Imaging differentiation of solid pseudopapillary neoplasms and neuroendocrine neoplasms of the pancreas.

Purpose: The present study aimed to compare the computed tomography (CT) and magnetic resonance imaging (MRI) features of solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (pNENs). Method: Lesion imaging features of 39 patients with SPNs and 127 patients with pNENs were retrospectively extracted from 104 CT and 91 MRI scans. Results: Compared to pNEN patients, SPN patients were significantly younger (mean age 51.8 yrs versus 32.7 yrs) and more often female (female: male ratio, 5.50:1 versus 1.19:1). Most SPNs and pNENs presented as well-defined lesions with an expansive growth pattern. SPNs more often appeared as round or ovoid lesions, compared to pNENs which showed a lobulated or irregular shape in more than half of cases (p<0.01). A surrounding capsule was detected in the majority of SPNs, but only in a minority of pNENs (<0.01). Hemorrhage occurred non-significantly more often in SPNs (p=0.09). Signal inhomogeneity in T1-fat-saturated (p<0.01) and T2-weighted imaging (p=0.046) as well as cystic degeneration (p<0.01) were more often observed in SPNs. Hyperenhancement in the arterial and portal-venous phase was more common in pNENs (p<0.01). Enlargement of locoregional lymph nodes (p<0.01) and liver metastases (p=0.03) were observed in some pNEN patients, but not in SPN patients. Multivariate logistic regression identified the presence of a capsule (p<0.01), absence of arterial hyperenhancement (p<0.01), and low patient age (p<0.01), as independent predictors for SPN. Conclusions: The present study provides three key features for differentiating SPNs from pNENs extracted from a large patient cohort: presence of a capsule, absence of arterial hyperenhancement, and low patient age.

Feasibility of Reduced Iodine Loads for Vascular Assessment Prior to Transcatheter Aortic Valve Implantation (TAVI) Using Spectral Detector CT.

Reduced iodine loads for computed tomography (CT)-based vascular assessment prior to transcatheter aortic valve implantation (TAVI) may be feasible in conjunction with a spectral detector CT scanner. This prospective single-center study considered 100 consecutive patients clinically referred for pre-TAVI CT. They were examined on a dual-layer detector CT scanner to obtain an ECG-gated cardiac scan and a non-ECG-gated aortoiliofemoral scan. Either a standard contrast media (SCM) protocol using 80 mL Iohexol 350 mgI/mL (iodine load: 28 gI) or a body-mass-index adjusted reduced contrast media (RCM) protocol using 40-70 mL Iohexol 350 mgI/mL (iodine load: 14-24.5 gI) were employed. Conventional images and virtual monoenergetic images at 40-80 keV were reconstructed. A threshold of 250 HU was set for sufficient attenuation along the arterial access pathway. A qualitative assessment used a five-point Likert scale. Sufficient attenuation in the thoracic aorta was observed for all patients in both groups using conventional images. In the abdominal, iliac, and femoral segments, sufficient attenuation was observed for the majority of patients when using virtual monoenergetic images (SCM: 96-100% of patients, RCM: 88-94%) without statistical difference between both groups. Segments with attenuation measurements below the threshold remained qualitatively assessable as well. Likert scores were 'excellent' for virtual monoenergetic images 50 keV and 55 keV in both groups (RCM: 1.2-1.4, SCM: 1.2-1.3). With diagnostic image quality maintained, it can be concluded that reduced iodine loads of 14-24.5 gI are feasible for pre-TAVI vascular assessment on a spectral detector CT scanner.

Reproducible Radiomics Features from Multi-MRI-Scanner Test-Retest-Study: Influence on Performance and Generalizability of Models.

BACKGROUND: Radiomics models trained on data from one center typically show a decline of performance when applied to data from external centers, hindering their introduction into large-scale clinical practice. Current expert recommendations suggest to use only reproducible radiomics features isolated by multiscanner test-retest experiments, which might help to overcome the problem of limited generalizability to external data. PURPOSE: To evaluate the influence of using only a subset of robust radiomics features, defined in a prior in vivo multi-MRI-scanner test-retest-study, on the performance and generalizability of radiomics models. STUDY TYPE: Retrospective. POPULATION: Patients with monoclonal plasma cell disorders. Training set (117 MRIs from center 1); internal test set (42 MRIs from center 1); external test set (143 MRIs from center 2-8). FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T; T1-weighted turbo spin echo. ASSESSMENT: The task for the radiomics models was to predict plasma cell infiltration, determined by bone marrow biopsy, noninvasively from MRI. Radiomics machine learning models, including linear regressor, support vector regressor (SVR), and random forest regressor (RFR), were trained on data from center 1, using either all radiomics features, or using only reproducible radiomics features. Models were tested on an internal (center 1) and a multicentric external data set (center 2-8). STATISTICAL TESTS: Pearson correlation coefficient r and mean absolute error (MAE) between predicted and actual plasma cell infiltration. Fisher's z-transformation, Wilcoxon signed-rank test, Wilcoxon rank-sum test; significance level P < 0.05. RESULTS: When using only reproducible features compared with all features, the performance of the SVR on the external test set significantly improved (r = 0.43 vs. r = 0.18 and MAE = 22.6 vs. MAE = 28.2). For the RFR, the performance on the external test set deteriorated when using only reproducible instead of all radiomics features (r = 0.33 vs. r = 0.44, P = 0.29 and MAE = 21.9 vs. MAE = 20.5, P = 0.10). CONCLUSION: Using only reproducible radiomics features improves the external performance of some, but not all machine learning models, and did not automatically lead to an improvement of the external performance of the overall best radiomics model. TECHNICAL EFFICACY: Stage 2.

Sensing Levofloxacin with an RNA Aptamer as a Bioreceptor.

To combat the growing threat of antibiotic resistance, environmental testing for antibiotic contamination is gaining an increasing role. This study aims to develop an easy-to-use assay for the detection of the fluoroquinolone antibiotic levofloxacin. Levofloxacin is used in human and veterinary medicine and has been detected in wastewater and river water. An RNA aptamer against levofloxacin was selected using RNA Capture-SELEX. The 73 nt long aptamer folds into three stems with a central three-way junction. It binds levofloxacin with a Kd of 6 µM and discriminates the closely related compound ciprofloxacin. Furthermore, the selection process was analyzed using a next-generation sequencing approach to better understand the sequence evolution throughout the selection. The aptamer was used as a bioreceptor for the development of a lateral flow assay. The biosensor exploited the innate characteristic of RNA Capture-SELEX to select aptamers that displace a complementary DNA oligonucleotide upon ligand binding. The lateral flow assay achieved a limit of visual detection of 100 µM. While the sensitivity of this assay constrains its immediate use in environmental testing, the present study can serve as a template for the selection of RNA aptamer-based biosensors.

Risk factors and management of biliary leakage after Endocystectomy for hepatic cystic echinococcosis.

BACKGROUND: Endocystectomy is a conservative surgical approach to managing cystic echinococcosis. Bile leakage is the main complication of this technique. The aim of this study was to evaluate the factors associated with bile leakage and to assess the outcomes and cost efficiency of strategies used to treat bile leakage. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent endocystectomy between 2005 and 2020 were included. The preoperative characteristics, intra- and postoperative outcomes, hospital costs, and cost efficiency (the Diagnosis-Related Group reimbursement minus the overall cost) were evaluated prospectively. A total of eighty patients with 142 cysts were included. Postoperative complications occurred in 17 patients (21%), including 11 patients with bile leakage (type A: 1, type B: 6 and type C: 4 patients, total 13%). Bile leakage was more frequent in patients with preoperative MRI signs of cysto-biliary fistulas or intraoperative visible cysto-biliary fistulas (p = 0.03 and p = 0.04, respectively) and in patients with cysts larger than 8 cm (p = 0.03). Patients with bile leakage who underwent reoperation (type C) had significantly shorter hospital stays (9 vs. 16 days, p<0.01) and better cost efficiency than those who received radiologic or endocscopic interventions (€2,072 vs. -€2,097 p = 0.01). No mortality was observed, and recurrence was seen in two patients. CONCLUSIONS/SIGNIFICANCE: Endocystectomy is a safe and efficient technique. Preoperative and intraoperative cysto-biliary fistulas and a cyst diameter larger than 8 cm are correlated to postoperative bile leakage. Early operative management of bile leakage reduces hospital stay and improves cost efficiency compared with radiologic or endoscopic treatments.

Hydride formation and dynamic phase changes during template-assisted Pd electrodeposition.

We investigated the structural evolution of electrochemically fabricated Pd nanowiresin situby means of grazing-incidence transmission small- and wide-angle x-ray scattering (GTSAXS and GTWAXS), x-ray fluorescence (XRF) and two-dimensional surface optical reflectance (2D-SOR). This shows how electrodeposition and the hydrogen evolution reaction (HER) compete and interact during Pd electrodepositon. During the bottom-up growth of the nanowires, we show thatß-phase Pd hydride is formed. Suspending the electrodeposition then leads to a phase transition fromß-phase Pd hydride toα-phase Pd. Additionally, we find that grain coalescence later hinders the incorporation of hydrogen in the Pd unit cell. GTSAXS and 2D-SOR provide complementary information on the volume fraction of the pores occupied by Pd, while XRF was used to monitor the amount of Pd electrodeposited.

Potential of ChatGPT and GPT-4 for Data Mining of Free-Text CT Reports on Lung Cancer.

Background The latest large language models (LLMs) solve unseen problems via user-defined text prompts without the need for retraining, offering potentially more efficient information extraction from free-text medical records than manual annotation. Purpose To compare the performance of the LLMs ChatGPT and GPT-4 in data mining and labeling oncologic phenotypes from free-text CT reports on lung cancer by using user-defined prompts. Materials and Methods This retrospective study included patients who underwent lung cancer follow-up CT between September 2021 and March 2023. A subset of 25 reports was reserved for prompt engineering to instruct the LLMs in extracting lesion diameters, labeling metastatic disease, and assessing oncologic progression. This output was fed into a rule-based natural language processing pipeline to match ground truth annotations from four radiologists and derive performance metrics. The oncologic reasoning of LLMs was rated on a five-point Likert scale for factual correctness and accuracy. The occurrence of confabulations was recorded. Statistical analyses included Wilcoxon signed rank and McNemar tests. Results On 424 CT reports from 424 patients (mean age, 65 years ± 11 [SD]; 265 male), GPT-4 outperformed ChatGPT in extracting lesion parameters (98.6% vs 84.0%, P < .001), resulting in 96% correctly mined reports (vs 67% for ChatGPT, P < .001). GPT-4 achieved higher accuracy in identification of metastatic disease (98.1% [95% CI: 97.7, 98.5] vs 90.3% [95% CI: 89.4, 91.0]) and higher performance in generating correct labels for oncologic progression (F1 score, 0.96 [95% CI: 0.94, 0.98] vs 0.91 [95% CI: 0.89, 0.94]) (both P < .001). In oncologic reasoning, GPT-4 had higher Likert scale scores for factual correctness (4.3 vs 3.9) and accuracy (4.4 vs 3.3), with a lower rate of confabulation (1.7% vs 13.7%) than ChatGPT (all P < .001). Conclusion When using user-defined prompts, GPT-4 outperformed ChatGPT in extracting oncologic phenotypes from free-text CT reports on lung cancer and demonstrated better oncologic reasoning with fewer confabulations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Hafezi-Nejad and Trivedi in this issue.

Pattern and time point of relapse in locally advanced esophagogastric adenocarcinoma after multimodal treatment: implications for a useful structured follow-up.

PURPOSE: Despite improvements in multimodal treatment of locally advanced esophagogastric adenocarcinoma, the majority of patients still relapses. The impact of structured follow-up for early detection of recurrence is unclear and controversially discussed. METHODS: Patients with locally advanced esophagogastric adenocarcinoma having received neoadjuvant/perioperative chemotherapy followed by tumor resection between 2009 and 2021, underwent a structured follow-up including three-monthly imaging during the first 2 years, followed by semiannual and annual examinations in year 3-4 and 5, respectively. Clinical outcome including pattern and time point of relapse was analyzed. RESULTS: Two hundred fifty-seven patients were included in this analysis. In 50.2% (n = 129) of patients, recurrent disease was diagnosed, with the majority (94.6%) relapsing within the first 2 years. The most common site of relapse were lymph node metastases followed by peritoneal carcinomatosis and hepatic and pulmonary metastases. 52.7% of patients presented with symptoms at the time of relapse. Cumulative risk and time point of relapse differed significantly between patient with a node-positive tumor (ypN+) after neoadjuvant treatment (high-risk group) and patients with node-negative primary tumor (ypN0) (low-risk group). High-risk patients had a significantly inferior disease-free survival (DFS) and overall survival (OS) with 11.1 and 29.0 months, respectively, whereas median DFS and OS were not reached for the low-risk group. CONCLUSIONS: The risk of relapse differs significantly between high- and low-risk patients. Only a part of relapses is associated with clinical symptoms. An individualized follow-up strategy is recommended for high- and low-risk patients considering the individual risk of relapse.

Vascular pathology in patients with alveolar echinococcosis: framework for assessment and clinical management - a retrospective case series.

OBJECTIVE: Alveolar echinococcosis (AE) is a parasitic liver disease with infiltrative growth similar to solid organ malignancies. Major vascular damage is frequent and often remains untreated until catastrophic events precipitate. Detailed clinical and radiological assessment is required to guide individualised treatment decisions. Standardised radiological reporting templates of malignancies with profiles resembling AE are candidates for adaptation. Our objectives are to describe vascular pathology in AE and establish a framework for structured evaluation as the basis for treatment decisions and monitoring. DESIGN: Retrospective case series. RESULTS: 69 patients (37.1%) had vascular involvement: portal vein (PV) 24.7%, hepatic vein (HV) 22.6% inferior vena cava (IVC) 13.4%. Significant stenosis/occlusion of vessels was present in 15.1% of PV, in 13.4% of HV and in 7.5% of IVC involvement. Vascular pathology needing specific treatment or monitoring was present in 8.6% of patients. The most frequent clinical presentation was high grade IVC stenosis or occlusion which was seen in 11 patients of the cohort. CONCLUSION: Advanced AE requires early multidisciplinary assessment to prevent progressive impairment of liver function due to vascular damage. The focus at first presentation is on complete evaluation of vascular (and biliary) involvement. The focus in non-resectable AE is on prevention of vascular (and biliary) complications while suppressing growth of AE lesions by benzimidazole treatment to improve the quality of life of patients. We developed a framework for standardised vascular assessment and follow-up of patients with AE to recognise and treat complications early.

Pulmonary cystic echinococcosis.

PURPOSE OF REVIEW: The aim of our review is to summarize specific clinical, diagnostic and treatment aspects of pulmonary cystic echinococcosis. The lung is the organ second most affected by cystic echinococcosis with approximately a quarter of cystic echinococcosis cysts. Most cysts are in the liver. Apart from the watch and wait approach for selected inactive cysts [cystic echinococcosis CE4, CE5], the well established WHO cystic echinococcosis cyst classification-based treatment of hepatic cystic echinococcosis cannot be applied to pulmonary cystic echinococcosis cysts. Some standard interventions can even be harmful when applied to pulmonary cystic echinococcosis cysts. RECENT FINDINGS: Cystic echinococcosis is one of the neglected tropical diseases (NTDs). Development of new diagnostics and treatment modalities is hampered by low investment into research and is accordingly slow. SUMMARY: Surgery is the mainstay of treatment for pulmonary cystic echinococcosis cysts. Parenchyma-sparing surgical techniques should be used whenever possible. Albendazole induces decay of the parasitic cyst membrane, opening of cystobronchial fistulas and cyst complications, which can be life threatening. It is strongly recommended to seek advice from expert centres, including differential diagnoses, treatment and a long-term management plan.

Echinococcoses - A Primer for Radiologists. / Die Echinokokkosen aus Sicht der Radiologie.

BACKGROUND: Cystic (CE) and alveolar (AE) echinococcoses are zoonotic parasitoses that may pose diagnostic problems due to their relative rarity in Middle Europe. METHODS: Based on a recent literature search and the observation of casuistics from a national echinococcosis treatment center, epidemiological, radiological, and therapeutic fundamentals are presented and important differences between AE and CE are discussed. RESULTS AND CONCLUSION: AE and CE must be regarded as completely different diseases, which differ from each other in every significant aspect. This applies not only to the epidemiological background of the patients but also to the biology of the diseases and their respective imaging features. KEY POINTS: · AE and CE are very distinct from one another and must be considered separately.. · AE is endemic in Middle Europe and is known as malignant parasitosis due to its destructive growth form.. · CE is primarily seen in Middle Europe in individuals with migration background and has a rather benign character.. CITATION FORMAT: · Weber TF, Mokry T, Stojkovic M. Die Echinokokkosen - Einblicke aus Sicht der Radiologie. Fortschr Röntgenstr 2023; 195: 1106 - 1121.

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively. RESULTS: A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated ( P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets. CONCLUSIONS: The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy.

Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma.

OBJECTIVE: To investigate the reproducibility of size measurements of focal bone marrow lesions (FL) in MRI in patients with monoclonal plasma cell disorders under variation of patient positioning and observer. METHODS: A data set from a prospective test-retest study was used, in which 37 patients with a total of 140 FL had undergone 2 MRI scans with identical parameters after patient repositioning. Two readers measured long and short axis diameter on the initial scan in T1 weighted, T2 weighted short tau inversion recovery and diffusion-weighted imaging sequences. The first reader additionally measured FL on the retest-scan. The Bland-Altman method was used to assess limits of agreement (LoA), and the frequencies of absolute size changes were calculated. RESULTS: In the simple test-retest experiment with one identical reader, a deviation of ≥1 mm / ≥2 mm / ≥3 mm for the long axis diameter in T1 weighted images was observed in 66% / 25% / 8% of cases. When comparing measurements of one reader on the first scan to the measurement of the other reader on the retest scan, a change of ≥1 mm / ≥3 mm / ≥5 mm for the long axis diameter in T1 weighted images was observed in 78% / 21% / 5% of cases. CONCLUSION: Small deviations in FL size are common and probably due to variation in patient positioning or inter-rater variability alone, without any actual biological change of the FL. Knowledge of the uncertainty associated with size measurements of FLs is critical for radiologists and oncologists when interpreting changes in FL size in clinical practice and in clinical trials. ADVANCES IN KNOWLEDGE: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders.Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change.Size changes of at least 6 mm or more in T1 weighted or T2 weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change.

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography.

OBJECTIVES: Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition. METHODS: For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated. RESULTS: Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002). CONCLUSIONS: Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases. KEY POINTS: • Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.

OBJECTIVES: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone. MATERIALS AND METHODS: In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. The quality of the automatic segmentation was evaluated on 15 manually segmented whole-body MRIs from 3 centers using the dice score. In 3 independent test sets from 3 centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation and manual ADC measurements from 2 independent radiologists was evaluated. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed MM who had undergone bone marrow biopsy, ADC measurements were correlated with biopsy results using Spearman correlation. RESULTS: The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, whereas the interrater experiment gave mean dice scores of 0.86, 0.86, and 0.77, respectively. The agreement between radiologists' manual ADC measurements and automatic ADC measurements was as follows: the bias between the first reader and the automatic approach was 49 × 10 -6 mm 2 /s, 7 × 10 -6 mm 2 /s, and -58 × 10 -6 mm 2 /s, and the bias between the second reader and the automatic approach was 12 × 10 -6 mm 2 /s, 2 × 10 -6 mm 2 /s, and -66 × 10 -6 mm 2 /s for the right pelvis, the left pelvis, and the sacral bone, respectively. The bias between reader 1 and reader 2 was 40 × 10 -6 mm 2 /s, 8 × 10 -6 mm 2 /s, and 7 × 10 -6 mm 2 /s, and the mean absolute difference between manual readers was 84 × 10 -6 mm 2 /s, 65 × 10 -6 mm 2 /s, and 75 × 10 -6 mm 2 /s. Automatically extracted ADC values significantly correlated with bone marrow plasma cell infiltration ( R = 0.36, P = 0.007). CONCLUSIONS: In this study, a nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations. This approach allows automatic, objective bone marrow ADC measurements, which agree well with manual ADC measurements and can help to overcome interrater variability or nonrepresentative measurements. Automatically extracted ADC values significantly correlate with bone marrow plasma cell infiltration and might be of value for automatic staging, risk stratification, or therapy response assessment.

In Vivo Repeatability and Multiscanner Reproducibility of MRI Radiomics Features in Patients With Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Study.

OBJECTIVES: Despite the extensive number of publications in the field of radiomics, radiomics algorithms barely enter large-scale clinical application. Supposedly, the low external generalizability of radiomics models is one of the main reasons, which hinders the translation from research to clinical application. The objectives of this study were to investigate reproducibility of radiomics features (RFs) in vivo under variation of patient positioning, magnetic resonance imaging (MRI) sequence, and MRI scanners, and to identify a subgroup of RFs that shows acceptable reproducibility across all different acquisition scenarios. MATERIALS AND METHODS: Between November 30, 2020 and February 16, 2021, 55 patients with monoclonal plasma cell disorders were included in this prospective, bi-institutional, single-vendor study. Participants underwent one reference scan at a 1.5 T MRI scanner and several retest scans: once after simple repositioning, once with a second MRI protocol, once at another 1.5 T scanner, and once at a 3 T scanner. Radiomics feature from the bone marrow of the left hip bone were extracted, both from original scans and after different image normalizations. Intraclass correlation coefficient (ICC) was used to assess RF repeatability and reproducibility. RESULTS: Fifty-five participants (mean age, 59 ± 7 years; 36 men) were enrolled. For T1-weighted images after muscle normalization, in the simple test-retest experiment, 110 (37%) of 295 RFs showed an ICC ≥0.8: 54 (61%) of 89 first-order features (FOFs), 35 (95%) of 37 volume and shape features, and 21 (12%) of 169 texture features (TFs). When the retest was performed with different technical settings, even after muscle normalization, the number of FOF/TF with an ICC ≥0.8 declined to 58/13 for the second protocol, 29/7 for the second 1.5 T scanner, and 49/7 for the 3 T scanner, respectively. Twenty-five (28%) of the 89 FOFs and 6 (4%) of the 169 TFs from muscle-normalized T1-weighted images showed an ICC ≥0.8 throughout all repeatability and reproducibility experiments. CONCLUSIONS: In vivo, only few RFs are reproducible with different MRI sequences or different MRI scanners, even after application of a simple image normalization. Radiomics features selected by a repeatability experiment only are not necessarily suited to build radiomics models for multicenter clinical application. This study isolated a subset of RFs, which are robust to variations in MRI acquisition observed in scanners from 1 vendor, and therefore are candidates to build reproducible radiomics models for monoclonal plasma cell disorders for multicentric applications, at least when centers are equipped with scanners from this vendor.

Asynchronous calibration of quantitative computed tomography bone mineral density assessment for opportunistic osteoporosis screening: phantom-based validation and parameter influence evaluation.

Asynchronous calibration could allow opportunistic screening based on routine CT for early osteoporosis detection. In this phantom study, a bone mineral density (BMD) calibration phantom and multi-energy CT (MECT) phantom were imaged on eight different CT scanners with multiple tube voltages (80-150 kVp) and image reconstruction settings (e.g. soft/hard kernel). Reference values for asynchronous BMD estimation were calculated from the BMD-phantom and validated with six calcium composite inserts of the MECT-phantom with known ground truth. Relative errors/changes in estimated BMD were calculated and investigated for influence of tube voltage, CT scanner and reconstruction setting. Reference values for 282 acquisitions were determined, resulting in an average relative error between calculated BMD and ground truth of - 9.2% ± 14.0% with a strong correlation (R2 = 0.99; p < 0.0001). Tube voltage and CT scanner had a significant effect on calculated BMD (p < 0.0001), with relative differences in BMD of 3.8% ± 28.2% when adapting reference values for tube voltage, - 5.6% ± 9.2% for CT scanner and 0.2% ± 0.2% for reconstruction setting, respectively. Differences in BMD were small when using reference values from a different CT scanner of the same model (0.0% ± 1.4%). Asynchronous phantom-based calibration is feasible for opportunistic BMD assessment based on CT images with reference values adapted for tube voltage and CT scanner model.

Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports.

Purpose: To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms. Materials and Methods: In this retrospective study, databases of three independent radiology departments were queried for SOR and FTOR dated from March 2018 to August 2021. An automated data mining and curation pipeline was developed to extract Response Evaluation Criteria in Solid Tumors-related TRCs for SOR for ground truth definition. The deep NLP bidirectional encoder representations from transformers (BERT) model and three feature-rich algorithms were trained on SOR to predict TRCs in FTOR. Models' F1 scores were compared against scores of radiologists, medical students, and radiology technologist students. Lexical and semantic analyses were conducted to investigate human and model performance on FTOR. Results: Oncologic findings and TRCs were accurately mined from 9653 of 12 833 (75.2%) queried SOR, yielding oncology reports from 10 455 patients (mean age, 60 years ± 14 [SD]; 5303 women) who met inclusion criteria. On 802 FTOR in the test set, BERT achieved better TRC classification results (F1, 0.70; 95% CI: 0.68, 0.73) than the best-performing reference linear support vector classifier (F1, 0.63; 95% CI: 0.61, 0.66) and technologist students (F1, 0.65; 95% CI: 0.63, 0.67), had similar performance to medical students (F1, 0.73; 95% CI: 0.72, 0.75), but was inferior to radiologists (F1, 0.79; 95% CI: 0.78, 0.81). Lexical complexity and semantic ambiguities in FTOR influenced human and model performance, revealing maximum F1 score drops of -0.17 and -0.19, respectively. Conclusion: The developed deep NLP model reached the performance level of medical students but not radiologists in curating oncologic outcomes from radiology FTOR.Keywords: Neural Networks, Computer Applications-Detection/Diagnosis, Oncology, Research Design, Staging, Tumor Response, Comparative Studies, Decision Analysis, Experimental Investigations, Observer Performance, Outcomes Analysis Supplemental material is available for this article. © RSNA, 2022.

A combined rotating disk electrode-surface x-ray diffraction setup for surface structure characterization in electrocatalysis.

Characterizing electrode surface structures under operando conditions is essential for fully understanding structure-activity relationships in electrocatalysis. Here, we combine in a single experiment high-energy surface x-ray diffraction as a characterizing technique with a rotating disk electrode to provide steady state kinetics under electrocatalytic conditions. Using Pt(111) and Pt(100) model electrodes, we show that full crystal truncation rod measurements are readily possible up to rotation rates of 1200 rpm. Furthermore, we discuss possibilities for both potentiostatic as well as potentiodynamic measurements, demonstrating the versatility of this technique. These different modes of operation, combined with the relatively simple experimental setup, make the combined rotating disk electrode-surface x-ray diffraction experiment a powerful technique for studying surface structures under operando electrocatalytic conditions.

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study.

OBJECTIVES: Disseminated bone marrow (BM) involvement is frequent in multiple myeloma (MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole BM. Reading of such whole-body scans is time-consuming, and yet radiologists can transfer only a small fraction of the information of the imaging data set to the report. This limits the influence that imaging can have on clinical decision-making and in research toward precision oncology. The objective of this feasibility study was to implement a concept for automatic, comprehensive characterization of the BM from wb-MRI, by automatic BM segmentation and subsequent radiomics analysis of 30 different BM spaces (BMS). MATERIALS AND METHODS: This retrospective multicentric pilot study used a total of 106 wb-MRI from 102 patients with (smoldering) MM from 8 centers. Fifty wb-MRI from center 1 were used for training of segmentation algorithms (nnU-Nets) and radiomics algorithms. Fifty-six wb-MRI from 8 centers, acquired with a variety of different MRI scanners and protocols, were used for independent testing. Manual segmentations of 2700 BMS from 90 wb-MRI were performed for training and testing of the segmentation algorithms. For each BMS, 296 radiomics features were calculated individually. Dice score was used to assess similarity between automatic segmentations and manual reference segmentations. RESULTS: The "multilabel nnU-Net" segmentation algorithm, which performs segmentation of 30 BMS and labels them individually, reached mean dice scores of 0.88 ± 0.06/0.87 ± 0.06/0.83 ± 0.11 in independent test sets from center 1/center 2/center 3-8 (interrater variability between radiologists, 0.88 ± 0.01). The subset from the multicenter, multivendor test set (center 3-8) that was of high imaging quality was segmented with high precision (mean dice score, 0.87), comparable to the internal test data from center 1. The radiomic BM phenotype consisting of 8880 descriptive parameters per patient, which result from calculation of 296 radiomics features for each of the 30 BMS, was calculated for all patients. Exemplary cases demonstrated connections between typical BM patterns in MM and radiomic signatures of the respective BMS. In plausibility tests, predicted size and weight based on radiomics models of the radiomic BM phenotype significantly correlated with patients' actual size and weight ( P = 0.002 and P = 0.003, respectively). CONCLUSIONS: This pilot study demonstrates the feasibility of automatic, objective, comprehensive BM characterization from wb-MRI in multicentric data sets. This concept allows the extraction of high-dimensional phenotypes to capture the complexity of disseminated BM disorders from imaging. Further studies need to assess the clinical potential of this method for automatic staging, therapy response assessment, or prediction of biopsy results.