Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework

Chung, Audrey G.; Khalvati, Farzad; Shafiee, Mohammad Javad; Haider, Masoom A.; Wong, Alexander

doi:10.1109/access.2015.2502220

Cited by 35 publications

(15 citation statements)

References 32 publications

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“…From the literature review (Table 1), it can be seen that the different classifiers, namely bagging (Parmar et al, 2015b), Bayesian (Parmar et al, 2015b), boosting (Parmar et al, 2015b), classification and regression tree (CART) , decision tree (Hawkins et al, 2014;Chaudhury, 2015;Parmar et al, 2015b;Velazquez et al, 2015), discriminant analysis (Balagurunathan et al, 2014b;Coquery et al, 2014;Parmar et al, 2015b), generalised linear (Parmar et al, 2015b), K-nearest neighbour (K-NN) Chaddad et al, 2015;Parmar et al, 2015b;Lian et al, 2016;Wu et al, 2016), logistic regression (Guo et al, 2015;Parmar et al, 2015a;Vallières et al, 2015;Ypsilantis et al, 2015;Coroller et al, 2016;Huang et al, 2016;Liang et al, 2016), naïve Bayes (Chaudhury, 2015;Emaminejad et al, 2016;Wu et al, 2016), neural network (Parmar et al, 2015b), random forest (Parmar et al, 2015b;Wu et al, 2016), and support vector machine (SVM) Chaudhury, 2015;Chung et al, 2015;Depeursinge et al, 2015;Khalvati et al, 2015;Parmar et al, 2015b;Upadhaya et al, 2015aUpadhaya et al, , 2015bYpsilantis et al, 2015;…”

Section: Discussionmentioning

confidence: 99%

Towards precision medicine: from quantitative imaging to radiomics

Acharya

Hagiwara

Sudarshan

et al. 2018

J. Zhejiang Univ. Sci. B

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Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

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Section: Discussionmentioning

confidence: 99%

Towards precision medicine: from quantitative imaging to radiomics

Acharya

Hagiwara

Sudarshan

et al. 2018

J. Zhejiang Univ. Sci. B

View full text Add to dashboard Cite

show abstract

“…This is done by further refining the results from RD-FM stage at voxel-resolution through a CRF framework (rADC-CRF) to reinforce the relative ADC map effect on the tumour region detection. Previously, it was reported [ 36 ] that a CRF framework can noticeably reduce the sparsely distributed tumour candidates on results produced by voxel-based approach. In our experiment, the CRF model is applied to relative ADC map and results show it further increases the specificity by reducing the number of false positive.…”

Section: Discussionmentioning

confidence: 99%

“…Conditional random fields were first proposed by Lafferty et al [ 34 ] and have previously been used for image labelling [ 35 ]. In addition, in [ 36 ], it was shown that CRF can be used to enforce the spatial constraints on prostate tumours such as compactness.…”

Section: Methodsmentioning

confidence: 99%

“…Here, we extend upon the CRF model proposed in [ 36 ] to leverage the full set of voxel-level quantitative radiomic features derived from relative ADC data while taking into account the spatial relationships and quantitative radiomics feature relationships between voxels to better enforce relative ADC characteristics. The CRF framework models the conditional probability of the binary label field Y and corresponding X observations as follows [ 36 ]: …”

Section: Methodsmentioning

confidence: 99%

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MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection

et al. 2018

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BackgroundQuantitative radiomic features provide a plethora of minable data extracted from multi-parametric magnetic resonance imaging (MP-MRI) which can be used for accurate detection and localization of prostate cancer. While most cancer detection algorithms utilize either voxel-based or region-based feature models, the complexity of prostate tumour phenotype in MP-MRI requires a more sophisticated framework to better leverage available data and exploit a priori knowledge in the field.MethodsIn this paper, we present MPCaD, a novel Multi-scale radiomics-driven framework for Prostate Cancer Detection and localization which leverages radiomic feature models at different scales as well as incorporates a priori knowledge of the field. Tumour candidate localization is first performed using a statistical texture distinctiveness strategy that leverages a voxel-resolution feature model to localize tumour candidate regions. Tumour region classification via a region-resolution feature model is then performed to identify tumour regions. Both voxel-resolution and region-resolution feature models are built upon and extracted from six different MP-MRI modalities. Finally, a conditional random field framework that is driven by voxel-resolution relative ADC features is used to further refine the localization of the tumour regions in the peripheral zone to improve the accuracy of the results.ResultsThe proposed framework is evaluated using clinical prostate MP-MRI data from 30 patients, and results demonstrate that the proposed framework exhibits enhanced separability of cancerous and healthy tissue, as well as outperforms individual quantitative radiomics models for prostate cancer detection.ConclusionQuantitative radiomic features extracted from MP-MRI of prostate can be utilized to detect and localize prostate cancer.

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“…The authors proposed a tumor candidate identification algorithm that identifies the regions of concern and constructs a comprehensive radiomics feature model to detect the much-suspected cancer regions. Further on the subject of prostate cancer in [20], a novel approach for automatic prostate tumor detection using a radiomics-driven conditional random field (RD-CRF) is proposed. The proposed method analyses the inter-voxel spatial and radiomics feature relationships to ensure that the identified tumor candidates exhibit specific tissue characteristics of cancer.…”

mentioning

confidence: 99%

Advanced Deep Learning Embedded Motion Radiomics Pipeline for Predicting Anti-PD-1/PD-L1 Immunotherapy Response in the Treatment of Bladder Cancer: Preliminary Results

et al. 2019

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A key objective of modern medicine is precision medicine, whose purpose is to personalize the treatment based on the specific characteristics of the patients and their illness. To guide treatment decisions, it is generally necessary to have a sample of the neoplastic tissue, which is obtained only with biopsies or similar invasive surgical procedures. As tumors are heterogeneous in their volume and change over time, a dynamic analysis of diagnostic medical images can provide a better understanding of the entire tumor, both in the screening and follow-up phase. In this work, the authors proposed the use of a radiomics pipeline which is able to characterize the possible response of the oncological patients to the anti- programmed death-ligand protein 1 (PD-L1) immunotherapeutic treatment. The immunotherapeutic treatment consists of a modern therapeutic approach in which the physicians try to reactivate the patient's immune system so that it recognizes and destroys cancer cells. The oncological biomarkers capable of characterizing patients who can benefit from immunotherapy from those who would not, are being studied. One of them is related to the expression of the PD-L1 inhibitor in the surface of neoplastic cells which are analyzed in this paper, considering that the analyzed immunotherapeutic treatment is of the anti-PD-L1 type. In this context, the authors propose a pipeline for an immunotherapy response prediction based on the analysis of only CT-scan images of patients with metastatic bladder cancer. Using a framework based on the use of deep Autoeconder network, CT-scan images were analyzed to extract the features capable of discriminating the patient's response to anti-PD-L1 immunotherapy treatment from those who are not. The preliminary results obtained (accuracy of approximately 86% with a sensitivity of approximately 80% against a specificity of approximately 89%) on the analyzed patient dataset, allows the confirmation of the feasibility of the proposed method. Although validated in a dataset containing patients with only one tumor histology (bladder cancer), the proposed method shows how modern radiomics techniques can contribute significantly in the implementation of non-invasive predictive systems that support the physician in the therapeutic choice. The idea of the authors is to create a form of oncological point of care on an embedded platform that allows physicians to always have a support tool in choosing the best therapy to suggest to the patient.

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Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework

Cited by 35 publications

References 32 publications

Towards precision medicine: from quantitative imaging to radiomics

Towards precision medicine: from quantitative imaging to radiomics

MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection

Advanced Deep Learning Embedded Motion Radiomics Pipeline for Predicting Anti-PD-1/PD-L1 Immunotherapy Response in the Treatment of Bladder Cancer: Preliminary Results

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