The Experts below are selected from a list of 2274 Experts worldwide ranked by ideXlab platform
Frederic D Mckenzie - One of the best experts on this subject based on the ideXlab platform.
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Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis
SIMULATION, 2012Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Ayyappa Vadlamudi, Bo Sun, John Semmes, Frederic D MckenzieAbstract: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%).
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Adjacent Slice prostate cancer prediction to inform maldi imaging biomarker analysis
Proceedings of SPIE, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, John O Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Adjacent Slice prostate cancer prediction to inform MALDI imaging biomarker analysis
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Prostate Cancer Region Prediction using MALDI Mass Spectra
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract:For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an Adjacent Slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue Slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an Adjacent Slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging Slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an Adjacent Slice.
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BIBE - Combining Prostate Cancer Region Predictions from MALDI Spectra Processing and Texture Analysis
2010 IEEE International Conference on BioInformatics and BioEngineering, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Frederic D Mckenzie, Bo Sun, O. John SemmesAbstract: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 apply a texture analysis technique on a high magnification optical image to predict PCa regions on an Adjacent tissue Slice. Second, we 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 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 (sen. 87.45%) but not specific (spe. 75%), and the prediction based on the MALDI spectra data is specific (spe. 100%) but less sensitive (sen. 50.98%). By combining those two results, a much better prediction for PCa regions on the Adjacent Slice can be achieved (sen. 80.39%, spe. 93.09%).
Shaohui Chuang - One of the best experts on this subject based on the ideXlab platform.
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Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis
SIMULATION, 2012Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Ayyappa Vadlamudi, Bo Sun, John Semmes, Frederic D MckenzieAbstract: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%).
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Adjacent Slice prostate cancer prediction to inform maldi imaging biomarker analysis
Proceedings of SPIE, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, John O Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Adjacent Slice prostate cancer prediction to inform MALDI imaging biomarker analysis
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Prostate Cancer Region Prediction using MALDI Mass Spectra
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract:For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an Adjacent Slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue Slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an Adjacent Slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging Slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an Adjacent Slice.
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BIBE - Combining Prostate Cancer Region Predictions from MALDI Spectra Processing and Texture Analysis
2010 IEEE International Conference on BioInformatics and BioEngineering, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Frederic D Mckenzie, Bo Sun, O. John SemmesAbstract: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 apply a texture analysis technique on a high magnification optical image to predict PCa regions on an Adjacent tissue Slice. Second, we 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 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 (sen. 87.45%) but not specific (spe. 75%), and the prediction based on the MALDI spectra data is specific (spe. 100%) but less sensitive (sen. 50.98%). By combining those two results, a much better prediction for PCa regions on the Adjacent Slice can be achieved (sen. 80.39%, spe. 93.09%).
Massimo Avoli - One of the best experts on this subject based on the ideXlab platform.
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Volume-conducted epileptiform events between Adjacent necortical Slices in an interface tissue chamber
Journal of neuroscience methods, 2005Co-Authors: Yuji Inaba, Massimo AvoliAbstract: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.
Julius O Nyalwidhe - One of the best experts on this subject based on the ideXlab platform.
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Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis
SIMULATION, 2012Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Ayyappa Vadlamudi, Bo Sun, John Semmes, Frederic D MckenzieAbstract: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%).
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Adjacent Slice prostate cancer prediction to inform maldi imaging biomarker analysis
Proceedings of SPIE, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, John O Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Adjacent Slice prostate cancer prediction to inform MALDI imaging biomarker analysis
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Prostate Cancer Region Prediction using MALDI Mass Spectra
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract:For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an Adjacent Slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue Slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an Adjacent Slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging Slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an Adjacent Slice.
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BIBE - Combining Prostate Cancer Region Predictions from MALDI Spectra Processing and Texture Analysis
2010 IEEE International Conference on BioInformatics and BioEngineering, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Frederic D Mckenzie, Bo Sun, O. John SemmesAbstract: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 apply a texture analysis technique on a high magnification optical image to predict PCa regions on an Adjacent tissue Slice. Second, we 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 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 (sen. 87.45%) but not specific (spe. 75%), and the prediction based on the MALDI spectra data is specific (spe. 100%) but less sensitive (sen. 50.98%). By combining those two results, a much better prediction for PCa regions on the Adjacent Slice can be achieved (sen. 80.39%, spe. 93.09%).
Dean A Troyer - One of the best experts on this subject based on the ideXlab platform.
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Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis
SIMULATION, 2012Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Ayyappa Vadlamudi, Bo Sun, John Semmes, Frederic D MckenzieAbstract: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%).
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Adjacent Slice prostate cancer prediction to inform maldi imaging biomarker analysis
Proceedings of SPIE, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, John O Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Adjacent Slice prostate cancer prediction to inform MALDI imaging biomarker analysis
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract: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.
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Medical Imaging: Computer-Aided Diagnosis - Prostate Cancer Region Prediction using MALDI Mass Spectra
Medical Imaging 2010: Computer-Aided Diagnosis, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, O. John Semmes, Frederic D MckenzieAbstract:For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an Adjacent Slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue Slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an Adjacent Slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging Slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an Adjacent Slice.
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BIBE - Combining Prostate Cancer Region Predictions from MALDI Spectra Processing and Texture Analysis
2010 IEEE International Conference on BioInformatics and BioEngineering, 2010Co-Authors: Ayyappa Vadlamudi, Shaohui Chuang, Xiaoyan Sun, Lisa H Cazares, Julius O Nyalwidhe, Dean A Troyer, Frederic D Mckenzie, Bo Sun, O. John SemmesAbstract: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 apply a texture analysis technique on a high magnification optical image to predict PCa regions on an Adjacent tissue Slice. Second, we 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 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 (sen. 87.45%) but not specific (spe. 75%), and the prediction based on the MALDI spectra data is specific (spe. 100%) but less sensitive (sen. 50.98%). By combining those two results, a much better prediction for PCa regions on the Adjacent Slice can be achieved (sen. 80.39%, spe. 93.09%).
