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PubMed2006

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models.

BACKGROUND: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. RESULTS: In this paper, we further refine and extend the hidden semi-Markov model (HSMM) initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. CONCLUSIONS: We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable to current similarity search methods.

Algorithms↗
PubMed2005

A scoring algorithm to predict survival for patients with metastatic clear cell renal cell carcinoma: a stratification tool for prospective clinical trials.

PURPOSE: We developed a clinically useful scoring algorithm to predict cancer specific survival for patients with clear cell metastatic renal cell carcinoma (RCC). MATERIALS AND METHODS: We studied 727 patients treated with radical nephrectomy for clear cell RCC from 1970 to 2000 who had distant metastases at nephrectomy (285) or in whom metastases subsequently developed (442). A scoring algorithm to predict cancer specific survival was developed using the regression coefficients from a Cox proportional hazards model. RESULTS: There were 606 deaths from clear cell RCC at a median of 1.0 years (range 0 to 14) following metastatic RCC. Constitutional symptoms at nephrectomy (+2), metastases to the bone (+2) or liver (+4), metastases in multiple simultaneous sites (+2), metastases at nephrectomy (+1) or within 2 years of nephrectomy (+3), complete resection of all metastatic sites (-5), tumor thrombus level I to IV (+3), and the primary pathological features of nuclear grade 4 (+3) and histological tumor necrosis (+2) were significantly associated with death from RCC. All patients started with a score of 0 and points were added or subtracted as indicated in parentheses. Cancer specific survival rates at 1 year were 85.1%, 72.1%, 58.8%, 39.0%, and 25.1%, respectively, for patients with scores of -5 to -1, scores of 0 to 2, scores of 3 to 6, scores of 7 or 8, and scores of 9 or more. CONCLUSIONS: This scoring algorithm can be used to predict cancer specific survival for patients with metastatic clear cell RCC.

Adenocarcinoma, Clear Cell↗
PubMed2004

Bayesian approach to discovering pathogenic SNPs in conserved protein domains.

The success rate of association studies can be improved by selecting better genetic markers for genotyping or by providing better leads for identifying pathogenic single nucleotide polymorphisms (SNPs) in the regions of linkage disequilibrium with positive disease associations. We have developed a novel algorithm to predict pathogenic single amino acid changes, either nonsynonymous SNPs (nsSNPs) or missense mutations, in conserved protein domains. Using a Bayesian framework, we found that the probability of a microbial missense mutation causing a significant change in phenotype depended on how much difference it made in several phylogenetic, biochemical, and structural features related to the single amino acid substitution. We tested our model on pathogenic allelic variants (missense mutations or nsSNPs) included in OMIM, and on the other nsSNPs in the same genes (from dbSNP) as the nonpathogenic variants. As a result, our model predicted pathogenic variants with a 10% false-positive rate. The high specificity of our prediction algorithm should make it valuable in genetic association studies aimed at identifying pathogenic SNPs.

Algorithms↗
PubMed2006

T-cell immunity to the folate receptor alpha is prevalent in women with breast or ovarian cancer.

PURPOSE: Studies have demonstrated that the generation of immunity to tumor antigens is associated with improved prognosis for many cancers. A candidate antigen is the folate receptor alpha (FRalpha), which is overexpressed in breast and ovarian cancers. Our goal in this study was to attain a better understanding of the extent of endogenous FRalpha immunity. METHODS: Using a CD4+ T cell epitope prediction algorithm, we predicted promiscuous epitopes of FRalpha, and tested for immunity in 30 breast (n = 17) or ovarian (n = 13) cancer patients and 18 healthy donors using enzyme-linked immunospot analysis. RESULTS: Fourteen peptides were predicted, seven each from the carboxy- and amino-terminus halves of the protein. More than 70% of patients demonstrated immunity to at least one FRalpha peptide. Patients responded to an average of 3 +/- 0.5 peptides, whereas healthy donors responded to 1 +/- 0.4 peptides (P = .004). Five peptides were recognized by more than 25% of patients. Responses to three peptides were higher (P < .05) in patients than in healthy donors, suggesting augmented immunity. Compared with healthy individuals, patients developed higher immunity to the amino-terminus half of the receptor (P = .03). There was no difference between each group in the responses to nonspecific (P = .2) and viral stimuli (P = .5). Lastly, patients demonstrated elevated levels of FRalpha antibodies consistent with a coordinated immune response. CONCLUSION: These findings demonstrate that the FRalpha is a target of the immune system in breast and ovarian cancer patients. Understanding which antigens are targeted by the immune system may be important for prognosis or immune-based therapies.

Adult↗
PubMed2002

Probing multiple effects on 15N, 13C alpha, 13C beta, and 13C' chemical shifts in peptides using density functional theory.

We have used density functional calculations on model peptides to study conformational effects on (15)N, (13)C alpha, (13)C beta, and (13)C' chemical shifts, associated with hydrogen bonding, backbone conformation, and side-chain orientation. The results show a significant dependence on the backbone torsion angles of the nearest three residues. Contributions to (15)N chemical shifts from hydrogen bonding (up to 8 ppm), backbone conformation (up to 13 ppm), side-chain orientation and neighborhood residue effects (up to 22 ppm) are significant, and a unified theory will be required to account for their behavior in proteins. In contrast to this, the dependence on sequence and hydrogen bonding is much less for (13)C alpha and (13)C beta chemical shifts (<0.5 ppm), and moderate for carbonyl carbon shifts (<2 ppm). The effects of side-chain orientation are mainly limited to the residue itself for both nitrogen and carbon, but the chi(1) effect is also significant for the nitrogen shift of the following residue and for the (13)C' shift of the preceding residue. The calculated results are used, in conjunction with an additive model of chemical shift contributions, to create an algorithm for prediction of (15)N and (13)C shifts in proteins from their structure; this includes a model to extrapolate results to regions of torsion angle space that have not been explicitly studied by density functional theory (DFT) calculations. Crystal structures of 20 proteins with measured shifts have been used to test the prediction scheme. Root mean square deviations between calculated and experimental shifts 2.71, 1.22, 1.31, and 1.28 ppm for N, C alpha, C beta, and C', respectively. This prediction algorithm should be helpful in NMR assignment, crystal and solution structure comparison, and structure refinement.

Carbon Isotopes↗
PubMed1996

Predictions of secondary structure using statistical analyses of electronic and vibrational circular dichroism and Fourier transform infrared spectra of proteins in H2O.

Vibrational circular dichroism (VCD) and Fourier transform IR (FTIR) methods for prediction of protein secondary structure are systematically compared using selective regression analysis. VCD and FTIR spectra over the amide I and II bands of 23 proteins dissolved in H2O were analyzed using the principal component method of factor analysis (PC/FA) and regression fits to fractional components (FC) of secondary structure. Predictive capability was determined by computing structures for proteins sequentially left out of the regression. All possible combinations of PC/FA spectral parameters (coefficients) were used to form a full set of restricted multiple regressions (RMR) of PC/FA coefficients with FC values, both independently for each spectral data set as well as for the VCD and FTIR sets grouped together and with similarly obtained electronic CD (ECD) data. The distribution of predictive error for a set of the best RMR relationships that use a given number of spectral coefficients was used to select the optimal prediction algorithm. Minimum predictive error resulted for a small subset (three to six) of spectral coefficients, which is consistent with our earlier findings using VCD measured for proteins in 2H2O and ECD data. Subtracting the average absorption spectrum from all the training set FTIR spectra before analysis yields more variance in the FTIR band shape and improves the predictive ability of the best PC/FA RMR to near that for the VCD. Both methods (FTIR and VCD) using data for proteins in H2O are somewhat better predictors than amide I' (in 2H2O) VCD alone and, for helix, worse than ECD alone. Combining FTIR and VCD data did not dramatically change the prediction results. Predictions are improved by combining both with ECD data, indicating that the improvement is due to using their very different structural sensitivities. The coupled H2O-based spectral analyses and the mixed amide I' + II VCD plus ECD analysis are comparable for the helix and sheet components, indicating that partial deuteration is not a major source of prediction error.

Circular Dichroism↗
PubMed2001

Estimating antemortem cognitive status of deceased subjects in a longitudinal study of dementia.

There was a five-year delay between the two waves of the Canadian Study of Health and Aging during which 2,982 participants died. Their cognitive status before death should be taken into account in estimating the incidence of dementia in the cohort. Information concerning antemortem cognitive status was available from death certificates and from an interview with a close relative of the decedent at the CSHA-2 follow-up. The interview included a direct question on whether the person had been diagnosed with dementia and questions covering cognitive signs and symptoms from which we formed an algorithm to predict probability of dementia. These sources of information were validated using a small sample of study participants who died within five months of undergoing the CSHA clinical examination. Sensitivity of the death certificate and the question regarding diagnosis of dementia was low (33% and 44%), although their specificity was very high. Accordingly, we combined these with the predictive algorithm to form an overall estimate of the probability of antemortem dementia. This raised the sensitivity to 82% (specificity 93%).

Aged↗
PubMed1995

A biologically-based algorithm for predicting human tissue: blood partition coefficients of organic chemicals.

A biologically-based algorithm for predicting the tissue: blood partition coefficients (PCs) of organic chemicals has been developed. The approach consisted of (i) describing tissues and blood in terms of their neutral lipid, phospholipid, and water contents, (ii) obtaining data on the solubility of chemicals in n-octanol and water, and (iii) calculating the tissue: blood PCs by assuming that the solubility of a chemical in n-octanol corresponds to its solubility in neutral lipids, the solubility in water corresponds to the solubility in tissue/blood water fraction, and the solubility in phospholipids is a function of solubility in water and n-octanol. The adequacy of this approach was verified by comparing the predicted values with previously published experimental data on human tissue (liver, lung, muscle, kidney, brain, adipose tissue): blood PCs for 23 organic chemicals. In the case of liver, lung, and muscle, the predicted PC values were in close agreement with the higher-end of the range of experimental PC values found in the literature. The predicted brain: and kidney: blood PCs were greater than the experimental PCs in most cases by approximately a factor of two. Whereas the adipose tissue: blood PCs of relatively less hydrophilic chemicals were adequately predicted, the predicted PCs for relatively more hydrophilic chemicals were much greater than the experimentally-determined values. There was a good agreement between the predicted and experimentally-determined blood solubility of the 23 chemicals chosen for this study, indicating that the over-estimation of tissue:blood PCs by the present method is not due to under-estimation of blood solubility of chemicals.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed2003

Comparative evaluation of three computerized algorithms for prediction of antiretroviral susceptibility from HIV type 1 genotype.

OBJECTIVES: To compare three methods for using HIV-1 genotype to predict antiretroviral drug susceptibility. METHODS: We applied three genotypic interpretation algorithms to 478 reverse transcriptase (RT) and 410 protease sequences for which phenotypic data were available. Sequences were obtained from clinical practice and from published sequences in the Stanford HIV-1 RT and Protease Sequence Database. The genotypic interpretation algorithms included: Stanford HIVdb program (HIVdb), the Visible Genetics/Bayer Diagnostics Guidelines 6.0 (VGI) and a genotypic interpretation program (AntiRetroScan, ARS) developed at the University of Siena, Italy. Genotypic interpretations were normalized to a three-level output: susceptible, intermediate and resistant. Discordances were defined as differences between genotype and phenotype for the same virus isolate. Discordances for which an isolate was considered susceptible by one test but resistant by another test were considered major discordances. RESULTS: The frequency of major discordances between genotype and phenotype was 10.6, 13.7 and 15.7% for ARS, VGI and HIVdb, respectively (P < 0.0001 for ARS versus HIVdb and for ARS versus VGI; P = 0.002 for VGI versus HIVdb). The correlation between genotype and phenotype was highest for non-nucleoside RT inhibitors and lowest for nucleoside RT inhibitors. Half of the major discordances involved stavudine, didanosine and zalcitabine. The concordance among the three genotypic algorithms was high, with weighted Kappa values ranging between 0.76 and 0.84 for the pairwise comparisons between each of the algorithms. CONCLUSIONS: Genotype interpretation algorithms correctly predict phenotype in 85-90% of cases, but the rate of concordance is not uniformly distributed among different drugs. These data provide insight into the potential additional benefit derived from phenotyping.

Algorithms↗
PubMed1988

The antigenic index: a novel algorithm for predicting antigenic determinants.

In this paper, we introduce a computer algorithm which can be used to predict the topological features of a protein directly from its primary amino acid sequence. The computer program generates values for surface accessibility parameters and combines these values with those obtained for regional backbone flexibility and predicted secondary structure. The output of this algorithm, the antigenic index, is used to create a linear surface contour profile of the protein. Because most, if not all, antigenic sites are located within surface exposed regions of a protein, the program offers a reliable means of predicting potential antigenic determinants. We have tested the ability of this program to generate accurate surface contour profiles and predict antigenic sites from the linear amino acid sequences of well-characterized proteins and found a strong correlation between the predictions of the antigenic index and known structural and biological data.

Algorithms↗
PubMed2006

Lipoprotein computational prediction in spirochaetal genomes.

Lipoproteins are of great interest in understanding the molecular pathogenesis of spirochaetes. Because spirochaete lipobox sequences exhibit more plasticity than those of other bacteria, application of existing prediction algorithms to emerging sequence data has been problematic. In this paper a novel lipoprotein prediction algorithm is described, designated SpLip, constructed as a hybrid of a lipobox weight matrix approach supplemented by a set of lipoprotein signal peptide rules allowing for conservative amino acid substitutions. Both the weight matrix and the rules are based on a training set of 28 experimentally verified spirochaetal lipoproteins. The performance of the SpLip algorithm was compared to that of the hidden Markov model-based LipoP program and the rules-based algorithm Psort for all predicted protein-coding genes of Leptospira interrogans sv. Copenhageni, L. interrogans sv. Lai, Borrelia burgdorferi, Borrelia garinii, Treponema pallidum and Treponema denticola. Psort sensitivity (13-35 %) was considerably less than that of SpLip (93-100 %) or LipoP (50-84 %) due in part to the requirement of Psort for Ala or Gly at the -1 position, a rule based on E. coli lipoproteins. The percentage of false-positive lipoprotein predictions by the LipoP algorithm (8-30 %) was greater than that of SpLip (0-1 %) or Psort (4-27 %), due in part to the lack of rules in LipoP excluding unprecedented amino acids such as Lys and Arg in the -1 position. This analysis revealed a higher number of predicted spirochaetal lipoproteins than was previously known. The improved performance of the SpLip algorithm provides a more accurate prediction of the complete lipoprotein repertoire of spirochaetes. The hybrid approach of supplementing weight matrix scoring with rules based on knowledge of protein secretion biochemistry may be a general strategy for development of improved prediction algorithms.

Algorithms↗
PubMed1996

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% of a base and/or apex biopsy were significant (p < or = 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%, specificity 71.3%, positive predictive value 72.9%, negative predictive value 84.7% and area under the receiver operating characteristic curve 85.9%. 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.

Aged↗
PubMed1997

A structure-based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets.

Binding of peptides to MHC class I molecules is a prerequisite for their recognition by cytotoxic T cells. Consequently, identification of peptides that will bind to a given MHC molecule must constitute a central part of any algorithm for prediction of T-cell antigenic peptides based on the amino acid sequence of the protein. Binding motifs, defined by anchor positions only, have proven to be insufficient to ensure binding, suggesting that other positions along the peptide sequence also affect peptide-MHC interaction. The second phase of prediction schemes therefore take into account the effect of all positions along the peptide sequence, and are based on position-dependent-coefficients that are used in the calculation of a peptide score. These coefficients can be extracted from a large ensemble of binding sequences that were tested experimentally, or derived from structural considerations, as in the algorithm developed by us recently. This algorithm uses the coordinates of solved complexes to evaluate the interactions of peptide amino acids with MHC contact residues, and results in a peptide score that reflects its binding energy. Here we present our analysis for peptide binding to four MHC alleles (HLA-A2, HLA-A68, HLA-B27 and H-2Kb), and compare the predictions of the algorithm to experimental binding data. The algorithm performs successfully in predicting peptide binding to MHC molecules with hydrophobic binding pockets but not when MHC molecules with hydrophilic, charged pockets are considered. For MHC molecules with hydrophobic pockets it is demonstrated how the algorithm succeeds in distinguishing binding from non-binding peptides, and in high ranking of immunogenic peptides within all overlapping same-length peptides spanning their respective protein sequences. The latter property of the algorithm makes it a useful tool in the rational design of peptide vaccines aimed at T-cell immunity.

Algorithms↗
PubMed2004

Predicting respiratory motion for four-dimensional radiotherapy.

Adapting radiation delivery to respiratory motion is made possible through corrective action based on real-time feedback of target position during respiration. The advantage of this approach lies with its ability to allow tighter margins around the target while simultaneously following its motion. A significant hurdle to the successful implementation of real-time target-tracking-based radiation delivery is the existence of a finite time delay between the acquisition of target position and the mechanical response of the system to the change in position. Target motion during the time delay leads to a resultant lag in the system's response to a change in tumor position. Predicting target position in advance is one approach to ensure accurate delivery. The aim of this manuscript is to estimate the predictive ability of sinusoidal and adaptive filter-based prediction algorithms on multiple sessions of patient respiratory patterns. Respiratory motion information was obtained from recordings of diaphragm motion for five patients over 60 sessions. A prediction algorithm that employed both prediction models-the sinusoidal model and the adaptive filter model-was developed to estimate prediction accuracy over all the sessions. For each session, prediction error was computed for several time instants (response time) in the future (0-1.8 seconds at 0.2-second intervals), based on position data collected over several signal-history lengths (1-7 seconds at 1-second intervals). Based on patient data included in this study, the following observations are made. Qualitative comparison of predicted and actual position indicated a progressive increase in prediction error with an increase in response time. A signal-history length of 5 seconds was found to be the optimal signal history length for prediction using the sinusoidal model for all breathing training modalities. In terms of overall error in predicting respiratory motion, the adaptive filter model performed better than the sinusoidal model. With the adaptive filter, average prediction errors of less than 0.2 cm (1sigma) are possible for response times less than 0.4 seconds. In comparing prediction error with system latency error (no prediction), the adaptive filter model exhibited lesser prediction errors as compared to the sinusoidal model, especially for longer response time values (>0.4 seconds). At smaller response time values (<0.4 seconds), improvements in prediction error reduction are required for both predictive models in order to maximize gains in position accuracy due to prediction. Respiratory motion patterns are inherently complex in nature. While linear prediction-based prediction models perform satisfactorily for shorter response times, their prediction accuracy significantly deteriorates for longer response times. Successful implementation of real-time target-tracking-based radiotherapy requires response times less than 0.4 seconds or improved prediction algorithms.

Algorithms↗
PubMed2003

A soft docking algorithm for predicting the structure of antibody-antigen complexes.

An efficient soft docking algorithm is described for predicting the mode of binding between an antibody and its antigen based on the three-dimensional structures of the molecules. The basic tools are the "simplified protein" model and the docking algorithm of Wodak and Janin. The side-chain flexibility of Arg, Lys, Asp, Glu, and Met residues on the protein surface is taken into account. A combined filtering technique is used to select candidate binding modes. After energy minimization, we calculate a scoring function, which includes electrostatic and desolvation energy terms. This procedure was applied to targets 04, 05, and 06 of CAPRI, which are complexes of three different camelid antibody VHH variable domains with pig alpha-amylase. For target 06, two native-like structures with a root-mean-square deviation < 4.0 A relative to the X-ray structure were found within the five top ranking structures. For targets 04 and 05, our procedure produced models where more than half of the antigen residues forming the epitope were correctly predicted, albeit with a wrong VHH domain orientation. Thus, our soft docking algorithm is a promising tool for predicting antibody-antigen recognition.

Algorithms↗
PubMed2002

A comparison of the abilities of nine scoring algorithms in predicting mortality.

OBJECTIVE: The purpose of this study was to compare the abilities of nine Abbreviated Injury Scale (AIS)- and (ICD-9)-based scoring algorithms in predicting mortality. METHODS: The scores collected on 76,871 incidents consist of four AIS-based algorithms (Injury Severity Score [ISS], New Injury Severity Score, Anatomic Profile Score [APS], and maximum AIS [maxAIS]), their four ICD to AIS mapped counterparts, and the ICD-9-based ISS (ICISS). A 10-fold cross-validation was performed and area under the receiver operating characteristic curve was used to determine algorithm discrimination. Hosmer-Lemeshow statistics were computed to gauge goodness-of-fit, and model refinement measured variance of predicted probabilities. RESULTS: Overall, the ICISS has the best discrimination and model refinement, whereas the APS has the best Hosmer-Lemeshow performance. ICD-9 to AIS mapped scores have worse discrimination than their AIS-based counterparts, but still show moderate performance. CONCLUSION: Differences in performance were relatively small. Complex scores such as the ICISS and the APS provide improvement in discrimination relative to the maxAIS and the ISS. Trauma registries should move to include the ICISS and the APS. The ISS and maxAIS perform moderately well and have bedside benefits.

Abbreviated Injury Scale↗
PubMed2002

Validation of a biopsy-based pathologic algorithm for predicting lymph node metastases in patients with clinically localized prostate carcinoma.

BACKGROUND: The authors validated an algorithm for the preoperative prediction of lymph node (LN) metastases in patients with clinically localized prostate carcinoma. The algorithm was applied to sextant biopsy material and radical retropubic prostatectomy (RRP) stage obtained from a cohort of men who were treated at the authors' institution. METHODS: Four hundred forty-three patients underwent systematic sextant biopsy and RRP with staging lymphadenectomy. The original algorithm was based on systematic sextant biopsy data and classified patients into three risk groups for LN metastases based on the biopsy result. If > or = 4 of 6 biopsies contained any Gleason Pattern 4 disease, then the patient was at high risk for LN metastases (45%). Patients with > or = 1 of 6 biopsies with dominant Gleason Pattern 4 disease (excluding high-risk patients) had an intermediate predicted risk (19%) of LN metastases. All other patients had a low predicted risk of LN metastases (2.2%). The authors assed the percentage of patients who were positive and negative for LN metastases and calculated the specificity and negative predictive value in the series when patients were classified according to the original algorithm. RESULTS: Twenty of 443 patients had intraoperative LN metastases. When applied to the current data, the Hamburg algorithm classified 404 patients in the low-risk group, 30 patients in the intermediate-risk group, and 9 patients in the high risk group. The incidence of LN metastases was 2.47% in the low-risk group, 20% in the intermediate-risk group, and 44.4% in the high-risk group. The negative predictive value for the low-risk group was 97.52%, and the specificity was 94.14%. CONCLUSIONS: The Hamburg algorithm proved a valid tool for the prediction of lymphatic spread in this validation study on data from the authors' institution. The algorithm may serve as a tool to select patients who do not need to undergo pelvic lymphadenectomy at the time they undergo RRP, hence reducing morbidity and expense. More importantly, with the increasing numbers of men undergoing treatment options in whom LN dissection is not performed, this validated algorithm provides an important selection basis regarding the appropriateness of a therapy that does not routinely include LN staging.

Algorithms↗