, 1.57; P= 0.2; extracapsular extension: C‐index, 0.62; HR, 2.06; P= 0.04).
CONCLUSIONS
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Risk assessment models that incorporate characteristics from the patient's own tumour specimen are more accurate than clinical‐only nomograms for predicting significant disease outcome.
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Systems‐based tools should provide useful information concerning the appropriate receipt of adjuvant therapy in the post‐surgical setting.