H. Cammann

发表

Sinus arrhythmia and heart rate in hypertonic disease.

K. Eckoldt, K. H. Bodmann, H. Cammann, 1976, Advances in cardiology.

Artificial neural networks and prostate cancer—tools for diagnosis and management

Kurt Miller, Klaus Jung, Henning Cammann, 2013, Nature Reviews Urology.

Re: Felix K.-H. Chun, Markus Graefen, Alberto Briganti, Andrea Gallina, Julia Hopp, Michael W. Kattan, Hartwig Huland and Pierre I. Karakiewicz. Initial biopsy outcome prediction--head-to-head comparison of a logistic regression-based nomogram versus artificial neural network. Eur Urol 2007;51:1236-

S. Loening, H. Cammann, C. Stephan, 2007, European urology.

Artificial neural networks: has the time come for their use in prostate cancer patients?

H. Cammann, C. Stephan, K. Jung, 2005, Nature Clinical Practice Urology.

PSA and new biomarkers within multivariate models to improve early detection of prostate cancer.

H. Cammann, C. Stephan, K. Jung, 2007, Cancer letters.

Reduced Serum Selenoprotein P Concentrations in German Prostate Cancer Patients

H. Cammann, C. Stephan, K. Jung, 2009, Cancer Epidemiology, Biomarkers & Prevention.

External Validation of an Artificial Neural Network and Two Nomograms for Prostate Cancer Detection

H. Cammann, C. Stephan, K. Miller, 2012, ISRN urology.

Internal validation of an artificial neural network for prostate biopsy outcome

H. Cammann, C. Stephan, K. Jung, 2010, International journal of urology : official journal of the Japanese Urological Association.

Re: Application of artificial intelligence to the management of urological cancer. M. F. Abbod, J. W. Catto, D. A. Linkens and F. C. Hamdy J Urol 2007; 178: 1150-1156.

H. Cammann, C. Stephan, K. Jung, 2008, The Journal of urology.

Diagnostic value of peripheral blood markers for acute rejection in liver transplanted-patients receiving anti IL-2R antibodies / Diagnostik der akuten Rejektion mittels peripherer Blutparameter bei lebertransplantierten Patienten unter IL-2 Rezeptorantikörper basierter Immunsuppression

H. Cammann, C. Müller, J. Klupp, 2008 .

Hormonal ‘minipuberty’ influences the somatic development of boys but not of girls up to the age of 6 years

H. Cammann, Volker Hesse, Klaus Oehler, 2015, Clinical endocrinology.

Comparative assessment of urinary prostate cancer antigen 3 and TMPRSS2:ERG gene fusion with the serum [-2]proprostate-specific antigen-based prostate health index for detection of prostate cancer.

H. Cammann, C. Stephan, K. Jung, 2013, Clinical chemistry.

Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score

H. Cammann, C. Stephan, K. Jung, 2014, Asian journal of andrology.

A (-5, -7) proPSA based artificial neural network to detect prostate cancer.

Klaus Jung, Henning Cammann, Carsten Stephan, 2006, European urology.

Multicenter evaluation of [-2]proprostate-specific antigen and the prostate health index for detecting prostate cancer.

H. Cammann, C. Stephan, K. Jung, 2013, Clinical chemistry.

New markers and multivariate models for prostate cancer detection.

Kurt Miller, Klaus Jung, Henning Cammann, 2009, Anticancer research.

A [‐2]proPSA‐based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases

H. Cammann, C. Stephan, K. Jung, 2009, The Prostate.

Benign prostatic hyperplasia-associated free prostate-specific antigen improves detection of prostate cancer in an artificial neural network.

H. Cammann, C. Stephan, K. Jung, 2009, Urology.

Three new serum markers for prostate cancer detection within a percent free PSA‐based artificial neural network

H. Cammann, C. Stephan, K. Jung, 2006, The Prostate.

Classification Models for Early Detection of Prostate Cancer

Thomas Tolxdorff, Henning Cammann, Carsten Stephan, 2008, Journal of biomedicine & biotechnology.

The percentage of prostate‐specific antigen (PSA) isoform [–2]proPSA and the Prostate Health Index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men aged ≤65 years

H. Cammann, C. Stephan, K. Jung, 2016, BJU international.

Prostate-Specific Antigen (PSA) Screening and New Biomarkers for Prostate Cancer (PCa)

H. Cammann, C. Stephan, K. Jung, 2014, EJIFCC.

Clinical utility of human glandular kallikrein 2 within a neural network for prostate cancer detection

H. Cammann, C. Stephan, G. Yousef, 2005, BJU international.

Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies.

Klaus Jung, Henning Cammann, Carsten Stephan, 2002, Clinical chemistry.

An artificial neural network for five different assay systems of prostate‐specific antigen in prostate cancer diagnostics

H. Cammann, C. Stephan, K. Jung, 2008, BJU international.

An artificial neural network considerably improves the diagnostic power of percent free prostate‐specific antigen in prostate cancer diagnosis: Results of a 5‐year investigation

Dietmar Schnorr, Steffen Hauptmann, Klaus Jung, 2002, International journal of cancer.

Comparison of two different artificial neural networks for prostate biopsy indication in two different patient populations.

H. Cammann, C. Stephan, K. Jung, 2007, Urology.

1497: Use of Propsa within Artificial Neural Networks to Improve Prostate Cancer Specificity

S. Loening, H. Cammann, C. Stephan, 2005 .

Re: Scott A. Tomlins, John R. Day, Robert J. Lonigro, et al. Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur Urol. In press. http://dx.doi.org/10.1016/j.eururo.2015.04.039.

H. Cammann, C. Stephan, K. Jung, 2015, European urology.

Early Detection of Prostate Cancer with Classifier Ensembles

Jörg D. Wichard, Thomas Tolxdorff, Henning Cammann, 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

Between-method differences in prostate-specific antigen assays affect prostate cancer risk prediction by nomograms.

H. Cammann, C. Stephan, K. Jung, 2011, Clinical chemistry.

Assay-specific artificial neural networks for five different PSA assays and populations with PSA 2–10 ng/ml in 4,480 men

B. G. Blijenberg, A. Haese*, H. Cammann, 2007, World Journal of Urology.

The Prostate ThreeNewSerumMarkers for ProstateCancer DetectionWithin a Percent Free PSA-Based ArtificialNeuralNetwork

H. Cammann, C. Stephan, K. Jung, 2005 .

Computer-Assisted Psychophysiological Regulation Diagnostics — New Possibilities for Preventive Health Care

G. S. Vasadze, H. Cammann, B. Fleischer, 1990 .

Avoiding pitfalls in applying prediction models, as illustrated by the example of prostate cancer diagnosis.

Klaus Jung, Henning Cammann, Carsten Stephan, 2011, Clinical chemistry.

Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine.

Peter Bartel, Kurt Miller, Henning Cammann, 2012, Urologic oncology.

Improved prostate cancer detection with a human kallikrein 11 and percentage free PSA-based artificial neural network

H. Cammann, C. Stephan, K. Jung, 2006, Biological chemistry.

Predictive modeling for the presence of prostate carcinoma using clinical, laboratory, and ultrasound parameters in patients with prostate‐specific antigen levels ≤ 10 ng/ml

Klaus Jung, Henning Cammann, Carsten Stephan, 2004, Cancer.

Multicenter evaluation of [-2]proprostate-specific antigen and the prostate health index for detecting prostate cancer.

H. Cammann, C. Stephan, K. Jung, 2013, Clinical chemistry.

Artificial neural network (ANN) velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity

H. Cammann, C. Stephan, K. Jung, 2008, BMC urology.

How to avoid misinterpretation of heart rate variability power spectra?

Henning Cammann, Josef Michel, H. Cammann, 2002, Comput. Methods Programs Biomed..