Atherosclerosis risk classification with computed tomography angiography: A radiologic-pathologic validation study.

BACKGROUND AND AIMS: The application of machine learning to assess plaque risk phenotypes on cardiovascular CT angiography (CTA) is an area of active investigation. Studies using accepted histologic definitions of plaque risk as ground truth for machine learning models are uncommon. The aim was to evaluate the accuracy of a machine-learning software for determining plaque risk phenotype as compared to expert pathologists (histologic ground truth). METHODS: Sections of atherosclerotic plaques paired with CTA were prospectively collected from patients undergoing carotid endarterectomy at two centers. Specimens were annotated for lipid-rich necrotic core, calcification, matrix, and intraplaque hemorrhage at 2 mm spacing and classified as minimal disease, stable plaque, or unstable plaque according to a modified American Heart Association histological definition. Phenotype is determined in two steps: plaque morphology is delineated according to histological tissue definitions, followed by a machine learning classifier. The performance in derivation and validation cohorts for plaque risk categorization and stenosis was compared to histologic ground truth at each matched cross-section. RESULTS: A total of 496 and 408 vessel cross-sections in the derivation and validation cohorts (from 30 and 23 patients, respectively). The software demonstrated excellent agreement in the validation cohort with histological ground truth plaque risk phenotypes with weighted kappa of 0.82 [0.78-0.86] and area under the receiver operating curve for correct identification of plaque type was 0.97 [0.96, 0.98], 0.95 [0.94, 0.97], 0.99 [0.99, 1.0] for unstable plaque, stable plaque, and minimal disease, respectively. Diameter stenosis correlated poorly to histologically defined plaque type; weighted kappa 0.25 in the validation cohort. CONCLUSIONS: A machine-learning software trained on histological ground-truth tissue inputs demonstrated high accuracy for identifying plaque stability phenotypes as compared to expert pathologists.

Ketamine for the Treatment of Chronic Pain: A Comprehensive Review.

BACKGROUND: Chronic pain significantly worsens the quality of life. Unlike neuropathic, musculoskeletal, postoperative pain, and cancer pain, chronic primary pain cannot be explained by an underlying disease or condition, making its treatment arduous. OBJECTIVES: This manuscript intends to provide a comprehensive review of the use of ketamine as a treatment option for specific chronic pain conditions. STUDY DESIGN: A review article. SETTING: A review of the literature. METHODS: A search was done on PubMed for relevant articles. RESULTS: A comprehensive review of the current understanding of chronic pain and the treatment of specific chronic pain conditions with ketamine. LIMITATIONS: Literature is scarce regarding the use of ketamine for the treatment of chronic pain. CONCLUSION: First-line treatment for many chronic pain conditions includes NSAIDs, antidepressants, anticonvulsants, and opioids. However, these treatment methods are unsuccessful in a subset of patients. Ketamine has been explored in randomized controlled trials (RCTs) as an alternative treatment option, and it has been demonstrated to improve pain symptoms, patient satisfaction, and quality of life. Conditions highlighted in this review include neuropathic pain, fibromyalgia, complex regional pain syndrome (CRPS), phantom limb pain (PLP), cancer pain, and post-thoracotomy pain syndrome. This review will discuss conditions, such as neuropathic pain, fibromyalgia, complex regional pain syndrome, and more and ketamine's efficacy and its supplementary benefits in the chronic pain patient population. As the opioid crisis in the United States continues to persist, this review aims to understand better multimodal analgesia, which can improve how chronic pain is managed.

Automated plaque analysis for the prognostication of major adverse cardiac events.

OBJECTIVE: The purpose of this study is to assess the value of an automated model-based plaque characterization tool for the prediction of major adverse cardiac events (MACE). METHODS: We retrospectively included 45 patients with suspected coronary artery disease of which 16 (33%) experienced MACE within 12 months. Commercially available plaque quantification software was used to automatically extract quantitative plaque morphology: lumen area, wall area, stenosis percentage, wall thickness, plaque burden, remodeling ratio, calcified area, lipid rich necrotic core (LRNC) area and matrix area. The measurements were performed at all cross sections, spaced at 0.5 mm, based on fully 3D segmentations of lumen, wall, and each tissue type. Discriminatory power of these markers and traditional risk factors for predicting MACE were assessed. RESULTS: Regression analysis using clinical risk factors only resulted in a prognostic accuracy of 63% with a corresponding area under the curve (AUC) of 0.587. Based on our plaque morphology analysis, minimal cap thickness, lesion length, LRNC volume, maximal wall area/thickness, the remodeling ratio, and the calcium volume were included into our prognostic model as parameters. The use of morphologic features alone resulted in an increased accuracy of 77% with an AUC of 0.94. Combining both clinical risk factors and morphological features in a multivariate logistic regression analysis increased the accuracy to 87% with a similar AUC of 0.924. CONCLUSION: An automated model based algorithm to evaluate CCTA-derived plaque features and quantify morphological features of atherosclerotic plaque increases the ability for MACE prognostication significantly compared to the use of clinical risk factors alone.

Atherosclerotic Plaque Tissue: Noninvasive Quantitative Assessment of Characteristics with Software-aided Measurements from Conventional CT Angiography.

Purpose To (a) evaluate whether plaque tissue characteristics determined with conventional computed tomographic (CT) angiography could be quantitated at higher levels of accuracy by using image processing algorithms that take characteristics of the image formation process coupled with biologic insights on tissue distributions into account by comparing in vivo results and ex vivo histologic findings and (b) assess reader variability. Materials and Methods Thirty-one consecutive patients aged 43-85 years (average age, 64 years) known to have or suspected of having atherosclerosis who underwent CT angiography and were referred for endarterectomy were enrolled. Surgical specimens were evaluated with histopathologic examination to serve as standard of reference. Two readers used lumen boundary to determine scanner blur and then optimized component densities and subvoxel boundaries to best fit the observed image by using semiautomatic software. The accuracy of the resulting in vivo quantitation of calcification, lipid-rich necrotic core (LRNC), and matrix was assessed with statistical estimates of bias and linearity relative to ex vivo histologic findings. Reader variability was assessed with statistical estimates of repeatability and reproducibility. Results A total of 239 cross sections obtained with CT angiography and histologic examination were matched. Performance on held-out data showed low levels of bias and high Pearson correlation coefficients for calcification (-0.096 mm2 and 0.973, respectively), LRNC (1.26 mm2 and 0.856), and matrix (-2.44 mm2 and 0.885). Intrareader variability was low (repeatability coefficient ranged from 1.50 mm2 to 1.83 mm2 among tissue characteristics), as was interreader variability (reproducibility coefficient ranged from 2.09 mm2 to 4.43 mm2). Conclusion There was high correlation and low bias between the in vivo software image analysis and ex vivo histopathologic quantitative measures of atherosclerotic plaque tissue characteristics, as well as low reader variability. Software algorithms can mitigate the blurring and partial volume effects of routine CT angiography acquisitions to produce accurate quantification to enhance current clinical practice. Clinical trial registration no. NCT02143102 © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on September 15, 2017.

Measurement Accuracy of Atherosclerotic Plaque Structure on CT Using Phantoms to Establish Ground Truth.

RATIONALE AND OBJECTIVES: The purpose of this study was to characterize analytic performance of software-aided arterial vessel structure measurements across a range of scanner settings for computed tomography angiography where ground truth is known. We characterized performance for measurands that may be efficiently measured for clinical cases without use of software, as well as those that may be done manually but which is generally not done due to the effort level required unless software is employed. MATERIALS AND METHODS: Four measurands (lumen area, stenosis, wall area, wall thickness) were evaluated using tissue-mimicking phantoms to estimate bias, heteroscedasticity, and limits of quantitation both pooled across scanner settings and individually for eight different settings. Reproducibility across scanner settings was also estimated. RESULTS: Measurements of lumen area have a near constant bias of +1.3 mm for measurements ranging from 3 mm2 to 40 mm2; stenosis bias is +7% across a 30%-70% range; wall area bias is +14% across a 50-450 mm2 range; and wall thickness bias is +1.2 mm across a 3-9 mm range. All measurements possess properties that make them suitable for measuring longitudinal change. Lumen area demonstrates the most sensitivity to scanner settings (bias from as low as +.1 mm to as high as +2.7 mm); wall thickness demonstrates negligible sensitivity. CONCLUSIONS: Variability across scanner settings for lumen measurands was generally higher than bias for a given setting. The converse was true for the wall measurands, where variability due to scanner settings was very low. Both bias and variability due to scanner settings of vessel structure were within clinically useful levels.

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.

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.

RATIONALE AND OBJECTIVES: Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile. MATERIALS AND METHODS: Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers. RESULTS: Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail. CONCLUSIONS: Nine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.