Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis.

Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

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.

Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study.

OBJECTIVES: To develop a CT-based radiomics model for preoperative prediction of lymph node (LN) metastasis in perihilar cholangiocarcinoma (pCCA). METHODS: The study enrolled consecutive pCCA patients from three independent Chinese medical centers. The Boruta algorithm was applied to build the radiomics signature for the primary tumor and LN. The k-means algorithm was employed to cluster the selected LNs based on the radiomics signature LN. Support vector machines were used to construct the prediction models. The diagnostic efficiency was measured by the area under the receiver operating characteristic curve (AUC). The optimal model was evaluated in terms of calibration, clinical usefulness, and prognostic value. RESULTS: A total of 214 patients were included in the study (mean age: 61.6 years ± 9.4; 130 male). The selected LNs were classified into two clusters, which were significantly correlated with LN metastasis in all cohorts (p < 0.001). The model incorporated the clinical risk factors, radiomics signature primary tumor, and the LN cluster obtained the best discrimination, with AUC values of 0.981 (95% CI: 0.962-1), 0.896 (95% CI: 0.810-0.982), and 0.865 (95% CI: 0.768-0.961) in the training, internal validation, and external validation cohorts, respectively. High-risk patients predicted by the optimal model had shorter overall survival than low-risk patients (median, 13.7 vs. 27.3 months, p < 0.001). CONCLUSIONS: The study proposed a radiomics model with good performance to predict LN metastasis in pCCA. As a noninvasive preoperative prediction tool, this model may help in patient risk stratification and personalized treatment. CLINICAL RELEVANCE STATEMENT: A CT-based radiomics model accurately predicts lymph node metastasis in perihilar cholangiocarcinoma patients. This noninvasive preoperative tool can aid in patient risk stratification and personalized treatment, potentially improving patient outcomes. KEY POINTS: • The radiomics model based on contrast-enhanced CT is a useful tool for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma. • Radiomics features extracted from lymph nodes show great potential for predicting lymph node metastasis. • The study is the first to identify a lymph node phenotype with a high probability of metastasis based on radiomics.

Imaging to Predict Prognosis in Hepatocellular Carcinoma: Current and Future Perspectives.

The focus of hepatocellular carcinoma (HCC) research for many years has been on noninvasive diagnosis. Standardized systematic algorithms composed of combinations of precise features now serve as diagnostic imaging markers of HCC and constitute a major innovation for liver imaging. In clinical practice, the diagnosis of HCC is based primarily on imaging and secondarily on pathologic analysis if the imaging features are not specific. Whereas accurate diagnosis is essential, the next phase of innovation for HCC will likely encompass predictive and prognostic markers. HCC is a biologically heterogeneous malignancy because of complex molecular, pathologic, and patient-level factors that impact the outcomes of treatment. In recent years, there have been many advances in systemic therapy to augment and extend the existing large cache of local and regional options. However, the guideposts for treatment decisions are neither sophisticated nor individualized. This review provides an overview of prognosis in HCC from the patient to the imaging feature level with a focus on future directions toward more individualized treatment guidance.

VICT2 Trait: Prognostic Alternative to Peritumoral Hepatobiliary Phase Hypointensity in HCC.

Background Peritumoral hepatobiliary phase (HBP) hypointensity is an established prognostic imaging feature in hepatocellular carcinoma (HCC), often associated with microvascular invasion (MVI). Similar prognostic features are needed for non-HBP MRI. Purpose To propose a non-hepatobiliary-specific MRI tool with similar prognostic value to peritumoral HBP hypointensity. Materials and Methods From December 2011 to November 2021, consecutive patients with HCC who underwent preoperative contrast-enhanced MRI were retrospectively enrolled and followed up until recurrence. All MRI scans were reviewed by two blinded radiologists with 7 and 10 years of experiences with liver MRI. A scoring system based on non-hepatobiliary-specific features that highly correlated with peritumoral HBP hypointensity was identified in a stratified sampling-derived training set of the gadoxetate disodium (EOB) group by means of multivariable logistic regression, and its values to predict MVI and recurrence-free survival (RFS) were assessed. Results There were 660 patients (551 men; median age, 53 years; IQR, 45-61 years) enrolled. Peritumoral portal venous phase hypoenhancement (odds ratio [OR] = 8.8), incomplete "capsule" (OR = 3.3), corona enhancement (OR, 2.6), and peritumoral mild-moderate T2 hyperintensity (OR, 2.2) (all P < .001) were associated with peritumoral HBP hypointensity and constituted the "VICT2 trait" (test set area under the receiver operating characteristic curve = 0.84; 95% CI: 0.78, 0.90). For the EOB group, both peritumoral HBP hypointensity (OR for MVI = 2.5, P = .02; hazard ratio for RFS = 2.5, P < .001) and the VICT2 trait (OR for MVI = 5.1, P < .001; hazard ratio for RFS = 2.3, P < .001) were associated with MVI and RFS, despite a higher specificity of the VICT2 trait for MVI (89% vs 80%, P = .01). These values of the VICT2 trait were confirmed in the extracellular contrast agent group (OR for MVI = 4.0; hazard ratio for RFS = 1.7; both P < .001). Conclusion Based on four non-hepatobiliary-specific MRI features, the VICT2 trait was comparable to peritumoral hepatobiliary phase hypointensity in predicting microvascular invasion and postoperative recurrence of hepatocellular carcinoma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Harmath in this issue.

Development of a Model including MRI Features for Predicting Advanced-stage Recurrence of Hepatocellular Carcinoma after Liver Resection.

Background Identifying patients at high risk for advanced-stage hepatocellular carcinoma (HCC) recurrence after liver resection may improve patient survival. Purpose To develop a model including MRI features for predicting postoperative advanced-stage HCC recurrence. Materials and Methods This single-center, retrospective study includes consecutive adult patients who underwent preoperative contrast-enhanced MRI and curative-intent resection for early- to intermediate-stage HCC (from December 2011 to April 2021). Three radiologists evaluated 52 qualitative features on MRI scans. In the training set, Fine-Gray proportional subdistribution hazard analysis was performed to identify clinical, laboratory, imaging, pathologic, and surgical variables to include in the predictive model. In the test set, the concordance index (C-index) was computed to compare the developed model with current staging systems. The Kaplan-Meier survival curves were compared using the log-rank test. Results The study included 532 patients (median age, 54 years; IQR, 46-62 years; 465 male patients), 302 patients from the training set (median age, 54 years; IQR, 46-63 years; 265 male patients), and 128 patients from the test set (median age, 53 years; IQR, 46-63 years; 108 male patients). Advanced-stage recurrence was observed in 38 of 302 (12.6%) and 15 of 128 (11.7%) of patients from the training and test sets, respectively. Serum neutrophil count (109/L), tumor size (in centimeters), and arterial phase hyperenhancement proportion on MRI scans were associated with advanced-stage recurrence (subdistribution hazard ratio range, 1.16-3.83; 95% CI: 1.02, 7.52; P value range, <.001 to .02) and included in the predictive model. The model showed better test set prediction for advanced-stage recurrence than four staging systems (2-year C-indexes, 0.82 [95% CI: 0.74, 0.91] vs 0.63-0.68 [95% CI: 0.52, 0.82]; P value range, .001-.03). Patients at high risk for HCC recurrence (model score, ≥15 points) showed increased advanced-stage recurrence and worse all-stage recurrence-free survival (RFS), advanced-stage RFS, and overall survival than patients at low risk for HCC recurrence (P value range, <.001 to .02). Conclusion A model combining serum neutrophil count, tumor size, and arterial phase hyperenhancement proportion predicted advanced-stage HCC recurrence better than current staging systems and may identify patients at high risk. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tsai and Mellnick in this issue.

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.

Comparative Performance of 2018 LI-RADS versus Modified LIRADS (mLI-RADS): An Individual Participant Data Meta-Analysis.

BACKGROUND: LI-RADS version 2018 (v2018) is used for non-invasive diagnosis of hepatocellular carcinoma (HCC). A recently proposed modification (known as mLI-RADS) demonstrated improved sensitivity while maintaining specificity and positive predictive value (PPV) of LI-RADS category 5 (definite HCC) for HCC. However, mLI-RADS requires multicenter validation. PURPOSE: To evaluate the performance of v2018 and mLI-RADS for liver lesions in a large, heterogeneous, multi-national cohort of patients at risk for HCC. STUDY TYPE: Systematic review and meta-analysis using individual participant data (IPD) [Study Protocol: https://osf.io/duys4]. POPULATION: 2223 observations from 1817 patients (includes all LI-RADS categories; females = 448, males = 1361, not reported = 8) at elevated risk for developing HCC (based on LI-RADS population criteria) from 12 retrospective studies. FIELD STRENGTH/SEQUENCE: 1.5T and 3T; complete liver MRI with gadoxetate disodium, including axial T2w images and dynamic axial fat-suppressed T1w images precontrast and in the arterial, portal venous, transitional, and hepatobiliary phases. Diffusion-weighted imaging was used when available. ASSESSMENT: Liver observations were categorized using v2018 and mLI-RADS. The diagnostic performance of each system's category 5 (LR-5 and mLR-5) for HCC were compared. STATISTICAL TESTS: The Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2 was applied to determine risk of bias and applicability. Diagnostic performances were assessed using the likelihood ratio test for sensitivity and specificity and the Wald test for PPV. The significance level was P < 0.05. RESULTS: 17% (2/12) of the studies were considered low risk of bias (244 liver observations; 164 patients). When compared to v2018, mLR-5 demonstrated higher sensitivity (61.3% vs. 46.5%, P < 0.001), similar PPV (85.3% vs. 86.3%, P = 0.89), and similar specificity (85.8% vs. 90.8%, P = 0.16) for HCC. DATA CONCLUSION: This study confirms mLR-5 has higher sensitivity than LR-5 for HCC identification, while maintaining similar PPV and specificity, validating the mLI-RADS proposal in a heterogeneous, international cohort. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Development and validation of the OSASH score to predict overall survival of hepatocellular carcinoma after surgical resection: a dual-institutional study.

OBJECTIVE: To develop and validate a risk score based on preoperative clinical-radiological parameters for predicting overall survival (OS) in patients undergoing surgical resection for hepatocellular carcinoma (HCC). METHODS: From July 2010 to December 2021, consecutive patients with surgically-proven HCC who underwent preoperative contrast-enhanced MRI were retrospectively enrolled. A preoperative OS risk score was constructed in the training cohort using a Cox regression model and validated in a propensity score-matched internal validation cohort and an external validation cohort. RESULTS: A total of 520 patients were enrolled, among whom 210, 210, and 100 patients were from the training, internal validation, and external validation cohorts, respectively. Independent predictors for OS included incomplete tumor "capsule," mosaic architecture, tumor multiplicity, and serum alpha-fetoprotein, which were incorporated into the "OSASH score." The C-index the OSASH score was 0.85, 0.81, and 0.62 in the training, internal, and external validation cohorts, respectively. Using 32 as the cutoff point, the OSASH score stratified patients into prognostically distinct low- and high-risk groups among all study cohorts and six subgroups (all p < 0.05). Furthermore, patients with BCLC stage B-C HCC and OSASH-low risk achieved comparable OS to that of patients with BCLC stage 0-A HCC and OSASH-high risk in the internal validation cohort (5-year OS rates, 74.7 vs. 77.8%; p = 0.964). CONCLUSION: The OSASH score may help predict OS in HCC patients undergoing hepatectomy and identify potential surgical candidates among those with BCLC stage B-C HCC. CLINICAL RELEVANCE STATEMENT: By incorporating three preoperative MRI features and serum AFP, the OSASH score may help predict postsurgical overall survival in patients with hepatocellular carcinoma and identify potential surgical candidates among those with BCLC stage B and C HCC. KEY POINTS: • The OSASH score incorporating three MRI features and serum AFP can be used to predict OS in HCC patients who received curative-intent hepatectomy. • The score stratified patients into prognostically distinct low- and high-risk strata in all study cohorts and six subgroups. • Among patients with BCLC stage B and C HCC, the score identified a subgroup of low-risk patients who achieved favorable outcomes after surgery.

Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

OBJECTIVES: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). METHODS: A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. RESULTS: The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001). CONCLUSION: DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. KEY POINTS: • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).

Prognostic MRI features to predict postresection survivals for very early to intermediate stage hepatocellular carcinoma.

OBJECTIVES: Contrast-enhanced MRI can provide individualized prognostic information for hepatocellular carcinoma (HCC). We aimed to investigate the value of MRI features to predict early (≤ 2 years)/late (> 2 years) recurrence-free survival (E-RFS and L-RFS, respectively) and overall survival (OS). MATERIALS AND METHODS: Consecutive adult patients at a tertiary academic center who received curative-intent liver resection for very early to intermediate stage HCC and underwent preoperative contrast-enhanced MRI were retrospectively enrolled from March 2011 to April 2021. Three masked radiologists independently assessed 54 MRI features. Uni- and multivariable Cox regression analyses were conducted to investigate the associations of imaging features with E-RFS, L-RFS, and OS. RESULTS: This study included 600 patients (median age, 53 years; 526 men). During a median follow-up of 55.3 months, 51% of patients experienced recurrence (early recurrence: 66%; late recurrence: 34%), and 17% died. Tumor size, multiple tumors, rim arterial phase hyperenhancement, iron sparing in solid mass, tumor growth pattern, and gastroesophageal varices were associated with E-RFS and OS (largest p = .02). Nonperipheral washout (p = .006), markedly low apparent diffusion coefficient value (p = .02), intratumoral arteries (p = .01), and width of the main portal vein (p = .03) were associated with E-RFS but not with L-RFS or OS, while the VICT2 trait was specifically associated with OS (p = .02). Multiple tumors (p = .048) and radiologically-evident cirrhosis (p < .001) were the only predictors for L-RFS. CONCLUSION: Twelve visually-assessed MRI features predicted postoperative E-RFS (≤ 2 years), L-RFS (> 2 years), and OS for very early to intermediate-stage HCCs. CLINICAL RELEVANCE STATEMENT: The prognostic MRI features may help inform personalized surgical planning, neoadjuvant/adjuvant therapies, and postoperative surveillance, thus may be included in future prognostic models. KEY POINTS: • Tumor size, multiple tumors, rim arterial phase hyperenhancement, iron sparing, tumor growth pattern, and gastroesophageal varices predicted both recurrence-free survival within 2 years and overall survival. • Nonperipheral washout, markedly low apparent diffusion coefficient value, intratumoral arteries, and width of the main portal vein specifically predicted recurrence-free survival within 2 years, while the VICT2 trait specifically predicted overall survival. • Multiple tumors and radiologically-evident cirrhosis were the only predictors for recurrence-free survival beyond 2 years.

Universal Liver Imaging Lexicon: Imaging Atlas for Research and Clinical Practice.

The use of standardized terms in assessing and reporting disease processes has well-established benefits, such as clear communication between radiologists and other health care providers, improved diagnostic accuracy and reproducibility, and the enhancement and facilitation of research. Recently, the Liver Imaging Reporting and Data System (LI-RADS) Steering Committee released a universal liver imaging lexicon. The current version of the lexicon includes 81 vetted and precisely defined terms that are relevant to acquisition of images using all major liver imaging modalities and contrast agents, as well as lesion- and organ-level features. Most terms in the lexicon are applicable to all patients undergoing imaging of the liver, and only a minority of the terms are strictly intended to be used for patients with high risk factors for hepatocellular carcinoma. This pictorial atlas familiarizes readers with the liver imaging lexicon and includes discussion of general concepts, providing sample definitions, schematics, and clinical examples for a subset of the terms in the liver imaging lexicon. The authors discuss general, technical, and imaging feature terms used commonly in liver imaging, with the goal of illustrating their use for clinical and research applications. Work of the U.S. Government published under an exclusive license with the RSNA. Online supplemental material is available for this article.

[Improve the Completion Rate of ECMO Placement Assistance for Nurses in the NICU].

BACKGROUND & PROBLEMS: The extracorporeal membrane oxygenation (ECMO) system can provide cardiopulmonary support to and reduce the mortality rate in severely ill newborns. According to our investigation, completion rate of the care process among staff nurses was only 63.5% in our ward. We assumed that the reasons for the above problems included: lack of care awareness, unfamiliarity with the ECMO operation process, inadequate instruments preparation, improper ECMO pipeline fixation, lack of designated space in the unit for placing ECMO supplies, lack of specialty care guidelines, lack of a regular inspection system, and lack of regular on-the-job education. PURPOSE: Improve awareness related to assisting ECMO placement among nurses in the neonatal intensive care unit and the completeness of care. RESOLUTIONS: 1. Create a care process guidebook describing the procedures for ECMO system placement in newborns to help nurse accomplish proper placement. 2. Establish the ECMO system consumables checklist and install an ECMO system-specialized toolbox to reduce the preparation time and smooth the process. 3. Regularly organize comprehensive nurse training and develop performance indicators to enhance ECMO system placement awareness and skills. RESULTS: The cognitive accuracy rate for the assisted placement of ECMO among nurses in the neonatal intensive care unit increased from 51.9% before improvement to 89.9% afterward. Also, the complete care rate of ECMO placement increased from 63.5% before improvement to 100% afterward. CONCLUSIONS: This project effectively improved the accuracy rate of nurses involved in assisting with ECMO placement, made the ECMO system placement process easier to implement, improved the care process completion rate, and improved newborn care quality.

Functional annotation of creeping bentgrass protein sequences based on convolutional neural network.

BACKGROUND: Creeping bentgrass (Agrostis soionifera) is a perennial grass of Gramineae, belonging to cold season turfgrass, but has poor disease resistance. Up to now, little is known about the induced systemic resistance (ISR) mechanism, especially the relevant functional proteins, which is important to disease resistance of turfgrass. Achieving more information of proteins of infected creeping bentgrass is helpful to understand the ISR mechanism. RESULTS: With BDO treatment, creeping bentgrass seedlings were grown, and the ISR response was induced by infecting Rhizoctonia solani. High-quality protein sequences of creeping bentgrass seedlings were obtained. Some of protein sequences were functionally annotated according to the database alignment while a large part of the obtained protein sequences was left non-annotated. To treat the non-annotated sequences, a prediction model based on convolutional neural network was established with the dataset from Uniport database in three domains to acquire good performance, especially the higher false positive control rate. With established model, the non-annotated protein sequences of creeping bentgrass were analyzed to annotate proteins relevant to disease-resistance response and signal transduction. CONCLUSIONS: The prediction model based on convolutional neural network was successfully applied to select good candidates of the proteins with functions relevant to the ISR mechanism from the protein sequences which cannot be annotated by database alignment. The waste of sequence data can be avoided, and research time and labor will be saved in further research of protein of creeping bentgrass by molecular biology technology. It also provides reference for other sequence analysis of turfgrass disease-resistance research.

CT/MRI and CEUS LI-RADS Major Features Association with Hepatocellular Carcinoma: Individual Patient Data Meta-Analysis.

Background The Liver Imaging Reporting and Data System (LI-RADS) assigns a risk category for hepatocellular carcinoma (HCC) to imaging observations. Establishing the contributions of major features can inform the diagnostic algorithm. Purpose To perform a systematic review and individual patient data meta-analysis to establish the probability of HCC for each LI-RADS major feature using CT/MRI and contrast-enhanced US (CEUS) LI-RADS in patients at high risk for HCC. Materials and Methods Multiple databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched for studies from January 2014 to September 2019 that evaluated the accuracy of CT, MRI, and CEUS for HCC detection using LI-RADS (CT/MRI LI-RADS, versions 2014, 2017, and 2018; CEUS LI-RADS, versions 2016 and 2017). Data were centralized. Clustering was addressed at the study and patient levels using mixed models. Adjusted odds ratios (ORs) with 95% CIs were determined for each major feature using multivariable stepwise logistic regression. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) (PROSPERO protocol: CRD42020164486). Results A total of 32 studies were included, with 1170 CT observations, 3341 MRI observations, and 853 CEUS observations. At multivariable analysis of CT/MRI LI-RADS, all major features were associated with HCC, except threshold growth (OR, 1.6; 95% CI: 0.7, 3.6; P = .07). Nonperipheral washout (OR, 13.2; 95% CI: 9.0, 19.2; P = .01) and nonrim arterial phase hyperenhancement (APHE) (OR, 10.3; 95% CI: 6.7, 15.6; P = .01) had stronger associations with HCC than enhancing capsule (OR, 2.4; 95% CI: 1.7, 3.5; P = .03). On CEUS images, APHE (OR, 7.3; 95% CI: 4.6, 11.5; P = .01), late and mild washout (OR, 4.1; 95% CI: 2.6, 6.6; P = .01), and size of at least 20 mm (OR, 1.6; 95% CI: 1.04, 2.5; P = .04) were associated with HCC. Twenty-five studies (78%) had high risk of bias due to reporting ambiguity or study design flaws. Conclusion Most Liver Imaging Reporting and Data System major features had different independent associations with hepatocellular carcinoma; for CT/MRI, arterial phase hyperenhancement and washout had the strongest associations, whereas threshold growth had no association. © RSNA, 2021 Online supplemental material is available for this article.

Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT.

BACKGROUND: Exploring the genomic landscape of hepatocellular carcinoma (HCC) provides clues for therapeutic decision-making. Phosphatidylinositol-3 kinase (PI3K) signaling is one of the key pathways regulating HCC aggressiveness, and its genomic alterations have been correlated with sorafenib response. In this study, we aimed to predict somatic mutations of the PI3K signaling pathway in HCC samples through machine-learning-based radiomic analysis. METHODS: HCC patients who underwent next-generation sequencing and preoperative contrast-enhanced CT were recruited from West China Hospital and The Cancer Genome Atlas for model training and validation, respectively. Radiomic features were extracted from volumes of interest (VOIs) covering the tumor (VOItumor) and peritumoral areas (5 mm [VOI5mm], 10 mm [VOI10mm], and 20 mm [VOI20mm] from tumor margin). Factor analysis, logistic regression analysis, least absolute shrinkage and selection operator, and random forest analysis were applied for feature selection and model construction. Model performance was characterized based on the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 132 HCC patients (mean age: 61.1 ± 14.7 years; 108 men) were enrolled. In the training set, the AUCs of radiomic signatures based on single CT phases were moderate (AUC 0.694-0.771). In the external validation set, the radiomic signature based on VOI10mm in arterial phase demonstrated the highest AUC (0.733) among all models. No improvement in model performance was achieved after adding the tumor radiomic features or manually assessed qualitative features. CONCLUSIONS: Machine-learning-based radiomic analysis had potential for characterizing alterations of PI3K signaling in HCC and could help identify potential candidates for sorafenib treatment.

Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely used for diagnosing hepatocellular carcinoma (HCC), however, with unsatisfactory sensitivity, complex ancillary features, and inadequate integration with gadoxetate disodium (EOB)-enhanced MRI. PURPOSE: To modify LI-RADS (mLI-RADS) on EOB-MRI. STUDY TYPE: Secondary analysis of a prospective observational study. POPULATION: Between July 2015 and September 2018, 224 consecutive high-risk patients (median age, 51 years; range, 26-83; 180 men; training/testing sets: 169/55 patients) with 742 (median size, 13 mm; interquartile range, 7-27; 498 HCCs) LR-3/4/5 observations. FIELD STRENGTH/SEQUENCE: 3.0 T T2 -weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar, and 3D T1 -weighted gradient echo sequences. ASSESSMENT: Three radiologists (with 5, 5, and 10 years of experience in liver MR imaging, respectively) blinded to the reference standard (histopathology or imaging follow-up) reviewed all MR images independently. In the training set, the optimal LI-RADS version 2018 (v2018) features selected by Random Forest analysis were used to develop mLI-RADS via decision tree analysis. STATISTICAL TESTS: In an independent testing set, diagnostic performances of mLI-RADS, LI-RADS v2018, and the Korean Liver Cancer Association (KLCA) guidelines were computed using a generalized estimating equation model and compared with McNemar's test. A two-tailed P < 0.05 was statistically significant. RESULTS: Five features (nonperipheral "washout," restricted diffusion, nonrim arterial phase hyperenhancement [APHE], mild-moderate T2 hyperintensity, and transitional phase hypointensity) constituted mLI-RADS, and mLR-5 was nonperipheral washout coupled with either nonrim APHE or restricted diffusion. In the testing set, mLI-RADS was significantly more sensitive (72%) and accurate (80%) than LI-RADS v2018 (sensitivity, 61%; accuracy 74%; both P < 0.001) and the KLCA guidelines (sensitivity, 64%; accuracy 74%; both P < 0.001), without sacrificing positive predictive value (mLI-RADS, 94%; LI-RADS v2018, 94%; KLCA guidelines, 92%). DATA CONCLUSION: In high-risk patients, the EOB-MRI-based mLI-RADS was simpler and more sensitive for HCC than LI-RADS v2018 while maintaining high positive predictive value. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptimal diagnostic sensitivity. PURPOSE: To propose data-driven modifications to the LI-RADS version 2018 (v2018) major feature system (rLI-RADS) on gadoxetate disodium (EOB)-enhanced magnetic resonance imaging (MRI) to improve sensitivity and simplicity while maintaining high positive predictive value (PPV) for detecting HCC. STUDY TYPE: Retrospective. POPULATION: Two hundred and twenty-four consecutive at-risk patients (training dataset: 169, independent testing dataset: 55) with 742 LR-3 to LR-5 liver observations (HCC: N = 498 [67%]) were analyzed from a prospective observational registry collected between July 2015 and September 2018. FIELD STRENGTH/SEQUENCE: 3.0 T/T2-weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar and three-dimensional (3D) T1-weighted gradient echo sequences. ASSESSMENT: All images were evaluated by three independent abdominal radiologists who were blinded to all clinical, pathological, and follow-up information. Composite reference standards of either histopathology or imaging follow-up were used. STATISTICAL TESTS: In the training dataset, LI-RADS v2018 major features were used to develop rLI-RADS based on their associated PPV for HCC. In an independent testing set, diagnostic performances of LI-RADS v2018 and rLI-RADS were computed using a generalized estimating equation model and compared with McNemar's test. A P value <0.05 was considered statistically significant. RESULTS: The median (interquartile range) size of liver observations was 13 mm (7-27 mm). The diagnostic table for rLI-RADS encompassed 9 cells, as opposed to 16 cells for LI-RADS v2018. In the testing set, compared to LI-RADS v2018, rLI-RADS category 5 demonstrated a significantly superior sensitivity (76% vs. 61%) while maintaining comparably high PPV (92.5% vs. 94.1%, P = 0.126). DATA CONCLUSION: Compared with LI-RADS v2018, rLI-RADS demonstrated improved simplicity and significantly superior diagnostic sensitivity for HCC in at-risk patients. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Prognostic implications of CT/MRI LI-RADS in hepatocellular carcinoma: State of the art and future directions.

Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. Management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics and clinical variables. However, these algorithms are unlikely to profile the full landscape of tumour aggressiveness and allow accurate prognosis stratification. Noninvasive imaging biomarkers on computed tomography (CT) or magnetic resonance imaging (MRI) exhibit a promising prospect to refine the prognostication of HCC. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, techniques, interpretation, reporting and data collection of liver imaging. At present, it has been widely accepted as an effective diagnostic system for HCC in at-risk patients. Emerging data have provided new insights into the potential of CT/MRI LI-RADS in HCC prognostication, which may help refine the prognostic paradigm of HCC that promises to direct individualized management and improve patient outcomes. Therefore, this review aims to summarize several prognostic imaging features at CT/MRI for patients with HCC; the available evidence regarding the use of LI-RDAS for evaluation of tumour biology and clinical outcomes, pitfalls of current literature, and future directions for LI-RADS in the management of HCC.

Predicting microvascular invasion in hepatocellular carcinoma: A dual-institution study on gadoxetate disodium-enhanced MRI.

BACKGROUND & AIMS: Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but its diagnosis mandates postoperative histopathologic analysis. We aimed to develop and externally validate a predictive scoring system for MVI. METHODS: From July 2015 to November 2020, consecutive patients underwent surgery for HCC with preoperative gadoxetate disodium (EOB)-enhanced MRI was retrospectively enrolled. All MR images were reviewed independently by two radiologists who were blinded to the outcomes. In the training centre, a radio-clinical MVI score was developed via logistic regression analysis against pathology. In the testing centre, areas under the receiver operating curve (AUCs) of the MVI score and other previous MVI schemes were compared. Overall survival (OS) and recurrence-free survival (RFS) were analysed by the Kaplan-Meier method with the log-rank test. RESULTS: A total of 417 patients were included, 195 (47%) with pathologically-confirmed MVI. The MVI score included: non-smooth tumour margin (odds ratio [OR] = 4.4), marked diffusion restriction (OR = 3.0), internal artery (OR = 3.0), hepatobiliary phase peritumoral hypointensity (OR = 2.5), tumour multifocality (OR = 1.6), and serum alpha-fetoprotein >400 ng/mL (OR = 2.5). AUCs for the MVI score were 0.879 (training) and 0.800 (testing), significantly higher than those for other MVI schemes (testing AUCs: 0.648-0.684). Patients with model-predicted MVI had significantly shorter OS (median 61.0 months vs not reached, P < .001) and RFS (median 13.0 months vs. 42.0 months, P < .001) than those without. CONCLUSIONS: A preoperative MVI score integrating five EOB-MRI features and serum alpha-fetoprotein level could accurately predict MVI and postoperative survival in HCC. Therefore, this score may aid in individualized treatment decision making.