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Frederic D Mckenzie – One of the best experts on this subject based on the ideXlab platform.

  • Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis
    SIMULATION, 2012
    Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Ayyappa Vadlamudi, Bo Sun, John Semmes, Frederic D Mckenzie

    Abstract:

    We present a three-step method to predict prostate cancer (PCa) regions on biopsy tissue samples based on high-confidence, low-resolution PCa regions marked by a pathologist. First, we will apply a texture-analysis technique on a high-magnification optical image to predict PCa regions on an Adjacent tissue Slice. Second, we will design a prediction model for the same purpose, using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue-imaging data from the Adjacent Slice. Finally, we will fuse those two results to obtain the PCa regions that will assist MALDI imaging biomarker identification. Experiment results show that the texture analysis-based prediction is sensitive (87.45%) but less specific (75%), and the prediction based on the MALDI spectra data processing is not sensitive (50.98%) but supremely specific (100%). By combining these two results, an optimized prediction for PCa regions on the Adjacent Slice can be achieved (sensitivity: 80.39%, specificity: 93.09%).

  • Adjacent Slice prostate cancer prediction to inform maldi imaging biomarker analysis
    Proceedings of SPIE, 2010
    Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, John O Semmes, Frederic D Mckenzie

    Abstract:

    Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer
    diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy
    samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging
    is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates.
    Unfortunately, traditional processing methods require histopathological examination on one Slice of a biopsy sample
    while the Adjacent Slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest
    confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to
    estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a
    significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the
    high confidence region to predict the true area of the tumor on the Adjacent MALDI imaged Slice.

  • Medical Imaging: Computer-Aided Diagnosis – Adjacent Slice prostate cancer prediction to inform MALDI imaging biomarker analysis
    Medical Imaging 2010: Computer-Aided Diagnosis, 2010
    Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D Mckenzie

    Abstract:

    Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer
    diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy
    samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging
    is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates.
    Unfortunately, traditional processing methods require histopathological examination on one Slice of a biopsy sample
    while the Adjacent Slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest
    confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to
    estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a
    significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the
    high confidence region to predict the true area of the tumor on the Adjacent MALDI imaged Slice.

Shaohui Chuang – One of the best experts on this subject based on the ideXlab platform.

  • Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis
    SIMULATION, 2012
    Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Ayyappa Vadlamudi, Bo Sun, John Semmes, Frederic D Mckenzie

    Abstract:

    We present a three-step method to predict prostate cancer (PCa) regions on biopsy tissue samples based on high-confidence, low-resolution PCa regions marked by a pathologist. First, we will apply a texture-analysis technique on a high-magnification optical image to predict PCa regions on an Adjacent tissue Slice. Second, we will design a prediction model for the same purpose, using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue-imaging data from the Adjacent Slice. Finally, we will fuse those two results to obtain the PCa regions that will assist MALDI imaging biomarker identification. Experiment results show that the texture analysis-based prediction is sensitive (87.45%) but less specific (75%), and the prediction based on the MALDI spectra data processing is not sensitive (50.98%) but supremely specific (100%). By combining these two results, an optimized prediction for PCa regions on the Adjacent Slice can be achieved (sensitivity: 80.39%, specificity: 93.09%).

  • Adjacent Slice prostate cancer prediction to inform maldi imaging biomarker analysis
    Proceedings of SPIE, 2010
    Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, John O Semmes, Frederic D Mckenzie

    Abstract:

    Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer
    diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy
    samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging
    is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates.
    Unfortunately, traditional processing methods require histopathological examination on one Slice of a biopsy sample
    while the Adjacent Slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest
    confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to
    estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a
    significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the
    high confidence region to predict the true area of the tumor on the Adjacent MALDI imaged Slice.

  • Medical Imaging: Computer-Aided Diagnosis – Adjacent Slice prostate cancer prediction to inform MALDI imaging biomarker analysis
    Medical Imaging 2010: Computer-Aided Diagnosis, 2010
    Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D Mckenzie

    Abstract:

    Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer
    diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy
    samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging
    is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates.
    Unfortunately, traditional processing methods require histopathological examination on one Slice of a biopsy sample
    while the Adjacent Slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest
    confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to
    estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a
    significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the
    high confidence region to predict the true area of the tumor on the Adjacent MALDI imaged Slice.

Massimo Avoli – One of the best experts on this subject based on the ideXlab platform.

  • Volume-conducted epileptiform events between Adjacent necortical Slices in an interface tissue chamber
    Journal of neuroscience methods, 2005
    Co-Authors: Yuji Inaba, Massimo Avoli

    Abstract:

    Abstract “Far-field” artifacts are presumed to contribute negligibly to the field potential activity recorded from brain Slices maintained in vitro. While performing paired intracellular and field potential recordings from rat neocortical Slices superfused with medium containing 4-aminopyridine + GABA receptor antagonists, we identified: (i) epileptiform discharges characterized by concomitant field oscillations (amplitude = 2.6–6.4 mV) and intracellular depolarizations as well as (ii) smaller amplitude (0.3–1.3 mV) field epileptiform events that were not associated with any intracellular activity. By placing an additional extracellular recording electrode into Adjacent Slices, we discovered that large amplitude, epileptiform discharges were generated concomitant to those seen in the first Slice at the field potential level only. In addition, we found in these Slices small amplitude, field discharges that were synchronous with those recorded intracellularly in the original Slice. Analysis of the changes in field potential amplitude over space demonstrated that this parameter was reduced by approximately 60% when the recording electrode was moved from the Slice generating the epileptiform activity to the bathing medium and further decreased in a quasi-linear mode when recordings were obtained from an Adjacent Slice. In conclusion, these observations indicate that brain Slices can, under appropriate conditions, produce field potentials that are of amplitude sufficient for being recorded from other Slices in the tissue chamber. These findings suggest that caution should be taken in assuming that field potential activity seen in an in vitro brain Slice is generated within the recorded tissue.