184 research outputs found
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies
In silico approach for the definition of radiomirnomic signatures for breast cancer differential diagnosis
Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. BACKGROUND: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. METHODS: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. RESULTS: We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. CONCLUSION: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis
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
Cost effectiveness of bilateral prophylactic mastectomy with and without different breast reconstruction techniques versus screening in women with high risk of breast cancer in the Canadian province of Ontario
We aimed to investigate the cost-effectiveness of bilateral prophylactic mastectomy (BPM) with and without different reconstruction for the purpose of determining which strategies represent value for money. We developed a decision analytic model to project the lifetime clinical and economic consequences of different strategies. The decision model was parameterized using 10-year follow up and cost data from Ontario administrative health databases and Ontario Cancer registry. Compared to the organized screening-based strategy, surgical strategies ranged from being more effective and cost-saving and up to being associated with an incremental cost effectiveness ratio (ICER) of 9,615. BPM with immediate one-stage ADM-assisted implant breast reconstruction is the most cost-effective strategy and appears to offer the highest value for money
BAYESIAN INTEGRATIVE ANALYSIS OF OMICS DATA
Technological innovations have produced large multi-modal datasets that range in multiplatform genomic data, pathway data, proteomic data, imaging data and clinical data. Integrative analysis of such data sets have potentiality in revealing important biological and clinical insights into complex diseases like cancer. This dissertation focuses on Bayesian methodology establishment in integrative analysis of radiogenomics and pathway driver detection applied in cancer applications. We initially present Radio-iBAG that utilizes Bayesian approaches in analyzing radiological imaging and multi-platform genomic data, which we establish a multi-scale Bayesian hierarchical model that simultaneously identifies genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. Our method is motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, wherein it identifies important magnetic resonance imaging features and the associated genomic platforms that are also significantly related with patient survival times. For another aspect of integrative analysis, we then present pathDrive that aims to detect key genetic and epigenetic upstream drivers that influence pathway activity. The method is applied into colorectal cancer incorporated with its four molecular subtypes. For each of the pathways that significantly differentiates subgroups, we detect important genomic drivers that can be viewed as “switches” for the pathway activity. To extend the analysis, finally, we develop proteomic based pathway driver analysis for multiple cancer types wherein we simultaneously detect genomic upstream factors that influence a specific pathway for each cancer type within the cancer group. With Bayesian hierarchical model, we detect signals borrowing strength from common cancer type to rare cancer type, and
simultaneously estimate their selection similarity. Through simulation study, our method is demonstrated in providing many advantages, including increased power and lower false discovery rates. We then apply the method into the analysis of multiple cancer groups, wherein we detect key genomic upstream drivers with proper biological interpretation. The overall framework and methodologies established in this dissertation illustrate further investigation in the field of integrative analysis of omics data, provide more comprehensive insight into biological mechanisms and processes, cancer development and progression
Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer
BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis
Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer
INTRODUCTION
Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice.
METHODS
More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer 'stem' cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account.
RESULTS
The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working.
CONCLUSIONS
With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years
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
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