The use of volumetric CT as an imaging biomarker in lung cancer

Blunt splenic injury: Assessment of follow-up CT utility using quantitative volumetry

Purpose:

Trials of non-operative management (NOM) have become the standard of care for blunt splenic injury (BSI) in hemodynamically stable patients. However, there is a lack of consensus regarding the utility of follow-up CT exams and relevant CT features. The purpose of this study is to determine imaging predictors of splenectomy on follow-up CT using quantitative volumetric measurements.

Methods:

Adult patients who underwent a trial of non-operative management (NOM) with follow-up CT performed for BSI between 2017 and 2019 were included (n = 51). Six patients (12% of cohort) underwent splenectomy; 45 underwent successful splenic salvage. Voxelwise measurements of splenic laceration, hemoperitoneum, and subcapsular hematoma were derived from portal venous phase images of admission and follow-up scans using 3D slicer. Presence/absence of pseudoaneurysm on admission and follow-up CT was assessed using arterial phase images. Multivariable logistic regression was used to determine independent predictors of decision to perform splenectomy.

Results:

Factors significantly associated with splenectomy in bivariate analysis incorporated in multivariate logistic regression included final hemoperitoneum volume (p = 0.003), final subcapsular hematoma volume (p = 0.001), change in subcapsular hematoma volume between scans (p = 0.09) and new/persistent pseudoaneurysm (p = 0.003). Independent predictors of splenectomy in the logistic regression were final hemoperitoneum volume (unit OR = 1.43 for each 100 mL change; 95% CI: 0.99–2.06) and new/persistent pseudoaneurysm (OR = 160.3; 95% CI: 0.91–28315.3). The AUC of the model incorporating both variables was significantly higher than AAST grading (0.91 vs. 0.59, p = 0.025). Mean combined effective dose for admission and follow up CT scans was 37.4 mSv.

Conclusion:

Follow-up CT provides clinically valuable information regarding the decision to perform splenectomy in BSI patients managed non-operatively. Hemoperitoneum volume and new or persistent pseudoaneurysm at follow-up are independent predictors of splenectomy.

Quantitative CT characterization of pediatric lung development using routine clinical imaging

BACKGROUND

The use of quantitative CT analysis in children is limited by lack of normal values of lung parenchymal attenuation. These characteristics are important because normal lung development yields significant parenchymal attenuation changes as children age.

OBJECTIVE

To perform quantitative characterization of normal pediatric lung parenchymal X-ray CT attenuation under routine clinical conditions in order to establish a baseline comparison to that seen in pathological lung conditions.

MATERIALS AND METHODS

We conducted a retrospective query of normal CT chest examinations in children ages 0-7 years from 2004 to 2014 using standard clinical protocol. During these examinations semi-automated lung parenchymal segmentation was performed to measure lung volume and mean lung attenuation.

RESULTS

We analyzed 42 CT examinations in 39 children, ages 3 days to 83 months (mean ± standard deviation [SD] = 42 ± 27 months). Lung volume ranged 0.10-1.72 liters (L). Mean lung attenuation was much higher in children younger than 12 months, with values as high as -380 Hounsfield units (HU) in neonates (lung volume 0.10 L). Lung volume decreased to approximately -650 HU by age 2 years (lung volume 0.47 L), with subsequently slower exponential decrease toward a relatively constant value of -860 HU as age and lung volume increased.

CONCLUSION

Normal lung parenchymal X-ray CT attenuation decreases with increasing lung volume and age; lung attenuation decreases rapidly in the first 2 years of age and more slowly thereafter. This change in normal lung attenuation should be taken into account as quantitative CT methods are translated to pediatric pulmonary imaging.

Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions

RATIONALE AND OBJECTIVES

This study aimed to review the current understanding and capabilities regarding use of imaging for noninvasive lesion characterization and its relationship to lung cancer screening and treatment.

MATERIALS AND METHODS

Our review of the state of the art was broken down into questions about the different lung cancer image phenotypes being characterized, the role of imaging and requirements for increasing its value with respect to increasing diagnostic confidence and quantitative assessment, and a review of the current capabilities with respect to those needs.

RESULTS

The preponderance of the literature has so far been focused on the measurement of lesion size, with increasing contributions being made to determine the formal performance of scanners, measurement tools, and human operators in terms of bias and variability. Concurrently, an increasing number of investigators are reporting utility and predictive value of measures other than size, and sensitivity and specificity is being reported. Relatively little has been documented on quantitative measurement of non-size features with corresponding estimation of measurement performance and reproducibility.

CONCLUSIONS

The weight of the evidence suggests characterization of pulmonary lesions built on quantitative measures adds value to the screening for, and treatment of, lung cancer. Advanced image analysis techniques may identify patterns or biomarkers not readily assessed by eye and may also facilitate management of multidimensional imaging data in such a way as to efficiently integrate it into the clinical workflow.

Three-dimensional Radiologic Assessment of Chemotherapy Response in Ewing Sarcoma Can Be Used to Predict Clinical Outcome

Purpose To compare the agreement of three-dimensional (3D) tumor measurements for therapeutic response assessment of Ewing sarcoma according to the Children's Oncology Group (COG) criteria, one-dimensional (1D) Response Evaluation Criteria in Solid Tumors (RECIST), and two-dimensional (2D) measurements defined by the World Health Organization (WHO) with tumor volume measurements as the standard of reference and to determine which method correlates best with clinical outcomes. Materials and Methods This retrospective study was approved by the institutional review board of three institutions. Seventy-four patients (mean age ± standard deviation, 14.5 years ± 6.5) with newly diagnosed Ewing sarcoma treated at three medical centers were evaluated. Primary tumor size was assessed on pre- and posttreatment magnetic resonance images according to 1D RECIST, 2D WHO, and 3D COG measurements. Tumor responses were compared with the standard of reference (tumor volume) on the basis of RECIST, COG, and WHO therapeutic response thresholds. Agreement between the percentage reduction measurements of the methods was assessed with concordance correlation, Bland-Altman analysis, and Spearman rank correlation. Agreement between therapeutic responses was assessed with Kendall tau and unweighted κ statistics. Tumor responses were compared with patient survival by using the log-rank test, Kaplan-Meier plots, and Cox regression. Results Agreement with the reference standard was significantly better for 3D measurement than for 1D and 2D measurements on the basis of RECIST and COG therapeutic response thresholds (concordance correlation of 0.41, 0.72, and 0.84 for 1D, 2D, and 3D measurements, respectively; P < .0001). Comparison of overall survival of responders and nonresponders demonstrated P values of .4133, .0112, .0032, and .0027 for 1D, 2D, 3D, and volume measurements, respectively, indicating that higher dimensional measurements were significantly better predictors of overall survival. Conclusion The 3D tumor measurements according to COG are better predictors of therapeutic response of Ewing sarcoma than 1D RECIST or 2D WHO measurements and show a significantly higher correlation with clinical outcomes. (©) RSNA, 2016 Online supplemental material is available for this article.

A mathematical simulation to assess variability in lung nodule size measurement associated with nodule-slice position

The purpose of this study is to assess the variance and error in nodule diameter measurement associated with variations in nodule-slice position in cross-sectional imaging. A computer program utilizing a standard geometric model was used to simulate theoretical slices through a perfectly spherical nodule of known size, position, and density within a background of "lung" of known fixed density. Assuming a threshold density, partial volume effect of a voxel was simulated using published slice and pixel sensitivity profiles. At a given slice thickness and nodule size, 100 scans were simulated differing only in scan start position, then repeated for multiple node sizes at three simulated slice thicknesses. Diameter was measured using a standard, automated algorithm. The frequency of measured diameters was tabulated; average errors and standard deviations (SD) were calculated. For a representative 5-mm nodule, average measurement error ranged from +10 to -23% and SD ranged from 0.07 to 0.99 mm at slice thicknesses of 0.75 to 5 mm, respectively. At fixed slice thickness, average error and SD decreased from peak values as nodule size increased. At fixed nodule size, SD increased as slice thickness increased. Average error exhibited dependence on both slice thickness and threshold. Variance and error in nodule diameter measurement associated with nodule-slice position exists due to geometrical limitations. This can lead to false interpretations of nodule growth or stability that could affect clinical management. The variance is most pronounced at higher slice thicknesses and for small nodule sizes. Measurement error is slice thickness and threshold dependent.

Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules

The Quantitative Imaging Biomarker Alliance (QIBA) is a multidisciplinary consortium sponsored by the RSNA to define processes that enable the implementation and advancement of quantitative imaging methods described in a QIBA profile document that outlines the process to reliably and accurately measure imaging features. A QIBA profile includes factors such as technical (product-specific) standards, user activities, and relationship to a clinically meaningful metric, such as with nodule measurement in the course of CT screening for lung cancer. In this report, the authors describe how the QIBA approach is being applied to the measurement of small pulmonary nodules such as those found during low-dose CT-based lung cancer screening. All sources of variance with imaging measurement were defined for this process. Through a process of experimentation, literature review, and assembly of expert opinion, the strongest evidence was used to define how to best implement each step in the imaging acquisition and evaluation process. This systematic approach to implementing a quantitative imaging biomarker with standardized specifications for image acquisition and postprocessing for a specific quantitative measurement of a pulmonary nodule results in consistent performance characteristics of the measurement (eg, bias and variance). Implementation of the QIBA small nodule profile may allow more efficient and effective clinical management of the diagnostic workup of individuals found to have suspicious pulmonary nodules in the course of lung cancer screening evaluation.

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm

PURPOSE

To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT).

MATERIALS AND METHODS

Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features.

RESULTS

Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998).

CONCLUSION

Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

Can oncology recapitulate paleontology? Lessons from species extinctions

Although we can treat cancers with cytotoxic chemotherapies, target them with molecules that inhibit oncogenic drivers, and induce substantial cell death with radiation, local and metastatic tumours recur, resulting in extensive morbidity and mortality. Indeed, driving a tumour to extinction is difficult. Geographically dispersed species of organisms are perhaps equally resistant to extinction, but >99.9% of species that have ever existed on this planet have become extinct. By contrast, we are nowhere near that level of success in cancer therapy. The phenomena are broadly analogous--in both cases, a genetically diverse population mutates and evolves through natural selection. The goal of cancer therapy is to cause cancer cell population extinction, or at least to limit any further increase in population size, to prevent the tumour burden from overwhelming the patient. However, despite available treatments, complete responses are rare, and partial responses are limited in duration. Many patients eventually relapse with tumours that evolve from cells that survive therapy. Similarly, species are remarkably resilient to environmental change. Paleontology can show us the conditions that lead to extinction and the characteristics of species that make them resistant to extinction. These lessons could be translated to improve cancer therapy and prognosis.

Lung Cancer Workshop XI: Tobacco-Induced Disease: Advances in Policy, Early Detection and Management

The Prevent Cancer Foundation Lung Cancer Workshop XI: Tobacco-Induced Disease: Advances in Policy, Early Detection and Management was held in New York, NY on May 16 and 17, 2014. The two goals of the Workshop were to define strategies to drive innovation in precompetitive quantitative research on the use of imaging to assess new therapies for management of early lung cancer and to discuss a process to implement a national program to provide high quality computed tomography imaging for lung cancer and other tobacco-induced disease. With the central importance of computed tomography imaging for both early detection and volumetric lung cancer assessment, strategic issues around the development of imaging and ensuring its quality are critical to ensure continued progress against this most lethal cancer.

Evaluation of an interactive science publishing tool: toward enabling three-dimensional analysis of medical images

RATIONALE AND OBJECTIVES

Higher resolution medical imaging platforms are rapidly emerging, but there is a challenge in applying these tools in a clinically meaningful way. The purpose of the current study was to evaluate a novel three-dimensional (3D) software imaging environment, known as interactive science publishing (ISP), in appraising 3D computed tomography images and to compare this approach with traditional planar (2D) imaging in a series of lung cancer cases.

MATERIALS AND METHODS

Twenty-four physician volunteers at different levels of training across multiple specialties were recruited to evaluate eight lung cancer-related clinical vignettes. The volunteers were asked to compare the performance of traditional 2D versus the ISP 3D imaging in assessing different visualization environments for diagnostic and measurement processes and to further evaluate the ISP tool in terms of general satisfaction, usability, and probable applicability.

RESULTS

Volunteers were satisfied with both imaging methods; however, the 3D environment had significantly higher ratings. Measurement performance was comparable using both traditional 2D and 3D image evaluation. Physicians not trained in 2D measurement approaches versus those with such training demonstrated better performance with ISP and preferred working in the ISP environment.

CONCLUSIONS

Recent postgraduates with only modest self-administered training performed equally well on 3D and 2D cases. This suggests that the 3D environment has no reduction in accuracy over the conventional 2D approach, while providing the advantage of a digital environment for cross-disciplinary interaction for shared problem solving. Exploration of more effective, efficient, self-directed training could potentially result in further improvement in image evaluation proficiency and potentially decrease training costs.

Volume-based response evaluation with consensual lesion selection: a pilot study by using cloud solutions and comparison to RECIST 1.1

RATIONALE AND OBJECTIVES

Lesion volume is considered as a promising alternative to Response Evaluation Criteria in Solid Tumors (RECIST) to make tumor measurements more accurate and consistent, which would enable an earlier detection of temporal changes. In this article, we report the results of a pilot study aiming at evaluating the effects of a consensual lesion selection on volume-based response (VBR) assessments.

MATERIALS AND METHODS

Eleven patients with lung computed tomography scans acquired at three time points were selected from Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) and proprietary databases. Images were analyzed according to RECIST 1.1 and VBR criteria by three readers working in different geographic locations. Cloud solutions were used to connect readers and carry out a consensus process on the selection of lesions used for computing response. Because there are not currently accepted thresholds for computing VBR, we have applied a set of thresholds based on measurement variability (-35% and +55%). The benefit of this consensus was measured in terms of multiobserver agreement by using Fleiss kappa (κfleiss) and corresponding standard errors (SE).

RESULTS

VBR after consensual selection of target lesions allowed to obtain κfleiss = 0.85 (SE = 0.091), which increases up to 0.95 (SE = 0.092), if an extra consensus on new lesions is added. As a reference, the agreement when applying RECIST without consensus was κfleiss = 0.72 (SE = 0.088). These differences were found to be statistically significant according to a z-test.

CONCLUSIONS

An agreement on the selection of lesions allows reducing the inter-reader variability when computing VBR. Cloud solutions showed to be an interesting and feasible strategy for standardizing response evaluations, reducing variability, and increasing consistency of results in multicenter clinical trials.

Clinical utility of quantitative imaging

Quantitative imaging (QI) is increasingly applied in modern radiology practice, assisting in the clinical assessment of many patients and providing a source of biomarkers for a spectrum of diseases. QI is commonly used to inform patient diagnosis or prognosis, determine the choice of therapy, or monitor therapy response. Because most radiologists will likely implement some QI tools to meet the patient care needs of their referring clinicians, it is important for all radiologists to become familiar with the strengths and limitations of QI. The Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force has explored the clinical application of QI and summarizes its work in this review. We provide an overview of the clinical use of QI by discussing QI tools that are currently used in clinical practice, clinical applications of these tools, approaches to reporting of QI, and challenges to implementing QI. It is hoped that these insights will help radiologists recognize the tangible benefits of QI to their patients, their referring clinicians, and their own radiology practice.

Issues with implementing a high-quality lung cancer screening program

After a comprehensive review of the evidence, the United States Preventive Services Task Force recently endorsed screening with low-dose computed tomography as an early detection approach that has the potential to significantly reduce deaths due to lung cancer. Prudent implementation of lung cancer screening as a high-quality preventive health service is a complex challenge. The clinical evaluation and management of high-risk cohorts in the absence of symptoms mandates an approach that differs significantly from that of symptom-detected lung cancer. As with other cancer screenings, it is essential to provide to informed at-risk individuals a safe, high-quality, cost-effective, and accessible service. In this review, the components of a successful screening program are discussed as we begin to disseminate lung cancer screening as a national resource to improve outcomes with this lethal cancer. This information about lung cancer screening will assist clinicians with communications about the potential benefits and harms of this service for high-risk individuals considering participation in the screening process.

A novel knowledge representation framework for the statistical validation of quantitative imaging biomarkers

Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging.

Comparison of 1D, 2D, and 3D nodule sizing methods by radiologists for spherical and complex nodules on thoracic CT phantom images

RATIONALE AND OBJECTIVES

To estimate and statistically compare the bias and variance of radiologists measuring the size of spherical and complex synthetic nodules.

MATERIALS AND METHODS

This study did not require the institutional review board approval. Six radiologists estimated the size of 10 synthetic nodules embedded within an anthropomorphic thorax phantom from computed tomography scans at 0.8- and 5-mm slice thicknesses. The readers measured the nodule size using unidimensional (1D) longest in-slice dimension, bidimensional (2D) area from longest in-slice and longest perpendicular dimension, and three-dimensional (3D) semiautomated volume. Intercomparisons of bias (difference between average and true size) and variance among methods were performed after converting the 2D and 3D estimates to a compatible 1D scale.

RESULTS

The relative biases of radiologists with the 3D tool were -1.8%, -0.4%, -0.7%, -0.4%, and -1.6% for 10-mm spherical, 20-mm spherical, 20-mm elliptical, 10-mm lobulated, and 10-mm spiculated nodules compared to 1.4%, -0.1%, -26.5%, -7.8%, and -39.8% for 1D. The three-dimensional measurements were significantly less biased than 1D for elliptical, lobulated, and spiculated nodules. The relative standard deviations for 3D were 7.5%, 3.9%, 3.6%, 9.7%, and 8.3% compared to 5.7%, 2.6%, 20.3%, 5.3%, and 16.4% for 1D. Unidimensional sizing was significantly less variable than 3D for the lobulated nodule and significantly more variable for the ellipsoid and spiculated nodules. Three-dimensional bias and variability were smaller for thin 0.8-mm slice data compared to thick 5.0-mm data.

CONCLUSIONS

The study shows that radiologist-controlled 3D volumetric lesion sizing can not only achieve smaller bias but also achieve similar or smaller variability compared to 1D sizing, especially for complex lesion shapes.

Consistency and efficiency of CT analysis of metastatic disease: semiautomated lesion management application within a PACS

OBJECTIVE

The purpose of this study was to evaluate the success, consistency, and efficiency of a semiautomated lesion management application within a PACS in the analysis of metastatic lesions in serial CT examinations of cancer patients.

MATERIALS AND METHODS

Two observers using baseline and follow-up CT data independently reviewed 93 target lesions (17 lung, five liver, 71 lymph node) in 50 patients with either metastatic bladder or prostate cancer. The observers measured the longest axis (or short axis for lymph nodes) of each lesion and made Response Evaluation Criteria in Solid Tumors (RECIST) determinations using manual and lesion management application methods. The times required for examination review, RECIST calculations, and data input were recorded. The Wilcoxon signed rank test was used to assess time differences, and Bland-Altman analysis was used to assess interobserver agreement within the manual and lesion management application methods. Percentage success rates were also reported.

RESULTS

With the lesion management application, most lung and liver lesions were semiautomatically segmented. Comparison of the lesion management application and manual methods for all lesions showed a median time saving of 45% for observer 1 (p<0.05) and 28% for observer 2 (p=0.05) on follow-up scans versus 28% for observer 1 (p<0.05) and 9% for observer 2 (p=0.087) on baseline scans. Variability of measurements showed mean percentage change differences of only 8.9% for the lesion management application versus 26.4% for manual measurements.

CONCLUSION

With the lesion management application method, most lung and liver lesions were successfully segmented semiautomatically; the results were more consistent between observers; and assessment of tumor size was faster than with the manual method.

Scatter correction associated with dedicated dual-source CT hardware improves accuracy of lung air measures

RATIONALE AND OBJECTIVES

Accurate assessment of air density used to quantitatively characterize amount and distribution of emphysema in chronic obstructive pulmonary disease (COPD) subjects has remained challenging. Hounsfield units (HU) within tracheal air can be considerably less negative than -1000 HU. This study has sought to characterize the effects of improved scatter correction used in dual-source pulmonary computed tomography (CT).

MATERIALS AND METHODS

Dual-source dual-energy (DSDE) and single-source (SS) scans taken at multiple energy levels and scan settings were acquired for quantitative comparison using anesthetized ovine (n = 6), swine (n = 13), and a lung phantom. Data were evaluated for the lung, inferior vena cava, and tracheal segments. To minimize the effect of cross-scatter, the phantom scans in the DSDE mode were obtained by reducing the current of one of the tubes to near zero.

RESULTS

A significant shift in mean HU values in the tracheal regions of animals and the phantom is observed, with values consistently closer to -1000 HU in DSDE mode. HU values associated with SS mode demonstrated a positive shift of up to 32 HU. In vivo tracheal air measurements demonstrated considerable variability with SS scanning, whereas these values were more consistent with DSDE imaging. Scatter effects in the lung parenchyma differed from adjacent tracheal measures.

CONCLUSION

Data suggest that the scatter correction introduced into the dual-energy mode of imaging has served to provide more accurate CT lung density measures sought to quantitatively assess the presence and distribution of emphysema in COPD subjects. Data further suggest that CT images, acquired without adequate scatter correction, cannot be corrected by linear algorithms given the variability in tracheal air HU values and the independent scatter effects on lung parenchyma.

An engineering view on megatrends in radiology: digitization to quantitative tools of medicine

Within six months of the discovery of X-ray in 1895, the technology was used to scan the interior of the human body, paving the way for many innovations in the field of medicine, including an ultrasound device in 1950, a CT scanner in 1972, and MRI in 1980. More recent decades have witnessed developments such as digital imaging using a picture archiving and communication system, computer-aided detection/diagnosis, organ-specific workstations, and molecular, functional, and quantitative imaging. One of the latest technical breakthrough in the field of radiology has been imaging genomics and robotic interventions for biopsy and theragnosis. This review provides an engineering perspective on these developments and several other megatrends in radiology.

Cardiac imaging approaches to evaluate drug-induced myocardial dysfunction

The ability to make informed benefit-risk assessments for potentially cardiotoxic new compounds is of considerable interest and importance at the public health, drug development, and individual patient levels. Cardiac imaging approaches in the evaluation of drug-induced myocardial dysfunction will likely play an increasing role. However, the optimal choice of myocardial imaging modality and the recommended frequency of monitoring are undefined. These decisions are complicated by the array of imaging techniques, which have varying sensitivities, specificities, availabilities, local expertise, safety, and costs, and by the variable time-course of tissue damage, functional myocardial depression, or recovery of function. This White Paper summarizes scientific discussions of members of the Cardiac Safety Research Consortium on the main factors to consider when selecting nonclinical and clinical cardiac function imaging techniques in drug development. We focus on 3 commonly used imaging modalities in the evaluation of cardiac function: echocardiography, magnetic resonance imaging, and radionuclide (nuclear) imaging and highlight areas for future research.

Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval

PURPOSE

To develop a method to quantify the margin sharpness of lesions on CT and to evaluate it in simulations and CT scans of liver and lung lesions.

METHODS

The authors computed two attributes of margin sharpness: the intensity difference between a lesion and its surroundings, and the sharpness of the intensity transition across the lesion boundary. These two attributes were extracted from sigmoid curves fitted along lines automatically drawn orthogonal to the lesion margin. The authors then represented the margin characteristics for each lesion by a feature vector containing histograms of these parameters. The authors created 100 simulated CT scans of lesions over a range of intensity difference and margin sharpness, and used the concordance correlation between the known parameter and the corresponding computed feature as a measure of performance. The authors also evaluated their method in 79 liver lesions (44 patients: 23 M, 21 F, mean age 61) and 58 lung nodules (57 patients: 24 M, 33 F, mean age 66). The methodology presented takes into consideration the boundary of the liver and lung during feature extraction in clinical images to ensure that the margin feature do not get contaminated by anatomy other than the normal organ surrounding the lesions. For evaluation in these clinical images, the authors created subjective independent reference standards for pairwise margin sharpness similarity in the liver and lung cohorts, and compared rank orderings of similarity used using our sharpness feature to that expected from the reference standards using mean normalized discounted cumulative gain (NDCG) over all query images. In addition, the authors compared their proposed feature with two existing techniques for lesion margin characterization using the simulated and clinical datasets. The authors also evaluated the robustness of their features against variations in delineation of the lesion margin by simulating five types of deformations of the lesion margin. Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests.

RESULTS

In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors' proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors' feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets.

CONCLUSIONS

The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

Integrating pathology and radiology disciplines: an emerging opportunity?

Pathology and radiology form the core of cancer diagnosis, yet the workflows of both specialties remain ad hoc and occur in separate "silos," with no direct linkage between their case accessioning and/or reporting systems, even when both departments belong to the same host institution. Because both radiologists' and pathologists' data are essential to making correct diagnoses and appropriate patient management and treatment decisions, this isolation of radiology and pathology workflows can be detrimental to the quality and outcomes of patient care. These detrimental effects underscore the need for pathology and radiology workflow integration and for systems that facilitate the synthesis of all data produced by both specialties. With the enormous technological advances currently occurring in both fields, the opportunity has emerged to develop an integrated diagnostic reporting system that supports both specialties and, therefore, improves the overall quality of patient care.

Drug development and clinical trials--the path to an approved cancer drug

Advances in our understanding of cancer biology have led to the discovery of a spectrum of new therapeutic targets. However, despite remarkable progress in the identification and characterization of novel mechanisms of the oncogenic process, the success rate for approval of oncology drugs remains low relative to other therapeutic areas. Innovative preclinical and clinical approaches, such as the use of advanced genomic technologies, as well as branched adaptive clinical trial designs, have the potential to accelerate the development and approval of highly effective oncology drugs, along with a matching diagnostic test to identify those patients most likely to benefit from the new treatment. To maximize the effectiveness of these new strategies, close collaboration between academic, industry, and regulatory agencies will be required. In this Review, we highlight new approaches in preclinical and clinical drug development that will help accelerate approval of drugs, and aim to provide more-effective treatments alongside companion diagnostic tests to ensure the right treatment is given to the right patient.

Measurement of tumor volumes improves RECIST-based response assessments in advanced lung cancer

OBJECTIVE

This study was designed to characterize the reproducibility of measurement for tumor volumes and their longest tumor diameters (LDs) and estimate the potential impact of using changes in tumor volumes instead of LDs as the basis for response assessments.

METHODS

We studied patients with advanced lung cancer who have been observed longitudinally with x-ray computed tomography in a multinational trial. A total of 71 time points from 10 patients with 13 morphologically complex target lesions were analyzed. A total of 6461 volume measurements and their corresponding LDs were made by seven independent teams using their own work flows and image analysis tools. Interteam agreement and overall interrater concurrence were characterized.

RESULTS

Interteam agreement between volume measurements was better than between LD measurements (ı = 0.945 vs 0.734, P = .005). The variability in determining the nadir was lower for volumes than for LDs (P = .005). Use of standard thresholds for the RECIST-based method and use of experimentally determined cutoffs for categorizing responses showed that volume measurements had a significantly greater sensitivity for detecting partial responses and disease progression. Earlier detection of progression would have led to earlier changes in patient management in most cases.

CONCLUSIONS

Our findings indicate that measurement of changes in tumor volumes is adequately reproducible. Using tumor volumes as the basis for response assessments could have a positive impact on both patient management and clinical trials. More authoritative work to qualify or discard changes in volume as the basis for response assessments should proceed.

Pulmonary tumor measurements from x-ray computed tomography in one, two, and three dimensions

RATIONALE AND OBJECTIVES

We evaluated the accuracy and reproducibility of three-dimensional (3D) measurements of lung phantoms and patient tumors from x-ray computed tomography (CT) and compared these to one-dimensional (1D) and two-dimensional (2D) measurements.

MATERIALS AND METHODS

CT images of three spherical and three irregularly shaped tumor phantoms were evaluated by three observers who performed five repeated measurements. Additionally, three observers manually segmented 29 patient lung tumors five times each. Follow-up imaging was performed for 23 tumors and response criteria were compared. For a single subject, imaging was performed on nine occasions over 2 years to evaluate multidimensional tumor response. To evaluate measurement accuracy, we compared imaging measurements to ground truth using analysis of variance. For estimates of precision, intraobserver and interobserver coefficients of variation and intraclass correlations (ICC) were used. Linear regression and Pearson correlations were used to evaluate agreement and tumor response was descriptively compared.

RESULTS

For spherical shaped phantoms, all measurements were highly accurate, but for irregularly shaped phantoms, only 3D measurements were in high agreement with ground truth measurements. All phantom and patient measurements showed high intra- and interobserver reproducibility (ICC >0.900). Over a 2-year period for a single patient, there was disagreement between tumor response classifications based on 3D measurements and those generated using 1D and 2D measurements.

CONCLUSION

Tumor volume measurements were highly reproducible and accurate for irregular, spherical phantoms and patient tumors with nonuniform dimensions. Response classifications obtained from multidimensional measurements suggest that 3D measurements provide higher sensitivity to tumor response.

X-ray computed tomography: semiautomated volumetric analysis of late-stage lung tumors as a basis for response assessments

Background. This study presents a semiautomated approach for volumetric analysis of lung tumors and evaluates the feasibility of using volumes as an alternative to line lengths as a basis for response evaluation criteria in solid tumors (RECIST). The overall goal for the implementation was to accurately, precisely, and efficiently enable the analyses of lesions in the lung under the guidance of an operator. Methods. An anthropomorphic phantom with embedded model masses and 71 time points in 10 clinical cases with advanced lung cancer was analyzed using a semi-automated workflow. The implementation was done using the Cognition Network Technology. Results. Analysis of the phantom showed an average accuracy of 97%. The analyses of the clinical cases showed both intra- and interreader variabilities of approximately 5% on average with an upper 95% confidence interval of 14% and 19%, respectively. Compared to line lengths, the use of volumes clearly shows enhanced sensitivity with respect to determining response to therapy. Conclusions. It is feasible to perform volumetric analysis efficiently with high accuracy and low variability, even in patients with late-stage cancer who have complex lesions.

A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective

As the detection and characterization of lung nodules are of paramount importance in thoracic radiology, various tools for making a computer-aided diagnosis (CAD) have been developed to improve the diagnostic performance of radiologists in clinical practice. Numerous studies over the years have shown that the CAD system can effectively help readers identify more nodules. Moreover, nodule malignancy and the response of malignant lung tumors to treatment can also be assessed using nodule volumetry. CAD also has the potential to objectively analyze the morphology of nodules and enhance the workflow during the assessment of follow-up studies. Therefore, understanding the current status and limitations of CAD for evaluating lung nodules is essential to effectively apply CAD in clinical practice.