Showing papers in "Radiology in 2018"
TL;DR: A broad overview of LI-RADS is provided, including its historic development, relationship to other imaging guidelines, composition, aims, and future directions, including the motivation for and key components of the 2018 update are understood.
Abstract: The Liver Imaging Reporting and Data System (LI-RADS) is composed of four individual algorithms intended to standardize the lexicon, as well as reporting and care, in patients with or at risk for hepatocellular carcinoma in the context of surveillance with US; diagnosis with CT, MRI, or contrast material-enhanced US; and assessment of treatment response with CT or MRI. This report provides a broad overview of LI-RADS, including its historic development, relationship to other imaging guidelines, composition, aims, and future directions. In addition, readers will understand the motivation for and key components of the 2018 update.
558 citations
TL;DR: The authors will explain the technical principles of photon-counting CT in nonmathematical terms for radiologists and clinicians to create opportunities for quantitative imaging relative to current CT technology.
Abstract: Photon-counting CT is an emerging technology with the potential to dramatically change clinical CT Photon-counting CT uses new energy-resolving x-ray detectors, with mechanisms that differ substantially from those of conventional energy-integrating detectors Photon-counting CT detectors count the number of incoming photons and measure photon energy This technique results in higher contrast-to-noise ratio, improved spatial resolution, and optimized spectral imaging Photon-counting CT can reduce radiation exposure, reconstruct images at a higher resolution, correct beam-hardening artifacts, optimize the use of contrast agents, and create opportunities for quantitative imaging relative to current CT technology In this review, the authors will explain the technical principles of photon-counting CT in nonmathematical terms for radiologists and clinicians Following a general overview of the current status of photon-counting CT, they will explain potential clinical applications of this technology
542 citations
TL;DR: Key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used are explained.
Abstract: In this article, we review some of the key methodologic points that should be considered with regard to clinical evaluation of artificial intelligence tools for use in medical diagnosis and prediction.
523 citations
TL;DR: Examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology and the future impact and natural extension of these techniques in radiology practice are discussed.
Abstract: Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.
© RSNA, 2018
501 citations
TL;DR: Many RFs were redundant and nonreproducible, and if all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs because of redundancy.
Abstract: The majority (94%) of the evaluated radiomics features for CT were not reproducible and were redundant. If all the CT parameters are held constant, then a smaller percentage (6%) of the radiomics f...
383 citations
TL;DR: U-Net demonstrates efficacy and precision in quickly generating accurate segmentations that can be used to extract relaxation times and morphologic characterization and values that can been used in the monitoring and diagnosis of OA.
Abstract: We aim to analyze how automatic segmentation performances translate in accuracy and precision to morphology and relaxometry in osteoarthritis compared with manual segmentations and increase the speed and accuracy of the work flow that uses quantitative MR imaging to study knee degenerative diseases.
300 citations
TL;DR: T2- Weighted-based radiomics showed better classification performance compared with qualitative assessment at T2-weighted and DW imaging for diagnosing pCR in patients with locally advanced rectal cancer after CRT.
Abstract: Purpose To investigate the value of T2-weighted-based radiomics compared with qualitative assessment at T2-weighted imaging and diffusion-weighted (DW) imaging for diagnosis of clinical complete response in patients with rectal cancer after neoadjuvant chemotherapy-radiation therapy (CRT). Materials and Methods This retrospective study included 114 patients with rectal cancer who underwent magnetic resonance (MR) imaging after CRT between March 2012 and February 2016. Median age among women (47 of 114, 41%) was 55.9 years (interquartile range, 45.4-66.7 years) and median age among men (67 of 114, 59%) was 55 years (interquartile range, 48-67 years). Surgical histopathologic analysis was the reference standard for pathologic complete response (pCR). For qualitative assessment, two radiologists reached a consensus. For radiomics, one radiologist segmented the volume of interest on high-spatial-resolution T2-weighted images. A random forest classifier was trained to separate the patients by their outcomes after balancing the number of patients in each response category by using the synthetic minority oversampling technique. Statistical analysis was performed by using the Wilcoxon rank-sum test, McNemar test, and Benjamini-Hochberg method. Results Twenty-one of 114 patients (18%) achieved pCR. The radiomic classifier demonstrated an area under the curve of 0.93 (95% confidence interval [CI]: 0.87, 0.96), sensitivity of 100% (95% CI: 0.84, 1), specificity of 91% (95% CI: 0.84, 0.96), positive predictive value of 72% (95% CI: 0.53, 0.87), and negative predictive value of 100% (95% CI: 0.96, 1). The diagnostic performance of radiomics was significantly higher than was qualitative assessment at T2-weighted imaging or DW imaging alone (P < .02). The specificity and positive predictive values were significantly higher in radiomics than were at combined T2-weighted and DW imaging (P < .0001). Conclusion T2-weighted-based radiomics showed better classification performance compared with qualitative assessment at T2-weighted and DW imaging for diagnosing pCR in patients with locally advanced rectal cancer after CRT. © RSNA, 2018 Online supplemental material is available for this article.
236 citations
Université de Montréal1, Duke University2, University of California, Davis3, Memorial Sloan Kettering Cancer Center4, Mayo Clinic5, Washington University in St. Louis6, University of Michigan7, American University of Beirut8, Georgetown University9, University of Massachusetts Medical School10, University of California, San Diego11, Naval Medical Center San Diego12, Thomas Jefferson University13, University of California, San Francisco14, University of Pennsylvania15
TL;DR: The quality of evidence supporting each LI-RADS major feature for diagnosis of HCC as well as of the LI- RADS imaging features suggesting malignancy other than HCC are assessed.
Abstract: The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS. © RSNA, 2017 Online supplemental material is available for this article.
221 citations
TL;DR: The purpose of the research roadmap is to highlight important information that is not known and to identify and prioritize needed research to determine if gadolinium retention adversely affects the function of human tissues and if vulnerable populations, such as children, are at greater risk for experiencing clinical disease.
Abstract: Gadolinium-based contrast agents (GBCAs) have revolutionized MRI, enabling physicians to obtain crucial life-saving medical information that often cannot be obtained with other imaging modalities. Since initial approval in 1988, over 450 million intravenous GBCA doses have been administered worldwide, with an extremely favorable pharmacologic safety profile; however, recent information has raised new concerns over the safety of GBCAs. Mounting evidence has shown there is long-term retention of gadolinium in human tissues. Further, a small subset of patients have attributed a constellation of symptoms to GBCA exposure, although the association of these symptoms with GBCA administration or gadolinium retention has not been proven by scientific investigation. Despite evidence that macrocyclic GBCAs show less gadolinium retention than linear GBCAs, the safety implications of gadolinium retention are unknown. The mechanism and chemical forms of gadolinium retention, as well as the biologic activity and clinical importance of these retained gadolinium species, remain poorly understood and underscore the need for additional research. In February 2018, an international meeting was held in Bethesda, Md, at the National Institutes of Health to discuss the current literature and knowledge gaps about gadolinium retention, to prioritize future research initiatives to better understand this phenomenon, and to foster collaborative standardized studies. The greatest priorities are to determine (a) if gadolinium retention adversely affects the function of human tissues, (b) if retention is causally associated with short- or long-term clinical manifestations of disease, and (c) if vulnerable populations, such as children, are at greater risk for experiencing clinical disease. The purpose of the research roadmap is to highlight important information that is not known and to identify and prioritize needed research. ©RSNA, 2018 Online supplemental material is available for this article .
192 citations
TL;DR: The authors review the technical basis, acquisition techniques, and results and limitations of US- and MR-based elastography techniques and discusses reliability, reproducibility, failure rate, and emerging advances.
Abstract: US and MR elastographic techniques have developed into accurate methods for quantitative, noninvasive diagnosis of liver fibrosis in a wide range of etiologies; however, interpretation of results should take into account potential confounding factors, pitfalls, and technical limitations.
191 citations
TL;DR: This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury.
Abstract: Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .
TL;DR: In general, CMB detection rate increases with field strength, with the use of three-dimensional sequences, and with postprocessing methods that use local perturbations of the MR phase to enhance T2* contrast.
Abstract: Cerebral microbleeds (CMBs), also referred to as microhemorrhages, appear on magnetic resonance (MR) images as hypointense foci notably at T2*-weighted or susceptibility-weighted (SW) imaging. CMBs are detected with increasing frequency because of the more widespread use of high magnetic field strength and of newer dedicated MR imaging techniques such as three-dimensional gradient-echo T2*-weighted and SW imaging. The imaging appearance of CMBs is mainly because of changes in local magnetic susceptibility and reflects the pathologic iron accumulation, most often in perivascular macrophages, because of vasculopathy. CMBs are depicted with a true-positive rate of 48%-89% at 1.5 T or 3.0 T and T2*-weighted or SW imaging across a wide range of diseases. False-positive "mimics" of CMBs occur at a rate of 11%-24% and include microdissections, microaneurysms, and microcalcifications; the latter can be differentiated by using phase images. Compared with postmortem histopathologic analysis, at least half of CMBs are missed with premortem clinical MR imaging. In general, CMB detection rate increases with field strength, with the use of three-dimensional sequences, and with postprocessing methods that use local perturbations of the MR phase to enhance T2* contrast. Because of the more widespread availability of high-field-strength MR imaging systems and growing use of SW imaging, CMBs are increasingly recognized in normal aging, and are even more common in various disorders such as Alzheimer dementia, cerebral amyloid angiopathy, stroke, and trauma. Rare causes include endocarditis, cerebral autosomal dominant arteriopathy with subcortical infarcts, leukoencephalopathy, and radiation therapy. The presence of CMBs in patients with stroke is increasingly recognized as a marker of worse outcome. Finally, guidelines for adjustment of anticoagulant therapy in patients with CMBs are under development. © RSNA, 2018.
TL;DR: Recommendations are made that cross-sectional enterography should be performed at diagnosis of Crohn's disease and considered for small bowel Crohn’s disease monitoring paradigms, and a useful morphologic construct describing how imaging findings evolve with disease progression and response is described.
Abstract: This guideline establishes a common expectation for the use of CT enterography and MR enterography in patients with small bowel Crohn’s disease, as well as elucidating anatomic structures to be systematically evaluated, the significance of specific imaging findings, and agreed-upon terms for describing imaging findings of small bowel Crohn’s disease inflammation and its complications.
TL;DR: Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy, thereby allowing for more precise imaging and screening.
Abstract: Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. © RSNA, 2018.
TL;DR: Clinically important lesions can be missed or their size can be underestimated at MP MR imaging, and new approaches to reduce this false-negative rate are needed.
Abstract: Multiparametric MR imaging has high sensitivity in the detection of clinically important prostate cancer on a per-patient basis; however, on a per-lesion basis, a considerable number of clinically important lesions are missed.
University of Washington1, Brown University2, American College of Radiology3, University of California, San Francisco4, University of Michigan5, University of Pennsylvania6, University of Alabama at Birmingham7, University of California, San Diego8, University of Texas Southwestern Medical Center9, University of Texas MD Anderson Cancer Center10, Oregon Health & Science University11, University of Chicago12, University of South Florida13
TL;DR: After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy.
Abstract: Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease Eighty (33%) of 242 patients experienced pCR Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 060; 95% confidence interval [CI]: 052, 068; P = 017) and after treatment (AUC = 061; 95% CI: 052, 069; P = 013) Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 076; 95% CI: 062, 089; P < 001) In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 072; 95% CI: 061, 083) over ΔADC alone (AUC = 057; 95% CI: 044, 070; P = 032) Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy © RSNA, 2018 Online supplemental material is available for this article
TL;DR: The FFRML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFRCFD approach, and both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.
Abstract: The results demonstrate that coronary CT angiography–derived fractional flow reserve using machine learning performs equally to computational fluid dynamics modeling in detecting lesion-specific ischemia; both algorithms outperform coronary CT angiography alone and quantitative coronary angiography.
TL;DR: Radiomic MRI phenotyping can improve survival prediction when integrated with clinical and genetic profiles and thus has potential as a practical imaging biomarker.
Abstract: Radiomic phenotyping based on multiparametric MRI data improves survival prediction when integrated into clinical and genetic status (O-6-methylguanine-DNA-methyltransferase promoter and isocitrate...
TL;DR: Practical guidance on how to implement and apply ACR TI-RADS is offered based on the authors' experience with the system.
Abstract: The purpose of this article is to present our perspective and provide practical advice to ultrasonography practitioners who adopt the American College of Radiology Thyroid Imaging Reporting and Data System.
TL;DR: RS provides response rates, tumor control, and survival outcomes comparable to curative-intent treatments for selected patients with early-stage HCC who have preserved liver function.
Abstract: Radiation segmentectomy provides local tumor control, prolonged time to progression, and overall survival outcomes comparable to radiofrequency ablation, resection, and transplantation for Barcelona Clinic Liver Cancer stage 0 or A patients. This treatment should be further investigated as a potentially curative treatment in patients not amenable to standard options.
TL;DR: Quantitative measurement of the mean apparent diffusion coefficient (ADC) improved differentiation of benign versus malignant prostate lesions, compared with clinical assessment, and Radiomic machine learning had comparable but not better performance than mean ADC assessment.
Abstract: Quantitative measurement of the mean apparent diffusion coefficient (ADC) was more accurate than prospective clinical assessment in classifying a lesion as clinically significant prostate cancer ra...
TL;DR: This proof-of-concept study indicates that TA of nonenhanced cine MR images allows for the diagnosis of subacute and chronic MI with high accuracy.
Abstract: Texture analysis is feasible and allows for the diagnosis of small and large subacute and chronic ischemic scars on nonenhanced cine MR images with high accuracy.
TL;DR: Clinicians should be aware of the strengths and weaknesses of US-based FNAB criteria in the management of thyroid nodules and take heed of the individual international society guidelines.
Abstract: The diagnostic performance of US-based fine-needle aspiration biopsy criteria differs according to different international society guidelines; therefore, clinicians need to be aware of the strengths and weaknesses of various US-based fine-needle aspiration biopsy criteria in the management of thyroid nodules.
TL;DR: A deep learning system for staging liver fibrosis by using CT images in the liver that outperformed the radiologist's interpretation, aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 index by using the area under the receiver operating characteristic curve (AUROC) and Obuchowski index.
Abstract: Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal venous phase CT images in 7461 patients with pathologically confirmed liver fibrosis. The diagnostic performance of the DLS was evaluated in separate test data sets for 891 patients. The influence of patient characteristics and CT techniques on the staging accuracy of the DLS was evaluated by logistic regression analysis. In a subset of 421 patients, the diagnostic performance of the DLS was compared with that of the radiologist's assessment, aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 index by using the area under the receiver operating characteristic curve (AUROC) and Obuchowski index. Results In the test data sets, the DLS had a staging accuracy of 79.4% (707 of 891) and an AUROC of 0.96, 0.97, and 0.95 for diagnosing significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4), respectively. At multivariable analysis, only pathologic fibrosis stage significantly affected the staging accuracy of the DLS (P = .016 and .013 for F1 and F2, respectively, compared with F4), whereas etiology of liver disease and CT technique did not. The DLS (Obuchowski index, 0.94) outperformed the radiologist's interpretation, APRI, and fibrosis-4 index (Obuchowski index range, 0.71-0.81; P ˂ .001) for staging liver fibrosis. Conclusion The deep learning system allows for accurate staging of liver fibrosis by using CT images. © RSNA, 2018 Online supplemental material is available for this article.
TL;DR: This study presented a radiogenomics map of non–small cell lung cancer that linked image phenotypes with ribonucleic acid signatures captured by metagenes and showed their association with molecular pathways.
Abstract: Our study presented a radiogenomics map of non–small cell lung cancer that linked image phenotypes with ribonucleic acid signatures captured by metagenes and showed their association with molecular pathways.
TL;DR: The authors will discuss the technical requirements and considerations for vessel wall image acquisition in general, describe their own vessel wall imaging protocol at 3 T and 7 T, show a step-by-step basic assessment of intracranial vessels wall imaging as performed at their institution, and summarize the commonly reported imaging characteristics of various intrac Cranial vessel wall diseases for direct clinical applicability.
Abstract: Intracranial vessel wall magnetic resonance (MR) imaging has gained much attention in the past decade and has become part of state-of-the-art MR imaging protocols to assist in diagnosing the cause of ischemic stroke. With intracranial vessel wall imaging, vessel wall characteristics have tentatively been described for atherosclerosis, vasculitis, dissections, Moyamoya disease, and aneurysms. With the increasing demand and subsequently increased use of intracranial vessel wall imaging in clinical practice, radiologists should be aware of the choices in imaging parameters and how they affect image quality, the clinical indications, methods of assessment, and limitations in the interpretation of these images. In this How I do It article, the authors will discuss the technical requirements and considerations for vessel wall image acquisition in general, describe their own vessel wall imaging protocol at 3 T and 7 T, show a step-by-step basic assessment of intracranial vessel wall imaging as performed at their institution-including commonly encountered artifacts and pitfalls-and summarize the commonly reported imaging characteristics of various intracranial vessel wall diseases for direct clinical applicability. Finally, future technical and clinical considerations for full implementation of intracranial vessel wall imaging in clinical practice, including the need for histologic validation and acquisition time reduction, will be discussed.
TL;DR: Whether computer-aided diagnosis approaches can increase PPV and reduce the false-positive rate in lung cancer screening for small nodules compared with human reading by thoracic radiologists is tested.
Abstract: Computer-aided diagnosis significantly reduces the low-dose CT screening false-positive rate and increases the positive predictive value of lung nodule evaluation.
TL;DR: The visual presence and severity of emphysema is associated with significantly increased mortality risk, independent of the quantitative severity ofEmphysem, in cigarette smokers.
Abstract: The Fleischner Society classification of emphysema provides a valid, reproducible index of emphysema severity that is associated with both physiologic impairment and mortality risk.
TL;DR: An overview of the state-of-the-art imaging techniques is presented for the evaluation of intrahepatic and perihilar cholangiocarcinoma, as well as complementary multimodality and multiparametric imaging approaches for a more comprehensive evaluation.
Abstract: For intrahepatic and perihilar cholangiocarcinoma (CC), up-to-date imaging techniques and multimodality multiparametric imaging approaches can improve diagnostic accuracy for tumor staging and resectability assessment. Current concepts regarding risk factors, cholangiocarcinogenesis, and premalignant tumors may also allow a better understanding of the tumor biology and imaging features of CC.
TL;DR: Automated methods can be used to identify findings in radiology reports and, with this method, a large labeled corpus can be generated for applications such as deep learning.
Abstract: Owing to the highly structured language of radiology, straightforward machine learning–based approaches can achieve state-of-the-art classification results on text corpora of radiology reports, generating large data sets of labeled reports needed for applications such as deep learning.