Estimating Local Cellular Density in Glioma Using MR Imaging Data

BACKGROUND AND PURPOSE:Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.MATERIALS AND METHODS:We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.RESULTS:The random forest methodology estimated cellular density with R² = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R² = 0.52). Outputs were also reported as graphic maps.CONCLUSIONS:Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

Increased cellular density (CD) is a hallmark of cancer and a key feature in histologic glioma analysis.¹ Mapping cellular density throughout a tumor would be a valuable tool to probe how tumors infiltrate and analyze the transition between diseased and healthy brain. However, measuring CD requires tissue, which entails additional risks and is expensive to obtain. There is no currently accepted clinical algorithm to translate imaging data into quantitative assessments of CD.There is great need for a method to estimate CD noninvasively in human patients with gliomas. In this article, we describe the development of such a method using MR imaging data inputs by correlating with multiple biopsy specimens acquired during a prospective human clinical trial. We obtained comprehensive MR imaging, including conventional, diffusion, perfusion, and permeability imaging sequences. We used machine learning approaches to correlate imaging findings with CD measurements from pathology, devised an algorithm to estimate CD from MR imaging inputs, and generated CD maps for the visual display of the predictions. We identified the most informative imaging data subset. This work has multiple applications in the diagnosis and treatment of patients with gliomas: For example, the method can be used to guide biopsy, resection, and surgery and delineate tumor borderzones both pre- and postoperatively.²     

Detrended fluctuation analysis detects altered coordination of running gait in athletes following a heavy period of training

Objectives

To investigate whether functional overreaching affects locomotor system behaviour when running at fixed relative intensities and if any effects were associated with changes in running performance.

A lymphoma patient with Cytomegalovirus retinitis and post‐autologous hematopoietic cell transplantation immune reconstitution uveitis: A case report and review of the literature

Cytomegalovirus (CMV) retinitis in hematologic malignancies in the absence of hematopoietic cell transplant (HCT) is uncommon. We report a case of a 54‐year‐old woman with peripheral T‐cell lymphoma who develops CMV retinitis and subsequently undergoes an autologous HCT, with eventual development of immune reconstitution uveitis. We further reviewed the PubMed literature on CMV retinitis in patients with lymphoma. We describe that CMV retinitis in patients with lymphoma has variable clinical presentations, may occur at any time during the course of the disease and chemotherapy, and is associated with significant morbidity.     

Industry Funding Is Correlated With Publication Productivity of US Academic Radiation Oncologists

Purpose

Industry payments to physicians are financial conflicts of interest and may influence research findings and medical decisions. We aim to (1) characterize industry payments within radiation oncology; and (2) explore the potential correlation between receiving disclosed industry payments and academic productivity.

Intravoxel incoherent motion imaging kinetics during chemoradiotherapy for human papillomavirus‐associated squamous cell carcinoma of the oropharynx: preliminary results from a prospective pilot study

This study aims to identify the temporal kinetics of intravoxel incoherent motion (IVIM) MRI in patients with human papillomavirus‐associated (HPV+) oropharyngeal squamous cell carcinoma. Patients were enrolled under an Institutional Review Board (IRB)‐approved protocol as part of an ongoing prospective clinical trial. All patients underwent two MRI studies: a baseline scan before chemoradiotherapy and a mid‐treatment scan 3–4 weeks after treatment initiation. Parametric maps representing pure diffusion coefficient (D), pseudo‐diffusion coefficient (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) were generated. The Mann–Whitney U‐test was used to assess the temporal variation of IVIM metrics. Bayesian quadratic discriminant analysis (QDA) was used to evaluate the extent to which mid‐treatment changes in IVIM metrics could be combined to predict sites that would achieve complete response (CR) in multivariate analysis. Thirty‐one patients were included in the final analysis with 59 lesions. Pretreatment ADC and D values of the CR lesions (n = 19) were significantly lower than those of non‐CR lesions (n = 33). Mid‐treatment ADC, D and f values were significantly higher (p < 0.0001) than pretreatment values for all lesions. Each increase in normalized ΔADC of size 0.1 yielded a 1.45‐fold increase in the odds of CR (p < 0.0003), each increase in normalized ΔD of size 0.1 yielded a 1.53‐fold increase in the odds of CR (p < 0.0002), and each unit increase in Δf yielded a 2.29‐fold increase in the odds of CR (p < 0.02). Combined ΔD and ΔADC were integrated into a multivariate prediction model and attained an AUC of 0.87 (95% confidence interval: 0.79, 0.96), as well as a sensitivity of 0.63, specificity of 0.85 and accuracy of 0.78, under leave‐one‐out cross‐validation. In conclusion, IVIM is feasible and potentially useful in the prediction and assessment of the early response of HPV+ oropharyngeal squamous cell carcinoma to chemoradiotherapy. Copyright © 2015 John Wiley & Sons, Ltd.