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Using multiscale texture and density features for near-term breast cancer risk analysis.

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Title: Using multiscale texture and density features for near-term breast cancer risk analysis.
Authors: Sun, Wenqing1, Tseng, Tzu‐Liang (Bill)1, Qian, Wei2, Zhang, Jianying3, Saltzstein, Edward C.4, Zheng, Bin5, Lure, Fleming2, Yu, Hui6, Zhou, Shi6
Source: Medical Physics. Jun2015, Vol. 42 Issue 6, p2853-2862. 10p.
Subjects: Digital image processing, Texture analysis (Image processing), Support vector machines, Mathematical combinations, Mammograms
Abstract: Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC= 0.729±0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Using multiscale texture and density features for near-term breast cancer risk analysis.
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  Data: <searchLink fieldCode="AR" term="%22Sun%2C+Wenqing%22">Sun, Wenqing</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tseng%2C+Tzu‐Liang+%28Bill%29%22">Tseng, Tzu‐Liang (Bill)</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Qian%2C+Wei%22">Qian, Wei</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jianying%22">Zhang, Jianying</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Saltzstein%2C+Edward+C%2E%22">Saltzstein, Edward C.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Bin%22">Zheng, Bin</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Lure%2C+Fleming%22">Lure, Fleming</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Yu%2C+Hui%22">Yu, Hui</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Shi%22">Zhou, Shi</searchLink><relatesTo>6</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jun2015, Vol. 42 Issue 6, p2853-2862. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Texture+analysis+%28Image+processing%29%22">Texture analysis (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+combinations%22">Mathematical combinations</searchLink><br /><searchLink fieldCode="DE" term="%22Mammograms%22">Mammograms</searchLink>
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  Data: Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. Methods: The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC= 0.729±0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Support vector machines
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      – SubjectFull: Mathematical combinations
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      – SubjectFull: Mammograms
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              M: 06
              Text: Jun2015
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