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

Measuring tumour hypoxia using oxygen-enhanced MRI in uterine cervical cancer patients

Abstract. Significance The capillaries are the smallest blood vessels in the body, typically imaged using video capillaroscopy to aid diagnosis of connective tissue diseases, such as systemic sclerosis. Video capillaroscopy allows visualization of morphological changes in the nailfold capillaries but does not provide any physiological information about the blood contained within the capillary network. Extracting parameters such as hemoglobin oxygenation could increase sensitivity for diagnosis and measurement of microvascular disease progression. Aim To design, construct, and test a low-cost multispectral imaging (MSI) system using light-emitting diode (LED) illumination to assess relative hemoglobin oxygenation in the nailfold capillaries. Approach An LED ring light was first designed and modeled. The ring light was fabricated using four commercially available LED colors and a custom-designed printed circuit board. The experimental system was characterized and results compared with the illumination model. A blood phantom with variable oxygenation was used to determine the feasibility of using the illumination-based MSI system for oximetry. Nailfold capillaries were then imaged in a healthy subject. Results The illumination modeling results were in close agreement with the constructed system. Imaging of the blood phantom demonstrated sensitivity to changing hemoglobin oxygenation, which was in line with the spectral modeling of reflection. The morphological properties of the volunteer capillaries were comparable to those measured in current gold standard systems. Conclusions LED-based illumination could be used as a low-cost approach to enable MSI of the nailfold capillaries to provide insight into the oxygenation of the blood contained within the capillary network.

Multispectral imaging of nailfold capillaries using light-emitting diode illumination

Abstract Systemic sclerosis (SSc)-related digital ischaemia is a major cause of morbidity, resulting from a combination of microvascular and digital artery disease. Photoacoustic imaging offers a newly available, non-invasive method of imaging digital artery structure and oxygenation. The aim of this study was to establish whether photoacoustic imaging could detect and measure vasculopathy in digital arteries, including the level of oxygenation, in patients with SSc and healthy controls. 22 patients with SSc and 32 healthy controls (HC) underwent photoacoustic imaging of the fingers. Vascular volume and oxygenation were assessed across eight fingers at the middle phalanx. In addition, oxygenation change during finger occlusion was measured at the non-dominant ring finger and the vascular network was imaged along the length of one finger for qualitative assessment. There was no statistically significant difference in vascular volume between patients with SSc and HC (mean of eight fingers; SSc, median 118.6 IQR [95.0–130.5] vs. HC 115.6 [97.8–158.9]) mm 3 . However, baseline oxygenation (mean 8 fingers) was lower in SSc vs. HC (0.373 [0.361–0.381] vs. 0.381 [0.373–0.385] arbitrary sO2 units respectively; p = 0.03). Hyperaemic oxygenation response following occlusion release was significantly lower in SSc compared to HC (0.379 [0.376–0.381] vs. 0.382 [0.377–0.385]; p = 0.03). Whilst vascular volume was similar between groups, digital artery oxygenation was decreased in patients with SSc as compared to HC, indicative of functional deficit. Photoacoustic imaging offers an exciting new method to image the vascular network in patients with SSc and the possibility to capture oxygenation as a functional measure. 

https://www.nature.com/articles/s41598-022-23826-1.pdf

Photoacoustic imaging is a novel tool to measure finger artery structure and oxygenation in patients with SSc

Abstract Objectives Nailfold capillaroscopy is key to timely diagnosis of SSc, but is often not used in rheumatology clinics because the images are difficult to interpret. We aimed to develop and validate a fully automated image analysis system to fill this gap. Methods We mimicked the image interpretation strategies of SSc experts, using deep learning networks to detect each capillary in the distal row of vessels and make morphological measurements. We combined measurements from multiple fingers to give a subject-level probability of SSc. We trained the system using high-resolution images from 111 subjects (group A) and tested on images from subjects not in the training set: 132 imaged at high-resolution (group B); 66 imaged with a low-cost digital microscope (group C). Roughly half of each group had confirmed SSc, and half were healthy controls or had primary RP (‘normal’). We also estimated the performance of SSc experts. Results We compared automated SSc probabilities with the known clinical status of patients (SSc versus ‘normal’), generating receiver operating characteristic curves (ROCs). For group B, the area under the ROC (AUC) was 97% (94–99%) [median (90% CI)], with equal sensitivity/specificity 91% (86–95%). For group C, the AUC was 95% (88–99%), with equal sensitivity/specificity 89% (82–95%). SSc expert consensus achieved sensitivity 82% and specificity 73%. Conclusion Fully automated analysis using deep learning can achieve diagnostic performance at least as good as SSc experts, and is sufficiently robust to work with low-cost digital microscope images.

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A deep learning system for quantitative assessment of microvascular abnormalities in nailfold capillary images

Poster: "ECR 2010 / C-0327 / Visual assessment of breast density in mammograms" by: "A P Sukha, N Barr, J Morris, M Berks, C R M Boggis, M Wilson, S M Astley; Manchester/UK"

Michael Berks

Papers

Measuring tumour hypoxia using oxygen-enhanced MRI in uterine cervical cancer patients

Multispectral imaging of nailfold capillaries using light-emitting diode illumination

Photoacoustic imaging is a novel tool to measure finger artery structure and oxygenation in patients with SSc

A deep learning system for quantitative assessment of microvascular abnormalities in nailfold capillary images

Visual assessment of breast density in mammograms