Quantitative Nuclear Grade Research Articles

Prognosis for Survival of Young Women with Breast Cancer by Quantitative p53 Immunohistochemistry.

p53 protein detected immunohistochemically has not been accepted as a biomarker for breast cancer patients because of disparate reports of the relationship between the amount of p53 protein detected and patient survival. The purpose of this study was to determine experimental conditions and methods of data analysis for which p53 stain intensity could be prognostic for survival of young breast cancer patients. A tissue microarray of specimens from 93 patients was stained with anti-p53 antibody, and stain intensity measured with a computer-aided image analysis system. A cut-point at one standard deviation below the mean of the distribution of p53 stain intensity separated patients into two groups with significantly different survival. These results were confirmed by Quantitative Nuclear Grade determined by DNA-specific Feulgen staining. P53 provided information beyond ER and PR status. Therefore, under the conditions reported here, p53 protein can be an effective prognostic factor for young breast cancer patients.

Prediction of Prostate-Specific Antigen Recurrence in Men with Long-term Follow-up Postprostatectomy Using Quantitative Nuclear Morphometry

Nuclear morphometric signatures can be calculated using nuclear size, shape, DNA content, and chromatin texture descriptors [nuclear morphometric descriptor (NMD)]. We evaluated the use of a patient-specific quantitative nuclear grade (QNG) alone and in combination with routine pathologic features to predict biochemical [prostate-specific antigen (PSA)] recurrence-free survival in patients with prostate cancer. The National Cancer Institute Cooperative Prostate Cancer Tissue Resource (NCI-CPCTR) tissue microarray was prepared from radical prostatectomy cases treated in 1991 to 1992. We assessed 112 cases (72 nonrecurrences and 40 PSA recurrences) with long-term follow-up. Images of Feulgen DNA-stained nuclei were captured and the NMDs were calculated using the AutoCyte system. Multivariate logistic regression was used to calculate QNG and pathology-based solutions for prediction of PSA recurrence. Kaplan-Meier survival curves and predictive probability graphs were generated. A QNG signature using the variance of 14 NMDs yielded an area under the receiver operator characteristic curve (AUC-ROC) of 80% with a sensitivity, specificity, and accuracy of 75% at a predictive probability threshold of > or =0.39. A pathology model using the pathologic stage and Gleason score yielded an AUC-ROC of 67% with a sensitivity, specificity, and accuracy of 70%, 50%, and 57%, respectively, at a predictive probability threshold of > or =0.35. Combining QNG, pathologic stage, and Gleason score yielded a model with an AUC-ROC of 81% with a sensitivity, specificity, and accuracy of 75%, 78%, and 77%, respectively, at a predictive probability threshold of > or =0.34. PSA recurrence is more accurately predicted using the QNG signature compared with routine pathology information alone. Inclusion of a morphometry signature, routine pathology, and new biomarkers should improve the prognostic value of information collected at surgery.

Using nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intent

We assessed the use of quantitative clinical and pathologic information to predict which patients would eventually require treatment for prostate cancer (CaP) in an expectant management (EM) cohort. We identified 75 men having prostate cancer with favorable initial biopsy characteristics; 30 developed an unfavorable biopsy (Gleason grade >6, >2 cores with cancer, >50% of a core with cancer, or a palpable nodule) requiring treatment and 45 maintained favorable biopsies throughout a median follow-up of 2.7 years. Demographic, clinical data and quantitative tissue histomorphometry determined by digital image analysis were analyzed. Logistic regression (LR) modeling generated a quantitative nuclear grade (QNG) signature based on the enrollment biopsy for differentiation of Favorable and Unfavorable groups using a variable LR selection criteria of P(z)<0.05. The QNG signature utilized 12 nuclear morphometric descriptors (NMDs) and had an area under the receiver operator characteristic curve (ROC-AUC) of 87% with a sensitivity of 82%, specificity of 70% and accuracy of 75%. A multivariable LR model combining QNG signature with clinical and pathological variables yielded an AUC-ROC of 88% and a sensitivity of 81%, specificity of 78% and accuracy of 79%. A LR model using prostate volume, PSA density, and number of pre-diagnosis biopsies resulted in an AUC-ROC of 68% and a sensitivity of 85%, specificity of 37% and accuracy of 56%. QNG using EM prostate biopsies improves the predictive accuracy of LR models based on traditional clinicopathologic variables in determining which patients will ultimately develop an unfavorable biopsy. Our QNG-based model must be rigorously, prospectively validated prior to use in the clinical arena.

Significant variations in nuclear structure occur between and within Gleason grading patterns 3, 4, and 5 determined by digital image analysis

Alterations in nuclei structure and DNA content captured from Gleason grading patterns 3, 4 and 5 of radical prostatectomy (RP) cases were determined by a computer-assisted microscope. Quantitative Nuclear Morphometry (QNM) profiles were created to evaluate variability in nuclear structure within each of these grades. A tissue microarray (TMA) was constructed using RP cases and the prostate cancer (PCa) TMA cores prepared from 20 GG-3, 9 GG-4, 10 GG-5 patterns, and 20 benign cancer-adjacent cases from RP archival paraffin blocks. Feulgen-stained nuclei were captured from 0.6 mm spots using the AutoCyte system. Pools of 1100 nuclei captured from each test group were used to calculate Multivariate Logistic Regression (MLR) models that generated predictive indices and predictive probabilities (PP) to make comparisons between and within each set of pooled nuclei. A single QNM profiles yielded areas of receiver operator characteristic curves (ROC) that distinguished differences among benign cancer-adjacent nuclei and GG-3 (ROC-AUC = 0.78); GG-4 (ROC-AUC = 0.86) and GG-5 (ROC-AUC = 0.88) with accuracies of 73%, 78% and 80% respectively. Applying PP plots generated from MLR models of GG 3, 4, and 5 nuclei clearly demonstrated marked heterogeneity within each of these three GG patterns. QNM signatures illustrate alterations in nuclei structure, based upon nuclear morphometry within each of these three GG patterns, and might signify potential variations in PCa disease risk of progression outcomes. In the future a modified system of Gleason grading that considers not only glandular architecture but also quantitative nuclear grade may ensure accuracy in prognosis.

Alterations in nuclear structure and expression of proPSA predict differences between native Japanese and Japanese-American prostate cancer

Objectives To differentiate the benign and/or malignant epithelial cells in prostate cancer (PCa) glands of native Japanese (NJ) and Japanese-American (JA) men using biomarkers. Methods Tissue microarrays from radical prostatectomy specimens of cancerous and adjacent benign areas from 25 NJ and 25 JA prostate glands were studied. Image analysis was used to quantify total prostate-specific antigen (PSA) and proPSA immunohistochemical staining, as well as the variance of several morphometric features from Feulgen-stained epithelial cell nuclei. Logistic regression analysis was applied to determine whether quantitative nuclear grade (QNG) calculations and PSA immunohistochemical staining could differentiate the two test groups. Results The QNG model differentiated changes in the benign epithelium of the two Japanese groups with an area under the receiver operating characteristic curve of 84% and accuracy of 82% ( P = 0.0001). A second QNG model differentiated changes in the malignant epithelium of the two groups with an area under the receiver operating characteristic curve of 84% and accuracy of 76% ( P = 0.0023). Logistic regression models combining proPSA immunohistochemical data and QNG from either benign or malignant tissue components yielded areas under the receiver operating characteristic curve of 96% and 91% ( P <0.0001) for differentiation of the JA and NJ groups, respectively. Conclusions Unique nuclear morphometric alterations demonstrated by QNG combined with proPSA immunohistologic localization independently predicted for significant differences between NJ and JA men with PCa. These preliminary observations indicate a basis for biologic and molecular alterations in the benign adjacent and malignant epithelium between these two groups.

Ability to predict metastasis based on pathology findings and alterations in nuclear structure of normal-appearing and cancer peripheral zone epithelium in the prostate.

Malignant transformation in the prostate produces significant alterations in glandular architecture (Gleason grade) and nuclear structure that provide valuable prognostic information. Normal-appearing nuclei (NN) adjacent to cancer may also have altered functions in response to malignancy. We studied NN adjacent to peripheral zone (PZ) prostate cancer (PCa), as well as the PZ cancer nuclei (CaN) using quantitative image cytometry. The nuclear structure information was combined with routine pathological findings to predict metastatic PCa progression and/or death. Tissue microarrays of normal-appearing and cancer areas were prepared from 182 pathologist-selected paraffin blocks. Feulgen-stained CaN and NN were captured from the tissue microarrays using the AutoCyte Pathology Workstation. Multivariate logistic regression was used to calculate quantitative nuclear grade (QNG) solutions based on nuclear morphometric descriptors determined from NN and CaN. Multivariate logistic regression and Kaplan-Meier plots were also used to predict risk for distant metastasis and/or PCa-specific death using QNG solutions and routine pathology. The pathology model yielded an area under the receiver operator characteristic curve of 72.5%. The QNG-NN and QNG-CaN solutions yielded an area under the receiver operator characteristic curve of 81.6 and 79.9%, respectively, but used different sets of nuclear morphometric descriptors. Kaplan-Meier plots for the pathology variables, the QNG-NN and QNG-CaN solutions, were combined with pathology to defined three statistically significantly distinct risk groups for distant metastasis and/or death (P < 0.0001). Alterations in cancer or normal-appearing nuclei adjacent to peripheral zone cancer areas can predict PCa progression and/or death. The QNG-NN and QNG-CA solutions could be combined with pathology variables to improve the prediction of distant metastasis.

288: Quantitative Nuclear Grade (QNG) Assessment of Non-Invasive Papillary Urothelial Neoplasms

You have accessJournal of UrologyDiscussed Poster, Sunday, May 9, 2004, 8:00 am - 12:00 pm1 Apr 2004288: Quantitative Nuclear Grade (QNG) Assessment of Non-Invasive Papillary Urothelial Neoplasms Theresa Y. Chan, Masood Khan, M. Craig Miller, Alan W. Partin, Jonathan I. Epstein, and Robert W. Veltri Theresa Y. ChanTheresa Y. Chan More articles by this author , Masood KhanMasood Khan More articles by this author , M. Craig MillerM. Craig Miller More articles by this author , Alan W. PartinAlan W. Partin More articles by this author , Jonathan I. EpsteinJonathan I. Epstein More articles by this author , and Robert W. VeltriRobert W. Veltri More articles by this author View All Author Informationhttps://doi.org/10.1016/S0022-5347(18)37550-5AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "288: Quantitative Nuclear Grade (QNG) Assessment of Non-Invasive Papillary Urothelial Neoplasms." The Journal of Urology, 171(4S), pp. 75–76 © 2016 by American Urological AssociationFiguresReferencesRelatedDetails Volume 171Issue 4SApril 2004Page: 75-76 Advertisement Copyright & Permissions© 2016 by American Urological AssociationMetricsAuthor Information Theresa Y. Chan More articles by this author Masood Khan More articles by this author M. Craig Miller More articles by this author Alan W. Partin More articles by this author Jonathan I. Epstein More articles by this author Robert W. Veltri More articles by this author Expand All Advertisement Loading ...

Quantitative alterations in nuclear structure predict prostate carcinoma distant metastasis and death in men with biochemical recurrence after radical prostatectomy.

Microscopic histologic grade has been the best predictor of prostate carcinoma (PCa) progression in men after surgical therapy. The ability to predict accurately, at the time of surgery, which patients are likely to develop metastatic PCa would enable optimization of disease management with adjuvant therapy. The authors assessed the ability of pathologic, nuclear morphometric, and chromatin parameters to predict metastatic PCa progression and/or death in 227 men with biochemical recurrence and long-term follow-up after undergoing radical prostatectomy. Multivariate logistic regression (LR) was used to calculate quantitative nuclear grade (QNG) solutions using the variances of 60 nuclear morphometric descriptors (NMDs) of nuclear size, shape, DNA content, and chromatin organization that predicted distant metastasis and/or PCa-specific death. An LR model also was generated to predict this outcome using a combination of pathologic variables and the best QNG solution. Cox proportional hazards models were generated, and Kaplan-Meier plots were used to display three risk groups based on pathology, QNG, and a combination of these variables. A multivariate LR model using pathology retained lymph node (LN) status, seminal vesicle status, and prostatectomy Gleason score, yielding an area under the curve-receiver operator characteristic (AUC-ROC) of 75% with an accuracy of 59% at 90% sensitivity. The best QNG solution used the variance of 25 NMDs, yielding an AUC-ROC of 84% and an accuracy of 70% at 90% sensitivity. The combined pathology-QNG model retained LN status, prostatectomy Gleason score, and QNG, yielding an AUC-ROC of 86% with an accuracy of 76% at 90% sensitivity. The Cox proportional hazards models produced the following significant univariate and multivariate hazard ratios: QNG, 3.5 and 2.9, respectively; LN, 2.7 and 1.8, respectively; and prostatectomy Gleason score, 2.8 and 2.1, respectively. Alterations in the structure of tumor nuclei measured by computer-assisted image analysis were strong predictors of PCa progression and death in men with long-term follow-up who had biochemical recurrence after undergoing radical prostatectomy. QNG solutions can serve as a new supplemental biomarker for accurate prediction of PCa progression at the time of surgery.

Risk biomarker assessment for breast cancer progression: replication precision of nuclear morphometry.

Nuclear morphometry is a method for quantitative measurement of histopathologic changes in the appearance of stained cell nuclei. Numerous studies have indicated that these assessments may provide clinically relevant information related to the degree of progression and malignant potential of breast neoplasia. Nuclear features are derived from computerized analysis of digitized microscope images, and a quantitative Feulgen stain for DNA was used. Features analyzed included: (1) DNA content; (2) nuclear size and shape; and (3) texture features, describing spatial features of chromatin distribution. In this study replicated measurements are described on a series of 54 breast carcinoma specimens of differing pathologic grades. Duplicate measurements were performed using two serial sections, which were processed and analyzed separately. The value of a single feature measurement, the nuclear area profile, was shown to be the strongest indicator of progression. A quantitative nuclear grade was derived and shown to be strongly correlated with not only the pathologic nuclear grade, but also with tubule formation, mitotic grade, and with the overall histopathologic grade. Analysis of replication precision showed that the standard methods of the histopathology laboratory, if practiced in a uniform manner, are sufficient to ensure reproducibility of these assessments. We argue that nuclear morphometry provides a standardized and reproducible framework for quantitative pathologic assessments.

Prediction of Pathological Stage in Patients with Clinical Stage T1c Prostate Cancer: The New Challenge

We developed an algorithm for predicting the likelihood of organ confined disease in patients with clinical stage T1c prostate cancer using biopsy pathology, computer assisted image analysis and serum prostate specific antigen (PSA). Of the 557 consecutive men enrolled in this study between October 1998 and January 2000 scheduled for radical prostatectomy at a single institution 386 (69%) presented with clinical stage T1c disease. Study exclusion criteria included neoadjuvant hormonal treatment with luteinizing hormone-releasing hormone, antiandrogen or 5alpha-reductase inhibitors. Preoperative serum, biopsy histology slides, clinical demographic information, prostatectomy pathology and prostate weight data were obtained. Biomarkers assessed included total PSA, complexed PSA, free PSA, the free-to-total PSA ratio, quantitative nuclear grade determined by image analysis, complexed PSA density, and biopsy Gleason grade and score. To determine patient specific quantitative nuclear grade values, images from approximately 125 cancer nuclei were captured per patient from the area of the biopsy section with the highest Gleason score. The variance in 60 nuclear size, shape and chromatin texture descriptors was calculated for each gallery of nuclei. Logistic regression was done to determine the most accurate combination of variables for predicting organ confined prostate cancer. Complete results and data were available on 255 of the 386 men (66%) with an average age plus or minus standard deviation of 58.8 +/- 6 years who had stage T1c disease, including 49 (19%) with pathologically nonorgan confined disease. Logistic regression analysis revealed that quantitative nuclear grade, biopsy Gleason score, total PSA, the calculated free-to-total PSA ratio, complexed PSA and complexed PSA density were univariately significant for predicting organ confined disease (p <0.05). On backward stepwise logistic regression only quantitative nuclear grade, complexed PSA density and Gleason score remained in a model yielding an area under the receiver operating characteristics curve of 82.4%. The quantitative nuclear grade biomarker was the strongest independent predictor of pathological stage in men with clinical stage T1c prostate cancer when combined with biopsy Gleason score and complexed PSA density data.

Standardization, analytical validation, and quality control of intermediate endpoint biomarkers

Standardized processes should be used in the identification and development of intermediate endpoint biomarkers (IEB) for the prediction of patient-specific disease outcomes. Using our own experiences, we outline some of our standardized processes. Using computer-assisted image analysis, we developed a new biomarker of genetic instability, termed quantitative nuclear grade (QNG). The QNG biomarker is derived using nuclear images analyzed from the tumor areas of Feulgen-stained 5-μm biopsy or radical prostatectomy tissue sections. From the variances of 41 to 60 different nuclear size, shape, and chromatin organization features, a QNG solution is computed using either logistic regression or artificial neural networks. QNG can then be used as an input for models that solve for a patient-specific probability to accurately predict disease outcomes. Preoperatively, QNG predicted both the pathologic stage and progression of prostate cancer using biopsies ( P <0.0001). Postoperatively, QNG proved extremely valuable in the prediction of biochemical progression using radical prostatectomy specimens with more than 10 years of follow-up ( P <0.0001). We also demonstrate the identification of novel, differentially expressed, prostate cancer genes using RNA fingerprinting methods and the clinical utility of testing for these genes in both blood and tissue samples. Also illustrated is the improvement of serum biomarker performance by combining molecular forms of PSA with new biomarkers. In conclusion, the development of new IEBs requires planning based upon an understanding of the molecular pathogenesis of disease. IEB selection and clinical evaluation should employ standardized methods of testing and validation, followed by publication. QNG is 1 example of a new, highly predictive, IEB for prostate cancer that has been developed using these processes.

Quantitative nuclear grade (QNG): a new image analysis-based biomarker of clinically relevant nuclear structure alterations.

This review addresses the potential clinical value of using quantitative nuclear morphometry information derived from computer-assisted image analysis for cancer detection and predicting outcomes such as tumor stage, recurrence, and progression. Today's imaging technology uses sophisticated hardware platforms coupled with powerful and user-friendly software packages that are commercially available as complete image analysis systems. There are many different mathematically derived nuclear morphometric descriptors (NMD's) (i.e. texture features) that can be calculated by these image analysis systems, but for the most part, these NMD's quantify nuclear size, shape, DNA content (ploidy), and chromatin organization (i.e. texture, both Markovian and non-Markovian) parameters. We have utilized commercially available image analysis systems and the NMD's calculated by these systems to create a mathematical solution, termed quantitative nuclear grade (QNG), for making clinical, diagnostic, and prognostic outcome predictions in both prostate and bladder cancer. A separate computational model is calculated for each outcome of interest using well-characterized and robust training, testing, and validation patient sample sets that adequately represent the selected population and clinical dilemma. A specific QNG solution may be calculated either by non-parametric statistical methods or non-linear mathematics employed by artificial neural networks (ANNs). The QNG solution, a measure of genomic instability, provides a unique independent variable to be used alone or to be included in an algorithm to assess a specific clinical outcome. This approach of customization of the nuclear morphometric descriptor (NMD) information through the calculation of a QNG solution mathematically adjusts for redundancy of features and reduces the complexity of the inputs used to create decision support tools for patient disease management. J. Cell. Biochem. Suppl. 35:151-157, 2000.

Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy

Objectives. To use pathologic, morphometric, DNA ploidy, and clinical data to develop and test a genetically engineered neural network (GENN) for the prediction of biochemical (prostate-specific antigen [PSA]) progression after radical prostatectomy in a select group of men with clinically localized prostate cancer. Methods. Two hundred fourteen men who underwent anatomic radical retropubic prostatectomy for clinically localized prostate cancer were selected on the basis of adequate follow-up, pathologic criteria indicating an intermediate risk of progression, and availability of archival tissue. The median age was 58.9 years (range 40 to 87). Men with Gleason score 5 to 7 and clinical Stage T1b-T2c tumors were included. Follow-up was a median of 9.5 years. Three GENNs were developed using pathologic findings (Gleason score, extraprostatic extension, surgical margin status), age, quantitative nuclear grade (QNG), and DNA ploidy. These networks were developed using three randomly selected training (n = 136) and testing (n = 35) sets. Different variable subsets were compared for the ability to maximize prediction of progression. Both standard logistic regression and Cox regression analyses were used concurrently to calculate progression risk. Results. Biochemical (PSA) progression occurred in 84 men (40%), with a median time to progression of 48 months (range 1 to 168). GENN models were trained using inputs consisting of (a) pathologic features and patient age; (b) QNG and DNA ploidy; and (c) all variables combined. These GENN models achieved an average accuracy of 74.4%, 63.1%, and 73.5%, respectively, for the prediction of progression in the training sets. In the testing sets, the three GENN models had an accuracy of 74.3%, 80.0%, and 78.1%, respectively. Conclusions. The GENN models developed show promise in predicting progression in select groups of men after radical prostatectomy. Neural networks using QNG and DNA ploidy as input variables performed as well as networks using Gleason score and staging information. All GENN models were superior to logistic regression modeling and to Cox regression analysis in prediction of PSA progression. The development of models using improved input variables and imaging systems in larger, well-characterized patient groups with long-term follow-up is ongoing.

Ability to predict biochemical progression using gleason score and a computer-generated quantitative nuclear grade derived from cancer cell nuclei

To determine the ability to predict prostate cancer progression using shape, size, and chromatin texture nuclear grading features preselected by logistic regression analyses based on expert-selected prostate cancer cell nuclei captured using a computer-assisted image analysis system. One hundred fifteen patients with clinically localized prostate cancer were identified at the Johns Hopkins medical institutions. The mean follow-up period was 10.4 +/- 1.7 years in 70 patients without disease progression, whereas the mean time to progression for the entire group was 3.8 +/- 2.5 years. Using 5-microns Feulgen-stained tissue sections, approximately 150 cancer cell nuclei were selected and captured for each case using a CAS-200 Image Analysis System. Thirty-eight different nuclear morphometric descriptors (NMDs) were calculated for each cell nucleus. The variance of the NMDs for each tumor was examined by univariate and multivariate logistic regression analyses and by Cox survival analyses to assess their ability to predict prostate cancer progression. Postoperative Gleason scoring was significantly correlated with disease progression (P < 0.00001; sensitivity, 73%; specificity, 84%; receiver operating characteristic curve area under the curve (ROC-AUC), 83%). Using backward stepwise logistic regression at a stringency of P < 0.05, the variances of 11 of the NMDs were found to be multivariately significant for progression prediction (P < 0.00001; sensitivity, 78%; specificity, 83%; ROC-AUC, 86%). A single value, termed the quantitative nuclear grade (QNG), was created from the variances of these, 11 multivariately significant NMDs using the logistic regression function. The QNG and the postoperative Gleason score were combined to create a model for the prediction of progression having a sensitivity of 89%, specificity of 84%, and ROC-AUC of 92%. These two parameters (QNG and Gleason score) clearly separated the patient sample into three statistically distinct risk groups and predicted the time to progression on the basis of Kaplan-Meier survival probability analysis. The QNG, combined with the postoperative Gleason score, may assist in the more accurate stratification of patients undergoing radical prostatectomy into low-, moderate-, and high-risk groups for cancer recurrence and may permit the early initiation of adjuvant therapy.

An Algorithm for Predicting Nonorgan Confined Prostate Cancer Using the Results Obtained from Sextant Core Biopsies with Prostate Specific Antigen Level

Purpose We determined the enhanced ability to predict nonorgan confined prostate cancer using several histopathological and quantitative nuclear imaging parameters combined with serum prostate specific antigen (PSA). Materials and Methods Several independent pathological and quantitative image analysis variables obtained from sextant biopsy specimens, as well as preoperative PSA were used. The study population included 210 patients with pathologically staged disease (192 with PSA). All variables were examined by univariate and multivariate logistic regression analyses to assess ability to predict disease organ confinement status. Results Univariate logistic regression analysis demonstrated that, in decreasing order, quantitative nuclear grade, preoperative PSA, total percent tumor involvement, number of positive sextant cores, preoperative Gleason score and involvement of more than 5 percent of a base and/or apex biopsy were significant (p less than or equal 0.006) for prediction of disease organ confinement status. Backward stepwise logistic regression was applied to these univariately significant variables, including deoxyribonucleic acid ploidy, to calculate a multivariate model for prediction of disease organ confinement status. This algorithm had a sensitivity of 85.7 percent, specificity 71.3 percent, positive predictive value 72.9 percent, negative predictive value 84.7 percent and area under the receiver operating characteristic curve 85.9 percent. Conclusions Information from pathological study of sextant prostate biopsies, preoperative PSA blood test and a new image analysis variable termed quantitative nuclear grade can be combined to create a multivariate algorithm that can predict more accurately nonorgan confined prostate cancer compared to previously reported methods.