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Determination of therapeutic benefit in intracranial tumors is intimately dependent on serial assessment of radiographic images. The Response Assessment in Neuro-Oncology (RANO) criteria were established in 2010 to provide an updated framework to better characterize tumor response to contemporary treatments. Since this initial update a number of RANO criteria have provided some basic principles for the interpretation of changes on MR images; however, the details of how to operationalize RANO and other criteria for use in clinical trials are ambiguous and not standardized. In this review article designed for the neuro-oncologist or treating clinician, we outline essential steps for performing radiographic assessments by highlighting primary features of the Imaging Charter (referred to as the Charter for the remainder of this article), a document that describes the clinical trial imaging methodology and methods to ensure operationalization of the Charter into the workings of a clinical trial. Lastly, we provide recommendations for specific changes to optimize this methodology for neuro-oncology, including image registration, requirement of growing tumor for eligibility in trials of recurrent tumor, standardized image acquisition guidelines, and hybrid reader paradigms that allow for both unbiased measurements and more comprehensive interpretation.

Background Since IDH-mutant (mIDH) low-grade gliomas (LGGs) progress slowly and have a relatively long survival, there is a significant need for earlier measurements of clinical benefit. Guidance using the LGG RANO criteria recommends serial bidirectional (2D) measurements on a single slice; however, questions remain as to whether volumetric (3D) measurements are better, since they would allow for more accurate measurements in irregular shaped lesions and allow readers to better assess areas of subtle change. Methods Twenty-one (out of 24) non-enhancing, recurrent mIDH1 LGGs were enrolled in a phase I, multicenter, open-label study of oral ivosidenib (NCT02073994), and with imaging pre- and post-treatment as part of this exploratory ad hoc analysis. 2D and 3D measurements on T2-weighted FLAIR images were centrally evaluated at an imaging contract research organization using a paired read and forced adjudication paradigm. The effects of 2D vs 3D measurements on progression-free survival (PFS), growth rate measurement variability, and reader concordance and adjudication rates were quantified. Results 3D volumetric measurements showed significantly longer estimated PFS (P = .0181), more stable (P = .0063) and considerably slower measures of tumor growth rate (P = .0037), the highest inter-reader agreement (weighted kappa = 0.7057), and significantly lower reader discordance rates (P = .0002) with 2D LGG RANO. Conclusion 3D volumetric measurements are better for determining response assessment in LGGs due to more stable measures of tumor growth rates (ie, less “yo-yo-ing” of measurements over time), highest inter-reader agreement, and lowest reader discordance rates. Continued evaluation in future studies is warranted to determine whether these measurements reflect clinical benefit.

Electronic waste (e-waste) is a global issue brought about by the short lifespan of electronics. Viable methods to relieve the inundated disposal system by repurposing the enormous amount of e-waste remain elusive. Inspired by the need for sustainable solutions, this study resulted in a multifaceted approach to upcycling compact discs (CDs). The once-ubiquitous plates can be transformed into stretchable and flexible biosensors. Our experiments and advanced prototypes show that effective, innovative biosensors can be developed at a low-cost. An affordable craft-based mechanical cutter allows pre-determined patterns to be scored on the recycled metal, an essential first step for producing stretchable, wearable electronics. The active metal harvested from the CDs was inert, cytocompatible, and capable of vital biopotential measurements. Additional studies examined the material’s resistive emittance, temperature sensing, real-time metabolite monitoring performance, and moisture-triggered transience. This sustainable approach for upcycling e-waste provides an advantageous research-based waste stream that does not require cutting-edge microfabrication facilities, expensive materials, and high-caliber engineering skills.

Objective: The aim of this research was to evaluate uterine-wall integrity 12 months after transcervical fibroid ablation (TFA) of uterine fibroids with the Sonata® system (Gynesonics Inc., Redwood City, CA). Materials and Methods: INTEGRITY is a secondary analysis of the FAST-EU clinical trial, a prospective, longitudinal, multicenter single-armed trial involving women with heavy menstrual bleeding secondary to fibroids who were treated at 7 academic and community hospitals in the United Kingdom, the Netherlands, and Mexico with transcervical, intrauterine, ultrasound-guided radiofrequency ablation (the Sonata system). TFA was performed on up to 5 fibroids per subject ranging from 1–5 cm in diameter as determined by magnetic resonance imaging (MRI). All measurements and comparisons, including uterine-wall thicknesses were derived from baseline and 12-month MRI scans by an independent core MRI center. Scans were analyzed to assess preservation of uterine-wall integrity and reviewed for uterine-wall anomalies after TFA with the Sonata system. Results: Twenty-nine patients had baseline and 12-month MRI with contrast enhancement. Minimum uterine-wall thicknesses in all visible slices were >2.5 mm in diameter. No areas on MRI indicated any loss of uterine-wall integrity, compared with baseline imaging; comparison of baseline and postablation uterine-wall thicknesses revealed no significant changes. Conclusion: Transcervical fibroid ablation with the Sonata system was associated with preservation of uterine-wall integrity in this patient cohort.

© 2019 American Society for Surgery of the Hand Purpose: Current evidence on predictors of outcomes after distal radius fracture is often based on retrospective analyses or may be confounded by fracture type. Using data from the Wrist and Radius Injury Surgical Trial (WRIST), a 24-site randomized study of distal radius fracture treatment, in which all fractures are severe enough to warrant surgery, we set out to perform a secondary data analysis to explore predictors of better or worse hand outcomes. Methods: The primary outcome measure was the Michigan Hand Outcomes Questionnaire (MHQ) summary score 12 months after treatment. We used a regression tree analysis with recursive partitioning to identify subgroups of participants who experienced similar outcomes (ie, MHQ score) and to determine which baseline or treatment factors they had in common. Results: Factors most predictive of 12-month MHQ score were pain at enrollment, education, age, and number of comorbidities. Specifically, participants who had a high school education or less and also reported severe pain had the lowest MHQ scores. Conversely, participants with less pain and more education and who were age 87 years or younger with one or no comorbid condition had the highest MHQ scores. Treatment type or radiographic measurements assessed on post-reduction films did not affect 12-month outcomes. Conclusions: These results identified patient characteristics that can be used by surgeons to identify subgroups of patients who may experience similar hand outcomes. Type of study/level of evidence: Prognostic III.

High-resolution computed tomography (HRCT) plays an indispensable role in the diagnosis of idiopathic pulmonary fibrosis (IPF). Due to unpredictability in progression and the short median survival of 2–5 years, it is critical to delineate the patients with rapid progression. The aim is to evaluate the predictability of IPF progression using the early quantitative changes. Automated texture-based quantitative lung fibrosis (QLF) was calculated from the anonymized HRCT. Two datasets were collected retrospectively: (1) a pilot study of 35 subjects with three sequential scans (baseline and 6 and 12 months) to obtain a threshold, where visual assessments were stable at 6 months but worsened at 12 months; (2) 157 independent subjects to test the threshold. Landmark Cox regressions were used to compare the progression-free survival (PFS) defined by pulmonary function using the threshold from the early changes in QLF. C-indexes were reported as estimations of the concordance of prediction. A threshold of 4% QLF change at 6 months corresponded to the mean change that worsened on HRCT visually at 12 months from the pilot study. Using the threshold, significant differences were found in the independent dataset (hazard ratio (HZ) = 5.92, p = 0.001 by Cox model, C-index = 0.71 at the most severe lobe; and HZ = 3.22, p = 0.012, C-index = 0.68 in the whole lung). Median PFS was 11.9 months for subjects with ≥ 4% changes, whereas median PFS was greater than 18 months for subjects with

Rationale and Objectives The purpose of this paper is to describe the integration of a commercial chest CT computer-aided detection (CAD) system into the clinical radiology reporting workflow and perform an initial investigation of its impact on radiologist efficiency. It seeks to complement research into CAD sensitivity and specificity of stand-alone systems, by focusing on report generation time when the CAD is integrated into the clinical workflow. Materials and Methods A commercial chest CT CAD software that provides automated detection and measurement of lung nodules, ascending and descending aorta, and pleural effusion was integrated with a commercial radiology report dictation application. The CAD system automatically prepopulated a radiology report template, thus offering the potential for increased efficiency. The integrated system was evaluated using 40 scans from a publicly available lung nodule database. Each scan was read using two methods: (1) without CAD analytics, i.e., manually populated report with measurements using electronic calipers, and (2) with CAD analytics to prepopulate the report for reader review and editing. Three radiologists participated as readers in this study. Results CAD assistance reduced reading times by 7%–44%, relative to the conventional manual method, for the three radiologists from opening of the case to signing of the final report. Conclusion This study provides an investigation of the impact of CAD and measurement on chest CTs within a clinical reporting workflow. Prepopulation of a report with automated nodule and aorta measurements yielded substantial time savings relative to manual measurement and entry.

Research in wearable electronics has paved the way for next-generation technology, sought to create point-of-care biosensors that combine chemical sensing on a biocompatible platform with a broad range of applications in human health monitoring. Despite significant progress, the microspatial mechanical mismatch and fluid-impermeable interface presented between skin and the electronics create adscititious problems in device lamination, conformality, and long-term monitoring. Herein, we engineered a skin-inspired, deterministically patterned, electrochemical biosensor that can be fully integrated with the curvilinear surface of the human body, while mechanically adapting to the natural stresses applied to the skin and allowing the mass transfer of gas and fluids. In particular, we developed mechanically-compliant lattice-structured biosensors for the continuous evaluation of lactate and oxygen. Systematic studies of the sensor performance were evaluated with variations in polymeric membranes and its ability to withstand commonplace harsh, multiaxial stresses.

A novel physics-based data augmentation (PBDA) is introduced, to provide a representative approach to introducing variance during training of a deep-learning model. Compared to traditional geometric-based data augmentation (GBDA), we hypothesize that PBDA will provide more realistic variation representative of potential imaging conditions that may be seen beyond the initial training data, and thereby train a more robust model (particularly in the scope of medical imaging). PBDA is tested in the context of false-positive reduction in nodule detection in low-dose lung CT and is shown to exhibit superior performance and robustness across a wide range of imaging conditions.

PurposeRecent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel.MethodA cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6mm, 1mm, and 2mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC≥0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA.ResultsAll included radiomic features lacked intercondition reproducibility (OCCC50% of radiomic features.ConclusionWe systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.

Background Pisiform excision and pisotriquetral arthrodesis are two surgical options for the treatment of pisotriquetral joint pain when conservative methods fail. However, it is unclear which option is best for patients who experience substantial, repetitive loading on their wrists and wish to preserve wrist flexibility and function. Case Description We present a case of bilateral ulnar-sided wrist pain related to the pisotriquetral joint in a 19-year-old collegiate diver. The pain was exacerbated by activities specific to this sport that requires wrist hyperextension, namely full weight-bearing on the hands (handstands), and has an impact on the hands and wrists upon water entry during dives due to direct palmar pressure. There were no radiographic signs of arthritis; however, there were bone marrow changes on magnetic resonance imaging (MRI). Management with rest, splinting, and corticosteroid injection failed to relieve the pain and precluded his ability to return to full-time diving. Treatment consisted of bilateral pisiform excision. Postoperatively, the patient returned to full-time competitive diving with resolution of his painful symptoms. Literature Review Pisiform excision has been shown to have successful outcomes in terms of return to play for lower impact athletes (such as badminton) but has not been reported in athletes who experience a high degree of force repetitively (such as gymnasts or divers). There is one report of pisotriquetral arthrodesis in a young gymnast with suboptimal results. Clinical Relevance This case report demonstrates that pisiform excision is a successful treatment for elite athletes who experience repetitive, palmar force on hyperextended wrists and subsequently develop ulnar-sided wrist pain.

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML). We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI − cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland–Altman analysis was used to compare peak ROI between CAD and manual method. The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732–0.968), 0.959 (95% CI 0.930–0.989), 0.792 (95% CI 0.716–0.869), and 0.825 (95% CI 0.703–0.948), respectively. On Bland–Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase. A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.

Infantile myofibroma or myofibromatosis is a myofibroblastic and fibroblastic proliferation that is most commonly reported in children younger than 2 years of age. It is a benign process composed histologically of a biphasic pattern of spindle-shaped cells surrounding a zone of less differentiated cells in a hemangiopericytoma-like pattern. We report this tumor in a unique presentation in the deep palm of a 2-year-old child without skin ulceration and with an intimate association with the median nerve. The well-circumscribed nature of the tumor facilitated complete excision with neural preservation. Final pathology was consistent with an unusual type of myofibroma or myofibromatosis. Conservative management with partial excision has been advocated for these masses because of potential surgical morbidity and its benign nature. This case report highlights the differential diagnosis of uncommon soft tissue tumors in the pediatric hand as well as the importance of a surgeon's surgical assessment in guiding treatment.

Author(s): Hoffman, John; Emaminejad, Nastaran; Wahi-Anwar, Muhammad; Kim, Grace H; Brown, Matthew; Young, Stefano; McNitt-Gray, Michael | Abstract: PurposeWith recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high-quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open-source high-throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large-scale quantitative imaging studies and to accelerate and improve quantitative imaging research.MethodsThe pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques.ResultsThe pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10ndays, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring.ConclusionsThe pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high-throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image-processing techniques to improve clinical quantitative imaging.

Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution’s pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes. We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8–14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0–13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%. The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

Purpose: To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. Methods: This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. Results: The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. Conclusions: As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy [Formula: see text] on test set across different tasks using a research database from multicenter clinical trials.

OBJECTIVE To examine changes in the extent of specific patterns of interstitial lung disease (ILD) as they transition from one pattern to another in response to immunosuppressive therapy in systemic sclerosis-related ILD (SSc-ILD). METHODS We evaluated changes in the quantitative extent of specific lung patterns of ILD using volumetric high-resolution computed tomography (HRCT) scans obtained at baseline and after 2 years of therapy in patients treated with either cyclophosphamide (CYC) for 1 year or mycophenolate mofetil (MMF) for 2 years in Scleroderma Lung Study II. ILD patterns included lung fibrosis, ground glass, honeycombing, and normal lung. Net change was calculated as the difference in the probability of change from one ILD pattern to another. Wilcoxon's signed rank test was used to compare the changes. RESULTS Forty-seven and 50 patients had baseline and follow-up scans in the CYC and MMF groups, respectively. Mean net improvements reflecting favorable changes from one ILD pattern to another in the whole lung in the CYC and MMF groups, respectively, were as follows: from lung fibrosis to a normal lung pattern, 21% and 19%; from a ground-glass pattern to a normal lung pattern, 30% and 28%; and from lung fibrosis to a ground-glass pattern, 5% and 0.5%. The mean overall improvement in transitioning from a ground-glass pattern or lung fibrosis to a normal lung pattern was significant for both treatments (all P < 0.001). CONCLUSION Significantly favorable transitions from both ground-glass and lung fibrosis ILD patterns to a normal lung pattern were observed in patients undergoing immunosuppressive treatment for SSc-ILD, suggesting the usefulness of examining these transitions for insights into the underlying pathobiology of treatment response.

Rationale: The Scleroderma Lung Study II (SLS II) demonstrated significant improvements in pulmonary function and dyspnea at 24 months compared with baseline when patients with symptomatic scleroderma–related interstitial lung disease (SSc-ILD) were treated with either cyclophosphamide for 1 year (followed for another year on placebo) or mycophenolate mofetil for 2 years in a randomized, double-blind clinical trial. Physiologic and clinical outcomes of SLS II have been published previously. Objectives: The aim of the study was to assess changes from baseline in the extent of SSc-ILD on high-resolution computed tomography (HRCT) measured in the SLS II participants using quantitative image analysis after 2 years and to determine whether these HRCT changes were correlated with the changes in physiologic and clinical measures over the same time interval. Methods: Ninety-seven of the 142 randomized subjects (cyclophosphamide group, 47 subjects; mycophenolate mofetil group, 50 subjects) participating in SLS II underwent thoracic volumetric thin-section HRCT at both baseline and 24 months. Quantitative computer-aided diagnosis scores using volumetric HRCT scans were obtained using a previously developed computer-aided system. The scores were quantitative lung fibrosis, quantitative ground glass, quantitative honeycomb, and quantitative interstitial lung disease (QILD), the latter representing the sum of quantitative lung fibrosis, quantitative ground glass, and quantitative honeycomb. These scores were obtained both for the whole lung and for individual lobes. Paired t tests were used for the combined (pooled) cyclophosphamide and mycophenolate mofetil groups to compare 24-month changes from baseline in both the whole lung and the lobe of maximal involvement as determined at baseline (worst lobe). Results: At the end of the 24-month trial, QILD in the whole lung was significantly reduced by a mean of 2.51% in the pooled groups (adjusted 95% confidence interval, −4.00 to −1.03%; P = 0.001). There was no significant difference in the QILD score improvement between the cyclophosphamide (−2.66%) and mycophenolate (−2.38%) groups when assessed separately (P = 0.88). For the pooled group, the 24-month changes in QILD scores in the whole lung correlated significantly with other outcomes, including 24-month changes in forced vital capacity (ρ = −0.37), single-breath diffusing capacity of the lung for carbon monoxide (ρ = −0.22), and breathlessness as measured by the Transition Dyspnea Index (ρ = −0.26). Conclusions: Treatment of SSc-ILD with either cyclophosphamide for 1 year, followed by placebo for a second year, or mycophenolate for 2 years was associated with a significant reduction (improvement) in the extent of HRCT SSc-ILD assessed by computer-aided diagnosis scores, which correlated well with one or more other measures of treatment response. These findings demonstrate that actual changes in lung structure accompany improvements in physiologic and/or symptomatic measures in SSc-ILD.

PURPOSE: We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. MATERIALS AND METHODS: In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multisubject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. RESULTS: Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. CONCLUSION: We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

The purpose of the study was to determine if enhancement features and qualitative imaging features on multiphasic multidetector computed tomography (MDCT) were associated with tumor grade in patients with clear cell renal cell carcinoma (ccRCC). In this retrospective, IRB approved, HIPAA-compliant, institutional review board-approved study with waiver of informed consent, 127 consecutive patients with 89 low grade (LG) and 43 high grade (HG) ccRCCs underwent preoperative four-phase MDCT in unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases. Previously published quantitative (absolute peak lesion enhancement, absolute peak lesion enhancement relative to normal enhancing renal cortex, 3D whole lesion enhancement and the wash-in/wash-out of enhancement within the 3D whole lesion ROI) and qualitative (enhancement pattern; presence of necrosis; pattern of; tumor margin; tumor–parenchymal interface, tumor–parenchymal interaction; intratumoral vascularity; collecting system infiltration; renal vein invasion; and calcification) assessments were obtained for each lesion independently by two fellowship-trained genitourinary radiologists. Comparisons between variables included χ2, ANOVA, and student t test. p values less than 0.05 were considered to be significant. Inter-reader agreement was obtained with the Gwet agreement coefficient (AC1) and standard error (SE) was reported. No significant differences were observed between the LG and HG ccRCC cohorts with respect to absolute peak lesion enhancement and relative lesion enhancement ratio. There was a significant inverse correlation between low and high grade ccRCC and tumor enhancement the NP (71 HU vs. 54 HU, p

Chronic non-healing wounds challenge tissue regeneration and impair infection regulation for patients afflicted with this condition. Next generation wound care technology capable of in situ physiological surveillance which can diagnose wound parameters, treat various chronic wound symptoms, and reduce infection at the wound noninvasively with the use of a closed loop therapeutic system would provide patients with an improved standard of care and an accelerated wound repair mechanism. The indicating biomarkers specific to chronic wounds include blood pressure, temperature, oxygen, pH, lactate, glucose, interleukin-6 (IL-6), and infection status. A wound monitoring device would help decrease prolonged hospitalization, multiple doctors' visits, and the expensive lab testing associated with the diagnosis and treatment of chronic wounds. A device capable of monitoring the wound status and stimulating the healing process is highly desirable. In this review, we discuss the impaired physiological states of chronic wounds and explain the current treatment methods. Specifically, we focus on improvements in materials, platforms, fabrication methods for wearable devices, and quantitative analysis of various biomarkers vital to wound healing progress.

Quantitative imaging in lung cancer CT seeks to characterize nodules through quantitative features, usually from a region of interest delineating the nodule. The segmentation, however, can vary depending on segmentation approach and image quality, which can affect the extracted feature values. In this study, we utilize a fully-automated nodule segmentation method – to avoid reader-influenced inconsistencies – to explore the effects of varied dose levels and reconstruction parameters on segmentation. Raw projection CT images from a low-dose screening patient cohort (N=59) were reconstructed at multiple dose levels (100%, 50%, 25%, 10%), two slice thicknesses (1.0mm, 0.6mm), and a medium kernel. Fully-automated nodule detection and segmentation was then applied, from which 12 nodules were selected. Dice similarity coefficient (DSC) was used to assess the similarity of the segmentation ROIs of the same nodule across different reconstruction and dose conditions. Nodules at 1.0mm slice thickness and dose levels of 25% and 50% resulted in DSC values greater than 0.85 when compared to 100% dose, with lower dose leading to a lower average and wider spread of DSC values. At 0.6mm, the increased bias and wider spread of DSC values from lowering dose were more pronounced. The effects of dose reduction on DSC for CAD-segmented nodules were similar in magnitude to reducing the slice thickness from 1.0mm to 0.6mm. In conclusion, variation of dose and slice thickness can result in very different segmentations because of noise and image quality. However, there exists some stability in segmentation overlap, as even at 1mm, an image with 25% of the lowdose scan still results in segmentations similar to that seen in a full-dose scan.

Deep learning methods have been widely and successfully applied to the medical imaging field. Specifically, fully convolutional neural networks have become the state-of-the-art supervised segmentation method in a variety of biomedical segmentation problems. Two fully convolutional networks were proposed to sequentially achieve accurate lobar segmentation. Firstly, a 2D ResNet-101 based network is proposed for lung segmentation and 575 chest CT scans from multicenter clinical trials were used with radiologist approved lung segmentation. Secondly, a 3D DenseNet based network is applied to segment the 5 lobes and a total of 1280 different CT scans were used with radiologist approved lobar segmentation as ground truth. The dataset includes various pathological lung diseases and stratified sampling was used to form training and test sets following a ratio of 4:1 to ensure a balanced number and type of abnormality present. A 3D CNN segmentation model was also built for lung segmentation to investigate the feasibility using current hardware. Using 5-fold cross validation a mean Dice coefficient of 0.988 ± 0.012 and Average Surface Distance of 0.562 ± 0.49 mm was achieved by the proposed 2D CNN on lung segmentation. 3D DenseNet on lobar segmentation achieved Dice score of 0.959 ± 0.087 and Average surface distance of 0.873 ± 0.61 mm.

A clinical validation of the bone scan lesion area (BSLA) as a quantitative imaging biomarker was performed in metastatic castration-resistant prostate cancer (mCRPC). BSLA was computed from whole-body bone scintigraphy at baseline and week 12 posttreatment in a cohort of 198 mCRPC subjects (127 treated and 71 placebo) from a clinical trial involving a different drug from the initial biomarker development. BSLA computation involved automated image normalization, lesion segmentation, and summation of the total area of segmented lesions on bone scan AP and PA views as a measure of tumor burden. As a predictive biomarker, treated subjects with baseline BSLA [Formula: see text] had longer survival than those with higher BSLA ([Formula: see text] and [Formula: see text]). As a surrogate outcome biomarker, subjects were categorized as progressive disease (PD) if the BSLA increased by a prespecified 30% or more from baseline to week 12 and non-PD otherwise. Overall survival rates between PD and non-PD groups were statistically different ([Formula: see text] and [Formula: see text]). Subjects without PD at week 12 had longer survival than subjects with PD: median 398 days versus 280 days. BSLA has now been demonstrated to be an early surrogate outcome for overall survival in different prostate cancer drug treatments.

Rationale and Objectives Median survival of patients with idiopathic pulmonary fibrosis (IPF) is 2–5 years. Sensitive imaging metrics can play a role in detecting early changes in therapeutic development. The aim of the present study was to compare known computed tomography (CT) histogram kurtosis and a classifier-based quantitative score to assess baseline severity and change over time in patients with IPF. Materials and Methods A total of 57 patients with at least baseline and paired follow-up scans were selected from an imaging database of standardized CT scans obtained from patients with IPF. CT histogram measurement of kurtosis and quantitative lung fibrosis (QLF) and quantitative interstitial lung disease (QILD) scores from a classification algorithm were calculated. Spearman rank correlations were used to assess associations between baseline severity and changes for all CT-derived measures compared to forced vital capacity (FVC) and carbon monoxide diffusion capacity (DLCO) (percent predicted). Results At baseline, mean (±SD) of kurtosis was 2.43 (±1.83). Mean (±SD) values of QLF and QILD scores were 20.7% (±13.4) and 43.3% (±20.0), respectively. All baseline histogram indices and QLF and QILD scores were correlated well with baseline FVC and DLCO. When assessing associations with changes in FVC and DLCO over time, only QLF score was statistically significant (ρ = −0.57; P

Background: Patients with IPF experience varying degrees of decline in lung function over time. The rate of decline can vary between patients and across over time and at present there is no reliable way to predict which subjects will decline faster. Aim: This study explores whether short term change in QLF score can predict fast from vs slow FVC decline. Methods: In an open-label phase 2 trial in patients with IPF subjects underwent standardized HRCT scans (QLF) and spirometry (FVC) performed at baseline and every 24 and 12 weeks, respectively. QLF was measured whore for the whole lung and worst lobe. Mixed effect models were used to estimate and compare against the reduction in FVC over repeated measurements with linear or quadratic growth models. Results: The aAnnual rate of reduction in mean FVC was 3.5% for the Week 24week QLF changes of Conclusion: Change in QLF of 4 percent at 6 months isn predictive of rapid FVC decline within 12 to 15 months

Objectives: To investigate regional transitions of interstitial lung disease patterns on HRCT and their association with symptom scores in patients with Idiopathic Pulmonary Fibrosis (IPF). This work uses mapping of voxels between baseline and follow-up scans to assess highly localized transitions in lung patterns. Methods: Anonymized HRCT images were obtained from 25 IPF subjects who had a baseline and follow-up surveillance HRCT scan at 6 months. A transitional interstitial lung disease (TILD) matrix of pattern transitions was computed for each paired of subject scans using the following steps: (1) lobar segmentation and classification of each voxel as one of 4 lung patterns (fibrotic, honeycomb, ground glass, or normal); (2) registration (mapping) of corresponding voxels between the paired scans; (3) for each mapped pair of voxels recording a count in the appropriate cell of the 4x4 TILD matrix; (4) summarizing the pattern transitions as percentages of total voxels (TILD scores). Associations between TILD scores and symptom scores, as well as comparisons between worse and non-worse groups, were performed. Results: TILD scores indicating worsening from ground glass to fibrotic patterns were associated with increasing the shortness of breath (rho=0.48; p=0.016). Significant differences in TILD scores were found between worse and non-worse groups in the shortness of breath (p=0.014). When symptoms were not worse the TILD scores were minimal in the right and left upper lobes. Conclusions: Using voxel-by-voxel TILD scores on paired HRCT scans 6 months apart, there were obvious transitions to worsening lung patterns when symptoms got worse.

Background: There is increasing interest in determining treatment efficacy early in the setting of patients with IPF. This is important in design of clinical trials in which decline in FVC is used to assess PFS as an endpoint. Aim: This study explores whether change in Quantitative Lung Fibrosis (QLF) score or FVC at 24 weeks is more sensitive in predicting PFS (where progression is defined as a decline in of > 10%) . Methods: In an open-label phase 2 trial in patients with idiopathic pulmonary fibrosis subjects underwent standardized HRCT scans (QLF) and spirometry (FVC) at baseline and and every 24 and 12 weeks respectively. Cox regression models and Kaplan-Meier (KM) Progression-Free-Survival plots was were performed to evaluate 24 week change in QLF of 4%, 3%, and 2% and FVC of 5% as a predictor of PFS. Harrell’s C index test was performed to assess the validity of the models. Results: The nNumber of subjects with both QLF scores and FVC at baseline, weeks 24, 48, 72 and 96 were 73, 66, 32, and 24 respectively. For the whole lung the hazard ratios for change in QLF of 4, 3, and 2% wereas 3.65, 3.6 and 3.7 respectively (all p Conclusion: A 24 week change in QLF > 2 % (worst lobe) rather change in FVC is a stronger predictor of PFS defined by FVC decline defined PFS in clinical trials.

Author(s): Young, Stefano; Lo, Pechin; Kim, Grace; Brown, Matthew; Hoffman, John; Hsu, William; Wahi-Anwar, Wasil; Flores, Carlos; Lee, Grace; Noo, Frederic; Goldin, Jonathan; McNitt-Gray, Michael | Abstract: PurposeLung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population.MethodsThe raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed.ResultsFrom the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case.ConclusionsCAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams.

For pediatric oncology patients, CT scans are performed to assess treatment response and disease progression. CAD may be used to detect lung nodules which would reflect metastatic disease. The purpose of this study was to investigate the effects of reducing radiation dose and varying slice thickness on CAD performance in the detection of solid lung nodules in pediatric patients. The dataset consisted of CT scans of 58 pediatric chest cases, from which 7 cases had lung nodules detected by radiologist, and a total of 28 nodules were marked. For each case, the original raw data (sinogram data) was collected and a noise addition model was used to simulate reduced-dose scans of 50%, 25% and 10% of the original dose. In addition, the original and reduced-dose raw data were reconstructed at slice thicknesses of 1.5 and 3 mm using a medium sharp (B45) kernel; the result was eight datasets (4 dose levels x 2 thicknesses) for each case An in-house CAD tool was applied on all reconstructed scans, and results were compared with the radiologist’s markings. Patient level mean sensitivities at 3mm thickness were 24%, 26%, 25%, 27%, and at 1.5 mm thickness were 23%, 29%, 35%, 36% for 10%, 25%, 50%, and 100% dose level, respectively. Mean FP numbers were 1.5, 0.9, 0.8, 0.7 at 3 mm and 11.4, 3.5, 2.8, 2.8 at 1.5 mm thickness for 10%, 25%, 50%, and 100% dose level respectively. CAD sensitivity did not change with dose level for 3mm thickness, but did change with dose for 1.5 mm. False Positives increased at low dose levels where noise values were high.

Background and objective Endobronchial valve (EBV) therapy is optimized in patients who demonstrate little or no collateral ventilation (CV). The accuracy of the Chartis System and visual assessment of high-resolution computerized tomography (HRCT) fissure completeness by a core radiology laboratory for classifying CV status was compared by evaluating the relationship of each method with target lobe volume reduction (TLVR) after EBV placement. Methods Retrospective HRCT fissure analysis of a study population who underwent catheter-based measurement of CV followed by complete occlusion of the targeted lobe by EBV. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the HRCT fissure analysis and the catheter-based measurement of CV for predicting TLVR was determined. Results Accuracy for correctly classifying TLVR with EBV was similar for Chartis System and HRCT fissure analysis (74 vs 77%). The sensitivity and specificity of the Chartis measurement were 86% and 61% and those of HRCT fissure analysis 75% and 79%. Patients with TLVR >= 350 mL had statistically significant improvement in respiratory function, exercise performance and quality of life measures. Conclusions When evaluating patients for likelihood of successful EBV therapy, the Chartis System CV assessment and HRCT fissure analysis appear to have comparable accuracy. Both techniques were found to be beneficial for EBV procedure planning.

Introduction High resolution computed tomography (HRCT) plays an indispensable role in the diagnosis of Idiopathic Pulmonary Fibrosis (IPF). Due to the short median survival of 2 to 5 years, understanding the chance of progression in the next follow-up of a patient is critical. The aim of this study is to test the predictability of progression in IPF of the near follow-up of a patient using the early quantitative changes in HRCT scans. Methods Volumetric anonymized HRCT scans were obtained. Two data sets were collected: a pilot study with 35 subjects in three sequential scans (baseline, 6 and 12 month scans) and validation study involving 157 subjects with clinical information. Quantitative lung fibrosis (QLF) scores were calculated for HRCT scans using an automated system. A log-rank test and Cox regression were used to test a prediction with early changes in QLF. Results In the pilot study mean changes in QLF increased by 4.33% at 6 months and 14.83% at 12 months, when the visual assessments changed from stable to worse. Mean QLF changes were ~1% for the stable cases during 12 months. Using 4% QLF differences at 6 months in the validation cohort, significant differences in progression were found (p Conclusion Early changes in a quantitative score from HRCT can predict progression in lung function. A quantitative texture-based score can play a pivotal role for informed and timely management decision for patients with IPF.

The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section "Discussion," a sentence was worded incorrectly.

625 Background: Clear cell renal cell carcinoma (ccRCC) is a highly vascularized tumor and has a heterogeneous molecular profile, but the correlation between its tumor biology and radiomic features have only recently been investigated. As 70% of ccRCCs are detected incidentally at imaging, and tumor phenotypes are only available after surgery, a non-invasive biomarker to predict ccRCC phenotypes and aggressiveness on imaging may be clinically valuable, as low grade lesions may undergo active surveillance. Methods: With IRB approval and HIPAA compliance, our study cohort comprised 92 consecutive patients with 102 ccRCCs (mean age, 62 years (SD ± 14.2) with histopathology and molecular endpoints imaged preoperatively on CT with a four-phase renal protocol [unenhanced (U), corticomedullary (C), nephrographic (N), excretory (E)]. Radiomic data was obtained by contouring the entire ccRCC in each phase to obtain a 3D tumor volume of interest (VOI). The mean enhancement in each phase the and wash-in and wash-out of enhancement was calculated for each ccRCC. Molecular data was obtained through immunohistochemistry of resected ccRCCs to assess carbonic anhydrase-IX (CAIX), microvessel density (MVD), phosphatase and tension homolog (PTEN), and tumor grade (TG). Categorical variables were analyzed with logistic regression and odds ratio (OR) was reported. Continuous variables were analyzed with linear regression and Pearson correlation coefficient (r2) was reported. P-values < .05 were considered significant. Results: Significant radiomic associations included TG and 3D tumor enhancement in the C (OR = 4.72, p = .030), N (OR = 17.71, p < .0001), and E phases (OR = 17.10, p < .0001), and wash-in from U to C (OR = 8.27, p = .004). MVD had a significant positive association with 3D tumor enhancement in the C phase (r2= 0.410, p < .0001), wash-in from U to C (r2= 0.435, p < .0001) and wash-out from the C to N (r2= 0.435, p = .001). There were no significant radiomic correlates with CAIX or PTEN expression. Conclusions: A 3D ccRCC VOI on multiphasic CT had significant correlations with TG and MVD, independent of clinical features, potentially becoming a predictive imaging biomarker of ccRCC aggressiveness and clinical outcome.

Purpose To assess the intra- and interscan reproducibility of LV twist using FAST. Assessing the reproducibility of the measurement of new MRI biomarkers is an important part of validation. Fourier Analysis of STimulated Echoes (FAST) is a new MRI tissue tagging method that has recently been shown to compare favorably with conventional estimates of left ventricular (LV) twist from cardiac tagged images, but with significantly reduced user interaction time. Materials and Methods Healthy volunteers (N = 10) were scanned twice using FAST over 1 week. On day 1, two measurements of LV twist were collected for intrascan comparisons. Measurements for LV twist were again collected on day 8 for interscan assessment. LV short-axis tagged images were acquired on a 3 Tesla (T) scanner to ensure detectability of tags during early and mid-diastole. Peak LV twist is reported as mean ± SD. Reproducibility was assessed using the concordance correlation coefficient (CCC) and the repeatability coefficient (RC) (95% confidence interval [CI] range). Results Mean peak twist measurements were 13.4 ± 4.3° (day 1, scan 1), 13.6 ± 3.7° (day 1, scan 2), and 13.0 ± 2.7° (day 8). Bland-Altman analysis resulted in intra- and interscan bias and 95% CI of −0.6° [−1.0°, 1.6°] and 1.4° (−1.0°, 3.0°), respectively. The Bland-Altman RC for peak LV twist was 2.6° and 4.0° for intra- and interscan, respectively. The CCC was 0.9 and 0.6 for peak LV twist for intra- and interscan, respectively. Conclusion FAST is a semi-automated method that provides a quick and quantitative assessment of LV systolic and diastolic twist that demonstrates high intrascan and moderate interscan reproducibility in preliminary studies. J. Magn. Reson. Imaging 2014;39:463–468. © 2013 Wiley Periodicals, Inc.

Lung cancer screening CT is already performed at low dose. There are many techniques to reduce the dose even further, but it is not clear how such techniques will affect nodule detectability. In this work, we used an in-house CAD algorithm to evaluate detectability. 90348 patients and their raw CT data files were drawn from the National Lung Screening Trial (NLST) database. All scans were acquired at ~2 mGy CTDIvol with fixed tube current, 1 mm slice thickness, and B50 reconstruction kernel on a Sensation 64 scanner (Siemens Healthcare). We used the raw CT data to simulate two additional reduced-dose scans for each patient corresponding to 1 mGy (50%) and 0.5 mGy (25%). Radiologists’ findings on the NLST reader forms indicated 65 nodules in the cohort, which we subdivided based on LungRADS criteria. For larger category 4 nodules, median sensitivities were 100% at all three dose levels, and mean sensitivity decreased with dose. For smaller nodules meeting the category 2 or 3 criteria, the dose dependence was less obvious. Overall, mean patient-level sensitivity varied from 38.5% at 100% dose to 40.4% at 50% dose, a difference of only 1.9%. However, the false-positive rate quadrupled from 1 per case at 100% dose to 4 per case at 25% dose. Dose reduction affected lung-nodule detectability differently depending on the LungRADS category, and the false-positive rate was very sensitive at sub-screening dose levels. Thus, care should be taken to adapt CAD for the very challenging noise characteristics of screening.

Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.

To investigate volumetric and density changes in the ipsilateral and contralateral lobes following volume reduction of an emphysematous target lobe.The study included 289 subjects with heterogeneous emphysema, who underwent bronchoscopic volume reduction of the most diseased lobe with endobronchial valves and 132 untreated controls. Lobar volume and low-attenuation relative area (RA) changes post-procedure were measured from computed tomography images. Regression analysis (Spearman's rho) was performed to test the association between change in the target lobe volume and changes in volume and density variables in the other lobes.The target lobe volume at full inspiration in the treatment group had a mean reduction of -0.45 L (SE = 0.034, P 0.0001), and was associated with volume increases in the ipsilateral lobe (rho = -0.68, P 0.0001) and contralateral lung (rho = -0.16, P = 0.006), and overall reductions in expiratory RA (rho = 0.31, P 0.0001) and residual volume (RV)/total lung capacity (TLC) (rho = 0.13, P = 0.03).When the volume of an emphysematous target lobe is reduced, the volume is redistributed primarily to the ipsilateral lobe, with an overall reduction. Image-based changes in lobar volumes and densities indicate that target lobe volume reduction is associated with statistically significant overall reductions in air trapping, consistent with expansion of the healthier lung.Computed tomography allows assessment of the treatment of emphysema with endobronchial valves. • Endobronchial valves can reduce the volume of an emphysematous lung lobe. • Compensatory expansion is greater in ipsilateral lobes than in the contralateral lung. • Reduced air trapping is measurable by RV/TLC and smaller low attenuation area.

The Scleroderma Lung Study showed the efficacy of cyclophosphamide in modestly improving the forced vital capacity (FVC) compared with placebo over 1 year. Using changes in texture-based scores that quantify lung fibrosis as the percentage involvement of reticulation patterns, the effectiveness of cyclophosphamide was re-assessed by examining its impact on quantitative lung fibrosis (QLF). Axial HRCT images were acquired (1-mm slice thickness, 10-mm increments) in the prone position at inspiration. A validated model for quantifying interstitial disease patterns was applied to images from 83 subjects at baseline and 12 months. Scores were calculated for six zones (upper, mid, lower of the right/left lung) and the whole lung. Average changes were compared. Correlations were performed between QLF and physiological and clinical scores. From the most severe zones identified at baseline, QLF scores decreased by 2.6% in the cyclophosphamide group, whereas they increased by 9.1% in the placebo group, leading to ~12% difference (p = 0.0027). Between-treatment difference in whole lung QLF was ~5% (p = 0.0190). Significant associations were observed between changes in QLF and FVC (r = −0.33), dyspnea score (r = −0.29), and consensus visual score (p = 0.0001). QLF scores provide an objective quantitative tool for assessing treatment efficacy in scleroderma-related interstitial lung disease.

Laser-induced forward transfer (LIFT) is a high-resolution direct-write technique, which can print a wide range of liquid materials without a nozzle. In this process, a pulsed laser initiates the expulsion of a high-velocity micro-jet of fluid from a thin donor film. LIFT involves a novel regime for impulsively driven free-surface jetting in that viscous forces developed in the thin film become relevant within the jet lifetime. In this work, time-resolved microscopy is used to study the dynamics of the laser-induced ejection process. We consider the influence of thin metal and thick polymer laser-absorbing layers on the flow actuation mechanism and resulting jet dynamics. Both films exhibit a mechanism in which flow is driven by the rapid expansion of a gas bubble within the liquid film. We present high-resolution images of the transient gas cavities, the resulting ejection of high aspect ratio external jets, as well as the first images of re-entrant jets formed during LIFT. These observations are interpreted in the context of similar work on cavitation bubble formation near free surfaces and rigid interfaces. Additionally, by increasing the laser beam size used on the polymer absorbing layer, we observe a transition to an alternate mechanism for jet formation, which is driven by the rapid expansion of a blister on the polymer surface. We compare the dynamics of these blister-actuated jets to those of the gas-actuated mechanism. Finally, we analyze these results in the context of printing sensitive ink materials.

BACKGROUND AND PURPOSE: Currently it is difficult to predict tumor response to anti-angiogenic therapy in individual patients. Our aim was to determine if ADC histogram analysis can stratify progression-free and overall survival in patients with newly diagnosed GBM treated “up-front” (ie, before tumor recurrence) with bevacizumab. MATERIALS AND METHODS: Up-front bevacizumab-treated and control patients (n = 59 and 62, respectively) with newly diagnosed GBM were analyzed by using an ADC histogram approach based on enhancing tumor. Progression-free and overall survival was determined by using Cox proportional HRs and the Kaplan-Meier method with logrank and Wilcoxon tests. RESULTS: For up-front bevacizumab-treated patients, lower ADC(L) was associated with significantly longer progression-free survival (median, 459 days for ADC(L) < 1200 versus 315 days for ADC(L) ≥ 1200 10(−6)mm(2)/s; P = .008, logrank test) and trended with longer overall survival (581 versus 429 days, P = .055). ADC values did not stratify progression-free or overall survival for patients in the control group (P = .92 and P = .22, respectively). Tumors with MGMT promoter methylation had lower ADC(L) values than unmethylated tumors (mean, 1071 versus 1183 10(−6)mm(2)/s; P = .01, 2-group t test). CONCLUSIONS: Pretreatment ADC histogram analysis can stratify progression-free survival in bevacizumab-treated patients with newly diagnosed GBM. Lower ADC is associated with tumor MGMT promoter methylation, which may, in part, account for the favorable outcome associated with low ADC(L) tumors.

Objective: We evaluated patterns of tumor progression in patients with recurrent glioblastoma who were treated with bevacizumab (BEV) alone or in combination with irinotecan (CPT-11) while participating in the BRAIN study. Methods: An independent neuroradiologist reviewed MRI scans at baseline and progression in patients who received BEV (n = 85) or BEV+CPT-11 (n = 82) while on BRAIN. Tumor patterns were scored as local, distant, diffuse, or multifocal. Median progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan-Meier methods. Hazard ratios for PFS and OS were estimated using a Cox regression model. Results: Twenty-eight percent of patients who participated in BRAIN had nonlocal disease at baseline (72% local disease). Sixty-seven (79%) patients treated with single-agent BEV and 57 (70%) patients treated with BEV+CPT-11 experienced disease progression while on BRAIN. Most patients in each treatment group did not have a change in the radiographic pattern of their tumor (i.e., “no shift”) at the time of progression. The proportion of BEV patients with no shift (82%) was greater than that of BEV+CPT-11 patients (53%, χ 2 p = 0.0004), and a greater proportion of BEV+CPT-11 patients (39%) compared with BEV patients (16%) experienced local-to-diffuse tumor pattern at progression (χ 2 p = 0.002). Patients treated with BEV or BEV+CPT-11 who had local-to-local or local-to-diffuse progression patterns had similar efficacy outcomes, including objective response, PFS, and OS. Conclusions: Most patients treated with BEV or BEV+CPT-11 on BRAIN did not experience a change from baseline in radiographic characteristics of disease at the time of progression.

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (" nodule�3 mm," " nodule

Background: Manually importing and analyzing image data can be time-consuming, prone to human error, and costly for large clinical trial datasets. This can lead to delays in quality control (QC) feedback to imaging sites and in obtaining data analysis results. Herein we describe the creation and application of a high-throughput review process for import, classification, labeling and QC of large multimodal clinical trial image datasets.Methods: Automated methods were used to remove patient identifying information, extract image header data, and filter image data for usability. A convolutional neural net was applied to estimate anatomy for CT images. Internal scores were assigned for each image series to identify the optimal series for labeling and reading of each anatomical region. Image QC reports were automatically generated for all patients.Results: In combined studies for which 204,492 series were received, 27,841 series were identified as usable and 13,415 series were labeled. Using this high-throughput method, total work-hours required per time point were reduced by an approximate factor of ten when compared to traditional review and labeling methods. Our anatomic classification system identified 95.7% of image series correctly, with the remaining series being manually corrected before labeling and analysis.Conclusions: A high-throughput image analysis pipeline was implemented in a large combined dataset of clinical trial image series. This pipeline can be applied across other studies and modalities for fast image data characterization, labeling and QC.

Emphysema is a disease that causes destruction of lung tissue and an overall reduction in lung compliance as air becomes trapped in the diseased regions. It affects an estimated 60 million people worldwide and up to 2 million Americans. As the disease progresses, the hyperinflated regions continue to expand, limiting the effective volume available for more viable lung tissue. New biomedical valves and stents are being developed that can be implanted in target airways to allow air to escape from the most diseased lung lobe, thereby reducing its volume and providing more space for the adjacent (healthier) lobe to expand. The challenge facing the clinician is to determine which patients are most likely to benefit from the treatment and which lobe/airway should be treated. This article describes imaging biomarkers and an automated computer-aided diagnosis system to perform patient selection and treatment targeting in emphysema.

The goal of this study was to develop a computer-aided therapeutic response (CADrx) system for early prediction of drug treatment response for glioblastoma multiforme (GBM) brain tumors with diffusion weighted (DW) MR images. In conventional Macdonald assessment, tumor response is assessed nine weeks or more post-treatment. However, we will investigate the ability of DW-MRI to assess response earlier, at five weeks post treatment. The apparent diffusion coefficient (ADC) map, calculated from DW images, has been shown to reveal changes in the tumor’s microenvironment preceding morphologic tumor changes. ADC values in treated brain tumors could theoretically both increase due to the cell kill (and thus reduced cell density) and decrease due to inhibition of edema. In this study, we investigated the effectiveness of features that quantify changes from pre- and post-treatment tumor ADC histograms to detect treatment response. There are three parts to this study: first, tumor regions were segmented on T1w contrast enhanced images by Otsu’s thresholding method, and mapped from T1w images onto ADC images by a 3D region of interest (ROI) mapping tool using DICOM header information, second, ADC histograms of the tumor region were extracted from both pre- and five weeks post-treatment scans, and fitted by a two-component Gaussian mixture model (GMM). The GMM features as well as standard histogram-based features were extracted. Finally, supervised machine learning techniques were applied for classification of responders or non-responders. The approach was evaluated with a dataset of 85 patients with GBM under chemotherapy, in which 39 responded and 46 did not, based on tumor volume reduction. We compared adaBoost, random forest and support vector machine classification algorithms, using ten-fold cross validation, resulting in the best accuracy of 69.41% and the corresponding area under the curve (Az) of 0.70.