Back

Longitudinal Case-Control Study of Active and Passive Dense Mammographic Breast Tissue

Batchelder, K. A.; White, B.; Cinelli, C.; Harrow, A.; Lary, C.; Khalil, A.

2024-02-18 oncology
10.1101/2024.02.17.24302978 medRxiv
Show abstract

Mammography is used as secondary prevention for breast cancer. Computer-aided detection and image-based short-term risk estimation were developed to improve the accuracy of mammography. However, most approaches inherently lack the ability to connect observations at the mammography level to observations of cancer onset and progression seen at a smaller scale, which can occur years before imageable cancer and lead to primary prevention. The Hurst exponent (H) can quantify mammographic tissue into regions of dense tissue undergoing active restructuring and regions that remain passive, with amounts of active and passive dense tissue that differ between cancer and controls at diagnosis. A longitudinal retrospective case-control study was conducted to test the hypothesis that differences can be detected before diagnosis and changes could signal developing cancer. Mammograms and reports were collected from 50 patients from Maine Medical Center in 2015 with at least a 5-year screening history. Age-matching patients within 2 years created a primary dataset, and within 5 years, a secondary dataset was created to test for sensitivity. The amount of passive (H [&ge;] 0.55) and active dense tissue (0.45 < H < 0.55) was calculated for each breast and was predicted by creating a linear mixed-effects model. Cancer status was a predictor for passive (p = 0.036) and active (p = 0.025) dense tissue using the primary dataset. However, when increasing the power, cancer status was a predictor for active dense tissue (p = 0.013), while breast status (p = 0.004), time (p = 0.009), and interaction (p = 0.038) were predictors for passive dense tissue. This suggests active dense tissue is a risk for cancer and passive dense tissue is an indication of developing cancer. Required Key MessagesO_LIMammographic dense breast tissue can be separated into regions of active and passive. C_LIO_LIThere is more active dense breast tissue in pathology-confirmed cancer cases than controls. C_LIO_LIIncreases in passive dense tissue in a breast could indicate a developing tumor. C_LI

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 19%
10.2%
2
JNCI Cancer Spectrum
10 papers in training set
Top 0.1%
9.2%
3
Breast Cancer Research
32 papers in training set
Top 0.1%
8.5%
4
Cancers
200 papers in training set
Top 0.8%
6.4%
5
Cancer Epidemiology, Biomarkers & Prevention
17 papers in training set
Top 0.1%
4.9%
6
PeerJ
261 papers in training set
Top 1%
4.3%
7
BMC Research Notes
29 papers in training set
Top 0.1%
4.0%
8
BMC Cancer
52 papers in training set
Top 0.6%
3.7%
50% of probability mass above
9
BMJ Open
554 papers in training set
Top 6%
3.6%
10
Scientific Reports
3102 papers in training set
Top 41%
3.1%
11
Diagnostics
48 papers in training set
Top 0.5%
3.1%
12
Annals of Biomedical Engineering
34 papers in training set
Top 0.4%
2.7%
13
PLOS Computational Biology
1633 papers in training set
Top 13%
2.1%
14
npj Breast Cancer
18 papers in training set
Top 0.1%
2.1%
15
Frontiers in Oncology
95 papers in training set
Top 2%
1.9%
16
The Journal of Clinical Endocrinology & Metabolism
35 papers in training set
Top 0.6%
1.8%
17
eLife
5422 papers in training set
Top 39%
1.8%
18
Frontiers in Bioinformatics
45 papers in training set
Top 0.2%
1.7%
19
JAMA Network Open
127 papers in training set
Top 2%
1.7%
20
Journal of Clinical Medicine
91 papers in training set
Top 4%
1.5%
21
Frontiers in Neuroscience
223 papers in training set
Top 4%
1.5%
22
Nature Communications
4913 papers in training set
Top 58%
1.0%
23
Frontiers in Medicine
113 papers in training set
Top 6%
0.9%
24
Frontiers in Artificial Intelligence
18 papers in training set
Top 0.6%
0.9%
25
Brain and Behavior
37 papers in training set
Top 2%
0.8%
26
Cancer Medicine
24 papers in training set
Top 1%
0.8%
27
iScience
1063 papers in training set
Top 34%
0.7%
28
Immunology
29 papers in training set
Top 1%
0.6%
29
Frontiers in Genetics
197 papers in training set
Top 11%
0.6%
30
Physiological Reports
35 papers in training set
Top 1%
0.6%