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Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

Overview of attention for article published in Breast Cancer Research, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
9 tweeters
facebook
1 Facebook page

Citations

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26 Dimensions

Readers on

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92 Mendeley
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Title
Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer
Published in
Breast Cancer Research, January 2016
DOI 10.1186/s13058-016-0682-8
Pubmed ID
Authors

H. R Ali, Aliakbar Dariush, Elena Provenzano, Helen Bardwell, Jean E Abraham, Mahesh Iddawela, Anne-Laure Vallier, Louise Hiller, Janet. A Dunn, Sarah J Bowden, Tamas Hickish, Karen McAdam, Stephen Houston, Mike J Irwin, Paul DP Pharoah, James D Brenton, Nicholas A Walton, Helena M Earl, Carlos Caldas, Ali, H Raza, Dariush, Aliakbar, Provenzano, Elena, Bardwell, Helen, Abraham, Jean E, Iddawela, Mahesh, Vallier, Anne-Laure, Hiller, Louise, Dunn, Janet A, Bowden, Sarah J, Hickish, Tamas, McAdam, Karen, Houston, Stephen, Irwin, Mike J, Pharoah, Paul D P, Brenton, James D, Walton, Nicholas A, Earl, Helena M, Caldas, Carlos, H. Raza Ali, Jean E. Abraham, Janet. A. Dunn, Sarah J. Bowden, Mike J. Irwin, Paul D. P. Pharoah, James D. Brenton, Nicholas A. Walton, Helena M. Earl

Abstract

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy. We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression. Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553). A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer. ClinicalTrials.gov NCT00070278 ; 03/10/2003.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Unknown 90 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 26%
Researcher 22 24%
Student > Master 10 11%
Professor > Associate Professor 9 10%
Other 7 8%
Other 10 11%
Unknown 10 11%
Readers by discipline Count As %
Medicine and Dentistry 36 39%
Computer Science 9 10%
Agricultural and Biological Sciences 9 10%
Engineering 8 9%
Biochemistry, Genetics and Molecular Biology 3 3%
Other 14 15%
Unknown 13 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 02 July 2017.
All research outputs
#3,311,486
of 13,534,740 outputs
Outputs from Breast Cancer Research
#498
of 1,521 outputs
Outputs of similar age
#68,108
of 269,298 outputs
Outputs of similar age from Breast Cancer Research
#12
of 26 outputs
Altmetric has tracked 13,534,740 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,521 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.6. This one has gotten more attention than average, scoring higher than 67% 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 269,298 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 74% of its contemporaries.
We're also able to compare this research output to 26 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 53% of its contemporaries.