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Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer

Overview of attention for article published in BMC Cancer, May 2018
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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Title
Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer
Published in
BMC Cancer, May 2018
DOI 10.1186/s12885-018-4448-9
Pubmed ID
Authors

Jon Whitney, German Corredor, Andrew Janowczyk, Shridar Ganesan, Scott Doyle, John Tomaszewski, Michael Feldman, Hannah Gilmore, Anant Madabhushi

Abstract

Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 115 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 12%
Researcher 9 8%
Student > Master 7 6%
Professor 7 6%
Student > Bachelor 6 5%
Other 26 23%
Unknown 46 40%
Readers by discipline Count As %
Medicine and Dentistry 22 19%
Computer Science 10 9%
Engineering 9 8%
Unspecified 6 5%
Agricultural and Biological Sciences 5 4%
Other 16 14%
Unknown 47 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 05 June 2018.
All research outputs
#6,995,389
of 23,083,773 outputs
Outputs from BMC Cancer
#1,842
of 8,378 outputs
Outputs of similar age
#121,478
of 331,095 outputs
Outputs of similar age from BMC Cancer
#48
of 177 outputs
Altmetric has tracked 23,083,773 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 8,378 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 77% 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 331,095 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 177 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.