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Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma.

Background Non-invasive characterization of a tumor's molecular features could enhance treatment management. Quantitative computed tomography (CT) based texture analysis (QTA) has been used to derive tumor heterogeneity information, and the appearance of the tumors has been shown to relate to patient outcome in non-small cell lung cancer (NSCLC) and other cancers. In this study, we examined the potential of tumoral QTA to differentiate K-ras mutant from pan-wildtype tumors and its prognostic potential using baseline pre-treatment non-contrast CT imaging in NSCLC. Methods Tumor DNA from patients with early-stage NSCLC was analyzed on the LungCarta Panel. Cases with a K-ras mutation or pan-wildtype for 26 oncogenes and tumor suppressor genes were selected for QTA. QTA was applied to regions of interest in the primary tumor. Non-parametric Mann Whitney test assessed the ability of the QTA, clinical and patient characteristics to differentiate between K-ras mutation from pan-wildtype. A recursive decision tree was developed to determine whether the differentiation of K-ras mutant from pan-wildtype tumors could be improved by sequential application of QTA parameters. Kaplan-Meier survival analysis assessed the ability of these markers to predict survival. Results QTA was applied to 48 cases identified, 27 had a K-ras mutation and 21 cases were pan-wildtype. Positive skewness and lower kurtosis were significantly associated with the presence of a K-ras mutation. A five node decision tree had sensitivity, specificity, and accuracy values (95% CI) of 96.3% (78.1–100), 81.0% (50.5–97.4), and 89.6% (72.9–97.0); respectively. Kurtosis was a significant predictor of OS and DFS, with a lower kurtosis value linked with poorer survival. Conclusions Lower kurtosis and positive skewness are significantly associated with K-ras mutations. A QTA feature such as kurtosis is prognostic for OS and DFS. Non-invasive QTA can differentiate the presence of K-ras mutation from pan-wildtype NSCLC and is associated with patient survival.

Tyrosine kinase inhibitors are the first line standard of care for treatment of metastatic renal cell carcinoma (RCC). Accurate response assessment in the setting of antiangiogenic therapies remains suboptimal as standard size-related response criteria do not necessarily accurately reflect clinical benefit, as they may be less pronounced or occur later in therapy than devascularisation. The challenge for imaging is providing timely assessment of disease status allowing therapies to be tailored to ensure ongoing clinical benefit. We propose that combined assessment of morphological, physiological and metabolic imaging parameters using 18F–fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F–FDG PET/MRI) will better reflect disease behaviour, improving assessment of response/non-response/relapse. The REMAP study is a single-centre prospective observational study. Eligible patients with metastatic renal cell carcinoma, planned for systemic therapy, with at least 2 lesions will undergo an integrated 18F–FDG PET and MRI whole body imaging with diffusion weighted and contrast-enhanced multiphasic as well as standard anatomical MRI sequences at baseline, 12 weeks and 24 weeks of systemic therapy allowing 18F–FDG standardised uptake value (SUV), apparent diffusion co-efficient (ADC) and normalised signal intensity (SI) parameters to be obtained. Standard of care contrast-enhanced computed tomography CT scans will be performed at equivalent time-points. CT response categorisation will be performed using RECIST 1.1 and alternative (modified)Choi and MASS criteria. The reference standard for disease status will be by consensus panel taking into account clinical, biochemical and conventional imaging parameters. Intra- and inter-tumoural heterogeneity in vascular, diffusion and metabolic response/non-response will be assessed by image texture analysis. Imaging will also inform the development of computational methods for automated disease status categorisation. The REMAP study will demonstrate the ability of integrated 18F–FDG PET-MRI to provide a more personalised approach to therapy. We suggest that 18F–FDG PET/MRI will provide superior sensitivity and specificity in early response/non-response categorisation when compared to standard CT (using RECIST 1.1 and alternative (modified)Choi or MASS criteria) thus facilitating more timely and better informed treatment decisions. The trial is approved by the Southeast London Research Ethics Committee reference 16/LO/1499 and registered on the NIHR clinical research network portfolio ISRCTN12114913 .