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

Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations.

$\textit{Objectives}$: To assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T. $\textit{Methods}$: Thirty-six female patients (median age 53, range 32-75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight $b$ values (0, 30, 60, 90, 120, 300, 600, 900 s/mm$^2$). Apparent diffusion coefficient (ADC), tissue diffusion coefficient ($D_t$), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha ($\alpha$) parameters were derived. Then $t$ tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels. $\textit{Results}$: All patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups ($p$ < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) ($p$ = 0.03, $p$ = 0.04). Overall, stretched-exponential parameters showed excellent repeatability. $\textit{Conclusion}$: DW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes.The study has received funding from the Addenbrookes Charitable Trust and the NIHR comprehensive Biomedical Research Centre (BRC) and the Experimental Cancer Medicine Centre (ECMC) awards to Cambridge University Hospitals NHS Foundation Trust in partnership with the University of Cambridge

A proposal of quantum-inspired machine learning for medical purposes: An application case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation

PET-MR Imaging of Hypoxia and Vascularity in Breast Cancer

Breast cancer is the most common cancer in the UK and in women globally. Imaging methods like mammography, ultrasound (US) and magnetic resonance imaging (MRI) play an important role in the diagnosis and management of breast cancer; they are generally utilised to provide anatomical or structural description of tumours in the clinical setting. It is widely accepted that the tumour microenvironment influences the phenotype, progression and treatment of breast cancer. This gave the impetus to move beyond tumour visualization in images to radiomics in order to provide additional disease characterisation and early biomarkers of tumour response. Due to their ability to assess physiological processes in vivo, positron emission tomography (PET) and MRI can provide non-invasive characterisation of the tumour microenvironment, including perfusion, vascular permeability, cellularity and hypoxia, which is associated with poor clinical outcome and metastasis. Clinical imaging studies in breast tumours have hitherto assessed tumour physiological parameters separately, with only few directly comparing data from these modalities. To this end, hybrid PET-MRI represents an attractive option as it can allow examination of functional processes and features of tumours simultaneously, while also conferring methodological advantages to the way imaging information is combined. The main aim of this thesis is to provide a better understanding of breast cancer pathophysiology using simultaneous PET and multi-parametric MRI. In particular, this work aims to explore relationships between imaging biomarkers of tumour vascularity measured by dynamic contrast-enhanced (DCE) MRI, cellularity using diffusion-weighted imaging (DWI) and hypoxic status using 18F-fluoromisonidazole (18F-FMISO) PET. Correlations between functional PET-MRI parameters and immunohistochemical (IHC) biomarkers of hypoxia and vascularity as well as MRI morphological tumour descriptors are also presented. The thesis concludes with an investigation of the utility of MRI markers of perfusion and surrogate markers of hypoxia to quantitatively monitor and predict pathological response in patients undergoing neoadjuvant chemotherapy (NACT) and provides projections for future work

Functional Magnetic Resonance Imaging of Breast Cancer

This thesis examines the use of magnetic resonance imaging (MRI) techniques in the detection of breast cancer and the prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NACT). This thesis compares the diagnostic performance of diffusion-weighted imaging (DWI) models in the breast using a systematic review and meta-analysis. Advanced diffusion models have been proposed that may improve the performance of standard DWI using the apparent diffusion coefficient (ADC) to discriminate between malignant and benign breast lesions. Pooling the results from 73 studies, comparable diagnostic accuracy is shown using the ADC and parameters from the intra-voxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) models. This work highlights a lack of standardisation in DWI protocols and methodology. Conventional acquisition techniques used in DWI often suffer from image artefacts and low spatial resolution. A multi-shot DWI technique, multiplexed sensitivity encoding (MUSE), can improve the image quality of DWI. A MUSE protocol has been optimised through a series of phantom experiments and validated in 20 patients. Comparing MUSE to conventional DWI, statistically significant improvements are shown in distortion and blurring metrics and qualitative image quality metrics such as lesion conspicuity and diagnostic confidence, increasing the clinical utility of DWI. This thesis investigates the use of dynamic contrast-enhanced MRI (DCE-MRI) in the detection of breast cancer and the prediction of pCR. Abbreviated MRI (ABB-MRI) protocols have gained increasing attention for the detection of breast cancer, acquiring a shortened version of a full diagnostic protocol (FDP-MRI) in a fraction of the time, reducing the cost of the examination. The diagnostic performance of abbreviated and full diagnostic protocols is systematically compared using a meta-analysis. Pooling 13 studies, equivalent diagnostic accuracy is shown for ABB-MRI in cohorts enriched with cancers, and lower but not significantly different diagnostic performance is shown in screening cohorts. Higher order imaging features derived from pre-treatment DCE-MRI could be used to predict pCR and inform decisions regarding targeted treatment, avoiding unnecessary toxicity. Using data from 152 patients undergoing NACT, radiomics features are extracted from baseline DCE-MRI and machine learning models trained to predict pCR with moderate accuracy. The stability of feature selection using logistic regression classification is demonstrated and a comparison of models trained using features from different time points in the dynamic series demonstrates that a full dynamic series enables the most accurate prediction of pCR.GE Healthcare funded PhD Studentshi

State-of-the-Art and Development Trend of Interventional Ultrasound in China

Interventional ultrasound (IUS) is an important branch of modern minimally invasive medicine that has been widely applied in clinical practice due to its unique techniques and advantages. As a relatively emerging field, IUS has progressed towards standardization, precision, intelligence, and cutting-edge directions alone with more than 40 years of development, which is becoming increasingly important techniques in clinical medicine. This article will briefly review the development and advancement of IUS for diagnosis and treatment in China in the era of precision medicine from the aspects of artificial intelligence, virtual navigation, molecular imaging, and nanotechnology

Evaluation of the vascular response to neoadjuvant chemotherapy in primary breast cancer.

Neoadjuvant chemotherapy (NAC) is being increasingly used in the treatment of primary breast cancer (PBC). With the primary tumour in situ, the neoadjuvant treatment setting allows an in vivo assessment of tumour chemo-responsiveness and permits an evaluation of the possible underlying biological mechanisms of response. Angiogenesis is critical for the growth and metastases of breast cancer and with the development of novel agents targeting this process, an understanding of the vascular effects of conventional chemotherapy will enable the rational design of future drug combinations. Functional magnetic resonance imaging (MRI) provides a non-invasive method for assessing tumour microvasculature. Using this technique, pre-treatment tumour vascularity and changes following two cycles of anthracycline-based NAC were measured in a series of patients with PBC. This demonstrated a significant reduction in the permeability and perfusion-related MRI parameters in tumours responding to treatment. The degree of change in K* was able to predict for pathological non-response with a positive predictive value of 84%. Further, an evaluation of the pathophysiological correlates of functional MRI demonstrated an association between the permability/perfusion-related parameters and aggressive tumour features. An evaluation of the effect of anthracycline-based NAC on immunohistochemically-derived measures of tumour angiogenesis was performed on a series of patients treated for PBC. A quantitative and a qualitative measure of tumour angiogenesis was performed (microvessel density MVD and pericyte coverage index PCI respectively), together with an assessment of VEGF expression. This demonstrated no change in MVD following treatment but a significant increase in PCI reflecting a reduction in the proportion of immature proliferating blood vessels. This was accompanied by a reduction in VEGF expression, which may be mediating this effect. These observations may have clinical importance as they may help identify patients who could benefit from alternative therapies early in their treatment course and they may assist in the rational design of combination cytotoxic and antiangiogenic treatment regimens

Patterns of injury and violence in Yaoundé Cameroon: an analysis of hospital data.

BackgroundInjuries are quickly becoming a leading cause of death globally, disproportionately affecting sub-Saharan Africa, where reports on the epidemiology of injuries are extremely limited. Reports on the patterns and frequency of injuries are available from Cameroon are also scarce. This study explores the patterns of trauma seen at the emergency ward of the busiest trauma center in Cameroon's capital city.Materials and methodsAdministrative records from January 1, 2007, through December 31, 2007, were retrospectively reviewed; information on age, gender, mechanism of injury, and outcome was abstracted for all trauma patients presenting to the emergency ward. Univariate analysis was performed to assess patterns of injuries in terms of mechanism, date, age, and gender. Bivariate analysis was used to explore potential relationships between demographic variables and mechanism of injury.ResultsA total of 6,234 injured people were seen at the Central Hospital of Yaoundé's emergency ward during the year 2007. Males comprised 71% of those injured, and the mean age of injured patients was 29 years (SD = 14.9). Nearly 60% of the injuries were due to road traffic accidents, 46% of which involved a pedestrian. Intentional injuries were the second most common mechanism of injury (22.5%), 55% of which involved unarmed assault. Patients injured in falls were more likely to be admitted to the hospital (p &lt; 0.001), whereas patients suffering intentional injuries and bites were less likely to be hospitalized (p &lt; 0.001). Males were significantly more likely to be admitted than females (p &lt; 0.001)DiscussionPatterns in terms of age, gender, and mechanism of injury are similar to reports from other countries from the same geographic region, but the magnitude of cases reported is high for a single institution in an African city the size of Yaoundé. As the burden of disease is predicted to increase dramatically in sub-Saharan Africa, immediate efforts in prevention and treatment in Cameroon are strongly warranted