Random Forest is a computationally efficient technique that can operate quickly over large datasets. It has been used in many recent research projects and real-world applications in diverse domains. However, the associated literature provides almost no directions about how many trees should be used to compose a Random Forest. The research reported here analyzes whether there is an optimal number of trees within a Random Forest, i.e., a threshold from which increasing the number of trees would bring no significant performance gain, and would only increase the computational cost. Our main conclusions are: as the number of trees grows, it does not always mean the performance of the forest is significantly better than previous forests (fewer trees), and doubling the number of trees is worthless. It is also possible to state there is a threshold beyond which there is no significant gain, unless a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be interesting in the biomedical domain. Additionally, datasets' density-based metrics proposed here probably capture some aspects of the VC dimension on decision trees and low-density datasets may require large capacity machines whilst the opposite also seems to be true.

How many trees in a random forest

With the increase in breast cancer risk over the years, there are many factors estimated that lead to it. However, till date which factor is majorly involved in development of breast cancer or which factor accounts more is not clearly evident. Mammography technique accounting for 80-90% of cancer being detected is believed to be the best method of detection. While mammographic density is manifested by increased proliferation of fat, stoma, epithelium and connective tissue, it is considered to be a risk factor for development of breast cancer. The current study was thus conducted to find out whether the mammographic density is actually a risk factor for development of breast cancer and to find out the better detection tool available. For this, the methodology adopted was review of various journals and studies already published with respect to mammographic density and its risk on development of breast cancer. The conclusion of the current study as well as from another comparable study was that the frequency of screening might be dependent on breast density and in such cases diagnostic techniques such as “digital mammography, ultra sonography and magnetic resonance imaging” may prove to be better detection tools. Moreover, recent studies have also suggested that mammographic density as a marker for risk of developing breast cancer holds true however, this fact needs to be evaluated further. Article DOI:  https://dx.doi.org/10.20319/lijhls.2016.22.4854 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Mammographic density and the risk and detection of breast cancer

https://www.zora.uzh.ch/id/eprint/182086/1/1-s2.0-S156899722030001X-main.pdf

Standardisation of nailfold capillaroscopy for the assessment of patients with Raynaud's phenomenon and systemic sclerosis

This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 19, No. 4. See the HTA programme website for further project information.

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The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme - a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone.

There has been considerable recent interest in the genetic, biological and epidemiological basis of mammographic density (MD), and the search for causative links between MD and breast cancer (BC) risk. This report will critically review the current literature on MD and summarize the current evidence for its association with BC. Keywords 'mammographic dens*', 'dense mammary tissue' or 'percent dens*' were used to search the existing literature in English on PubMed and Medline. All reports were critically analyzed. The data were assigned to one of the following aspects of MD: general association with BC, its relationship with the breast hormonal milieu, the cellular basis of MD, the generic variations of MD, and its significance in the clinical setting. MD adjusted for age, and BMI is associated with increased risk of BC diagnosis, advanced tumour stage at diagnosis and increased risk of both local recurrence and second primary cancers. The MD measures that predict BC risk have high heritability, and to date several genetic markers associated with BC risk have been found to also be associated with these MD risk predictors. Change in MD could be a predictor of the extent of chemoprevention with tamoxifen. Although the biological and genetic pathways that determine and perhaps modulate MD remain largely unresolved, significant inroads are being made into the understanding of MD, which may lead to benefits in clinical screening, assessment and treatment strategies. This review provides a timely update on the current understanding of MD's association with BC risk.

Mammographic density-a review on the current understanding of its association with breast cancer

First-in-Human Technique Translation of Oxygen-Enhanced MRI to an MR Linac System in Patients with Head and Neck Cancer.

21158 screening mammograms were obtained, 10024 acquired using full field digital mammography (FFDM) and 11134 acquired using film-screen mammography For each mammogram, data were collected on recall for further assessment due to detection of microcalcification, use of needle biopsy, and presence of microcalcifications in biopsy specimens 61.5% of women who had a core biopsy following digital mammography had microcalcifications detected, compared with 65.8% for analogue mammography but this difference was not significant (p=0.71) Rates of detection of microcalcifications in women screened by the two methods were similar It was also found that the recall rate for assessment for women screened digitally (6.1%) was significantly higher than the recall rate for those screened by analogue mammography (3.3%), 95% confidence interval 2.2% - 3.4% Screening using digital mammography leads to a higher recall rate for assessment than analogue mammography, although similar rates of detection of microcalcifications occur with both imaging modalities.

Comparison of microcalcification detection rates and recall rates in digital and analogue mammography

/pdf/madym-a-c-toolkit-for-quantitative-dce-mri-analysis-2w3mwl1tgm.pdf

Madym: A C++ toolkit for quantitative DCE-MRI analysis

Polarisation-sensitive optical coherence tomography (PS-OCT) offers a novel, non-invasive method of assessing skin fibrosis in the multisystem disease systemic sclerosis (SSc) by measuring collagen retardance. This study aimed to assess retardance as a biomarker in SSc. Thirty-one patients with SSc and 27 healthy controls (HC) underwent PS-OCT imaging. 'Skin score' was assessed by clinical palpation (0-3 scale). A subset of ten patients and ten age/sex-matched HC had a biopsy and longitudinal imaging. Histological assessment included quantification of epidermal thickness, collagen content (to assess fibrosis) and matrix metalloproteinase (MMP) activity (in situ zymography). PS-OCT images were assessed for epidermal thickness (structure) and fibrosis (retardance). Positive correlation was observed between epidermal thickness as measured by histology and structural PS-OCT (r = 0.79; p < 0.001). Retardance was: HC mean 0.21 (SD 0.21) radian/pixel; SSc skin score 0, 0.30 (0.19); skin score 1, 0.11 (0.16); skin score 2, 0.06 (0.12); skin score 3, 0.36 (0.35). Longitudinal retardance decreased at one-week across groups, increasing at one-month for HC/skin score 0-1; HC biopsy site retardance suggests scarring is akin to fibrosis. Relationships identified between retardance with both biopsy and skin score data indicate that retardance warrants further investigation as a suitable biomarker for SSc-related fibrosis. 

/pdf/polarisation-sensitive-optical-coherence-tomography-1k6lievl.pdf

Polarisation-sensitive optical coherence tomography measurement of retardance in fibrosis, a non-invasive biomarker in patients with systemic sclerosis

A recent study has shown that breast cancer risk can be reduced by taking Tamoxifen, but only if this results in at least a
10% point reduction in mammographic density. When mammographic density is quantified visually, it is impossible to
assess reader accuracy using clinical images as the ground truth is unknown.
Our aim was to compare three models of assessing density change and to determine reader accuracy in identifying
reductions of 10% points or more. We created 100 synthetic, mammogram-like images comprising 50 pairs designed to
simulate natural reduction in density within each pair. Model I: individual images were presented to readers and density
assessed. Model II: pairs of images were displayed together, with readers assessing density for each image. Model III:
pairs of images were displayed together, and readers asked whether there was at least a 10% point reduction in density.
Ten expert readers participated.
Readers' estimates of percentage density were significantly closer to the truth (6.8%-26.4%) when images were assessed
individually rather than in pairs (9.6%-29.8%). Measurement of change was significantly more accurate in Model II than
Model I (p<0.005). Detecting density changes of at least 10% points in image pairs, mean accuracy was significantly
(p<0.005) lower (58%-88%) when change was calculated from density assessments than in Model III (74%-92%).
Our results suggest that where readers need to identify change in density, images should be displayed alongside one
another. In our study, less accurate assessors performed better when asked directly about the magnitude of the change.

Michael Berks

Papers

First-in-Human Technique Translation of Oxygen-Enhanced MRI to an MR Linac System in Patients with Head and Neck Cancer.

Comparison of microcalcification detection rates and recall rates in digital and analogue mammography

Madym: A C++ toolkit for quantitative DCE-MRI analysis

Polarisation-sensitive optical coherence tomography measurement of retardance in fibrosis, a non-invasive biomarker in patients with systemic sclerosis

Assessment of change in breast density: reader performance using synthetic mammographic images