Radiomic biomarkers informative of cancerous transformation in neurofibromatosis-1 plexiform tumors.

BACKGROUND: This study explores whether objective, quantitative radiomic biomarkers derived from magnetic resonance (MR), positron emission tomography (PET), and computed tomography (CT) may be useful in reliably distinguishing malignant peripheral nerve sheath tumors (MPNST) from benign plexiform neurofibromas (PN). METHODS: A registration and segmentation pipeline was established using a cohort of NF1 patients with histopathological diagnosis of PN or MPNST, and medical imaging of the PN including MR and PET-CT. The corrected MR datasets were registered to the corresponding PET-CT via landmark-based registration. PET standard-uptake value (SUV) thresholds were used to guide segmentation of volumes of interest: MPNST-associated PET-hot regions (SUV≥3.5) and PN-associated PET-elevated regions (2.0

Impact of advanced detector technology and iterative reconstruction on low-dose quantitative assessment of lung computed tomography density in a biological lung model.

PURPOSE: Quantitative computed tomography (QCT)-derived measures of lung density are valued methods for objectively characterizing lung parenchymal and peripheral airways disease and are being used in a growing number of lung disease focused trials. Detector and reconstruction improvements in CT technology have allowed for significant radiation dose reduction in image acquisition with comparable qualitative image quality. We report the impact of detector type and reconstruction type on QCT lung density measures in relation to decreasing dose indices. METHODS: Two sets of studies were completed in an in vivo pig model with a SOMATOM Definition Flash CT system: (a) prior to system upgrade with conventional detectors (UFC) and filtered back projection (FBP), and (b) post system upgrade with integrated electronic detectors (STELLAR) and iterative reconstruction (SAFIRE). CT data were acquired across estimated CT volume dose indices (CTDIvol ) ranging from 0.75 to 15 mGy at both inspiratory and expiratory breath holds. Semiautomated lung segmentations allowed calculation of histogram median, kurtosis, and 15th percentile. Percentage of voxels below -910 HU and -950 HU (inspiratory), and -856 HU (expiratory) were also examined. The changes in these QCT metrics from dose reduction (15 mGy down to 0.75 mGy) were calculated relative to paired reference values (15 mGy). Results were compared based on detector and reconstruction type. RESULTS: In this study, STELLAR detectors improved concordance with 15 mGy values down to 3 mGy for inspiratory scans and 6 mGy for expiratory scans. The addition of SAFIRE reconstruction in all acquired measurements resulted in minimal deviation from reference values at 0.75 mGy. CONCLUSION: The use of STELLAR integrated electronic detectors and SAFIRE iterative reconstruction may allow for comparable lung density measures with CT dose indices down to 0.75 mGy.

Exploration of the volumetric composition of human lung cancer nodules in correlated histopathology and computed tomography.

Gaining a complete and comprehensive understanding of lung cancer nodule histological compositions and how these tissues are represented in radiological data is important not only for expanding the current knowledge base of cancer growth and development but also has potential implications for classification standards, radiological diagnosis methods and for the evaluation of treatment response. In this study we generate large scale histological segmentations of the cancerous and non-cancerous tissues within resected lung nodules. We have implemented a processing pipeline which allows for the direct correlation between histological data and spatially corresponding computed tomography data. Utilizing these correlated datasets we evaluated the statistical separation between Hounsfield Unit (HU) histogram values for each tissue type. The findings of this study revealed that lung cancer nodules contain a complex intermixing of cellular tissue types and that trends exist in the relationship between these tissue types. It was found that the mean Hounsfield Unit values for isolated lung cancer nodules imaged with computed tomography, had statistically significantly different values for non-solid bronchoalveolar carcinoma, solid cancerous tumor, blood, and inactive fibrotic stromal tissue.

An automated segmentation approach for highlighting the histological complexity of human lung cancer.

Lung cancer nodules, particularly adenocarcinoma, contain a complex intermixing of cellular tissue types: incorporating cancer cells, fibroblastic stromal tissue, and inactive fibrosis. Quantitative proportions and distributions of the various tissue types may be insightful for understanding lung cancer growth, classification, and prognostic factors. However, current methods of histological assessment are qualitative and provide limited opportunity to systematically evaluate the relevance of lung nodule cellular heterogeneity. In this study we present both a manual and an automatic method for segmentation of tissue types in histological sections of resected human lung cancer nodules. A specialized staining approach incorporating immunohistochemistry with a modified Masson's Trichrome counterstain was employed to maximize color contrast in the tissue samples for automated segmentation. The developed, clustering-based, fully automated segmentation approach segments complete lung nodule cross-sectional histology slides in less than 1 min, compared to manual segmentation which requires multiple hours to complete. We found the accuracy of the automated approach to be comparable to that of the manual segmentation with the added advantages of improved time efficiency, removal of susceptibility to human error, and 100% repeatability.