↓ Skip to main content

Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer

Overview of attention for article published in Radiology, February 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#43 of 10,272)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
72 news outlets
blogs
2 blogs
twitter
14 X users
facebook
1 Facebook page

Citations

dimensions_citation
83 Dimensions

Readers on

mendeley
100 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer
Published in
Radiology, February 2018
DOI 10.1148/radiol.2017170273
Pubmed ID
Authors

Sebastian Bickelhaupt, Paul Ferdinand Jaeger, Frederik Bernd Laun, Wolfgang Lederer, Heidi Daniel, Tristan Anselm Kuder, Lorenz Wuesthof, Daniel Paech, David Bonekamp, Alexander Radbruch, Stefan Delorme, Heinz-Peter Schlemmer, Franziska Hildegard Steudle, Klaus Hermann Maier-Hein

Abstract

Purpose To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue-optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods This institutional study included 222 women at two independent study sites (site 1: training set of 95 patients; mean age ± standard deviation, 58.6 years ± 6.6; 61 malignant and 34 benign lesions; site 2: independent test set of 127 patients; mean age, 58.2 years ± 6.8; 61 malignant and 66 benign lesions). All women presented with a finding suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0-1500 sec/mm2) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. A radiomics feature model was developed by using a random forest regressor. The fixed model was tested on an independent test set. Conventional interpretations of MR imaging were also assessed for comparison. Results The radiomics feature model reduced false-positive results from 66 to 20 (specificity 70.0% [46 of 66]) at the predefined sensitivity of greater than 98.0% [60 of 61] in the independent test set, with BI-RADS 4a and 4b lesions benefiting from the analysis (specificity 74.0%, [37 of 50]; 60.0% [nine of 15]) and BI-RADS 5 lesions showing no added benefit. The model significantly improved specificity compared with the median apparent diffusion coefficient (P < .001) and apparent kurtosis coefficient (P = .02) alone. Conventional reading of dynamic contrast material-enhanced MR imaging provided sensitivity of 91.8% (56 of 61) and a specificity of 74.2% (49 of 66). Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model (P = .001). Conclusion A radiomics model based on kurtosis diffusion-weighted imaging performed by using MR imaging machines from different vendors allowed for reliable differentiation between malignant and benign breast lesions in both a training and an independent test data set.©RSNA, 2018 Online supplemental material is available for this article.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 18%
Researcher 15 15%
Other 6 6%
Student > Postgraduate 6 6%
Student > Bachelor 6 6%
Other 20 20%
Unknown 29 29%
Readers by discipline Count As %
Medicine and Dentistry 35 35%
Engineering 12 12%
Computer Science 5 5%
Neuroscience 2 2%
Physics and Astronomy 2 2%
Other 6 6%
Unknown 38 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 577. 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 12 June 2018.
All research outputs
#40,612
of 25,382,440 outputs
Outputs from Radiology
#43
of 10,272 outputs
Outputs of similar age
#944
of 344,345 outputs
Outputs of similar age from Radiology
#1
of 97 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,272 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.2. This one has done particularly well, scoring higher than 99% 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 344,345 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.