MR Elastography for Classification of Focal Liver Lesions Using Viscoelastic Parameters: A Pilot Study Based on Intrinsic and Extrinsic Activations.

BACKGROUND: Intrinsic activation MR elastography (iMRE) uses cardiovascular pulsations to assess tissue viscoelastic properties. Applying it to focal liver lesions extends its capabilities. PURPOSE: To assess the viscoelastic parameters of focal liver lesions measured by iMRE and compare its diagnostic performance with extrinsic MRE (eMRE) for differentiating malignant and benign lesions. STUDY TYPE: Prospective. POPULATION: A total of 55 participants underwent MRI with research MRE sequences; 32 participants with 17 malignant and 15 benign lesions underwent both iMRE and eMRE. FIELD STRENGTH/SEQUENCE: iMRE at ~1 Hz heart rate used a 3 T scanner with a modified four-dimensional (4D)-quantitative flow gradient-echo phase contrast and low-velocity encoding cardiac-triggered technique. eMRE employed a gradient-echo sequence at 30, 40, and 60 Hz. ASSESSMENT: Liver displacements were measured using 4D-phase contrast and reconstructed via a nonlinear inversion algorithm to determine shear stiffness (SS) and damping ratio (DR). iMRE parameters were normalized to the corresponding values from the spleen. Lesions were manually segmented, and image quality was reviewed. STATISTICAL TESTS: Kruskal-Wallis, Mann-Whitney, Dunn's test, and areas under receiver operating characteristic curves (AUC) were assessed. RESULTS: SS was significantly higher in malignant than benign lesions with iMRE at 1 Hz (3.69 ± 1.31 vs. 1.63 ± 0.45) and eMRE at 30 Hz (3.76 ± 1.12 vs. 2.60 ± 1.26 kPa), 40 Hz (3.76 ± 1.12 vs. 2.60 ± 1.26 kPa), and 60 Hz (7.32 ± 2.87 vs. 2.48 ± 1.12 kPa). DR was also significantly higher in malignant than benign lesions at 40 Hz (0.36 ± 0.11 vs. 0.21 ± 0.01) and 60 Hz (0.89 ± 0.86 vs. 0.22 ± 0.09). The AUC were 0.86 for iMRE SS, 0.87-0.98 for eMRE SS, 0.47 for iMRE DR, and 0.62-0.86 for eMRE DR. DATA CONCLUSION: Cardiac-activated iMRE can characterize liver lesions and differentiate malignant from benign lesions through normalized SS maps. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction.

Colorectal liver metastases (CLM) affect almost half of all colon cancer patients and the response to systemic chemotherapy plays a crucial role in patient survival. While oncologists typically use tumor grading scores, such as tumor regression grade (TRG), to establish an accurate prognosis on patient outcomes, including overall survival (OS) and time-to-recurrence (TTR), these traditional methods have several limitations. They are subjective, time-consuming, and require extensive expertise, which limits their scalability and reliability. Additionally, existing approaches for prognosis prediction using machine learning mostly rely on radiological imaging data, but recently histological images have been shown to be relevant for survival predictions by allowing to fully capture the complex microenvironmental and cellular characteristics of the tumor. To address these limitations, we propose an end-to-end approach for automated prognosis prediction using histology slides stained with Hematoxylin and Eosin (H&E) and Hematoxylin Phloxine Saffron (HPS). We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We propose a semi-supervised model to perform tissue classification from sparse annotations, producing segmentation and feature maps. Specifically, we use an attention-based approach that weighs the importance of different slide regions in producing the final classification results. Finally, we exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer model in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction. We evaluate our approach on an in-house clinical dataset of 258 CLM patients, achieving superior performance compared to other comparative models with a c-index of 0.804 (0.014) for OS and 0.735 (0.016) for TTR, as well as on two public datasets. The proposed approach achieves an accuracy of 86.9% to 90.3% in predicting TRG dichotomization. For the 3-class TRG classification task, the proposed approach yields an accuracy of 78.5% to 82.1%, outperforming the comparative methods. Our proposed pipeline can provide automated prognosis for pathologists and oncologists, and can greatly promote precision medicine progress in managing CLM patients.

Is concurrent LR-5 associated with a higher rate of hepatocellular carcinoma in LR-3 or LR-4 observations? An individual participant data meta-analysis.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) does not consider factors extrinsic to the observation of interest, such as concurrent LR-5 observations. PURPOSE: To evaluate whether the presence of a concurrent LR-5 observation is associated with a difference in the probability that LR-3 or LR-4 observations represent hepatocellular carcinoma (HCC) through an individual participant data (IPD) meta-analysis. METHODS: Multiple databases were searched from 1/2014 to 2/2023 for studies evaluating the diagnostic accuracy of CT/MRI for HCC using LI-RADS v2014/2017/2018. The search strategy, study selection, and data collection process can be found at https://osf.io/rpg8x . Using a generalized linear mixed model (GLMM), IPD were pooled across studies and modeled simultaneously with a one-stage meta-analysis approach to estimate positive predictive value (PPV) of LR-3 and LR-4 observations without and with concurrent LR-5 for the diagnosis of HCC. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS: Twenty-nine studies comprising 2591 observations in 1456 patients (mean age 59 years, 1083 [74%] male) were included. 587/1960 (29.9%) LR-3 observations in 1009 patients had concurrent LR-5. The PPV for LR-3 observations with concurrent LR-5 was not significantly different from the PPV without LR-5 (45.4% vs 37.1%, p = 0.63). 264/631 (41.8%) LR-4 observations in 447 patients had concurrent LR-5. The PPV for LR-4 observations with concurrent LR-5 was not significantly different from LR-4 observations without concurrent LR-5 (88.6% vs 69.5%, p = 0.08). A sensitivity analysis for low-risk of bias studies (n = 9) did not differ from the primary analysis. CONCLUSION: The presence of concurrent LR-5 was not significantly associated with differences in PPV for HCC in LR-3 or LR-4 observations, supporting the current LI-RADS paradigm, wherein the presence of synchronous LR-5 may not alter the categorization of LR-3 and LR-4 observations.

Quantitative Ultrasound and Ultrasound-Based Elastography for Chronic Liver Disease: Practical Guidance, From the AJR Special Series on Quantitative Imaging.

Quantitative ultrasound (QUS) and ultrasound-based elastography techniques are emerging as non-invasive effective methods for assessing chronic liver disease. They are more accurate than B-mode imaging alone and more accessible than MRI as alternatives to liver biopsy. Early detection and monitoring of diffuse liver processes such as steatosis, inflammation, and fibrosis play an important role in guiding patient management. The most widely available and validated techniques are attenuation-based QUS techniques and shear-wave elastography techniques that measure shear-wave speed. Other techniques are supported by a growing body of evidence and are increasingly commercialized. This review explains general physical concepts of QUS and ultrasound-based elastography techniques for evaluating chronic liver disease. The first section describes QUS techniques relying on attenuation, backscatter, and speed of sound. The second section discusses ultrasound-based elastography techniques analyzing shear-wave speed, shear-wave dispersion, and shear-wave attenuation. With an emphasis on clinical implementation, each technique's diagnostic performance along with thresholds for various clinical applications are summarized, to provide guidance on analysis and reporting for radiologists. Measurement methods, advantages, and limitations are also discussed. The third section explores developments in quantitative contrast-enhanced and vascular ultrasound that are relevant to chronic liver disease evaluation.

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis.

OBJECTIVE: The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. METHODS: We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set. RESULTS: For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone. CONCLUSION: Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

Inter-visit and inter-reader reproducibility of multi-parametric diffusion-weighted MR imaging in longitudinally imaged patients with metabolic dysfunction-associated fatty liver disease and healthy volunteers.

BACKGROUND: Despite the widespread use of diffusion-weighted imaging (DWI) in metabolic dysfunction-associated fatty liver disease (MAFLD), MRI acquisition and quantification techniques vary in the literature suggesting the need for established and reproducible protocols. The goal of this study was to assess inter-visit and inter-reader reproducibility of DWI- and IVIM-derived parameters in patients with MAFLD and healthy volunteers using extensive sampling of the "fast" compartment, non-rigid registration, and exclusion voxels with poor fit quality. METHODS: From June 2019 to April 2023, 31 subjects (20 patients with biopsy-proven MAFLD and 11 healthy volunteers) were included in this IRB-approved study. Subjects underwent MRI examinations twice within 40 days. 3.0 T DWI was acquired using a respiratory-triggered spin-echo diffusion-weighted echo-planar imaging sequence (b-values of 0, 10, 20, 30, 40, 50, 100, 200, 400, 800 s/mm2). DWI series were co-registered prior to voxel-wise non-linear regression of the IVIM model and voxels with poor fit quality were excluded (normalized root mean squared error ≥ 0.05). IVIM parameters (perfusion fraction, f; diffusion coefficient, D; and pseudo-diffusion coefficient, D*), and apparent diffusion coefficients (ADC) were computed from manual segmentation of the right liver lobe performed by two analysts on two MRI examinations. RESULTS: All results are reported for f, D, D*, and ADC respectively. For inter-reader agreement on the first visit, ICC were of 0.985, 0.994, 0.986, and 0.993 respectively. For intra-reader agreement of analyst 1 assessed on both imaging examinations, ICC between visits were of 0.805, 0.759, 0.511, and 0.850 respectively. For inter-reader agreement on the first visit, mean bias and 95 % limits of agreement were (0.00 ± 0.03), (-0.01 ± 0.03) × 10-3 mm2/s, (0.70 ± 10.40) × 10-3 mm2/s, and (-0.02 ± 0.04) × 10-3 mm2/s respectively. For intra-reader agreement of analyst 1, mean bias and 95 % limits of agreement were (0.01 ± 0.09) × 10-3 mm2/s, (-0.01 ± 0.21) × 10-3 mm2/s, (-13.37 ± 56.19) × 10-3 mm2/s, and (-0.01 ± 0.16) × 10-3 mm2/s respectively. Except for parameter D* that was associated with between-subjects parameter variability (P = 0.009), there was no significant variability between subjects, examinations, or readers. CONCLUSION: With our approach, IVIM parameters f, D, D*, and ADC provided excellent inter-reader agreement and good to very good inter-visit or intra-reader agreement, thus showing the reproducibility of IVIM-DWI of the liver in MAFLD patients and volunteers.

Between-Visit Reproducibility of Shear Wave Viscoelastography in Volunteers and Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease.

OBJECTIVE: To assess the reproducibility of six ultrasound (US)-determined shear wave (SW) viscoelastography parameters for assessment of mechanical properties of the liver in volunteers and patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD) or metabolic dysfunction-associated steatohepatitis (MASH). METHODS: This prospective, cross-sectional, institutional review board-approved study included 10 volunteers and 20 patients with MASLD or MASH who underwent liver US elastography twice, at least 2 weeks apart. SW speed (SWS), Young's modulus (E), shear modulus (G), SW attenuation (SWA), SW dispersion (SWD), and viscosity were computed from radiofrequency data recorded on a research US scanner. Linear mixed models were used to consider the sonographer on duty as a confounder. The reproducibility of measurements was assessed by intraclass correlation coefficient (ICC), coefficient of variation (CV), reproducibility coefficient (RDC), and Bland-Altman analyses. RESULTS: The sonographer performing the exam had no impact on viscoelastic parameters (P > .05). ICCs of SWS, E, G, SWA, SWD, and viscosity were, respectively, 0.89 (95% confidence intervals [CI]: 0.79-0.95), 0.81 (95% CI: 0.79-0.95), 0.90 (95% CI: 0.80-0.95), 0.96 (95% CI: 0.93-0.98), 0.78 (95% CI: 0.60-0.89), and 0.90 (95% CI: 0.80-0.95); CVs were 11.9, 23.3, 24.2, 10.1, 29.0, and 32.2%; RDCs were 33.0, 64.5, 66.9, 27.7, 80.3, and 89.2%, and Bland-Altman mean biases and 95% limits of agreement were -0.05 (-0.45, 0.35) m/s, -0.61 (-5.33, 4.10) kPa, -0.25 (-2.06, 1.56) kPa, -0.01 (-0.27, 0.26) Np/m/Hz, -0.09 (-7.09, 6.91) m/s/kHz, and -0.33 (-2.60, 1.94) Pa/s, between the two visits. CONCLUSION: US-determined viscoelastography parameters can be measured with high reproducibility and consistency between two visits 2 weeks apart on the same ultrasound machine.

Mycobacterium vaccae attenuates airway inflammation by inhibiting autophagy and activating PI3K/Akt signaling pathway in OVA-induced allergic airway inflammation mouse model.

PURPOSE: The etiology of asthma remains elusive, with no known cure. Based on accumulating evidence, autophagy, a self-degradation process that maintains cellular metabolism and homeostasis, participates in the development of asthma. Mycobacterium vaccae vaccine (M. vaccae), an immunomodulatory agent, has previously been shown to effectively alleviate airway inflammation and airway remodeling. However, its therapeutic effect on asthma via the regulation of autophagy remains unknown. Therefore, this study aimed to investigate the impact of M. vaccae in attenuating asthma airway inflammation via autophagy-mediated pathways. METHODS: Balb/c mice were used to generate an ovalbumin (OVA)-immunized allergic airway model and were subsequently administered either M. vaccae or M. vaccae + rapamycin (an autophagy activator) prior to each challenge. Next, airway inflammation, mucus secretion, and airway remodeling in mouse lung tissue were assessed via histological analyses. Lastly, the expression level of autophagy proteins LC3B, Beclin1, p62, and autolysosome was determined both in vivo and in vitro, along with the expression level of p-PI3K, PI3K, p-Akt, and Akt in mouse lung tissue. RESULTS: The findings indicated that aerosol inhalation of M. vaccae in an asthma mouse model has the potential to decrease eosinophil counts, alleviate airway inflammation, mucus secretion, and airway remodeling through the inhibition of autophagy. Likewise, M. vaccae could reduce the levels of OVA-specific lgE, IL-5, IL-13, and TNF-α in asthma mouse models by inhibiting autophagy. Furthermore, this study revealed that M. vaccae also suppressed autophagy in IL-13-stimulated BEAS-2B cells. Moreover, M. vaccae may activate the PI3K/Akt signaling pathway in the lung tissue of asthmatic mice. CONCLUSION: In summary, the present study suggests that M. vaccae may contribute to alleviating airway inflammation and remodeling in allergic asthma by potentially modulating autophagy and the PI3K/Akt signaling pathway. These discoveries offer a promising avenue for the development of therapeutic interventions targeting allergic airway inflammation.

Simulating federated learning for steatosis detection using ultrasound images.

We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private dataset (153 patients; 1530 images) and a public dataset (55 patient; 550 images) were included in this retrospective study. The datasets contained patients with metabolic dysfunction-associated fatty liver disease (MAFLD) with biopsy-proven steatosis grades and control individuals without steatosis. We employed four data partitioning strategies to simulate FL scenarios and we assessed four FL algorithms. We investigated the impact of class imbalance and the mismatch between the global and local data distributions on the learning outcome. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. AUCs were 0.93 (95% CI 0.92, 0.94) for source-based partitioning scenario with FedAvg, 0.90 (95% CI 0.89, 0.91) for a centralized model, and 0.83 (95% CI 0.81, 0.85) for a model trained in a single-center scenario. When data was perfectly balanced on the global level and each site had an identical data distribution, the model yielded an AUC of 0.90 (95% CI 0.88, 0.92). When each site contained data exclusively from one single class, irrespective of the global data distribution, the AUC fell in the range of 0.34-0.70. FL applied to B-mode US images provide performance comparable to a centralized model and higher than single-center scenario. Global data imbalance and local data heterogeneity influenced the learning outcome.

Changes in the Biology and Susceptibility of Weevil (Cylas formicarius) to the Insecticide Spinetoram as a Response to Cadmium Contamination.

The sweet potato weevil Cylas formicarius is a notorious underground pest in sweet potato (Ipomoea batatas L.). However, little is known about the effects of cadmium (Cd) stress on weevil biology and resistance to pesticides and biotic agents. Therefore, we fed sweet potato weevils with Cd-contaminated sweet potato and assessed adult food intake and survival and larval developmental duration and mortality rates, as well as resistance to the insecticide spinetoram and susceptibility to the entomopathogenic fungus Beauveria bassiana. With increasing Cd concentration, the number of adult weevil feeding holes, adult survival and life span, and larval developmental duration decreased significantly, whereas larval mortality rates increased significantly. However, at the lowest Cd concentration (30 mg/L), adult feeding was stimulated. Resistance of adult sweet potato weevils to spinetoram increased at low Cd concentration, whereas Cd contamination did not affect sensitivity to B. bassiana. Thus, Cd contamination affected sweet potato weevil biology and resistance, and further studies will investigate weevil Cd accumulation and detoxification mechanisms.

Interpretation, Reporting, and Clinical Applications of Liver MR Elastography.

Chronic liver disease is highly prevalent and often leads to fibrosis or cirrhosis and complications such as liver failure and hepatocellular carcinoma. The diagnosis and staging of liver fibrosis is crucial to determine management and mitigate complications. Liver biopsy for histologic assessment has limitations such as sampling bias and high interreader variability that reduce precision, which is particularly challenging in longitudinal monitoring. MR elastography (MRE) is considered the most accurate noninvasive technique for diagnosing and staging liver fibrosis. In MRE, low-frequency vibrations are applied to the abdomen, and the propagation of shear waves through the liver is analyzed to measure liver stiffness, a biomarker for the detection and staging of liver fibrosis. As MRE has become more widely used in clinical care and research, different contexts of use have emerged. This review focuses on the latest developments in the use of MRE for the assessment of liver fibrosis; provides guidance for image acquisition and interpretation; summarizes diagnostic performance, along with thresholds for diagnosis and staging of liver fibrosis; discusses current and emerging clinical applications; and describes the latest technical developments.

Human-scale navigation of magnetic microrobots in hepatic arteries.

Using external actuation sources to navigate untethered drug-eluting microrobots in the bloodstream offers great promise in improving the selectivity of drug delivery, especially in oncology, but the current field forces are difficult to maintain with enough strength inside the human body (>70-centimeter-diameter range) to achieve this operation. Here, we present an algorithm to predict the optimal patient position with respect to gravity during endovascular microrobot navigation. Magnetic resonance navigation, using magnetic field gradients in clinical magnetic resonance imaging (MRI), is combined with the algorithm to improve the targeting efficiency of magnetic microrobots (MMRs). Using a dedicated microparticle injector, a high-precision MRI-compatible balloon inflation system, and a clinical MRI, MMRs were successfully steered into targeted lobes via the hepatic arteries of living pigs. The distribution ratio of the microrobots (roughly 2000 MMRs per pig) in the right liver lobe increased from 47.7 to 86.4% and increased in the left lobe from 52.2 to 84.1%. After passing through multiple vascular bifurcations, the number of MMRs reaching four different target liver lobes had a 1.7- to 2.6-fold increase in the navigation groups compared with the control group. Performing simulations on 19 patients with hepatocellular carcinoma (HCC) demonstrated that the proposed technique can meet the need for hepatic embolization in patients with HCC. Our technology offers selectable direction for actuator-based navigation of microrobots at the human scale.

Risk Factors for Hospitalization Duration Longer Than 24 Hours Following Percutaneous Radiofrequency Ablation of Liver Tumours.

PURPOSE: Prior studies have described complications of radiofrequency ablation (RFA) of liver tumours. The aim of this study was to identify risk factors for hospitalization duration longer than 24 hours following RFA of liver tumours. METHODS: This retrospective, single-centre study included patients with liver tumours undergoing RFA between October 2017 and July 2020. Medical records were reviewed to collect patient, tumours, and procedure characteristics for each RFA session. The association between potential risk factors and duration of hospitalization (less than or more than 24 hours) was analyzed using univariate and multivariate logistic regressions. RESULTS: Our study included 291 patients (mean age: 65.2 ± 11.2 [standard deviation]; 201 men) undergoing 324 RFA sessions. Sixty-eight sessions (21.0%) resulted in hospitalization of more than 24 hours. Multivariate analysis identified each additional needle insertion per session (OR 1.4; 95% CI [1.1-1.9]; P = .02), RFA performed in segment V (OR 2.8; 95% CI [1.4-5.7]; P = .004), and use of artificial pneumothorax (OR 14.5; 95% CI [1.4-146.0]; P = .02) as potential risk factors. A history of hepatic encephalopathy (OR 2.6; 95% CI [1.1-6.0]; P = .03) was only significant in univariate analysis. Post-hoc, subgroup analysis of patients with hepatocellular carcinoma (69.8%) did not identify other risk factors. CONCLUSION: Risk factors for a hospitalization duration longer than 24 hours include a higher number of needle insertions per session, radiofrequency ablation in segment V, and use of an artificial pneumothorax.

LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis.

Background The independent contribution of each Liver Imaging Reporting and Data System (LI-RADS) CT or MRI ancillary feature (AF) has not been established. Purpose To evaluate the association of LI-RADS AFs with hepatocellular carcinoma (HCC) and malignancy while adjusting for LI-RADS major features through an individual participant data (IPD) meta-analysis. Materials and Methods Medline, Embase, Cochrane Central Register of Controlled Trials, and Scopus were searched from January 2014 to January 2022 for studies evaluating the diagnostic accuracy of CT and MRI for HCC using LI-RADS version 2014, 2017, or 2018. Using a one-step approach, IPD across studies were pooled. Adjusted odds ratios (ORs) and 95% CIs were derived from multivariable logistic regression models of each AF combined with major features except threshold growth (excluded because of infrequent reporting). Liver observation clustering was addressed at the study and participant levels through random intercepts. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2. Results Twenty studies comprising 3091 observations (2456 adult participants; mean age, 59 years ± 11 [SD]; 1849 [75.3%] men) were included. In total, 89% (eight of nine) of AFs favoring malignancy were associated with malignancy and/or HCC, 80% (four of five) of AFs favoring HCC were associated with HCC, and 57% (four of seven) of AFs favoring benignity were negatively associated with HCC and/or malignancy. Nonenhancing capsule (OR = 3.50 [95% CI: 1.53, 8.01]) had the strongest association with HCC. Diffusion restriction (OR = 14.45 [95% CI: 9.82, 21.27]) and mild-moderate T2 hyperintensity (OR = 10.18 [95% CI: 7.17, 14.44]) had the strongest association with malignancy. The strongest negative associations with HCC were parallels blood pool enhancement (OR = 0.07 [95% CI: 0.01, 0.49]) and marked T2 hyperintensity (OR = 0.18 [95% CI: 0.07, 0.45]). Seventeen studies (85%) had a high risk of bias. Conclusion Most LI-RADS AFs were independently associated with HCC, malignancy, or benignity as intended when adjusting for major features. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Crivellaro in this issue.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: A meta-analysis.

Background & Aims: Pathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease. Methods: Using individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland-Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies. Results: Eligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80-0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5-36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology. Conclusions: Histological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values. Impact and implications: Magnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.

Individual Participant Data Meta-Analysis of LR-5 in LI-RADS Version 2018 versus Revised LI-RADS for Hepatocellular Carcinoma Diagnosis.

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11-30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sirlin and Chernyak in this issue.

Telomerase-activated Au@DNA nanomachine for targeted chemo-photodynamic synergistic therapy.

We successfully designed a smart activatable nanomachine for cancer synergistic therapy. Photodynamic therapy (PDT) and chemotherapy can be activated by intracellular telomerase while anti-cancer drugs can be effectively transported into tumour cells. An Sgc8 aptamer was designed, which can specifically distinguish tumour cells from normal cells and perform targeted therapy. The nanomachine entered the tumour cells by recognising PTK7, which is overexpressed on the surface of cancer cells. Then, the "switch" of the system was opened by TP sequence extension under telomerase stimulus. So, the chemotherapeutic drug DOX was released to achieve the chemotherapy, and the Ce6 labelled Sgc8-apt was released to activate the PDT. It was found that if no telomerase existed, the Ce6 would always be in an "off" state and could not activate the PDT. Telomerase is the key to controlling the activation of the PDT, which effectively reduces the damage photosensitisers cause to normal cells. Using in vitro and in vivo experiments, the nanomachine shows an excellent performance in targeted synergistic therapy, which is expected to be utilised in the future.