A5067110590- AI in cancer detection
- Epigenetics and DNA Methylation
- Digital Radiography and Breast Imaging
- Global Cancer Incidence and Screening
- MicroRNA in disease regulation
- Bioinformatics and Genomic Networks
- Radiomics and Machine Learning in Medical Imaging
- Infrared Thermography in Medicine
- Cancer Risks and Factors
- Birth, Development, and Health
- BRCA gene mutations in cancer
- Breast Cancer Treatment Studies
- Genetic Associations and Epidemiology
- Nutritional Studies and Diet
- Genetic Syndromes and Imprinting
- Colorectal Cancer Screening and Detection
- Intergenerational Family Dynamics and Caregiving
- Diet and metabolism studies
- Cancer Genomics and Diagnostics
- Gene expression and cancer classification
- Genetic factors in colorectal cancer
- Health, Environment, Cognitive Aging
- Childhood Cancer Survivors' Quality of Life
- RNA modifications and cancer
- RNA Research and Splicing
The University of Melbourne
2016-2025
Thomas Jefferson University Hospital
2022
Cancer Council Victoria
2013
St Vincent's Hospital
2013
The University of Queensland
2013
Smoking has been reported to be associated with peripheral blood DNA methylation, but the causal aspects of association have rarely investigated. We aimed investigate and underlying causation between smoking methylation. The methylation profile from blood, collected as dried spots stored on Guthrie cards, was measured for 479 Australian women including 66 monozygotic twin pairs, dizygotic 215 sisters twins 130 families using Infinium HumanMethylation450K BeadChip array. Linear regression...
Cancer genetics has to date focused on epithelial malignancies, identifying multiple histotype-specific pathways underlying cancer susceptibility. Sarcomas are rare malignancies predominantly derived from embryonic mesoderm. To identify specific mesenchymal cancers, we performed whole-genome germline sequencing 1644 sporadic cases and 3205 matched healthy elderly controls. Using an extreme phenotype design, a combined rare-variant burden ontologic analysis identified two sarcoma-specific...
Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI cannot perform at the level multi-reader systems used by screening programs countries such as Australia, Sweden, and UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia conduct detailed simulations five potential AI-integrated pathways, examine...
Abstract Background DNA methylation-based biological age (DNAm age) is an important biomarker for adult health. Studies in specific ranges have found widely varying results about its genetic and environmental causes of variation. However, these studies are not able to provide a comprehensive view the variation over lifespan. Results In order investigate DNAm across lifespan, we pooled genome-wide methylation data 4217 people aged 0–92 years from 1871 families. was calculated using Horvath...
Supplemental material is available for this article.Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in issue.
Investigating the genetic and environmental causes of variation in genome-wide average DNA methylation (GWAM), a global measure from HumanMethylation450 array, might give better understanding influences on methylation.We measured GWAM for 2299 individuals aged 0 to 90 years seven twin and/or family studies. We estimated familial correlations, modelled correlations with cohabitation history fitted variance components models GWAM.The correlation pairs was ∼0.8 at birth, decreased age during...
Abstract Background Mammographic density (MD) phenotypes, including percent (PMD), area of dense tissue (DA), and non-dense (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes highly heritable. However, only a small proportion their variance is explained by identified genetic variants. Methods We conducted genome-wide association study, as well transcriptome-wide study (TWAS), age- BMI-adjusted DA, NDA, PMD in up to 27,900 European-ancestry women from the...
Abstract Background While the UK Biobank has been widely used for cancer research, its representativeness of population in terms incidence not thoroughly investigated. Methods We conducted a prospective cohort study 466,163 participants who were cancer-free at recruitment. Standardised ratios (SIRs) calculated all cancers combined and 25 cancers, by comparing incidences with national incidences. Variations SIR age, sex deprivation measures Results Over median follow-up period 12 years,...
<p>Supplementary Figure S1 is a stylised illustration of DEPTH’s use sliding windows to identify genomic susceptibility regions.</p>
<p>Supplementary Figure S2 shows the estimation of genetic ancestry AGOG, GliomaScan and GICC samples by way scatter plot PC1 versus PC2.</p>
Mammographic density, the area of mammographic image that appears white or bright, predicts breast cancer risk. We estimated proportions variance explained by questionnaire-measured risk factors and unmeasured residual familial factors.For 544 MZ 339 DZ twin pairs 1,558 non-twin sisters from 1,564 families, density was measured using computer-assisted method Cumulus. associations multilevel mixed-effects linear regression studied aspects a multivariate normal model.The age, body mass index...
When measured using the computer-assisted method CUMULUS, mammographic density adjusted for age and body mass index predicts breast cancer risk. We asked if new measures defined by higher brightness thresholds gave better risk predictions. The Korean Breast Cancer Study included 213 women diagnosed with invasive 630 controls matched at full-field digital mammogram menopausal status. Mammographic was CUMULUS conventional threshold (Cumulus), in effect two increasingly thresholds, which we...
Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age body mass index (BMI), is a well-established risk factor breast cancer. We asked if higher thresholds better separate women with without Methods: studied Australian women, 354 cancer over-sampled early-onset family history, 944 unaffected controls frequency-matched at mammogram. measured mammographic dense area percent using CUMULUS software which we call Cumulus, two increasingly...
Purpose To compare three mammographic density measures defined by different pixel intensity thresholds as predictors of breast cancer risk for two digital systems. Materials and Methods The Korean Breast Cancer Study included 398 women with invasive 737 control participants matched age at mammography (±1 year), examination date, system, menopausal status. Mammographic was measured using the automated Laboratory Individualized Radiodensity Assessment (LIBRA) software semiautomated Cumulus...