The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Gabriela Spulber - One of the best experts on this subject based on the ideXlab platform.
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reproducible segmentation of white matter hyperintensities using a new Statistical Definition
Magnetic Resonance Materials in Physics Biology and Medicine, 2017Co-Authors: Soheil Damangir, Eric Westman, Andrew Simmons, Hugo Vrenken, Larsolof Wahlund, Gabriela SpulberAbstract:Objectives We present a method based on a proposed Statistical Definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples.
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Reproducible segmentation of white matter hyperintensities using a new Statistical Definition
Magnetic Resonance Materials in Physics Biology and Medicine, 2017Co-Authors: Soheil Damangir, Eric Westman, Andrew Simmons, Hugo Vrenken, Larsolof Wahlund, Gabriela SpulberAbstract:Objectives We present a method based on a proposed Statistical Definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. Materials and methods T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer’s disease) were used. The segmentation was performed using a proposed Definition for WMH based on the one-tailed Kolmogorov–Smirnov test. Results The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85–0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83–0.94) that exceeded intra-rater similarity (Dice 0.75–0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed Definition has better accuracy and reproducibility in the test dataset used. Conclusion Overall, the presented Definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.
Andrew Simmons - One of the best experts on this subject based on the ideXlab platform.
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reproducible segmentation of white matter hyperintensities using a new Statistical Definition
Magnetic Resonance Materials in Physics Biology and Medicine, 2017Co-Authors: Soheil Damangir, Eric Westman, Andrew Simmons, Hugo Vrenken, Larsolof Wahlund, Gabriela SpulberAbstract:Objectives We present a method based on a proposed Statistical Definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples.
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Reproducible segmentation of white matter hyperintensities using a new Statistical Definition
Magnetic Resonance Materials in Physics Biology and Medicine, 2017Co-Authors: Soheil Damangir, Eric Westman, Andrew Simmons, Hugo Vrenken, Larsolof Wahlund, Gabriela SpulberAbstract:Objectives We present a method based on a proposed Statistical Definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. Materials and methods T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer’s disease) were used. The segmentation was performed using a proposed Definition for WMH based on the one-tailed Kolmogorov–Smirnov test. Results The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85–0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83–0.94) that exceeded intra-rater similarity (Dice 0.75–0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed Definition has better accuracy and reproducibility in the test dataset used. Conclusion Overall, the presented Definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.
P Giraud - One of the best experts on this subject based on the ideXlab platform.
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probability of mediastinal involvement in non small cell lung cancer a Statistical Definition of the clinical target volume for 3 dimensional conformal radiotherapy
International Journal of Radiation Oncology Biology Physics, 2006Co-Authors: P Giraud, Yann De Rycke, A Lavole, B Milleron, J M Cosset, Kenneth E RosenzweigAbstract:Purpose: Conformal irradiation (3D-CRT) of non–small-cell lung carcinoma (NSCLC) is largely based on precise Definition of the nodal clinical target volume (CTVn). A reduction of the number of nodal stations to be irradiated would facilitate tumor dose escalation. The aim of this study was to design a mathematical tool based on documented data to predict the risk of metastatic involvement for each nodal station. Methods and Materials: We reviewed the large surgical series published in the literature to identify the main pretreatment parameters that modify the risk of nodal invasion. The probability of involvement for the 17 nodal stations described by the American Thoracic Society (ATS) was computed from all these publications. Starting with the primary site of the tumor as the main characteristic, we built a probabilistic tree for each nodal station representing the risk distribution as a function of each tumor feature. Statistical analysis used the inversion of probability trees method described by Weinstein and Feinberg. Validation of the software based on 134 patients from two different populations was performed by receiver operator characteristic (ROC) curves and multivariate logistic regression. Results: Analysis of all of the various parameters of pretreatment staging relative to each level of the ATS map results in 20,000 different combinations. The first parameters included in the tree, depending on tumor site, were histologic classification, metastatic stage, nodal stage weighted as a function of the sensitivity and specificity of the diagnostic examination used (positron emission tomography scan, computed tomography scan), and tumor stage. Software is proposed to compute a predicted probability of involvement of each nodal station for any given clinical presentation. Double cross validation confirmed the methodology. A 10% cutoff point was calculated from ROC and logistic model giving the best prediction of mediastinal lymph node involvement. Conclusion: To more accurately define the CTVn in NSCLC three-dimensional conformal radiotherapy, we propose a software that evaluates the risk of mediastinal lymph node involvement from easily accessible individual pretreatment parameters.
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Probability of mediastinal involvement in non–small-cell lung cancer: a Statistical Definition of the clinical target volume for 3-dimensional conformal radiotherapy?
International Journal of Radiation Oncology Biology Physics, 2005Co-Authors: P Giraud, Yann De Rycke, A Lavole, B Milleron, J M Cosset, Kenneth E RosenzweigAbstract:Purpose: Conformal irradiation (3D-CRT) of non–small-cell lung carcinoma (NSCLC) is largely based on precise Definition of the nodal clinical target volume (CTVn). A reduction of the number of nodal stations to be irradiated would facilitate tumor dose escalation. The aim of this study was to design a mathematical tool based on documented data to predict the risk of metastatic involvement for each nodal station. Methods and Materials: We reviewed the large surgical series published in the literature to identify the main pretreatment parameters that modify the risk of nodal invasion. The probability of involvement for the 17 nodal stations described by the American Thoracic Society (ATS) was computed from all these publications. Starting with the primary site of the tumor as the main characteristic, we built a probabilistic tree for each nodal station representing the risk distribution as a function of each tumor feature. Statistical analysis used the inversion of probability trees method described by Weinstein and Feinberg. Validation of the software based on 134 patients from two different populations was performed by receiver operator characteristic (ROC) curves and multivariate logistic regression. Results: Analysis of all of the various parameters of pretreatment staging relative to each level of the ATS map results in 20,000 different combinations. The first parameters included in the tree, depending on tumor site, were histologic classification, metastatic stage, nodal stage weighted as a function of the sensitivity and specificity of the diagnostic examination used (positron emission tomography scan, computed tomography scan), and tumor stage. Software is proposed to compute a predicted probability of involvement of each nodal station for any given clinical presentation. Double cross validation confirmed the methodology. A 10% cutoff point was calculated from ROC and logistic model giving the best prediction of mediastinal lymph node involvement. Conclusion: To more accurately define the CTVn in NSCLC three-dimensional conformal radiotherapy, we propose a software that evaluates the risk of mediastinal lymph node involvement from easily accessible individual pretreatment parameters.
Soheil Damangir - One of the best experts on this subject based on the ideXlab platform.
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reproducible segmentation of white matter hyperintensities using a new Statistical Definition
Magnetic Resonance Materials in Physics Biology and Medicine, 2017Co-Authors: Soheil Damangir, Eric Westman, Andrew Simmons, Hugo Vrenken, Larsolof Wahlund, Gabriela SpulberAbstract:Objectives We present a method based on a proposed Statistical Definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples.
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Reproducible segmentation of white matter hyperintensities using a new Statistical Definition
Magnetic Resonance Materials in Physics Biology and Medicine, 2017Co-Authors: Soheil Damangir, Eric Westman, Andrew Simmons, Hugo Vrenken, Larsolof Wahlund, Gabriela SpulberAbstract:Objectives We present a method based on a proposed Statistical Definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples. Materials and methods T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer’s disease) were used. The segmentation was performed using a proposed Definition for WMH based on the one-tailed Kolmogorov–Smirnov test. Results The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85–0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83–0.94) that exceeded intra-rater similarity (Dice 0.75–0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed Definition has better accuracy and reproducibility in the test dataset used. Conclusion Overall, the presented Definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.
Kenneth E Rosenzweig - One of the best experts on this subject based on the ideXlab platform.
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probability of mediastinal involvement in non small cell lung cancer a Statistical Definition of the clinical target volume for 3 dimensional conformal radiotherapy
International Journal of Radiation Oncology Biology Physics, 2006Co-Authors: P Giraud, Yann De Rycke, A Lavole, B Milleron, J M Cosset, Kenneth E RosenzweigAbstract:Purpose: Conformal irradiation (3D-CRT) of non–small-cell lung carcinoma (NSCLC) is largely based on precise Definition of the nodal clinical target volume (CTVn). A reduction of the number of nodal stations to be irradiated would facilitate tumor dose escalation. The aim of this study was to design a mathematical tool based on documented data to predict the risk of metastatic involvement for each nodal station. Methods and Materials: We reviewed the large surgical series published in the literature to identify the main pretreatment parameters that modify the risk of nodal invasion. The probability of involvement for the 17 nodal stations described by the American Thoracic Society (ATS) was computed from all these publications. Starting with the primary site of the tumor as the main characteristic, we built a probabilistic tree for each nodal station representing the risk distribution as a function of each tumor feature. Statistical analysis used the inversion of probability trees method described by Weinstein and Feinberg. Validation of the software based on 134 patients from two different populations was performed by receiver operator characteristic (ROC) curves and multivariate logistic regression. Results: Analysis of all of the various parameters of pretreatment staging relative to each level of the ATS map results in 20,000 different combinations. The first parameters included in the tree, depending on tumor site, were histologic classification, metastatic stage, nodal stage weighted as a function of the sensitivity and specificity of the diagnostic examination used (positron emission tomography scan, computed tomography scan), and tumor stage. Software is proposed to compute a predicted probability of involvement of each nodal station for any given clinical presentation. Double cross validation confirmed the methodology. A 10% cutoff point was calculated from ROC and logistic model giving the best prediction of mediastinal lymph node involvement. Conclusion: To more accurately define the CTVn in NSCLC three-dimensional conformal radiotherapy, we propose a software that evaluates the risk of mediastinal lymph node involvement from easily accessible individual pretreatment parameters.
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Probability of mediastinal involvement in non–small-cell lung cancer: a Statistical Definition of the clinical target volume for 3-dimensional conformal radiotherapy?
International Journal of Radiation Oncology Biology Physics, 2005Co-Authors: P Giraud, Yann De Rycke, A Lavole, B Milleron, J M Cosset, Kenneth E RosenzweigAbstract:Purpose: Conformal irradiation (3D-CRT) of non–small-cell lung carcinoma (NSCLC) is largely based on precise Definition of the nodal clinical target volume (CTVn). A reduction of the number of nodal stations to be irradiated would facilitate tumor dose escalation. The aim of this study was to design a mathematical tool based on documented data to predict the risk of metastatic involvement for each nodal station. Methods and Materials: We reviewed the large surgical series published in the literature to identify the main pretreatment parameters that modify the risk of nodal invasion. The probability of involvement for the 17 nodal stations described by the American Thoracic Society (ATS) was computed from all these publications. Starting with the primary site of the tumor as the main characteristic, we built a probabilistic tree for each nodal station representing the risk distribution as a function of each tumor feature. Statistical analysis used the inversion of probability trees method described by Weinstein and Feinberg. Validation of the software based on 134 patients from two different populations was performed by receiver operator characteristic (ROC) curves and multivariate logistic regression. Results: Analysis of all of the various parameters of pretreatment staging relative to each level of the ATS map results in 20,000 different combinations. The first parameters included in the tree, depending on tumor site, were histologic classification, metastatic stage, nodal stage weighted as a function of the sensitivity and specificity of the diagnostic examination used (positron emission tomography scan, computed tomography scan), and tumor stage. Software is proposed to compute a predicted probability of involvement of each nodal station for any given clinical presentation. Double cross validation confirmed the methodology. A 10% cutoff point was calculated from ROC and logistic model giving the best prediction of mediastinal lymph node involvement. Conclusion: To more accurately define the CTVn in NSCLC three-dimensional conformal radiotherapy, we propose a software that evaluates the risk of mediastinal lymph node involvement from easily accessible individual pretreatment parameters.
