Breast cancer detection using infrared thermal imaging and a deep learning model

Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models

BREAST CANCER RISK AND DETECTION USING GENES, MAMMOGRAPHIC DENSITY AND MAMMOGRAMS

Ph.DDOCTOR OF PHILOSOPH

An Investigation of Global and Local Radiomic Features for Customized Self-Assessment Mammographic Test Sets for Radiologists in China in Comparison with Those in Australia

Self-assessment test sets have demonstrated being effective tools to improve radiologists’ diagnostic skills through immediate error feedback. Current sets use a one-size-fits-all approach in selecting challenging cases, overlooking cohort-specific weaknesses. This thesis assessed feasibility of using a comprehensive set of handcrafted global radiomic features (Stage 1, Chapter 3) as well as handcrafted (Stage 2, Chapter 4) and deep-learning based (Stage 3, Chapter 5) local radiomic features to identify challenging mammographic cases for Chinese and Australian radiologists. In the first stage, global handcrafted radiomic features and Random Forest models analyzed mammography datasets involving 36 radiologists from China and Australia independently assessing 60 dense mammographic cases. The results were used to build and evaluate models’ performance in case difficulty prediction. The second stage focused on local handcrafted radiomic features, utilizing the same dataset but extracting features from error-related local mammographic areas to analyze features linked to diagnostic errors. The final stage introduced deep learning, specifically Convolutional Neural Network (CNN), using an additional test set and radiologists’ readings to identify features linked to false positive errors. Stage 1 found that global radiomic features effectively detected false positive and false negative errors. Notably, Australian radiologists showed less predictable errors than their Chinese counterparts. Feature normalization did not improve model performance. In Stage 2, the model showed varying success rates in predicting false positives and false negatives among the two cohorts, with specific mammographic regions more prone to errors. In Stage 3, the transferred ResNet-50 architecture performed the best for both cohorts. In conclusion, the thesis affirmed the importance of radiomic features in improving curation of cohort-specific self-assessment mammography test sets

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Determinants of interval cancer and tumor size among breast cancer screening participants

Breast cancer is the most common cancer of women in Sweden and globally. In the more affluent countries, mammography screening has been in place for a few decades and has successfully reduced mortality. However, there is increasing interest in enhancing the impact of screening by going from the current age-based screening system to a risk-based system. There are two risk components that must be taken into account – the underlying breast cancer risk and the risk of delayed detection. Mammographic density, the amount of dense tissue in the breast, has been shown to be a risk factor for both. In this thesis, my aim was to identify novel determinants of delayed breast cancer detection by studying observed cases of interval cancer or large cancer at diagnosis. The potential risk factors for delayed detection were based on negative mammograms and other data that can be determined before diagnosis. Study I to III, were based on a retrospective case-only population, while Study IV was based on a prospective cohort. In Study I, we developed an estimate of the longitudinal fluctuation in mammographic percent density between screenings. Based on our results, we concluded that women that were subsequently diagnosed with interval cancer had higher density fluctuations than women with screen-detected cancer. In Study II, we went beyond density and examined 32 other image features which were computer-extracted from digitized mammograms. We identified two novel features that were associated with an increased risk of interval cancer compared to screen-detected cancer. One feature seemed to be related to the shape of the entire dense area, being flat rather than round increased the risk of interval cancer, possibly due to making clinical detection easier. The other feature seemed to be related to whether the density was more concentrated or instead was interspersed with fatty streaks. When density was more concentrated, the risk of interval cancer increased, possibly by making mammographic detection more difficult. In Study III, we determined risk factors for the cancer diagnosis being delayed until the cancer had reached a size larger than 2 cm. High density and high body mass index (BMI) were already known risk factors in general. Our aim was to understand if different factors were involved depending on the detection mode, screen-detection or interval cancer detection. We found that high BMI increased the risk of large cancer markedly among interval cancers and somewhat among screen-detected cancers. High density was associated with large cancer only among screen-detected cases. In survival analysis, we showed that high BMI increased the risk of disease progression, but only among women with interval cancer. In Study IV, we found that the localized density category at the site of the subsequent cancer was often different compared to the overall density. We examined the effect of high localized density, independent of overall density, and found that it was strongly associated with large cancer at diagnosis. In addition, it was associated with interval cancer among the less aggressive node-negative cases. It remains to be elucidated whether this effect is purely due to visual masking or also due to an association with biological characteristics of the tumor microenvironment. In conclusion, we have identified several novel determinants of delayed breast cancer detection, which could be further validated in trials of risk-stratified screening

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Mammographic breast density and hormonal, proliferative and stromal histopathologic markers in benign and malignant tissue of different ethnicities

Analysis of the association of mammographic brast density with histopathologic markers

Breast cancer, medical imaging, and cancer genetics. A new genetic concept regarding the causes and prevention strategies of cancer is presented

Breast cancer is the most common cancer type in the United Kingdom. Many women with breast cancer do not show any noticeable symptoms in their early stages, hence regular breast screening is important. In this research focus is on medical imaging and its role in breast cancer screening, diagnosis, and treatment monitoring. Around 10% of all cancers are caused by inherited gene mutations which may cause cancer to run in families. Though, majority of cancer cases (up to 90%) are caused by acquired gene mutations which may also appear to run in families when family members share a particular environment or exposure. Genetic testing is conducted in this research on a number of participants to investigate the cancer cases found among their families. The findings of this research show that significant improvements have taken place in the emergence of hybrid imaging modalities used for breast imaging, through the fusion of different imaging techniques. The findings also provide evidence that similar to cancers caused by inherited gene mutations, cancers caused by non-inherited gene mutations may also appear to run in families when family members share certain environments and exposures or lifestyle behaviours. As a result, a new genetic concept of cancer essential to understand and control the disease is presented in this work which links between the human population origins and migrations, environmental factors and gene mutations, and the development of cancer. Furthermore, a number of cancer prevention strategies are recommended in this study to prevent people from getting the disease