3,285 research outputs found
DEVELOPING NOVEL COMPUTER-AIDED DETECTION AND DIAGNOSIS SYSTEMS OF MEDICAL IMAGES
Reading medical images to detect and diagnose diseases is often difficult and has large inter-reader variability. To address this issue, developing computer-aided detection and diagnosis (CAD) schemes or systems of medical images has attracted broad research interest in the last several decades. Despite great effort and significant progress in previous studies, only limited CAD schemes have been used in clinical practice. Thus, developing new CAD schemes is still a hot research topic in medical imaging informatics field. In this dissertation, I investigate the feasibility of developing several new innovative CAD schemes for different application purposes. First, to predict breast tumor response to neoadjuvant chemotherapy and reduce unnecessary aggressive surgery, I developed two CAD schemes of breast magnetic resonance imaging (MRI) to generate quantitative image markers based on quantitative analysis of global kinetic features. Using the image marker computed from breast MRI acquired pre-chemotherapy, CAD scheme enables to predict radiographic complete response (CR) of breast tumors to neoadjuvant chemotherapy, while using the imaging marker based on the fusion of kinetic and texture features extracted from breast MRI performed after neoadjuvant chemotherapy, CAD scheme can better predict the pathologic complete response (pCR) of the patients. Second, to more accurately predict prognosis of stroke patients, quantifying brain hemorrhage and ventricular cerebrospinal fluid depicting on brain CT images can play an important role. For this purpose, I developed a new interactive CAD tool to segment hemorrhage regions and extract radiological imaging marker to quantitatively determine the severity of aneurysmal subarachnoid hemorrhage at presentation and correlate the estimation with various homeostatic/metabolic derangements and predict clinical outcome. Third, to improve the efficiency of primary antibody screening processes in new cancer drug development, I developed a CAD scheme to automatically identify the non-negative tissue slides, which indicate reactive antibodies in digital pathology images. Last, to improve operation efficiency and reliability of storing digital pathology image data, I developed a CAD scheme using optical character recognition algorithm to automatically extract metadata from tissue slide label images and reduce manual entry for slide tracking and archiving in the tissue pathology laboratories.
In summary, in these studies, we developed and tested several innovative approaches to identify quantitative imaging markers with high discriminatory power. In all CAD schemes, the graphic user interface-based visual aid tools were also developed and implemented. Study results demonstrated feasibility of applying CAD technology to several new application fields, which has potential to assist radiologists, oncologists and pathologists improving accuracy and consistency in disease diagnosis and prognosis assessment of using medical image
Surrogate endpoints for early-stage breast cancer: a review of the state of the art, controversies, and future prospects
Breast cancer subtypes; Neoadjuvant therapy; Surrogate markersSubtipos de cáncer de mama; Terapia neoadyuvante; Marcadores sustitutosSubtipus de càncer de mama; Teràpia neoadjuvant; Marcadors substitutsDrug approval for early-stage breast cancer (EBC) has been historically granted in the context of registration trials based on adequate outcomes such as disease-free survival and overall survival. Improvements in long-term outcomes have made it more difficult to demonstrate the clinical benefit of a new cancer drug in large, randomized, comparative clinical trials. Therefore, the use of surrogate endpoints rather than traditional measures allows for cancer drug trials to proceed with smaller sample sizes and shorter follow-up periods, which reduces drug development time. Among surrogate endpoints for breast cancer, the increase in pathological complete response (pCR) rates was considered appropriate for accelerated drug approval. The association between pCR and long-term outcomes was strongest in patients with aggressive tumor subtypes, such as triple-negative and human epidermal growth factor receptor 2 (HER2)-positive/hormone receptor-negative breast cancers. Whereas in hormone receptor-positive/HER2-negative EBC, the most accepted surrogate markers for endocrine therapy–based trials include changes in Ki67 and the preoperative endocrine prognostic index. Beyond the classic endpoints, further prognostic tools are required to provide EBC patients with individualized and effective therapies, and the neoadjuvant setting provides an excellent platform for drug development and biomarker discovery. Nowadays, the availability of multigene signatures is offering a standardized quantitative and reproducible tool to potentiate the efficacy of standard treatment for high-risk patients and develop de-escalated treatments for patients at lower risk of relapse. In this article, we first evaluate the surrogacies used for long-term outcomes and the underlying evidence supporting the use of each surrogate endpoint for the accelerated or regular drug approval process in EBC. Next, we provide an overview of the most recent studies and innovative strategies in a (neo)adjuvant setting as a platform to accelerate new drug approval. Finally, we highlight some clinical trials aimed at tailoring systemic treatment of EBC using prognosis-related factors or early biomarkers of drug sensitivity or resistance
Imaging Biomarkers for Precision Medicine in Locally Advanced Breast Cancer
Guidelines from the American National Comprehensive Cancer Network (NCCN)recommend neoadjuvant chemotherapy (NAC) to patients with locally advanced breast cancer (LABC) to downstage tumors before surgery. However, only a small fraction (15-17%) of LABC patients achieve complete pathologic response (pCR), i.e. no residual tumor in the breast, after treatment. Measuring tumor response during
53 neoadjuvant chemotherapy can potentially help physicians adapt treatment thus, potentially improving the pCR rate. Recently, imaging biomarkers that are used to measure the tumor’s functional and biological features have been studied as pre-treatment markers for pCR or as an indicator for intra-treatment tumor response. Also, imaging biomarkers have been the focus of intense research to characterize tumor heterogeneity as well as to advance our understanding of the principle mechanisms behind chemoresistance. Advances in investigational radiology are moving rapidly to high-resolution imaging, capturing metabolic data, performing tissue characterization and statistical modelling of imaging biomarkers, with an endpoint of personalized medicine in breast cancer treatment. In this commentary, we present studies within the framework of imaging biomarkers used to measure breast tumor response to chemotherapy. Current studies are showing that significant progress has been made in the accuracy of measuring tumor response either before or during chemotherapy, yet the challenges at the forefront of these works include translational gaps such as needing large-scale clinical trials for validation, and standardization of imaging methods. However, the ongoing research is showing that imaging biomarkers may play an important role in personalized treatments for LABC
Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy
Purpose: This study comprises two subprojects. In subproject one, the study
purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using
quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS)
in locally advanced breast cancer (LABC) during chemotherapy. In subproject
two, DOS-based functional maps were analysed with texture-based image
features to predict breast cancer response before the start of NAC.
Patients and Measurements: The institution’s ethics review board approved
this study. For subproject one, subjects (n=22) gave written consent before
participating in the study. Participants underwent non-invasive, DOS and QUS
imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before
surgical removal of the tumour (mastectomy and/or lumpectomy);
corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were
determined from tumour ultrasound data using spectral analysis. In the same
patients, DOS was used to measure parameters relating to tumour haemoglobin
and tissue composition such as %Water and %Lipids. Discriminant analysis
and receiver-operating characteristic (ROC) analyses were used to correlate the
measured imaging parameters to Miller-Payne pathological response during
treatment. Additionally, multivariate analysis was carried out for pairwise DOS
and QUS parameter combinations to determine if an increase in the
classification accuracy could be obtained using combination DOS and QUS
parametric models.
For subproject two, 15 additional patients we recruited after first giving
their written informed consent. A pooled analysis was completed for all DOS
baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients
planned for NAC had functional DOS maps and associated textural features
generated. A grey-level co-occurrence matrix (texture) analysis was completed
for parameters associated with haemoglobin, tissue composition, and optical
properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total
haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering
amplitude [SA]) prior to treatment. Textural features included contrast (con),
vi
correlation (cor), energy (ene), and homogeneity (hom). Patients were
classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological
response criteria after treatment completion. In order to test if baseline
univariate texture features could predict treatment response, a receiver
operating characteristic (ROC) analysis was performed, and the optimal
sensitivity, specificity and area under the curve (AUC) was calculated using
Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to
test 40 DOS-texture features and all possible bivariate combinations using a
naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were
included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and
validation and ROC analysis were performed to find the maximum sensitivity
and specificity of bivariate DOS-texture features.
Results: For subproject one, individual DOS and QUS parameters, including
the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin
(HbT) were significant markers for response outcome after one week of
treatment (p<0.01). Multivariate (pairwise) combinations increased the
sensitivity, specificity and AUC at this time; the SI+HbO2 showed a
sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment.
For subproject two, the results indicated that textural characteristics of
pre-treatment DOS parametric maps can differentiate treatment response
outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst
univariate parameters in predicting response to chemotherapy: sensitivity (%Sn)
and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of
87.8%. The highest predictors using multivariate (binary) combination features
were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0,
a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model.
Conclusion: DOS and QUS demonstrated potential as coincident markers for
treatment response and may potentially facilitate response-guided therapies.
Also, the results of this study demonstrated that DOS-texture analysis can be
used to predict breast cancer response groups prior to starting NAC using
baseline DOS measurements
Evaluation of the current knowledge limitations in breast cancer research: a gap analysis
BACKGROUND
A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients.
METHODS
Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action.
RESULTS
Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds).
CONCLUSION
Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care
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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
Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies
The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on non-invasive, radiological imaging to diagnose, stage, and treat these malignancies. Oesophageal, gastric, and colorectal malignancies require specialist investigation and treatment due to the complex nature of the anatomy, biology, and subsequent treatment strategies. As cancer imaging techniques develop, many opportunities to improve tumour detection, diagnostic accuracy and treatment monitoring present themselves. This review article aims to report current imaging practice, advances in various radiological modalities in relation to GI luminal tumour sites and describes opportunities for GI radiologists to improve patient outcomes
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