Cost-effectiveness of olaparib maintenance therapy when used with and without restriction by BRCA1/2 mutation status for platinum-sensitive relapsed ovarian cancer.

Objectives: To determine whether olaparib maintenance therapy, used with and without restriction by BRCA1/2 mutation status, is cost-effective at the population level for platinum-sensitive relapsed ovarian cancer in Singapore.Methods: A partitioned survival model compared three management strategies: 1) treat all patients with olaparib; 2) test for germline BRCA1/2 mutation, followed by targeted olaparib use in mutation carriers only; 3) observe all patients. Mature overall survival (OS) data from Study 19 and a 15-year time horizon were used and direct medical costs were applied. Sensitivity analyses were conducted to explore uncertainties.Results: Treating all patients with olaparib was the most costly and effective strategy, followed by targeted olaparib use, and observation of all patients. Base-case incremental cost-effectiveness ratios (ICERs) for all-olaparib and targeted use strategies were SGD133,394 (USD100,926) and SGD115,736 (USD87,566) per quality-adjusted life year (QALY) gained, respectively, compared to observation. ICERs were most sensitive to the cost of olaparib, time horizon and discount rate for outcomes. When these parameters were varied, ICERs remained above SGD92,000 (USD69,607)/QALY.Conclusions: At the current price, olaparib is not cost-effective when used with or without restriction by BRCA1/2 mutation status in Singapore, despite taking into account potential OS improvement over a long time horizon.

An Asian Perspective on Gender Differences in Clinical Outcomes and Echocardiographic Profiles of Patients With Medically Managed Severe Aortic Stenosis.

BACKGROUND: Gender differences in valvular heart disease are increasingly recognised. A prior study has suggested better surgical outcomes in women with symptomatic aortic stenosis (AS). We investigate gender differences in medically managed severe AS. METHOD: We studied 347 patients with severe AS (aortic valve area index <0.6 cm2/m2) in terms of baseline clinical background, echocardiographic characteristics, and clinical outcomes. Appropriate univariate and multivariate models were employed, while Kaplan-Meier curves were constructed to compare mortality outcomes. RESULTS: In total, 205 (59%) patients were women. Despite higher incidences of hypertension (75.6% vs 47.3%) and diabetes mellitus (46.5% vs 29.5%) in women, women had improved survival (Kaplan-Meier log-rank = 6.24, p = 0.012). After adjusting for age (hazard ratio [HR], 1.034; 95% confidence interval [CI], 1.014-1.054), hypertension (HR, 1.469; 95% CI, 0.807-2.673), diabetes (HR, 1.219; 95% CI, 0.693-2.145), and indexed aortic valve area (HR 0.145, 95% CI 0.031-0.684) on multivariate analyses, female gender remained independently associated with lower mortality (HR, 0.561; 95%, CI 0.332-0.947). Women tended to have smaller body surface area (BSA), left ventricular (LV) internal diastolic diameter, and smaller LV outflow tract diameter but were similar to men in terms of LV ejection fraction, AS severity, and patterns of LV remodelling. CONCLUSIONS: Women appeared to have better outcomes compared to men in medically managed severe AS. These gender differences warrant further study and may affect prognosis, follow-up, and timing of valve surgery.

Subgroup analysis reveals molecular heterogeneity and provides potential precise treatment for pancreatic cancers.

BACKGROUND: The relationship between molecular heterogeneity and clinical features of pancreatic cancer remains unclear. In this study, pancreatic cancer was divided into different subgroups to explore its specific molecular characteristics and potential therapeutic targets. PATIENTS AND METHODS: Expression profiling data were downloaded from The Cancer Genome Atlas database and standardized. Bioinformatics techniques such as unsupervised hierarchical clustering was used to explore the optimal molecular subgroups in pancreatic cancer. Clinical pathological features and pathways in each subgroup were also analyzed to find out the potential clinical applications and initial promotive mechanisms of pancreatic cancer. RESULTS: Pancreatic cancer was divided into three subgroups based on different gene expression features. Patients included in each subgroup had specific biological features and responded significantly different to chemotherapy. CONCLUSION: Three distinct subgroups of pancreatic cancer were identified, which means that patients in each subgroup might benefit from targeted individual management.

Lymph node segmentation by dynamic programming and active contours.

PURPOSE: Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. METHODS: The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. RESULTS: The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists' manual segmentations were 84.6%, 86.2%, and 88.3%. CONCLUSIONS: The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes.

Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data.

PURPOSE: Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making. METHODS: To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis. RESULTS: On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively. CONCLUSION: Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC.

Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan (p > 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74-0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean ± std: ICC = 0.96 ± 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Reproducibility of radiomics for deciphering tumor phenotype with imaging.

Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.

Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 µl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

Hybrid detection of lung nodules on CT scan images.

PURPOSE: The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules. METHODS: The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithms were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule. RESULTS: The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%. CONCLUSIONS: The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.

Variability in assessing treatment response: metastatic colorectal cancer as a paradigm.

PURPOSE: The cutoff values currently used to categorize tumor response to therapy are neither biologically based nor tailored for measurement reproducibility with contemporary imaging modalities. Sources and magnitudes of discordance in response assessment in metastatic colorectal cancer (mCRC) are unknown. EXPERIMENTAL DESIGN: A subset of patients' CT images of chest, abdomen, and pelvis were randomly chosen from a multicenter clinical trial evaluating insulin-like growth factor receptor type 1-targeted therapy in mCRC. Using Response Evaluation Criteria in Solid Tumors (RECIST), three radiologists selected target lesions and measured "uni" (maximal diameter), "bi" (product of maximal diameter and maximal perpendicular diameter), and "vol" (volume) on baseline and 6-week posttherapy scans in the following ways: (i) each radiologist independently selected and measured target lesions and (ii) one radiologist's target lesions were blindly remeasured by the others. Variability in relative change of tumor measurements was analyzed using linear mixed effects models. RESULTS: Three radiologists independently selected 138, 101, and 146 metastatic target lesions in the liver, lungs, lymph nodes, and other organs (e.g., peritoneal cavity) in 29 patients. Of 198 target lesions total, 33% were selected by all three, 28% by two, and 39% by one radiologist. With independent selection, the variability in relative change of tumor measurements was 11% (uni), 19% (bi), and 22% (vol), respectively. When measuring the same lesions, the corresponding numbers were 8%, 14%, and 12%. CONCLUSIONS: The relatively low variability in change of mCRC measurements suggests that response criteria could be modified to allow more accurate and sensitive CT assessment of anticancer therapy efficacy.

Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study.

PURPOSE: To explore the effects of computed tomography (CT) slice thickness and reconstruction algorithm on quantification of image features to characterize tumors using a chest phantom. MATERIALS AND METHODS: Twenty-two phantom lesions of known sizes (10 and 20 mm), shapes (spherical, elliptical, lobulated, and spiculated), and densities [-630, -10, and +100 Hounsfield Unit (HU)] were inserted into an anthropomorphic thorax phantom and scanned three times with relocations. The raw data were reconstructed using six imaging settings, i.e., a combination of three slice thicknesses of 1.25, 2.5, and 5 mm and two reconstruction kernels of lung and standard. Lesions were segmented and 14 image features representing lesion size, shape, and texture were calculated. Differences in the measured image features due to slice thickness and reconstruction algorithm were compared using linear regression method by adjusting three confounding variables (size, density, and shape). RESULTS: All 14 features were significantly different between 1.25 and 5 mm slice images. The 1.25 and 2.5 mm slice thicknesses were better than 5 mm for volume, density mean, density SD gray-level co-occurrence matrix (GLCM) energy and homogeneity. As for the reconstruction algorithm, there was no significant difference in uni-dimension, volume, shape index 9, and compactness. Lung reconstruction was better for density mean, whereas standard reconstruction was better for density SD. CONCLUSIONS: CT slice thickness and reconstruction algorithm can significantly affect the quantification of image features. Thinner (1.25 and 2.5 mm) and thicker (5 mm) slice images should not be used interchangeably. Sharper and smoother reconstructions significantly affect the density-based features.

Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field.

PURPOSE: Lung lesions vary considerably in size, density, and shape, and can attach to surrounding anatomic structures such as chest wall or mediastinum. Automatic segmentation of the lesions poses a challenge. This work communicates a new three-dimensional algorithm for the segmentation of a wide variety of lesions, ranging from tumors found in patients with advanced lung cancer to small nodules detected in lung cancer screening programs. METHODS: The authors' algorithm uniquely combines the image processing techniques of marker-controlled watershed, geometric active contours as well as Markov random field (MRF). The user of the algorithm manually selects a region of interest encompassing the lesion on a single slice and then the watershed method generates an initial surface of the lesion in three dimensions, which is refined by the active geometric contours. MRF improves the segmentation of ground glass opacity portions of part-solid lesions. The algorithm was tested on an anthropomorphic thorax phantom dataset and two publicly accessible clinical lung datasets. These clinical studies included a same-day repeat CT (prewalk and postwalk scans were performed within 15 min) dataset containing 32 lung lesions with one radiologist's delineated contours, and the first release of the Lung Image Database Consortium (LIDC) dataset containing 23 lung nodules with 6 radiologists' delineated contours. The phantom dataset contained 22 phantom nodules of known volumes that were inserted in a phantom thorax. RESULTS: For the prewalk scans of the same-day repeat CT dataset and the LIDC dataset, the mean overlap ratios of lesion volumes generated by the computer algorithm and the radiologist(s) were 69% and 65%, respectively. For the two repeat CT scans, the intra-class correlation coefficient (ICC) was 0.998, indicating high reliability of the algorithm. The mean relative difference was -3% for the phantom dataset. CONCLUSIONS: The performance of this new segmentation algorithm in delineating tumor contour and measuring tumor size illustrates its potential clinical value for assisting in noninvasive diagnosis of pulmonary nodules, therapy response assessment, and radiation treatment planning.

Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes.

PURPOSE: To retrospectively identify quantitative computed tomographic (CT) features that correlate with epidermal growth factor receptor (EGFR) mutation in surgically resected lung adenocarcinomas stratified by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS) classification in an East Asian cohort of patients known to have a high prevalence of EGFR mutations. MATERIALS AND METHODS: An institutional review board approved this study and waived informed consent. In 153 surgically resected lung adenocarcinomas, EGFR mutation was determined by direct DNA sequencing. Histologic subtype was classified according to IASLC/ATS/ERS classification of lung adenocarcinoma. At preoperative chest CT, the percentage of ground-glass opacity (GGO) volume and total tumor volume of each tumor were measured by using a semiautomated algorithm. Distribution of EGFR mutation according to histologic subtype, percentage of GGO volume, and total tumor volume was evaluated by using the Fisher exact test, the Student t test, trend analysis, and multiple logistic regression analysis. RESULTS: Exon 21 missense mutation was more frequent in lepidic predominant adenocarcinomas than in other histologic subtypes (odds ratio, 3.44; 95% confidence interval: 1.53, 7.74; P = .003). GGO volume percentage in tumors with exon 21 missense mutation (61.7% ± 31.9 [standard deviation]) was significantly higher than that in EGFR wild-type tumors (30.0% ± 38.5) (P = .0001) and exon 19-mutated tumors (28.9% ± 37.7) (P = .0006). A significant trend of prevalence of exon 21 missense mutation increasing along with increasing GGO volume (P = .0008) was found. CONCLUSION: GGO volume percentage in tumors with exon 21 missense mutation was significantly higher than that in tumors with other EGFR mutation status. This can be related to the fact that exon 21 missense mutation was significantly more frequent in lepidic predominant adenocarcinomas, including adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic predominant invasive adenocarcinoma, according to IASLE/ATS/ERS classification.

Exploring intra- and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals.

OBJECTIVE: Understanding magnitudes of variability when measuring tumor size may be valuable in improving detection of tumor change and thus evaluating tumor response to therapy in clinical trials and care. Our study explored intra- and inter-reader variability of tumor uni-dimensional (1D), bi-dimensional (2D), and volumetric (VOL) measurements using manual and computer-aided methods (CAM) on CT scans reconstructed at different slice intervals. MATERIALS AND METHODS: Raw CT data from 30 patients enrolled in oncology clinical trials was reconstructed at 5, 2.5, and 1.25 mm slice intervals. 118 lesions in the lungs, liver, and lymph nodes were analyzed. For each lesion, two independent radiologists manually and, separately, using computer software, measured the maximum diameter (1D), maximum perpendicular diameter, and volume (CAM only). One of them blindly repeated the measurements. Intra- and inter-reader variability for the manual method and CAM were analyzed using linear mixed-effects models and Bland-Altman method. RESULTS: For the three slice intervals, the maximum coefficients of variation for manual intra-/inter-reader variability were 6.9%/9.0% (1D) and 12.3%/18.0% (2D), and for CAM were 5.4%/9.3% (1D), 11.3%/18.8% (2D) and 9.3%/18.0% (VOL). Maximal 95% reference ranges for the percentage difference in intra-reader measurements for manual 1D and 2D, and CAM VOL were (-15.5%, 25.8%), (-27.1%, 51.6%), and (-22.3%, 33.6%), respectively. CONCLUSIONS: Variability in measuring the diameter and volume of solid tumors, manually and by CAM, is affected by CT slice interval. The 2.5mm slice interval provides the least measurement variability. Among the three techniques, 2D has the greatest measurement variability compared to 1D and 3D.

Multiphasic contrast-enhanced MRI: single-slice versus volumetric quantification of tumor enhancement for the assessment of renal clear-cell carcinoma fuhrman grade.

PURPOSE: To assess the association between clear-cell carcinoma pathology grade at nephrectomy and magnetic resonance imaging (MRI) tumor enhancement. MATERIALS AND METHODS: The Institutional Review Board approved this retrospective study and waived the informed consent requirement. In all, 32 patients underwent multiphase contrast-enhanced MRI prior to nephrectomy. MRI tumor enhancement was measured using two approaches: 1) the most enhancing portion of the tumor on a single slice and 2) volumetric analysis of enhancement in the entire tumor. Associations between pathological grade, tumor size, and enhancement were evaluated using the Kruskal-Wallis test and generalized logistic regression models. RESULTS: No significant association between pathology grade and enhancement was found when measurements were made on a single slice. When measured in the entire tumor, significant associations were found between higher pathology grades and lower mean, median, top 10%, top 25%, and top 50% tumor enhancement (P < 0.001-0.002). On multivariate analysis the association between grade and enhancement remained significant (P = 0.041-0.043), but tumor size did not make an additional contribution beyond tumor enhancement alone in differentiating between tumor grades. CONCLUSION: There is significant association between tumor grade and enhancement, but only when measured in the entire tumor and not on the most enhancing portion on a single slice.

Assessing the effect of CT slice interval on unidimensional, bidimensional and volumetric measurements of solid tumours.

OBJECTIVES: To study the magnitude of differences in tumour unidimensional (1D), bidimensional (2D) and volumetric (VOL) measurements determined from computed tomography (CT) images reconstructed at 5, 2.5 and 1.25 mm slice intervals. MATERIALS AND METHODS: A total of 118 lesions in lung, liver and lymph nodes were selected from 30 patients enrolled in early phase clinical trials. Each CT scan was reconstructed at 5, 2.5 and 1.25 mm slice intervals during the image acquisition. Lesions were semi-automatically segmented on each interval image series and supervised by a radiologist. 1D, 2D and VOL were computed based on the final segmentation results. Average measurement differences across different slice intervals were obtained using linear mixed-effects analysis of variance models. RESULTS: Lesion diameters ranged from 6.1 to 80.1 mm (median 18.4 mm). The largest difference was seen between 1.25 and 5 mm (mean difference of 7.6% for 1D [P < 0.0001], 13.1% for 2D [P < 0.0001], -5.7% for VOL [P = 0.0001]). Mean differences between 1.25 and 2.5 mm were all within ±3.5% (within ±6% confidence interval). For VOL, there was a larger average difference between measurements on different slice intervals for the smaller lesions (<10 mm) compared with the larger lesions. CONCLUSIONS: Different slice intervals may give different 1D, 2D and VOL measurements. In clinical practice, it would be prudent to use the same slice interval for consecutive measurements.

Informatics methods to enable sharing of quantitative imaging research data.

INTRODUCTION: The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. METHODS: We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. RESULTS: There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. CONCLUSIONS: As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.

Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography.

There are lots of work being done to develop computer-assisted diagnosis and detection (CAD) technologies and systems to improve the diagnostic quality for pulmonary nodules. Another way to improve accuracy of diagnosis on new images is to recall or find images with similar features from archived historical images which already have confirmed diagnostic results, and the content-based image retrieval (CBIR) technology has been proposed for this purpose. In this paper, we present a method to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs. Seventy-seven biopsy-confirmed CT cases of SPNs were included in this study. A total of 67 features were extracted by a feature extraction procedure, and around 25 features were finally selected after 300 genetic generations. We constructed the SVM-based classifier with the selected features and evaluated the performance of the classifier by comparing the classification results of the SVM-based classifier with six senior radiologists' observations. The evaluation results not only showed that most of the selected features are characteristics frequently considered by radiologists and used in CAD analyses previously reported in classifying SPNs, but also indicated that some newly found features have important contribution in differentiating benign from malignant SPNs in SVM-based feature space. The results of this research can be used to build the highly efficient feature index of a CBIR system for CT images with pulmonary nodules.