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Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

Overview of attention for article published in BMC Medical Imaging, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#31 of 376)
  • Good Attention Score compared to outputs of the same age (79th percentile)

Mentioned by

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6 tweeters
patent
1 patent

Citations

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97 Dimensions

Readers on

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149 Mendeley
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Title
Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models
Published in
BMC Medical Imaging, August 2015
DOI 10.1186/s12880-015-0069-9
Pubmed ID
Authors

Farzad Khalvati, Alexander Wong, Masoom A. Haider

Abstract

Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 146 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 22%
Researcher 28 19%
Student > Master 19 13%
Student > Bachelor 13 9%
Student > Doctoral Student 10 7%
Other 23 15%
Unknown 23 15%
Readers by discipline Count As %
Computer Science 33 22%
Medicine and Dentistry 31 21%
Engineering 22 15%
Physics and Astronomy 14 9%
Agricultural and Biological Sciences 9 6%
Other 13 9%
Unknown 27 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 September 2019.
All research outputs
#2,960,268
of 15,814,075 outputs
Outputs from BMC Medical Imaging
#31
of 376 outputs
Outputs of similar age
#49,013
of 236,846 outputs
Outputs of similar age from BMC Medical Imaging
#1
of 1 outputs
Altmetric has tracked 15,814,075 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 376 research outputs from this source. They receive a mean Attention Score of 2.0. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 236,846 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them