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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.

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.

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.

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive decline in lung function. Natural history of IPF is unknown and the prediction of disease progression at the time of diagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosis of IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictive model for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, there are two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans and their follow-up status; and (b) simultaneously selecting important features from high-dimensional space, and optimizing the prediction performance. We resolved the first challenge by implementing a study design and having an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-up visits. For the second challenge, we integrated the feature selection with prediction by developing an algorithm using a wrapper method that combines quantum particle swarm optimization to select a small number of features with random forest to classify early patterns of progression. We applied our proposed algorithm to analyze anonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields a parsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROI level. These results are superior to other popular feature selections and classification methods, in that our method produces higher accuracy in prediction of progression and more balanced sensitivity and specificity with a smaller number of selected features. Our work is the first approach to show that it is possible to use only baseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence.

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.

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.

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.

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.

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.

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

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.

To retrospectively investigate the prevalence of tracheal collapse in an emphysema cohort. The occurrence of a large degree of tracheal collapse may have important implications for the clinical management of respiratory symptoms and air trapping in patients with emphysema.Paired full-inspiratory and end-expiratory thin-section volumetric computed tomographic scans were available for 1071 long-term smokers with clinically and physiologically confirmed emphysema. The percentage reduction in the cross-sectional tracheal luminal area from full-inspiration to end-expiration was automatically computed at 2.5-mm intervals along the centerline of the trachea using customized software.Maximal tracheal collapse did not follow a normal distribution in the emphysema cohort (P.0001, skewness/kurtosis tests for normality); the median collapse was 18% (intraquartile range, 11%-30%). Statistically significant differences were found in the distribution of maximal collapse by gender (P.005, Wilcoxon rank sum test). Overall, 10.5% of men and 17.1% of women showed evidence of tracheomalacia on the basis of the criterion of a reduction of 50% or greater in cross-sectional tracheal luminal area at end-expiration.This study offers insights into the prevalence of tracheal collapse in a cohort of patients with emphysema; future work is needed to determine the possible relationship between tracheal collapse and air trapping in subjects with emphysema.

Objectives The aim is to investigate whether the 12-month quantitative changes in high-resolution CT (HRCT) measures of interstitial lung disease (ILD) are different, and to understand how they change, in patients with scleroderma-related ILD who receive drug therapy versus placebo. Methods HRCT images were acquired at baseline and at 12 months in 83 participants in Scleroderma Lung Study I, a clinical trial comparing treatment with oral cyclophosphamide versus placebo. A computer-aided model was used to quantify the extent of fibrotic reticulation, ground glass and honeycomb patterns and quantitative ILD (QILD: sum of these patterns) in the whole lung and the lung zone (upper, middle or lower) of maximal disease involvement. Results Mean QILD score decreased by 3.9% in the cyclophosphamide group while increasing by 4.2% in the placebo group in the most severe zone (p=0.01) and decreased by 3.2% in the cyclophosphamide group while increasing by 2.2% in the placebo group in the whole lung (p=0.03). Transitional probabilities demonstrated greater changes from a fibrotic to either a ground glass or normal pattern in the cyclophosphamide group and the reverse in the placebo group. Conclusions Changes in quantitative HRCT measures of ILD provide a sensitive indication of disease progression and response to treatment. Trial registration number NCT00004563; Post-results.

Symptomatic localized adenomyosis is generally treated conservatively, or with more radical treatments such as hysterectomy. An effective non-invasive therapy is needed, especially for those who wish to preserve their fertility. MR-guided Focused Ultrasound ablation (MRgFUS) has been shown as an effective treatment for symptomatic uterine leiomyomas with a large non-perfused volume (NPV) immediately after treatment. Our specific aim was to compare the change in NPV in subjects with localized adenomyomas treated with MRgFUS, with the change in NPV in subjects treated with MRgFUS for symptomatic uterine leiomyomas to determine if similar results were achieved in the localized adenomyoma cohort.

PURPOSE: Texture features have been investigated as a biomarker of response and malignancy. Because these features reflect local differences in density, they may be influenced by acquisition and reconstruction parameters. The purpose of this study was to investigate the effects of radiation dose level and reconstruction method on features derived from lung lesions. METHODS: With IRB approval, 33 lung tumor cases were identified from clinically indicated thoracic CT scans in which the raw projection (sinogram) data were available. Based on a previously-published technique, noise was added to the raw data to simulate reduced-dose versions of each case at 25%, 10% and 3% of the original dose. Original and simulated reduced dose projection data were reconstructed with conventional and two iterative-reconstruction settings, yielding 12 combinations of dose/recon conditions. One lesion from each case was contoured. At the reference condition (full dose, conventional recon), 17 lesions were randomly selected for repeat contouring (repeatability). For each lesion at each dose/recon condition, 151 texture measures were calculated. A paired differences approach was employed to compare feature variation from repeat contours at the reference condition to the variation observed in other dose/recon conditions (reproducibility). The ratio of standard deviation of the reproducibility to repeatability was used as the variation measure for each feature. RESULTS: The mean variation (standard deviation) across dose levels and kernel was significantly different with a ratio of 2.24 (±5.85) across texture features (p=0.01). The mean variation (standard deviation) across dose levels with conventional recon was also significantly different with 2.30 (7.11) (p=0.025). The mean variation across reconstruction settings of original dose has a trend in showing difference with 1.35 (2.60) among all features (p=0.09). CONCLUSION: Texture features varied considerably with variations in dose and reconstruction condition. Care should be taken to standardize these conditions when using texture as a quantitative feature. This effort supported in part by a grant from the National Cancer Institute's Quantitative Imaging Network (QIN): U01 CA181156; The UCLA Department of Radiology has a Master Research Agreement with Siemens Healthcare; Dr. McNitt-Gray has previously received research support from Siemens Healthcare.

Purpose To determine whether conventional nodule densitometry or analysis based on contrast enhancement maps of indeterminate lung nodules imaged with contrast-enhanced CT can distinguish benign from malignant lung nodules.

Rationale and Objectives The objective is to study the incremental effects of using a computer-aided lung nodule detection (CAD) system on the performance of a large pool of observers. Materials and Methods A set of eight thin-section computed tomographic data sets with limited longitudinal coverage, containing a total of 22 lung nodules, was analyzed by using the automated nodule detection system. When applied to all eight cases, the CAD system alone achieved a detection rate of 86.4%, with 2.64 false-positive results per case. This study included 202 observers at a national radiology meeting: 39 thoracic radiologists, 95 nonthoracic radiologists, and 68 nonradiologists. Each participant read from one to eight cases in random order, first without and then with CAD system output available. Observer performance in nodule detection was measured before and after CAD was made available. Differences in performance of groups of observers before and after CAD were tabulated by mean, median, and SD in detection rate and number of false-positive results and tested by using nonparametric methods. Results In an analysis involving only the first randomly selected case read by all 202 participants, there were statistically significant increases in nodule detection rates and numbers of false-positive results for all types of observers. There was a significant difference in detection rates between radiologists and nonradiologists before CAD, but after CAD, there was no significant difference in detection rates between these observer types. In a second analysis involving 13 participants who read all eight cases, mean detection rates were 64.0% before CAD and 81.9% after CAD. Mean numbers of false-positive results were 0.144 per case before CAD and 0.173 after CAD. Conclusion In a large observer study, use of a CAD system for nodule detection resulted in an incremental increase in detection rate, but also led to an increase in number of false-positive results. Also, CAD appears to be an equalizer of detection rates between observers of different levels of experience.

Rationale and objectives To investigate the performance of observers with different levels of experience in distinguishing between benign and malignant solitary pulmonary nodules (SPN) on CT, and to determine the effects on interpretation of three different conditions: image data alone, the addition of clinical data, and the addition of output from a computer-aided diagnosis (CAD) system. Materials and methods 28 thin-section CT datasets of SPNs with proven diagnoses (15 malignant and 13 benign) were used to measure observer performance. Readers were categorized according to their experience and read the cases in random order. For each case readers were asked to assign a level of confidence on a scale from 0.0–1.0 (0.0 benign, 1.0 malignant) for the diagnosis of the nodule. Each reader scored the cases based on review of image data alone (phase 1), then with limited clinical data (phase 2), and finally with CAD output (phase 3). To assess performance, multiple reader multiple case (MRMC) receiver operating characteristic (ROC) analysis was used. Results 2 thoracic radiologists, 1 thoracic radiology fellow, 2 nonthoracic radiologists, and 3 radiology residents read the cases. The average area under the ROC curve for all readers (A z ) at each stage was 0.68, 0.75, and 0.81, for image data alone, with clinical data, and with CAD output respectively. The difference in performance between phases (2 and 3) and (1 and 3) was significantly different ( P = 0.018 and P = 0.020). However, the difference between phases (1 and 2) was not significantly different ( P = 0.155). Conclusion Diagnostic performance increased significantly with the addition of CAD output. With further validation CAD output may play a significant role in SPN management.

Purpose: A method to discriminate different types of renal cell carcinoma (RCC) was developed using attenuation values observed in multiphasic contrast-enhanced CT. This work evaluates the sensitivity of this RCC discrimination task at different CT radiation dose levels. Methods: We selected 5 cases of kidney lesion patients who had undergone four-phase CT scans covering the abdomen to the lilac crest. Through an IRB-approved study, the scans were conducted on 64-slice CT scanners (Definition AS/Definition Flash, Siemens Healthcare) using automatic tube-current modulation (TCM). The protocol included an initial baseline unenhanced scan, followed by three post-contrast injection phases. CTDIvol (32 cm phantom) measured between 9 to 35 mGy for any given phase. As a preliminary study, we limited the scope to the cortico-medullary phase—shown previously to be the most discriminative phase. A previously validated method was used to simulate a reduced dose acquisition via adding noise to raw CT sinogram data, emulating corresponding images at simulated doses of 50%, 25%, and 10%. To discriminate the lesion subtype, ROIs were placed in the most enhancing region of the lesion. The mean HU value of an ROI was extracted and used to discriminate to the worst-case RCC subtype, ranked in the order of clear cell, papillary, chromophobe and the benign oncocytoma. Results: Two patients exhibited a change of worst case RCC subtype between original and simulated scans, at 25% and 10% doses. In one case, the worst-case RCC subtype changed from oncocytoma to chromophobe at 10% and 25% doses, while the other case changed from oncocytoma to clear cell at 10% dose. Conclusion: Based on preliminary results from an initial cohort of 5 patients, worst-case RCC subtypes remained constant at all simulated dose levels except for 2 patients. Further study conducted on more patients will be needed to confirm our findings. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant Support from: U01 CA181156

An automated system was developed for detecting lung micronodules on thin-section computed tomographic images and was applied to data from 15 subjects with 77 lung nodules. The automated system, without user interaction, achieved a sensitivity of 100% for nodules (>3 mm in diameter) and 70% for micronodules (

e16049 Background: Clear cell renal cell carcinoma (ccRCC) is the most common tumor of the kidney. Up to 70% are incidentally detected on multiphasic CT. The prognosis for patients with ccRCC is related to Fuhrman grade (FG) and is diagnosed by biopsy or excision. There is a great need for a non-invasive method to assess tumor grade which may help inform clinical decision-making. The purpose of our study is to determine if contrast enhancement on CT predicts FG and microvessel density (MVD) of ccRCC lesions and to assess which combination of quantitative and qualitative radiological features and clinical features predict high FG ccRCC lesions. Methods: With IRB approval for this HIPAA-compliant retrospective study, our pathology and imaging databases were queried to obtain a cohort of ccRCC with a preoperative multiphasic (unenhanced (U), corticomedullary (C), nephrographic (N), and excretory (E)) CT scan. Tumors were stained with CD4 to quantify % MVD. Spearman’s rank correlation was calculated to test the strength of the association between CT enhancement, %MVD and FG. Stepwise logistic regression analysis was performed to determine the quantitative and/or qualitative feature with the highest performance in predicting high FG tumor. The multivariate logistic regression analysis was evaluated using ROC curves and AUCs. Results: Our cohort had 127 patients with 89 low-grade tumors and 43 high-grade tumors. The %wash-in of enhancement from the U to the C phase showed a significant correlation with %MVD of the tumor (R2= 0.181,p < . 001) as did enhancement of the tumor in the early C phase with %MVD (R2= 0.159,p < . 001). There was a significant inverse correlation with %MVD and FG (R2= 0.137,p < . 001). T-stage, tumor size, %MVD and presence of renal vein invasion were determined to be significant independent predictors of high grade lesions with an AUC of .838 (95% CI .748-.927). Conclusions: Lower grade ccRCC tumors have a higher MVD. Therefore, contrast material washes in at a faster rate from the UN to the CM phase, enabling low grade to be discriminated from high-grade tumors on CT.

Bone tumor segmentation on bone scans has recently been adopted as a basis for objective tumor assessment in several phase II and III clinical drug trials. Interpretation can be difficult due to the highly sensitive but non-specific nature of bone tumor appearance on bone scans. In this paper we present a machine learning approach to segmenting tumors on bone scans, using intensity and context features aimed at addressing areas prone to false positives. We computed the context features using landmark points, identified by a modified active shape model. We trained a random forest classifier on 100 and evaluated on 73 prostate cancer subjects from a multi-center clinical trial. A reference segmentation was provided by a board certified radiologist. We evaluated our learning based method using the Jaccard index and compared against the state of the art, rule based method. Results showed an improvement from 0.50 ±0.31 to 0.57 ±0.27. We found that the context features played a significant role in the random forest classifier, helping to correctly classify regions prone to false positives.

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patient's baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic, a priori model to detect candidate nodules on the baseline scan of a previously unseen patient. A user then confirms or rejects nodule candidates to establish baseline results. For analysis of follow-up scans of that particular patient, a patient-specific model is derived from these baseline results. This model describes expected features (location, volume and shape) of previously segmented nodules so that the system can relocalize them automatically on follow-up. On the baseline scans of 17 subjects, a radiologist identified a total of 36 nodules, of which 31 (86%) were detected automatically by the system with an average of 11 false positives (FPs) per case. In follow-up scans 27 of the 31 nodules were still present and, using patient-specific models, 22 (81%) were correctly relocalized by the system. The system automatically detected 16 out of a possible 20 (80%) of new nodules on follow-up scans with ten FPs per case.

Whole-body bone scintigraphy (or bone scan) is a highly sensitive method for visualizing bone metastases and is the accepted standard imaging modality for detection of metastases and assessment of treatment outcomes. The development of a quantitative biomarker using computer-aided detection on bone scans for treatment response assessment may have a significant impact on the evaluation of novel oncologic drugs directed at bone metastases. One of the challenges to lesion segmentation on bone scans is the non-specificity of the radiotracer, manifesting as high activity related to non-malignant processes like degenerative joint disease, sinuses, kidneys, thyroid and bladder. In this paper, we developed an automated bone scan lesion segmentation method that implements intensity normalization, a two-threshold model, and automated detection and removal of areas consistent with non-malignant processes from the segmentation. The two-threshold model serves to account for outlier bone scans with elevated and diffuse intensity distributions. Parameters to remove degenerative joint disease were trained using a multi-start Nelder-Mead simplex optimization scheme. The segmentation reference standard was constructed manually by a panel of physicians. We compared the performance of the proposed method against a previously published method. The results of a two-fold cross validation show that the overlap ratio improved in 67.0% of scans, with an average improvement of 5.1% points.

Purpose: CT lung screening is already performed at low doses. In this study, we investigated the effects of further dose reduction on a lung-nodule CAD detection algorithm. Methods: The original raw CT data and images from 348 patients were obtained from our local database of National Lung Screening Trial (NLST) cases. 61 patients (17.5%) had at least one nodule reported on the NLST reader forms. All scans were acquired with fixed mAs (25 for standard-sized patients, 40 for large patients) on a 64-slice scanner (Sensation 64, Siemens Healthcare). All images were reconstructed with 1-mm slice thickness, B50 kernel. Based on a previously-published technique, we added noise to the raw data to simulate reduced-dose versions of each case at 50% and 25% of the original NLST dose (i.e. approximately 1.0 and 0.5 mGy CTDIvol). For each case at each dose level, a CAD detection algorithm was run and nodules greater than 4 mm in diameter were reported. These CAD results were compared to “truth”, defined as the approximate nodule centroids from the NLST forms. Sensitivities and false-positive rates (FPR) were calculated for each dose level, with a sub-analysis by nodule LungRADS category. Results: For larger category 4 nodules, median sensitivities were 100% at all three dose levels, and mean sensitivity decreased with dose. For the more challenging category 2 and 3 nodules, the dose dependence was less obvious. Overall, mean subject-level sensitivity varied from 38.5% at 100% dose to 40.4% at 50% dose, a difference of only 1.9%. However, median FPR quadrupled from 1 per case at 100% dose to 4 per case at 25% dose. Conclusions: Dose reduction affected nodule detectability differently depending on the LungRADS category, and FPR was very sensitive at sub-screening levels. Care should be taken to adapt CAD for the very challenging noise characteristics of screening. Funding support: NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.

4564Background: Up to 70% of renal masses are found incidentally on imaging. Pretreatment percutaneous biopsy is commonly used to diagnose tumor grade. A non-invasive method to assess grade on imag...

e16067Background: The incidence of renal cell carcinoma has steadily increased, in part due the increased number of incidentally detected renal neoplasms on imaging. Although a small renal mass can...

The development and evaluation of a computer-aided bone scan analysis technique to quantify changes in tumor burden and assess treatment effects in prostate cancer clinical trials.We have developed and report on a commercial fully automated computer-aided detection (CAD) system. Using this system, scan images were intensity normalized, and then lesions were identified and segmented by anatomic region-specific intensity thresholding. Detected lesions were compared against expert markings to assess the accuracy of the CAD system. The metrics Bone Scan Lesion Area, Bone Scan Lesion Intensity, and Bone Scan Lesion Count were calculated from identified lesions, and their utility in assessing treatment effects was evaluated by analyzing before and after scans from metastatic castration-resistant prostate cancer patients: 10 treated and 10 untreated. In this study, patients were treated with cabozantinib, a MET/vascular endothelial growth factor inhibitor resulting in high rates of resolution of bone scan abnormalities.Our automated CAD system identified bone lesion pixels with 94% sensitivity, 89% specificity, and 89% accuracy. Significant differences in changes from baseline were found between treated and untreated groups in all assessed measurements derived by our system. The most significant measure, Bone Scan Lesion Area, showed a median (interquartile range) change from baseline at week 6 of 7.13% (27.61) in the untreated group compared with -73.76% (45.38) in the cabozantinib-treated group (P=0.0003).Our system accurately and objectively identified and quantified metastases in bone scans, allowing for interpatient and intrapatient comparison. It demonstrates potential as an objective measurement of treatment effects, laying the foundation for validation against other clinically relevant outcome measures.