Conventional and novel [18F]FDG PET/CT features as predictors of CAR-T cell therapy outcome in large B-cell lymphoma.

Relapse and toxicity limit the effectiveness of chimeric antigen receptor T-cell (CAR-T) therapy for large B-cell lymphoma (LBCL), yet biomarkers that predict outcomes and toxicity are lacking. We examined radiomic features extracted from pre-CAR-T 18F-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) scans (n = 341) of 180 patients (121 male; median age, 66 years). Three conventional (maximum standardized uptake value [SUVmax], metabolic tumor volume [MTV], total lesion glycolysis [TLG]) and 116 novel radiomic features were assessed, along with inflammatory markers, toxicities, and outcomes. At both pre-apheresis and pre-infusion time points, conventional PET features of disease correlated with elevated inflammatory markers. At pre-infusion, MTV was associated with grade ≥ 2 cytokine release syndrome (odds ratio [OR] for 100 mL increase: 1.08 [95% confidence interval (CI), 1.01-1.20], P = 0.031), and SUVmax was associated with failure to achieve complete response (CR) (OR 1.72 [95% CI, 1.24-2.43], P < 0.001). Higher pre-apheresis and pre-infusion MTV values were associated with shorter progression-free survival (PFS) (HR for 10-unit increase: 1.11 [95% CI, 1.05-1.17], P < 0.001; 1.04 [95% CI, 1.02-1.07], P < 0.001) and shorter overall survival (HR for 100-unit increase: 1.14 [95% CI, 1.07-1.21], P < 0.001; 1.04 [95% CI, 1.02-1.06], P < 0.001). A combined MTV and LDH measure stratified patients into high and low PFS risk groups. Multiple pre-infusion novel radiomic features were associated with CR. These quantitative conventional [18F]FDG PET/CT features obtained before CAR-T cell infusion, which were correlated with inflammation markers, may provide prognostic biomarkers for CAR-T therapy efficacy and toxicity. The use of conventional and novel radiomic features may thus help identify high-risk patients for earlier interventions.

How young radiologists use contrast media and manage adverse reactions: an international survey.

OBJECTIVES: To collect real-world data about the knowledge and self-perception of young radiologists concerning the use of contrast media (CM) and the management of adverse drug reactions (ADR). METHODS: A survey (29 questions) was distributed to residents and board-certified radiologists younger than 40 years to investigate the current international situation in young radiology community regarding CM and ADRs. Descriptive statistics analysis was performed. RESULTS: Out of 454 respondents from 48 countries (mean age: 31.7 ± 4 years, range 25-39), 271 (59.7%) were radiology residents and 183 (40.3%) were board-certified radiologists. The majority (349, 76.5%) felt they were adequately informed regarding the use of CM. However, only 141 (31.1%) received specific training on the use of CM and 82 (18.1%) about management ADR during their residency. Although 266 (58.6%) knew safety protocols for handling ADR, 69.6% (316) lacked confidence in their ability to manage CM-induced ADRs and 95.8% (435) expressed a desire to enhance their understanding of CM use and handling of CM-induced ADRs. Nearly 300 respondents (297; 65.4%) were aware of the benefits of contrast-enhanced ultrasound, but 249 (54.8%) of participants did not perform it. The preferred CM injection strategy in CT parenchymal examination and CT angiography examination was based on patient's lean body weight in 318 (70.0%) and 160 (35.2%), a predeterminate fixed amount in 79 (17.4%) and 116 (25.6%), iodine delivery rate in 26 (5.7%) and 122 (26.9%), and scan time in 31 (6.8%) and 56 (12.3%), respectively. CONCLUSION: Training in CM use and management ADR should be implemented in the training of radiology residents. CRITICAL RELEVANCE STATEMENT: We highlight the need for improvement in the education of young radiologists regarding contrast media; more attention from residency programs and scientific societies should be focused on training about contrast media use and the management of adverse drug reactions. KEY POINTS: • This survey investigated training of young radiologists about use of contrast media and management adverse reactions. • Most young radiologists claimed they did not receive dedicated training. • An extreme heterogeneity of responses was observed about contrast media indications/contraindications and injection strategy.

Perceptions of radiologists on structured reporting for cancer imaging-a survey by the European Society of Oncologic Imaging (ESOI).

OBJECTIVES: To assess radiologists' current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template. MATERIALS AND METHODS: An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients. RESULTS: A total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists' experience (p < 0.001), and experience also showed low but significant correlations with communication with clinicians (r = - 0.27, p = 0.003), report quality (r = 0.19, p = 0.043), and error rate (r = - 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality (p = 0.036). CONCLUSION: Radiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians. CLINICAL RELEVANCE STATEMENT: Radiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement. KEY POINTS: • The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice. • The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%). • Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans.

Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis.

BACKGROUND: The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. METHODS: In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1-3 vs 4-5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. FINDINGS: In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942-0·956), accuracy of 0·890 (0·879-0·901), sensitivity of 0·868 (0·851-0·885), and specificity of 0·913 (0·899-0·925); LARS-max achieved an AUC of 0·949 (0·942-0·956), accuracy of 0·868 (0·858-0·879), sensitivity of 0·909 (0·896-0·924), and specificity of 0·826 (0·808-0·843); and LARS-ptct achieved an AUC of 0·939 (0·930-0·948), accuracy of 0·875 (0·864-0·887), sensitivity of 0·836 (0·817-0·855), and specificity of 0·915 (0·901-0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938-0·966), accuracy of 0·907 (0·888-0·925), sensitivity of 0·874 (0·843-0·904), and specificity of 0·949 (0·921-0·960); LARS-max achieved an AUC of 0·952 (0·937-0·965), accuracy of 0·898 (0·878-0·916), sensitivity of 0·899 (0·871-0·926), and specificity of 0·897 (0·871-0·922); and LARS-ptct achieved an AUC of 0·932 (0·915-0·948), accuracy of 0·870 (0·850-0·891), sensitivity of 0·827 (0·793-0·863), and specificity of 0·913 (0·889-0·937). INTERPRETATION: Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. FUNDING: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.

68Ga-Pentixafor PET/MRI for Treatment Response Assessment in Mantle Cell Lymphoma: Comparison Between Changes in Lesion CXCR4 Expression on PET and Lesion Size and Diffusivity on MRI.

PURPOSE: The aim of this study was to compare CXCR4 imaging with 68Ga-pentixafor PET to MRI for treatment response assessment in patients with mantle cell lymphoma (MCL). PATIENTS AND METHODS: Twenty-two posttreatment 68Ga-pentixafor PET/MRI scans of 16 patients (7 women and 9 men; mean age, 69.9 ± 7.9) with a total of 67 target lesions on baseline PET/MRI were analyzed. Rates of complete remission per lesion and per scan, according to MRI (based on lesion size) and 68Ga-pentixafor PET (based on SUV decrease to lower than liver and blood pool uptake), were compared using McNemar tests. The t tests and Pearson correlation coefficients (r) were used to compare rates of change in lesion diameter products (DPs) on MRI, and standardized uptake values (SUVmax, SUVmean) on PET, relative to baseline. RESULTS: At interim PET/MRI, 18/32 (56.3%) target lesions met CR criteria on 68Ga-pentixafor PET, and 16/32 (50.0%) lesions met size-based MRI criteria for CR (P = 0.63). At end-of-treatment PET/MRI, 40/57 (70.2%) target lesions met 68Ga-pentixafor PET criteria for CR, and 27/57 (47.4%) lesions met size-based MRI criteria for CR (P = 0.021). Complete remission after treatment was observed more frequently on 68Ga-pentixafor PET (11/22 scans, 54.5%) than on MRI (6/22 scans, 27.3%) (P = 0.031). Rates of change did not differ significantly between lesion DP (-69.20% ± 34.62%) and SUVmax (-64.59% ± 50.78%, P = 0.22), or DP and SUVmean (-60.15 ± 64.58, P = 0.064). Correlations were strong between DP and SUVmax (r = 0.71, P < 0.001) and DP and SUVmean (r = 0.73, P < 0.001). CONCLUSIONS: In MCL patients, 68Ga-pentixafor PET may be superior for assessment of complete remission status than anatomic MRI using lesion size criteria, especially at the end of treatment.

ComBat Harmonization for MRI Radiomics: Impact on Nonbinary Tissue Classification by Machine Learning.

OBJECTIVES: The aims of this study were to determine whether ComBat harmonization improves multiclass radiomics-based tissue classification in technically heterogeneous MRI data sets and to compare the performances of 2 ComBat variants. MATERIALS AND METHODS: One hundred patients who had undergone T1-weighted 3D gradient echo Dixon MRI (2 scanners/vendors; 50 patients each) were retrospectively included. Volumes of interest (2.5 cm 3 ) were placed in 3 disease-free tissues with visually similar appearance on T1 Dixon water images: liver, spleen, and paraspinal muscle. Gray-level histogram (GLH), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM) radiomic features were extracted. Tissue classification was performed on pooled data from the 2 centers (1) without harmonization, (2) after ComBat harmonization with empirical Bayes estimation (ComBat-B), and (3) after ComBat harmonization without empirical Bayes estimation (ComBat-NB). Linear discriminant analysis with leave-one-out cross-validation was used to distinguish among the 3 tissue types, using all available radiomic features as input. In addition, a multilayer perceptron neural network with a random 70%:30% split into training and test data sets was used for the same task, but separately for each radiomic feature category. RESULTS: Linear discriminant analysis-based mean tissue classification accuracies were 52.3% for unharmonized, 66.3% for ComBat-B harmonized, and 92.7% for ComBat-NB harmonized data. For multilayer perceptron neural network, mean classification accuracies for unharmonized, ComBat-B-harmonized, and ComBat-NB-harmonized test data were as follows: 46.8%, 55.1%, and 57.5% for GLH; 42.0%, 65.3%, and 71.0% for GLCM; 45.3%, 78.3%, and 78.0% for GLRLM; and 48.1%, 81.1%, and 89.4% for GLSZM. Accuracies were significantly higher for both ComBat-B- and ComBat-NB-harmonized data than for unharmonized data for all feature categories (at P = 0.005, respectively). For GLCM ( P = 0.001) and GLSZM ( P = 0.005), ComBat-NB harmonization provided slightly higher accuracies than ComBat-B harmonization. CONCLUSIONS: ComBat harmonization may be useful for multicenter MRI radiomics studies with nonbinary classification tasks. The degree of improvement by ComBat may vary among radiomic feature categories, among classifiers, and among ComBat variants.

Impact of ComBat Harmonization on PET Radiomics-Based Tissue Classification: A Dual-Center PET/MRI and PET/CT Study.

Our purpose was to determine whether ComBat harmonization improves 18F-FDG PET radiomics-based tissue classification in pooled PET/MRI and PET/CT datasets. Methods: Two hundred patients who had undergone 18F-FDG PET/MRI (2 scanners and vendors; 50 patients each) or PET/CT (2 scanners and vendors; 50 patients each) were retrospectively included. Gray-level histogram, gray-level cooccurrence matrix, gray-level run-length matrix, gray-level size-zone matrix, and neighborhood gray-tone difference matrix radiomic features were calculated for volumes of interest in the disease-free liver, spleen, and bone marrow. For individual feature classes and a multiclass radiomic signature, tissue was classified on ComBat-harmonized and unharmonized pooled data, using a multilayer perceptron neural network. Results: Median accuracies in training and validation datasets were 69.5% and 68.3% (harmonized), respectively, versus 59.5% and 58.9% (unharmonized), respectively, for gray-level histogram; 92.1% and 86.1% (harmonized), respectively, versus 53.6% and 50.0% (unharmonized), respectively, for gray-level cooccurrence matrix; 84.8% and 82.8% (harmonized), respectively, versus 62.4% and 58.3% (unharmonized), respectively, for gray-level run-length matrix; 87.6% and 85.6% (harmonized), respectively, versus 56.2% and 52.8% (unharmonized), respectively, for gray-level size-zone matrix; 79.5% and 77.2% (harmonized), respectively, versus 54.8% and 53.9% (unharmonized), respectively, for neighborhood gray-tone difference matrix; and 86.9% and 84.4% (harmonized), respectively, versus 62.9% and 58.3% (unharmonized), respectively, for radiomic signature. Conclusion: ComBat harmonization may be useful for multicenter 18F-FDG PET radiomics studies using pooled PET/MRI and PET/CT data.

Radiologist-Level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans.

PURPOSE: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. MATERIALS AND METHODS: In this retrospective study, 38 229 examinations (composed of 64 063 individual breast scans from 14 475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years ± 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60 108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. RESULTS: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P < .001 for both; n = 250). CONCLUSION: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.Keywords: MRI, Breast, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning AlgorithmsPublished under a CC BY 4.0 license. Supplemental material is available for this article.

Development and evaluation of a computer-based decision support system for diffuse lung diseases at high-resolution computed tomography.

BACKGROUND: High-resolution computed tomography (HRCT) is essential in narrowing the possible differential diagnoses of diffuse and interstitial lung diseases. PURPOSE: To investigate the value of a novel computer-based decision support system (CDSS) for facilitating diagnosis of diffuse lung diseases at HRCT. MATERIAL AND METHODS: A CDSS was developed that includes about 100 different illustrations of the most common HRCT signs and patterns and describes the corresponding pathologies in detail. The logical set-up of the software facilitates a structured evaluation. By selecting one or more CT patterns, the program generates a ranked list of the most likely differential diagnoses. Three independent and blinded radiology residents initially evaluated 40 cases with different lung diseases alone; after at least 12 weeks, observers re-evaluated all cases using the CDSS. RESULTS: In 40 patients, a total of 113 HRCT patterns were evaluated. The percentage of correctly classified patterns was higher with CDSS (96.8%) compared to assessment without CDSS (90.3%; P < 0.01). Moreover, the percentage of correct diagnosis (81.7% vs. 64.2%) and differential diagnoses (89.2% vs. 38.3%) were superior with CDSS compared to evaluation without CDSS (both P < 0.01). CONCLUSION: Addition of a CDSS using a structured approach providing explanations of typical HRCT patterns and graphical illustrations significantly improved the performance of trainees in characterizing and correctly identifying diffuse lung diseases.

Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status.

Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.

Head and neck single- and dual-energy CT: differences in radiation dose and image quality of 2nd and 3rd generation dual-source CT.

OBJECTIVES: To compare radiation dose and image quality of single-energy (SECT) and dual-energy (DECT) head and neck CT examinations performed with second- and third-generation dual-source CT (DSCT) in matched patient cohorts. METHODS: 200 patients (mean age 55.1 ± 16.9 years) who underwent venous phase head and neck CT with a vendor-preset protocol were retrospectively divided into four equal groups (n = 50) matched by gender and BMI: second (Group A, SECT, 100-kV; Group B, DECT, 80/Sn140-kV), and third-generation DSCT (Group C, SECT, 100-kV; Group D, DECT, 90/Sn150-kV). Assessment of radiation dose was performed for an average scan length of 27 cm. Contrast-to-noise ratio measurements and dose-independent figure-of-merit calculations of the submandibular gland, thyroid, internal jugular vein, and common carotid artery were analyzed quantitatively. Qualitative image parameters were evaluated regarding overall image quality, artifacts and reader confidence using 5-point Likert scales. RESULTS: Effective radiation dose (ED) was not significantly different between SECT and DECT acquisition for each scanner generation (p = 0.10). Significantly lower effective radiation dose (p < 0.01) values were observed for third-generation DSCT groups C (1.1 ± 0.2 mSv) and D (1.0 ± 0.3 mSv) compared to second-generation DSCT groups A (1.8 ± 0.1 mSv) and B (1.6 ± 0.2 mSv). Figure-of-merit/contrast-to-noise ratio analysis revealed superior results for third-generation DECT Group D compared to all other groups. Qualitative image parameters showed non-significant differences between all groups (p > 0.06). CONCLUSION: Contrast-enhanced head and neck DECT can be performed with second- and third-generation DSCT systems without radiation penalty or impaired image quality compared with SECT, while third-generation DSCT is the most dose efficient acquisition method. ADVANCES IN KNOWLEDGE: Differences in radiation dose between SECT and DECT of the dose-vulnerable head and neck region using DSCT systems have not been evaluated so far. Therefore, this study directly compares radiation dose and image quality of standard SECT and DECT protocols of second- and third-generation DSCT platforms.

Can Follow-up be Avoided for Probably Benign US Masses with No Enhancement on MRI?

OBJECTIVES: To assess whether no enhancement on pre-treatment MRI can rule out malignancy of additional US mass(es) initially assessed as BI-RADS 3 or 4 in women with newly diagnosed breast cancer. METHODS: This retrospective study included consecutive women from 2010-2018 with newly diagnosed breast cancer; at least one additional breast mass (distinct from index cancer) assigned a BI-RADS 3 or 4 on US; and a bilateral contrast-enhanced breast MRI performed within 90 days of US. All malignant masses were pathologically proven; benign masses were pathologically proven or defined as showing at least 2 years of imaging stability. Incidence of malignant masses and NPV were calculated on a per-patient level using proportions and exact 95% CIs. RESULTS: In 230 patients with 309 additional masses, 140/309 (45%) masses did not enhance while 169/309 (55%) enhanced on MRI. Of the 140 masses seen in 105 women (mean age, 54 years; range 28-82) with no enhancement on MRI, all had adequate follow-up and 140/140 (100%) were benign, of which 89/140 (63.6%) were pathologically proven and 51/140 (36.4%) demonstrated at least 2 years of imaging stability. Pre-treatment MRI demonstrating no enhancement of US mass correlate(s) had an NPV of 100% (95% CI 96.7-100.0). CONCLUSIONS: All BI-RADS 3 and 4 US masses with a non-enhancing correlate on pre-treatment MRI were benign. The incorporation of MRI, when ordered by the referring physician, may decrease unnecessary follow-up imaging and/or biopsy if the initial US BI-RADS assessment and management recommendation were to be retrospectively updated. KEY POINTS: • Of 309 BI-RADS 3 or 4 US masses with a corresponding mass on MRI, 140/309 (45%) demonstrated no enhancement whereas 169/309 (55%) demonstrated enhancement • All masses classified as BI-RADS 3 or 4 on US without enhancement on MRI were benign • MRI can rule out malignancy in non-enhancing US masses with an NPV of 100.

Can Dual-energy CT-based Virtual Monoenergetic Imaging Improve the Assessment of Hypodense Liver Metastases in Patients With Hepatic Steatosis?

RATIONALE AND OBJECTIVES: To evaluate the impact of noise-optimized virtual monoenergetic imaging (VMI) on lesion demarcation and measuring accuracy of hypoattenuating liver metastases in patients with fatty liver disease compared to standard reconstructions. MATERIALS AND METHODS: Twenty-eight patients (mean age 62.2 ± 7.7 years) with fatty liver disease and hypoattenuating liver metastases who underwent unenhanced and contrast-enhanced portal-venous dual-energy CT (DECT) were enrolled. Standard linearly blended and VMI series were reconstructed in 10-keV intervals. Lesion-to-parenchyma contrast-to-noise ratio (CNR) was calculated and the best VMI series was further investigated in a subjective evaluation of overall image quality and lesion demarcation. Size measurements were performed independently by measuring all hypodense lesions (n = 58) twice in a predefined sequence. Inter- and intra-rater agreement was assessed using intra-class correlation coefficient (ICC) statistics. RESULTS: The calculated CNR was greatest at 40-keV VMI (4.3 ± 2.6), significantly higher compared to standard reconstructions (2.9 ± 1.9; p < 0.001). Subjective ratings for overall image quality showed no significant difference between the 2 reconstruction techniques (both medians 4; p = 0.147), while lesion margin demarcation was found to be superior for 40-keV VMI (median 5; p ≤ 0.001). Inter- (ICC, 0.98 for 40-keV VMI; ICC, 0.93 for standard reconstruction) and intra-rater (ICC, 0.99 for 40-keV VMI; ICC, 0.94 for standard image series) analysis showed an excellent agreement for lesion measurements in both reconstruction techniques. CONCLUSION: Noise-optimized VMI reconstructions significantly improve contrast and lesion demarcation of hypoattenuating liver metastases in patients with the fatty liver disease compared to standard reconstruction.

Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: effects of post-reconstruction methods in a dual-center study.

OBJECTIVES: To evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling. METHODS: One hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization. RESULTS: Using unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72-0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82-0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79-0.92) was revealed. CONCLUSION: Radiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification. KEY POINTS: • Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation. • Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing. • When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation.

Prevalence and prognostic impact of nonischemic late gadolinium enhancement in stress cardiac magnetic resonance.

AIM: To assess the prevalence and prognostic significance of NI-LGE in patients undergoing stress-CMR. METHODS: Stress-CMR with either dipyridamole or adenosine was performed in 283 patients (228 men, 81%) including perfusion imaging, wall motion evaluation and LGE. Follow-up was completed in all enrolled patients (median time: 1850 days; interquartile range: 1225-2705 days). Composite endpoint included cardiac death, ventricular tachycardia, myocardial infarction, stroke, hospitalization for cardiac cause and coronary revascularization performed beyond 90 days from stress-CMR scans. RESULTS: One hundred and twelve patients (40%) had negative LGE (no-LGE), 140 patients (49%) I-LGE and 31 patients (11%) NI-LGE. Twenty-five events occurred in the no-LGE group, 68 in I-LGE and 11 in the NI-LGE group. On survival curves, patients with NI-LGE had worse prognosis than patients with no-LGE regardless of the presence of inducible perfusion defects. No significant prognostic differences were found between I-LGE and NI-LGE. CONCLUSION: NI-LGE can be detected in 11% of patients during stress-CMR providing a diagnosis of nonischemic cardiac disease. Patients with NI-LGE have worse prognosis than those with no-LGE.

Influence of contrast material density and kV setting on detectability of calcified plaques on coronary CT angiography.

PURPOSE: To analyze the impact of tube potential and iodine concentration on the visibility of calcified plaques in coronary computed tomography angiography (cCTA). METHODS & MATERIALS: 164 consecutive patients (65.9 % men and a mean age of 57.1 ±â€¯11.3 years) with suspected coronary artery disease underwent calcium scoring (CaSc) scan followed by cCTA with topogram-based automated tube voltage selection (70 kV, 80 kV, 90 kV, 100 kV or 120 kV). In 127 Patients (HC), we injected 50 mL of contrast material (CM) with a concentration of 400 mg iodine per ml and in 37 patients (LC) 50 mL iodine concentration of 280 mg/mL. Sensitivity of cCTA for detecting calcified plaques was calculated with CaSc serving as gold standard. Density of CM enhanced coronary vessels and calcified plaques were quantified by region-of-interest (ROI) measurements in unenhanced and cCTA image series. RESULTS: Overall sensitivity of cCTA to detect calcified plaques was significantly higher using LC compared to HC (79 % vs. 73 %; p = 0.0035). The impact of LC was impressive at 70 kV with an improved sensitivity of 70 % vs. 57.1 % in HC (p = 0.0082). Furthermore, density values of HC enhanced coronary vessels exceeded those of calcified plaques, especially at low kV levels. In LC, except for the 70 kV setting, higher density values were shown for calculi than enhanced vessels. CONCLUSION: Low kV cCTA in routine using highly concentrated CM leads to reduced calcified plaque perceptibility and hence potentially underestimation of stenosis. Thus, low kV cCTA using CM with lower iodine concentration is necessary. In addition, a dose reduction up to 77.7 % can also be benefited.

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging.

The aim of our intra-individual comparison study was to investigate and compare the potential of radiomics analysis of contrast-enhanced mammography (CEM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status, and tumor grade in patients with primary breast cancer. This retrospective study included 48 female patients with 49 biopsy-proven breast cancers who underwent pretreatment breast CEM and MRI. Radiomics analysis was performed by using MaZda software. Radiomics parameters were correlated with tumor histology (invasive vs. non-invasive), hormonal status (HR+ vs. HR-), and grading (low grade G1 + G2 vs. high grade G3). CEM radiomics analysis yielded classification accuracies of up to 92% for invasive vs. non-invasive breast cancers, 95.6% for HR+ vs. HR- breast cancers, and 77.8% for G1 + G2 vs. G3 invasive cancers. MRI radiomics analysis yielded classification accuracies of up to 90% for invasive vs. non-invasive breast cancers, 82.6% for HR+ vs. HR- breast cancers, and 77.8% for G1+G2 vs. G3 cancers. Preliminary results indicate a potential of both radiomics analysis of DCE-MRI and CEM for non-invasive assessment of tumor-invasiveness, hormone receptor status, and tumor grade. CEM may serve as an alternative to MRI if MRI is not available or contraindicated.

Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics.

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77-0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75-0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

In vitro testing of a funnel-shaped tip catheter model to decrease clot migration during mechanical thrombectomy.

One limitation of mechanical thrombectomy (MT) is clot migration during procedure. This might be caused by abruption of the trapped thrombus at the distal access catheter (DAC) tip during stent-retriever retraction due to the cylindrical shaped tip of the DAC. Aiming to solve this problem, this study evaluates the proof-of-concept of a new designed funnel-shaped tip, in an experimental in vitro setting. Two catheter models, one with a funnel-shaped tip and one with a cylindrical-shaped tip, were compared in an experimental setup. For MT a self-made vessel model and thrombi generated from pig's blood were used. MT was performed 20 times for each device using two different stent-retrievers, 10 times respectively. For the funnel-shaped model: for both stent-retrievers (Trevo XP ProVue 3/20 mm; Trevo XP ProVue 4/20 mm) MT was successful at first pass in 9/10 (90%), respectively. For the cylindrical-shaped model: MT was successful at first pass in 5/10 (50%) with the smaller stent-retriever and in 6/10 (60%) with the larger stent-retriever. The experiments show a better recanalization rate for funnel-shaped tips, than for cylindrical-shaped tips. These results are indicating a good feasibility for this new approach, thus the development of a prototype catheter seems reasonable.

Subsequent Marking under Ultrasound Guidance of Vacuum-Assisted Breast Biopsy Areas after Receipt of Histology: A Feasibility Study of a New Technique.

PURPOSE: The aim of this study was to evaluate the feasibility and the accuracy of a secondary, metachronous ultrasound (US)-guided marking of the stereotactic vacuum-assisted breast biopsy (ST-VABB) area. MATERIALS AND METHODS: The institutional ethics committee approved the study. The retrospective study included 98 patients. In ST-VABB of 45 women, no tissue markers were deployed at the biopsy site, even if no residual calcifications remained. After histology proved the necessity for a subsequent operation, the biopsy site was marked under US guidance using a coil marker. All interventions were technically successful. No complications occurred. Mammography was done to visualize the coil deployment. The distances from the center of the lesion and the biopsy cavity to the coil location were measured in both planes to evaluate the accuracy of the marking procedure. RESULTS: In 24 of the 46 cases, the whole lesion was biopsied without residual elements. The mean time between ST-VABB and sonographic marking of the lesion was 9.7 days (median 6.5). The biopsy cavity could be detected in 40 (87%) cases and thus marked exactly. The mean time of US-guided marking was 12.5 min. The mean distance between the coil and the target lesion was 0.6 ± 1.5 cm in the craniocaudal (cc) view and 0.5 ± 1.5 cm in the mediolateral (ml) view for all markings. The mean delta value from the distance nipple-original lesion and from the distance nipple-coil was 0.85 ± 1.2 cm (median 0.5) in the cc view and 0.88 ± 1.2 cm (median 0.6) in the ml view for all cases. Clip migration was not observed. CONCLUSION: Our study demonstrates the feasibility and the technical success of secondary metachronous coil marking of the biopsy site under US guidance after receipt of histology. This approach seems to be a cost-effective alternative to the standard procedure of the primary coil marking especially in all completely removed lesions. It may offer advantages for allergic patients.