Showing papers in "Radiology in 2016"
TL;DR: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Abstract: In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
4,773 citations
TL;DR: Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine.
Abstract: Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. © RSNA, 2016 Online supplemental material is available for this article.
531 citations
TL;DR: Experienced radiologists achieved moderate reproducibility for PI-RADS version 2, and neither required nor benefitted from a training session, although was weak for DCE in PZ.
Abstract: Six experienced radiologists achieved moderate reproducibility for Prostate Imaging Reporting and Data System version 2 and neither required nor benefitted from a training session; agreement tended to be better in the peripheral zone than the transition zone, although it was weak for dynamic contrast–enhanced imaging in the peripheral zone.
408 citations
TL;DR: This review examines how radiology benefits from NLP, taking a close look at the individual studies in terms of tasks, the NLP methodology and tools used, and their application purpose and performance results.
Abstract: Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed.
387 citations
TL;DR: The results of this study showed global BBB leakage in patients with early AD that is associated with cognitive decline that may be part of a cascade of pathologic events that eventually lead to cognitive decline and dementia.
Abstract: MR imaging was used to show global, diffusely distributed leakage of the blood-brain barrier in patients with early Alzheimer disease, which suggests that a compromised blood-brain barrier is part of the early pathology of Alzheimer disease and might be part of a cascade of pathologic events that eventually lead to cognitive decline.
379 citations
University of Chicago1, NorthShore University HealthSystem2, University of Wisconsin-Madison3, University of North Carolina at Chapel Hill4, University of Texas MD Anderson Cancer Center5, Memorial Sloan Kettering Cancer Center6, University of Miami7, University of Pittsburgh8, National Institutes of Health9
TL;DR: Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence, and is associated with significant associations between radiomics signatures and multigene assay recurrence scores.
Abstract: Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R2 = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence. © RSNA, 2016 Online supplemental material is available for this article.
370 citations
TL;DR: A review of the clinical literature, mainly focusing on current outstanding issues, is given, followed by some innovative proposals for future improvements.
Abstract: The concept of diffusion magnetic resonance (MR) imaging emerged in the mid-1980s, together with the first images of water diffusion in the human brain, as a way to probe tissue structure at a microscopic scale, although the images were acquired at a millimetric scale. Since then, diffusion MR imaging has become a pillar of modern clinical imaging. Diffusion MR imaging has mainly been used to investigate neurologic disorders. A dramatic application of diffusion MR imaging has been acute brain ischemia, providing patients with the opportunity to receive suitable treatment at a stage when brain tissue might still be salvageable, thus avoiding terrible handicaps. On the other hand, it was found that water diffusion is anisotropic in white matter, because axon membranes limit molecular movement perpendicularly to the nerve fibers. This feature can be exploited to produce stunning maps of the orientation in space of the white matter tracts and brain connections in just a few minutes. Diffusion MR imaging is now also rapidly expanding in oncology, for the detection of malignant lesions and metastases, as well as monitoring. Water diffusion is usually largely decreased in malignant tissues, and body diffusion MR imaging, which does not require any tracer injection, is rapidly becoming a modality of choice to detect, characterize, or even stage malignant lesions, especially for breast or prostate cancer. After a brief summary of the key methodological concepts beyond diffusion MR imaging, this article will give a review of the clinical literature, mainly focusing on current outstanding issues, followed by some innovative proposals for future improvements.
362 citations
TL;DR: Current limitations and future developments of ASL techniques to improve clinical applicability, such as multiple inversion time ASL sequences to assess alterations of transit time, reproducibility and quantification of cerebral blood flow, and to measure cerebrovascular reserve, will be reviewed.
Abstract: Arterial spin labeling (ASL) is a magnetic resonance (MR) imaging technique used to assess cerebral blood flow noninvasively by magnetically labeling inflowing blood. In this article, the main labeling techniques, notably pulsed and pseudocontinuous ASL, as well as emerging clinical applications will be reviewed. In dementia, the pattern of hypoperfusion on ASL images closely matches the established patterns of hypometabolism on fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) images due to the close coupling of perfusion and metabolism in the brain. This suggests that ASL might be considered as an alternative for FDG, reserving PET to be used for the molecular disease-specific amyloid and tau tracers. In stroke, ASL can be used to assess perfusion alterations both in the acute and the chronic phase. In arteriovenous malformations and dural arteriovenous fistulas, ASL is very sensitive to detect even small degrees of shunting. In epilepsy, ASL can be used to assess the epileptogenic focus, both in peri- and interictal period. In neoplasms, ASL is of particular interest in cases in which gadolinium-based perfusion is contraindicated (eg, allergy, renal impairment) and holds promise in differentiating tumor progression from benign causes of enhancement. Finally, various neurologic and psychiatric diseases including mild traumatic brain injury or posttraumatic stress disorder display alterations on ASL images in the absence of visualized structural changes. In the final part, current limitations and future developments of ASL techniques to improve clinical applicability, such as multiple inversion time ASL sequences to assess alterations of transit time, reproducibility and quantification of cerebral blood flow, and to measure cerebrovascular reserve, will be reviewed. © RSNA, 2016 Online supplemental material is available for this article.
332 citations
TL;DR: An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated.
Abstract: The authors of this study identified an 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastomas and that demonstrates improved performance compared with that of established clinical and radiologic risk models.
308 citations
TL;DR: Methods for controlling the quality of fluorine 18 fluorodeoxyglucose PET imaging conditions to ensure the comparability of PET images from different time points to allow quantitative expression of the changes in PET measurements and assessment of overall treatment response in PET studies are described.
Abstract: Collection of metabolic response data in a standardized fashion will greatly aid future pooling of study data and meta-analyses to allow the best determination of the predictive and prognostic value of the PET Response Criteria in Solid Tumors (PERCIST 1.0) in a variety of clinical settings and with a variety of treatments.
277 citations
TL;DR: Tumor size of less than 3 cm and ablation margins greater than 5 mm are essential for satisfactory local tumor control and are associated with shorter OS.
Abstract: Attaining sufficient ablation margins (>5 mm, and, ideally, >10 mm) can result in significant lowering of local tumor progression rates after percutaneous radiofrequency ablation of colorectal cancer liver metastases; in addition, a modified clinical risk score for ablation can be used as a prognostic stratification tool.
TL;DR: The spectrum of findings associated with congenital Zika virus infections in the IPESQ in northeastern Brazil is illustrated to aid the radiologist in identifying Zika virus infection at imaging.
Abstract: We illustrate the spectrum of findings associated with congenital Zika virus infection in the Instituto de Pesquisa in Campina Grande State Paraiba in northeastern Brazil, where the congenital infection has been particularly severe.
TL;DR: "Worrisome" imaging features, such as tumor dimension, nonsmooth tumor margins, peritumoral enhancement, and TTPVI, have high accuracy in the prediction of MVI in HCC.
Abstract: Some “worrisome” imaging features, such as nonsmooth tumor margins, peritumoral enhancement, two-trait predictor of venous invasion (“internal arteries” and “hypoattenuating halos”), and large tumor size were used to significantly predict the presence of microvascular invasion in hepatocellular carcinoma.
TL;DR: Fast reperfusion leads to improved functional outcome among patients with acute stroke treated with stent retrievers and detailed attention to workflow with iterative feedback and aggressive time goals may have contributed to efficient workflow environments.
Abstract: Purpose To study the relationship between functional independence and time to reperfusion in the Solitaire with the Intention for Thrombectomy as Primary Endovascular Treatment for Acute Ischemic Stroke (SWIFT PRIME) trial in patients with disabling acute ischemic stroke who underwent endovascular therapy plus intravenous tissue plasminogen activator (tPA) administration versus tPA administration alone and to investigate variables that affect time spent during discrete steps. Materials and Methods Data were analyzed from the SWIFT PRIME trial, a global, multicenter, prospective study in which outcomes were compared in patients treated with intravenous tPA alone or in combination with the Solitaire device (Covidien, Irvine, Calif). Between December 2012 and November 2014, 196 patients were enrolled. The relation between time from (a) symptom onset to reperfusion and (b) imaging to reperfusion and clinical outcome was analyzed, along with patient and health system characteristics that affect discrete steps in patient workflow. Multivariable logistic regression was used to assess relationships between time and outcome; negative binomial regression was used to evaluate effects on workflow. The institutional review board at each site approved the trial. Patients provided written informed consent, or, at select sites, there was an exception from having to acquire explicit informed consent in emergency circumstances. Results In the stent retriever arm of the study, symptom onset to reperfusion time of 150 minutes led to 91% estimated probability of functional independence, which decreased by 10% over the next hour and by 20% with every subsequent hour of delay. Time from arrival at the emergency department to arterial access was 90 minutes (interquartile range, 69-120 minutes), and time to reperfusion was 129 minutes (interquartile range, 108-169 minutes). Patients who initially arrived at a referring facility had longer symptom onset to groin puncture times compared with patients who presented directly to the endovascular-capable center (275 vs 179.5 minutes, P < .001). Conclusion Fast reperfusion leads to improved functional outcome among patients with acute stroke treated with stent retrievers. Detailed attention to workflow with iterative feedback and aggressive time goals may have contributed to efficient workflow environments. (©) RSNA, 2016 Online supplemental material is available for this article.
TL;DR: In this paper, the authors evaluated the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma.
Abstract: Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. Materials and Methods Retrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, RTK I "PGFRA," RTK II "classic"), MGMT promoter methylation status, and hallmark copy number variations (EGFR, PDGFRA, MDM4, and CDK4 amplification; PTEN, CDKN2A, NF1, and RB1 loss). Univariate analyses (voxel-lesion symptom mapping for tumor location, Wilcoxon test for all other MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics. Results There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR [P < .01], 61% for RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters was not significant. Conclusion The authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma. © RSNA, 2016 Online supplemental material is available for this article.
TL;DR: In a general clinical practice environment, hepatic MR elastography is a robust imaging method with a high success rate in a broad spectrum of patients and shows the complex association between liver stiffness and hepatic pathophysiology.
Abstract: With a technical failure rate of less than 5.6%, the use of hepatic MR elastography to assess changes in liver tissue mechanics, which are associated with many pathophysiologic factors, may yield important information regarding staging of liver disease without invasive biopsy.
TL;DR: The state of the art in psychoradiology is presented, as well as perspectives regarding preparations in the field of radiology for its evolution, and the main challenges and future directions in this field are described.
Abstract: The characterization of imaging biomarkers associated with psychiatric illness can facilitate diagnostic and therapeutic practice by providing an objective way to select patients for optimal therapies and to track treatment effects on brain systems.
University of California, San Francisco1, Brown University2, American College of Radiology3, Hospital of the University of Pennsylvania4, University of Washington5, University of Alabama at Birmingham6, Medical University of South Carolina7, Memorial Sloan Kettering Cancer Center8, University of Texas Southwestern Medical Center9, Georgetown University10, University of Chicago11, University of Pennsylvania12
TL;DR: Breast tumor FTV measured by MR imaging is a strong predictor of RFS, even in the presence of PCR and RCB class, and models combining MR imaging, histopathology, and breast cancer subtype demonstrated the strongest predictive performance in this study.
Abstract: Results of this study show that MR imaging measurements of breast tumor volume by using a standardized method are effective for prediction of recurrence-free survival as early as one cycle of treatment.
TL;DR: Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.
Abstract: Our results suggest that advanced diffusion-weighted imaging techniques by using biexponential and stretched exponential models and diffusion kurtosis imaging can provide more valuable information for the grading of gliomas; water molecular diffusion heterogeneity index and mean kurtosis may be more accurate in the grading of gliomas compared with the other diffusion parameters, which would be helpful in improving therapy strategies and prognosis.
TL;DR: Both TIRADS and the ATA guidelines provide effective malignancy risk stratification for thyroid nodules, and Nodules that do not meet the criteria for a specific pattern with the AtA guidelines have a relatively high risk of malignancies.
Abstract: An improvement in performance may be achieved with a broader pattern classification of the present American Thyroid Association guidelines, and this can contribute to more accurate malignancy risk stratification.
TL;DR: The manifestations of these in clinical practice-urate and bone marrow edema detection, metal artifact reduction, and tendon analysis, with potential in arthrography, bone densitometry, and metastases surveillance are discussed.
Abstract: The principal advantages of dual-energy computed tomography (CT) over conventional CT in the musculoskeletal setting relate to the additional information provided regarding tissue composition, artifact reduction, and image optimization. This article discusses the manifestations of these in clinical practice-urate and bone marrow edema detection, metal artifact reduction, and tendon analysis, with potential in arthrography, bone densitometry, and metastases surveillance. The basic principles of dual-energy CT physics and scanner design will also be discussed. © RSNA, 2016.
TL;DR: A rapid technique for quantitative abdominal imaging was developed that allows simultaneous quantification of multiple tissue properties within one 19-second breath hold, with measurements comparable to those in published literature.
Abstract: A rapid technique for quantitative abdominal imaging was developed by using a fast imaging with steady-state free precession MR fingerprinting acquisition in combination with the Bloch-Siegert B1 mapping method, allowing simultaneous quantification of T1 and T2 in the abdomen within a 19-second breath hold.
TL;DR: This study found a statistically significant relationship between head impact exposure and change of FA fractional anisotropy value of whole, core, and terminals of left IFOF and right SLF's terminals where WM and gray matter intersect, in the absence of a clinically diagnosed concussion.
Abstract: Purpose To examine the effects of subconcussive impacts resulting from a single season of youth (age range, 8-13 years) football on changes in specific white matter (WM) tracts as detected with diffusion-tensor imaging in the absence of clinically diagnosed concussions. Materials and Methods Head impact data were recorded by using the Head Impact Telemetry system and quantified as the combined-probability risk-weighted cumulative exposure (RWECP). Twenty-five male participants were evaluated for seasonal fractional anisotropy (FA) changes in specific WM tracts: the inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus, and superior longitudinal fasciculus (SLF). Fiber tracts were segmented into a central core and two fiber terminals. The relationship between seasonal FA change in the whole fiber, central core, and the fiber terminals with RWECP was also investigated. Linear regression analysis was conducted to determine the association between RWECP and change in fiber tract FA during the season. Results There were statistically significant linear relationships between RWEcp and decreased FA in the whole (R2 = 0.433; P = .003), core (R2 = 0.3649; P = .007), and terminals (R2 = 0.5666; P < .001) of left IFOF. A trend toward statistical significance (P = .08) in right SLF was observed. A statistically significant correlation between decrease in FA of the right SLF terminal and RWECP was also observed (R2 = 0.2893; P = .028). Conclusion This study found a statistically significant relationship between head impact exposure and change of FA fractional anisotropy value of whole, core, and terminals of left IFOF and right SLF's terminals where WM and gray matter intersect, in the absence of a clinically diagnosed concussion. © RSNA, 2016.
TL;DR: The current state of photoacoustic imaging is presented, including techniques and instrumentation, followed by a discussion of potential clinical applications of this technique for the detection and management of cancer.
Abstract: With the availability of commercial preclinical imaging systems along with the ability to build relatively inexpensive custom systems, an increase in photoacoustic imaging investigation rate is expected in the coming years for various clinical applications including and beyond cancer detection and characterization.
TL;DR: The performance of PCD showed no statistically significant difference compared with EID when the abdomen was evaluated in a conventional scan mode, and PCD provides spectral information, which may be used for material decomposition.
Abstract: The photon-counting detector system showed equivalent performance to clinical energy-integrating detectors when the abdomen was evaluated in a conventional scanning mode with the added advantage of providing spectral information that may be used for material decomposition.
TL;DR: CT imaging features of lung adenocarcinomas in combination with clinical variables can be used to prognosticate EGFR mutation status better than use of clinical variables alone.
Abstract: Purpose To retrospectively identify the relationship between epidermal growth factor receptor (EGFR) mutation status, predominant histologic subtype, and computed tomographic (CT) characteristics in surgically resected lung adenocarcinomas in a cohort of Asian patients. materials and Methods This study was approved by the institutional review board, with waiver of informed consent. Preoperative chest CT findings were retrospectively evaluated in 385 surgically resected lung adenocarcinomas. A total of 30 CT descriptors were assessed. EGFR mutations at exons 18-21 were determined by using the amplification refractory mutation system. Multiple logistic regression analyses were performed to identify independent factors of harboring EGFR mutation status. The final model was selected by using the backward elimination method, and two areas under the receiver operating characteristic curve (ROC) were compared with the nonparametric approach of DeLong, DeLong, and Clarke-Pearson. Results EGFR mutations were found in 168 (43.6%) of 385 patients. Mutations were found more frequently in (a) female patients (P < .001); (b)those who had never smoked (P < .001); (c)those with lepidic predominant adenocarcinomas (P = .001) or intermediate pathologic grade (P < .001); (e) smaller tumors (P < .001); (f)tumors with spiculation (P = .019), ground-glass opacity (GGO) or mixed GGO (P < .001), air bronchogram (P = .006), bubblelike lucency (P < .001), vascular convergence (P = .024), thickened adjacent bronchovascular bundles (P = .027), or pleural retraction (P < .001); and (g) tumors without pleural attachment (P = .004), a well-defined margin (P = .010), marked heterogeneous enhancement (P = .001), severe peripheral emphysema (P = .002), severe peripheral fibrosis (P = .013), or lymphadenopathy (P = .028). The most important and significantly independent prognostic factors of harboring EGFR-activating mutation for the model with both clinical variables and CT features were those who had never smoked and those with smaller tumors, bubblelike lucency, homogeneous enhancement, or pleural retraction when adjusting for histologic subtype, pathologic grade, or thickened adjacent bronchovascular bundles. ROC curve analysis showed that use of clinical variables combined with CT features (area under the ROC curve = 0.778) was superior to use of clinical variables alone (area under the ROC curve = 0.690). Conclusion CT imaging features of lung adenocarcinomas in combination with clinical variables can be used to prognosticate EGFR mutation status better than use of clinical variables alone. (©) RSNA, 2016 Online supplemental material is available for this article.
TL;DR: In large cohorts, extracellular volume fraction (ECV) has been shown to quantify the full extent of myocardial fibrosis in noninfarcted myocardium and may predict outcomes at least as effectively as left ventricular ejection fraction.
Abstract: While cardiovascular magnetic resonance (MR) has become the noninvasive tool of choice for the assessment of myocardial viability and for the detection of acute myocardial edema, cardiac T1 mapping is believed to further extend the ability of cardiovascular MR to characterize the myocardium. Fundamentally, cardiovascular MR can improve diagnosis of disease that historically has been challenging to establish with other imaging modalities. For example, decreased native T1 values appear highly specific to detect and quantify disease severity related to myocardial iron overload states or glycosphingolipid accumulation in Anderson-Fabry disease, whereas high native T1 values are observed with edema, amyloid, and other conditions. Cardiovascular MR can also improve the assessment of prognosis with parameters that relate to myocardial structure and composition that complement the familiar functional parameters around which contemporary cardiology decision making revolves. In large cohorts, extracellular volume fraction (ECV) has been shown to quantify the full extent of myocardial fibrosis in noninfarcted myocardium. ECV may predict outcomes at least as effectively as left ventricular ejection fraction. This uncommon statistical observation (of potentially being more strongly associated with outcomes than ejection fraction) suggests prime biologic importance for the cardiac interstitium that may rank highly in the hierarchy of vast myocardial changes occurring in cardiac pathophysiology. This article presents current and developing clinical applications of cardiac T1 mapping and reviews the existing evidence on their diagnostic and prognostic value in various clinical conditions. This article also contextualizes these advances and explores how T1 mapping and ECV may affect major "global" issues such as diagnosis of disease, risk stratification, and paradigms of disease, and ultimately how we conceptualize patient vulnerability.
TL;DR: PET imaging characteristics associated with distant metastasis that could potentially help practitioners to tailor appropriate therapy for individual patients with early-stage NSCLC were identified.
Abstract: Purpose To identify quantitative imaging biomarkers at fluorine 18 ((18)F) positron emission tomography (PET) for predicting distant metastasis in patients with early-stage non-small cell lung cancer (NSCLC). Materials and Methods In this institutional review board-approved HIPAA-compliant retrospective study, the pretreatment (18)F fluorodeoxyglucose PET images in 101 patients treated with stereotactic ablative radiation therapy from 2005 to 2013 were analyzed. Data for 70 patients who were treated before 2011 were used for discovery purposes, while data from the remaining 31 patients were used for independent validation. Quantitative PET imaging characteristics including statistical, histogram-related, morphologic, and texture features were analyzed, from which 35 nonredundant and robust features were further evaluated. Cox proportional hazards regression model coupled with the least absolute shrinkage and selection operator was used to predict distant metastasis. Whether histologic type provided complementary value to imaging by combining both in a single prognostic model was also assessed. Results The optimal prognostic model included two image features that allowed quantification of intratumor heterogeneity and peak standardized uptake value. In the independent validation cohort, this model showed a concordance index of 0.71, which was higher than those of the maximum standardized uptake value and tumor volume, with concordance indexes of 0.67 and 0.64, respectively. The prognostic model also allowed separation of groups with low and high risk for developing distant metastasis (hazard ratio, 4.8; P = .0498, log-rank test), which compared favorably with maximum standardized uptake value and tumor volume (hazard ratio, 1.5 and 2.0, respectively; P = .73 and 0.54, log-rank test, respectively). When combined with histologic types, the prognostic power was further improved (hazard ratio, 6.9; P = .0289, log-rank test; and concordance index, 0.80). Conclusion PET imaging characteristics associated with distant metastasis that could potentially help practitioners to tailor appropriate therapy for individual patients with early-stage NSCLC were identified. (©) RSNA, 2016 Online supplemental material is available for this article.
TL;DR: In this paper, the diagnostic performance of intravoxel incoherent motion (IVIM) parameters and apparent diffusion coefficient (ADC) to assess response to combined chemotherapy and radiation therapy (CRT) in patients with rectal cancer by using histogram analysis derived from whole-tumor volumes and single-section regions of interest (ROIs).
Abstract: Purpose To determine the diagnostic performance of intravoxel incoherent motion (IVIM) parameters and apparent diffusion coefficient (ADC) to assess response to combined chemotherapy and radiation therapy (CRT) in patients with rectal cancer by using histogram analysis derived from whole-tumor volumes and single-section regions of interest (ROIs). Materials and Methods The institutional review board approved this retrospective study of 31 patients with rectal cancer who underwent magnetic resonance (MR) imaging before and after CRT, including diffusion-weighted imaging with 34 b values prior to surgery. Patient consent was not required. ADC, perfusion-related diffusion fraction (f), slow diffusion coefficient (D), and fast diffusion coefficient (D*) were calculated on MR images acquired before and after CRT by using biexponential fitting. ADC and IVIM histogram metrics and median values were obtained by using whole-tumor volume and single-section ROI analyses. All ADC and IVIM parameters obtained before and after CRT were compared with histopathologic findings by using t tests with Holm-Sidak correction. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of IVIM parameters derived from whole-tumor volume and single-section ROIs for prediction of histopathologic response. Results Extreme values aside, results of histogram analysis of ADC and IVIM were equivalent to median values for tumor response assessment (P > .06). Prior to CRT, none of the median ADC and IVIM diffusion metrics correlated with subsequent tumor response (P > .36). Median D and ADC values derived from either whole-volume or single-section analysis increased significantly after CRT (P ≤ .01) and were significantly higher in good versus poor responders (P ≤ .02). Median IVIM f and D* values did not significantly change after CRT and were not associated with tumor response to CRT (P > .36). Interobserver agreement was excellent for whole-tumor volume analysis (range, 0.91-0.95) but was only moderate for single-section ROI analysis (range, 0.50-0.63). Conclusion Median D and ADC values obtained after CRT were useful for discrimination between good and poor responders. Histogram metrics did not add to the median values for assessment of tumor response. Volumetric analysis demonstrated better interobserver reproducibility when compared with single-section ROI analysis. (©) RSNA, 2016 Online supplemental material is available for this article.
TL;DR: Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts, which could have substantial effects on clinical practice patterns.
Abstract: Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. (©) RSNA, 2015 Online supplemental material is available for this article.