Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging

To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from 1H‐MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer.

[1]  H. Hricak,et al.  1H magnetic resonance spectroscopy of prostate cancer: biomarkers for tumor characterization. , 2008, Cancer biomarkers : section A of Disease markers.

[2]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[3]  Anant Madabhushi,et al.  Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS , 2013, Medical Image Anal..

[4]  G. Lockwood,et al.  Predicting prostate biopsy outcome: artificial neural networks and polychotomous regression are equivalent models , 2011, International Urology and Nephrology.

[5]  Peter Bartel,et al.  Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine. , 2012, Urologic oncology.

[6]  Paulo J. G. Lisboa,et al.  The Use of Artificial Neural Networks in Decision Support in Cancer: a Systematic Review , 2005 .

[7]  Mesut Remzi,et al.  An artificial neural network to predict the outcome of repeat prostate biopsies. , 2003, Urology.

[8]  Ashutosh Tewari,et al.  Predicting the outcome of prostate biopsy in a racially diverse population: a prospective study. , 2002, Urology.

[9]  Sung Il Hwang,et al.  Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine , 2009, European Radiology.

[10]  N. deSouza,et al.  The effect of experimental conditions on the detection of spermine in cell extracts and tissues , 2009, NMR in biomedicine.

[11]  Peter L Choyke,et al.  Imaging prostate cancer: a multidisciplinary perspective. , 2007, Radiology.

[12]  H. Cammann,et al.  Internal validation of an artificial neural network for prostate biopsy outcome , 2010, International journal of urology : official journal of the Japanese Urological Association.

[13]  Kjell Arne Kvistad,et al.  Applications of neural network analyses to in vivo 1H magnetic resonance spectroscopy of epilepsy patients , 1999, Epilepsy Research.

[14]  H. Hricak,et al.  Chronic prostatitis: MR imaging and 1H MR spectroscopic imaging findings--initial observations. , 2004, Radiology.

[15]  Leo L. Cheng,et al.  Metabolomic Imaging for Human Prostate Cancer Detection , 2010, Science Translational Medicine.

[16]  G. De Meerleer,et al.  A qualitative approach to combined magnetic resonance imaging and spectroscopy in the diagnosis of prostate cancer. , 2010, European journal of radiology.

[17]  P Finne,et al.  Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. , 2000, Urology.

[18]  Kazutaka Saito,et al.  Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy. , 2008, European urology.

[19]  H. Hricak,et al.  Detection of prostate cancer with MR spectroscopic imaging: an expanded paradigm incorporating polyamines. , 2007, Radiology.

[20]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[21]  Guido Schwarzer,et al.  Artificial neural networks for diagnosis and prognosis in prostate cancer. , 2002, Seminars in urologic oncology.

[22]  M. Kattan,et al.  Correlation of proton MR spectroscopic imaging with gleason score based on step-section pathologic analysis after radical prostatectomy. , 2005, Radiology.

[23]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[24]  T. Scheenen,et al.  Prostate MRI and 3D MR spectroscopy: how we do it. , 2010, AJR. American journal of roentgenology.

[25]  G. Hagberg,et al.  From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods , 1998, NMR in biomedicine.

[26]  Risto A. Kauppinen,et al.  Diagnostic assessment of brain tumours and non-neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks , 1999, Journal of Cancer Research and Clinical Oncology.

[27]  A. Hall,et al.  Adaptive Switching Circuits , 2016 .

[28]  A. Haese*,et al.  Critical assessment of tools to predict clinically insignificant prostate cancer at radical prostatectomy in contemporary men , 2008, Cancer.

[29]  Mesut Remzi,et al.  Novel artificial neural network for early detection of prostate cancer. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  R. Lenkinski,et al.  Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imagery , 2012, Journal of magnetic resonance imaging : JMRI.

[31]  R. Meijer,et al.  The value of an artificial neural network in the decision-making for prostate biopsies , 2009, World Journal of Urology.

[32]  Anant Madabhushi,et al.  Simultaneous segmentation of prostatic zones using Active Appearance Models with multiple coupled levelsets , 2013, Comput. Vis. Image Underst..

[33]  P. Scardino,et al.  Critical review of prostate cancer predictive tools. , 2009, Future oncology.

[34]  W. El-Deredy,et al.  Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: a review , 1997, NMR in biomedicine.

[35]  E. Crawford,et al.  Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer. , 2003, Oncology.

[36]  M. Kattan,et al.  Transition zone prostate cancer: metabolic characteristics at 1H MR spectroscopic imaging--initial results. , 2003, Radiology.

[37]  F. Jiru Introduction to post-processing techniques. , 2008, European journal of radiology.

[38]  Jan Aasly,et al.  Applications of neural network analyses to in vivo 1H magnetic resonance spectroscopy of Parkinson disease patients , 2002, Journal of magnetic resonance imaging : JMRI.