A Bayesian Nonparametric model for textural pattern heterogeneity

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumor heterogeneity through patterns of enhancement, texture, morphology, and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of Gray-Level Co-occurrence Matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero-inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine-learning approaches currently used in cancer radiomics with simulation studies.Comment: 45 pages, 7 figures, 1 Tabl

Targeting hypoxia-inducible factor-1α (HIF-1α) in combination with antiangiogenic therapy: a phase I trial of bortezomib plus bevacizumab.

PurposeWe hypothesized that bortezomib, an agent that suppresses HIF-1α transcriptional activity, when combined with bevacizumab, would obviate the HIF-1α resistance pathway. The objectives of this phase I trial were to assess safety and biological activity of this combination.Experimental designPatients with advanced, refractory malignancies were eligible. Patients received bevacizumab and bortezomib (3-week cycle) with dose expansions permitted if responses were seen and for assessing correlates. Pharmacodynamic assessment included plasma VEGF, VEGFR2, 20S proteasome inhibition, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and HIF-1α tumor expression.ResultsNinety-one patients were treated (median=6 prior treatments). The FDA-approved doses of both drugs were safely reached, and the recommended phase 2 dose (RP2D) is bevacizumab 15 mg/kg with bortezomib 1.3 mg/m(2). Four patients attained partial response (PR) and seven patients achieved stable disease (SD) ≥ 6 months (Total SD ≥ 6 months/PR=11 (12%)). The most common drug-related toxicities included thrombocytopenia (23%) and fatigue (19%). DCE-MRI analysis demonstrated no dose-dependent decreases in K(trans) although analysis was limited by small sample size (N=12).ConclusionCombination bevacizumab and bortezomib is well-tolerated and has demonstrated clinical activity in patients with previously treated advanced malignancy. Pharmacodynamic assessment suggests that inhibition of angiogenic activity was achieved

DCE-MRI images of rat tumor model. 3 consecutive imaging sessions in 12 rats.

DCE-MRI images of rat tumor model. 3 consecutive imaging sessions in 12 rats

CT Perfusion Characteristics Identify Metastatic Sites in Liver

Tissue perfusion plays a critical role in oncology because growth and migration of cancerous cells require proliferation of new blood vessels through the process of tumor angiogenesis. Computed tomography (CT) perfusion is an emerging functional imaging modality that measures tissue perfusion through dynamic CT scanning following intravenous administration of contrast medium. This noninvasive technique provides a quantitative basis for assessing tumor angiogenesis. CT perfusion has been utilized on a variety of organs including lung, prostate, liver, and brain, with promising results in cancer diagnosis, disease prognostication, prediction, and treatment monitoring. In this paper, we focus on assessing the extent to which CT perfusion characteristics can be used to discriminate liver metastases from neuroendocrine tumors from normal liver tissues. The neuroendocrine liver metastases were analyzed by distributed parameter modeling to yield tissue blood flow (BF), blood volume (BV), mean transit time (MTT), permeability (PS), and hepatic arterial fraction (HAF), for tumor and normal liver. The result reveals the potential of CT perfusion as a tool for constructing biomarkers from features of the hepatic vasculature for guiding cancer detection, prognostication, and treatment selection

Radiologic manifestations of immune-related adverse events in patients with metastatic melanoma undergoing anti-CTLA-4 antibody therapy.

OBJECTIVE: Monoclonal antibodies against cytotoxic T-lymphocyte antigen 4 (CTLA-4) used for treatment of metastatic melanoma produce inflammatory immune-related adverse events. The purpose of the current study was to retrospectively identify and characterize the radiologic manifestations of immune-related adverse events and to evaluate the possible association between these events and clinical responses to anti-CTLA-4 therapy. MATERIALS AND METHODS: We retrospectively reviewed the images and medical records of 119 patients with metastatic melanoma treated with anti-CTLA-4 at our institution and assessed the presence of radiologic manifestations of immune-related adverse events and the clinical responses to therapy. The responses were categorized as progressive or controlled disease. The controlled disease category included stable disease, partial response, and complete response according to the Response Evaluation Criteria in Solid Tumors, version 1.1. RESULTS: Radiologic manifestations of immune-related adverse events were found in 20 patients (16.8%). Clinically evident manifestations included colitis, hypophysitis, thyroiditis, and arthritis. Clinically silent manifestations were benign lymphadenopathy and inflammatory changes in the soft tissues, such as myositis, fasciitis, and retroperitoneal fat haziness. There was a significant association between the incidence of radiologic manifestations of immune-related adverse events and clinical responses to anti-CTLA-4 therapy. The disease control rates were 18% for the entire group, 55% for the group with, and 10% for the group without radiologic manifestations of immune-related adverse events. In three patients (2.5%), lymphadenopathy related to radiologic manifestations of immune-related adverse events was interpreted as suspected metastasis but was proved benign at biopsy. CONCLUSION: Radiologic manifestations of immune-related adverse events are associated with significant clinical benefit of anti-CTLA-4 therapy. In the era of developing immune checkpoint-targeted therapy for metastatic melanoma, radiologists should be alert to the possibility of these manifestations, which can mimic radiologic disease progression

A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization.

Computed tomography perfusion (CTp) is an emerging functional imaging technology that provides a quantitative assessment of the passage of fluid through blood vessels. Tissue perfusion plays a critical role in oncology due to the proliferation of networks of new blood vessels typical of cancer angiogenesis, which triggers modifications to the vasculature of the surrounding host tissue. In this article, we consider a Bayesian semiparametric model for the analysis of functional data. This method is applied to a study of four interdependent hepatic perfusion CT characteristics that were acquired under the administration of contrast using a sequence of repeated scans over a period of 590 seconds. More specifically, our modeling framework facilitates borrowing of information across patients and tissues. Additionally, the approach enables flexible estimation of temporal correlation structures exhibited by mappings of the correlated perfusion biomarkers and thus accounts for the heteroskedasticity typically observed in those measurements, by incorporating change-points in the covariance estimation. This method is applied to measurements obtained from regions of liver surrounding malignant and benign tissues, for each perfusion biomarker. We demonstrate how to cluster the liver regions on the basis of their CTp profiles, which can be used in a prediction context to classify regions of interest provided by future patients, and thereby assist in discriminating malignant from healthy tissue regions in diagnostic settings

A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization.

Computed tomography perfusion (CTp) is an emerging functional imaging technology that provides a quantitative assessment of the passage of fluid through blood vessels. Tissue perfusion plays a critical role in oncology due to the proliferation of networks of new blood vessels typical of cancer angiogenesis, which triggers modifications to the vasculature of the surrounding host tissue. In this article, we consider a Bayesian semiparametric model for the analysis of functional data. This method is applied to a study of four interdependent hepatic perfusion CT characteristics that were acquired under the administration of contrast using a sequence of repeated scans over a period of 590 seconds. More specifically, our modeling framework facilitates borrowing of information across patients and tissues. Additionally, the approach enables flexible estimation of temporal correlation structures exhibited by mappings of the correlated perfusion biomarkers and thus accounts for the heteroskedasticity typically observed in those measurements, by incorporating change-points in the covariance estimation. This method is applied to measurements obtained from regions of liver surrounding malignant and benign tissues, for each perfusion biomarker. We demonstrate how to cluster the liver regions on the basis of their CTp profiles, which can be used in a prediction context to classify regions of interest provided by future patients, and thereby assist in discriminating malignant from healthy tissue regions in diagnostic settings

Predictive classification of correlated targets with application to detection of metastatic cancer using functional CT imaging

Perfusion computed tomography (CTp) is an emerging functional imaging modality that uses physiological models to quantify characteristics pertaining to the passage of fluid through blood vessels. Perfusion characteristics provide physiological correlates for neovascularization induced by tumor angiogenesis. Thus CTp offers promise as a non-invasive quantitative functional imaging tool for cancer detection, prognostication, and treatment monitoring. In this article, we develop a Bayesian probabilistic framework for simultaneous supervised classification of multivariate correlated objects using separable covariance. The classification approach is applied to discriminate between regions of liver that contain pathologically verified metastases from normal liver tissue using five perfusion characteristics. The hepatic regions tend to be highly correlated due to common vasculature. We demonstrate that simultaneous Bayesian classification yields dramatic improvements in performance in the presence of strong correlation among intra-subject units, yet remains competitive with classical methods in the presence of weak or no correlation

Clinical and Pathological Complete Remission in a Patient With Metastatic Renal Cell Carcinoma (mRCC) Treated With Sunitinib: Is mRCC Curable With Targeted Therapy?

AbstractWe report a patient with metastatic clear-cell renal cell carcinoma (mRCC) who presented with primary tumor in situ in the left kidney and metastases to bone, liver, lungs, and brain. After over 5 years of sunitinib therapy and subsequent cytoreductive left nephrectomy, the patient achieved radiographic complete response (CR) and had pathologic CR in the nephrectomy specimen. Durable clinical and pathological CRs are possible with targeted agents, even with primary tumor in situ and widely disseminated metastases. Ongoing research will define the optimal duration of systemic therapy in exceptional responders and identify the molecular determinants of response and resistance