Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements--a work in progress.

OBJECTIVE: Computed tomography texture analysis (CTTA) is a method of quantifying lesion heterogeneity based on distribution of pixel intensities within a region of interest. This study investigates the ability of CTTA to distinguish different hypervascular liver lesions and compares CTTA parameters by creating a proof-of-concept model to distinguish between different lesions. METHODS: Following institutional review board approval, CTTA software (TexRAD Ltd) was used to retrospectively analyze 17 cases of focal nodular hyperplasia, 19 hepatic adenomas, 25 hepatocellular carcinomas, and 19 cases of normal liver parenchyma using arterial phase scans. Two radiologists read the same image series used by the CTTA software and reported their best guess diagnosis. Computed tomography texture analysis parameters were computed from regions of interest using spatial band-pass filters to quantify heterogeneity. Random-forest method was used to construct a predictive model from these parameters, and a separate regression model was created using a subset of parameters. RESULTS: The random-forest model successfully distinguished the 3 lesion types and normal liver with predicted classification performance accuracy for 91.2% for adenoma, 94.4% for focal nodular hyperplasia, and 98.6% for hepatocellular carcinoma. This error prediction was generated using a subset of data points not used in generation of the model, but not on discrete prospective cases. In contrast, the 2 human readers using the same image series data analyzed by the CTTA software had lower accuracies, of 72.2% and 65.6%, respectively. The explicit regression model with a subset of image parameters had intermediate overall accuracy of 84.9%. CONCLUSIONS: Computed tomography texture analysis may prove valuable in lesion characterization. Differentiation between common hypervascular lesion types could be aided by the judicious incorporation of texture parameters into clinical analysis.

Short communication: Subcellular motion compensation for minimally invasive microscopy, in vivo: evidence for oxygen gradients in resting muscle.

RATIONALE: In vivo microscopy seeks to observe dynamic subcellular processes in a physiologically relevant context. A primary limitation of optical microscopy in vivo is tissue motion, which prevents physiological time course observations or image averaging. OBJECTIVE: To develop and demonstrate motion compensation methods that can automatically track image planes within biological tissues, including the tissue displacements associated with large changes in blood flow, and to evaluate the effect of global hypoxia on the regional kinetics and steady state levels of mitochondrial NAD(P)H. METHODS AND RESULTS: A dynamic optical microscope, with real-time prospective tracking and retrospective image processing, was used collect high-resolution images through cellular responses to various perturbations. The subcellular metabolic response to hypoxia was examined in vivo. Mitochondria closest to the capillaries were significantly more oxidized at rest (67+/-3%) than the intrafibrillar mitochondria (83+/-3%; P<0.0001) in the same cell. CONCLUSIONS: These data are consistent with the hypothesis that a significant oxygen gradient from capillary to muscle core exists at rest, thereby reducing the oxidative load on the muscle cell.

Three-dimensional motion tracking for high-resolution optical microscopy, in vivo.

When conducting optical imaging experiments, in vivo, the signal to noise ratio and effective spatial and temporal resolution is fundamentally limited by physiological motion of the tissue. A three-dimensional (3D) motion tracking scheme, using a multiphoton excitation microscope with a resonant galvanometer, (512 × 512 pixels at 33 frames s(-1)) is described to overcome physiological motion, in vivo. The use of commercially available graphical processing units permitted the rapid 3D cross-correlation of sequential volumes to detect displacements and adjust tissue position to track motions in near real-time. Motion phantom tests maintained micron resolution with displacement velocities of up to 200 µm min(-1), well within the drift observed in many biological tissues under physiologically relevant conditions. In vivo experiments on mouse skeletal muscle using the capillary vasculature with luminal dye as a displacement reference revealed an effective and robust method of tracking tissue motion to enable (1) signal averaging over time without compromising resolution, and (2) tracking of cellular regions during a physiological perturbation.

CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.

RATIONALE AND OBJECTIVES: Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model. MATERIALS AND METHODS: CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases. RESULTS: The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity = 89%, specificity = 99%), clear cell RCCs in 91% of cases (sensitivity = 91%, specificity = 97%), cysts in 100% of cases (sensitivity = 100%, specificity = 100%), and papillary RCCs in 100% of cases (sensitivity = 100%, specificity = 98%). CONCLUSIONS: CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.

Prepaid entitlements: a new challenge for physician-patient relationships.

KIE: The transition from a fee-for-service model to a prepaid health care system can sometimes result in stresses for both physicians and patients. Both could feel trapped as patients approach the system with objectives that may be unrealistic, while physicians feel threatened or suspect that patients are abusing the services. Based on their experience in a large multispecialty academic group practice, the authors have developed management strategies to improve the physician patient relationship in prepaid care settings. These include review of marketing efforts, patient education to foster realistic expectations, a policy for dealing with dissatisfied patients and physicians, a strong central administrative physician to resolve differences of opinion, and continuing physician orientation and education to improve judgment and attitudes.^ieng