US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Complementary Role of CEUS and CT/MR LI-RADS for Diagnosis of Recurrent HCC.

PURPOSE: We retrospectively compared the diagnostic performance of contrast-enhanced ultrasonography (CEUS) and contrast-enhanced computer tomography-magnetic resonance imaging (CT/MRI) for recurrent hepatocellular carcinoma (HCC) after curative treatment. MATERIALS AND METHODS: After curative treatment with 421 ultrasound (US) detected lesions, 303 HCC patients underwent both CEUS and CT/MRI. Each lesion was assigned a Liver Imaging Reporting and Data System (LI-RADS) category according to CEUS and CT/MRI LI-RADS. Receiver-operating characteristic (ROC) curves were computed to determine the optimal diagnosis algorithms for CEUS, CT and MRI. The diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were compared between CEUS and CT/MRI. RESULTS: Among the 421 lesions, 218 were diagnosed as recurrent HCC, whereas 203 lesions were diagnosed as benign. In recurrent HCC, CEUS detected more arterial hyperenhancement (APHE) and washout than CT and more APHE than MRI. CEUS yielded better diagnostic performance than CT (AUC: 0.981 vs. 0.958) (p = 0.024) comparable diagnostic performance to MRI (AUC: 0.952 vs. 0.933) (p > 0.05) when using their optimal diagnostic criteria. CEUS missed 12 recurrent HCCs, CT missed one, and MRI missed none. The detection rate of recurrent HCC on CEUS (94.8%, 218/230) was lower than that on CT/MRI (99.6%, 259/260) (p = 0.001). Lesions located on the US blind spots and visualization score C would hinder the ability of CEUS to detect recurrent HCC. CONCLUSION: CEUS demonstrated excellent diagnostic performance but an inferior detection rate for recurrent HCC. CEUS and CT/MRI played a complementary role in the detection and characterization of recurrent HCC.

Contrast-enhanced US to Improve Diagnostic Performance of O-RADS US Risk Stratification System for Malignancy.

Background The Ovarian-Adnexal Reporting and Data System (O-RADS) has limited specificity for malignancy. Contrast-enhanced US can help distinguish malignant from benign lesions, but its added value to O-RADS has not yet been assessed. Purpose To establish a diagnostic model combining O-RADS and contrast-enhanced US and to validate whether O-RADS plus contrast-enhanced US has a better diagnostic performance than O-RADS alone. Materials and Methods This prospective study included participants from May 2018 to March 2021 who underwent contrast-enhanced US before surgery and had lesions categorized as O-RADS 3, 4, or 5 by US, with a histopathologic reference standard. From April 2021 to July 2022, participants with pathologically confirmed ovarian-adnexal lesions were recruited for the validation group. In the pilot group, the initial enhancement time and enhancement intensity in comparison with the uterine myometrium, contrast agent distribution pattern, and dynamic changes in enhancement of lesions were assessed. Contrast-enhanced US features were used to calculate contrast-enhanced US scores for benign (score ≤2) and malignant (score ≥4) lesions. Lesions were then re-rated according to O-RADS category plus contrast-enhanced US scores. Receiver operating characteristic curves were constructed and compared using the DeLong method. The combined system was validated in an independent group. Results The pilot group included 76 women (mean age, 44 years ± 13 [SD]), and the validation group included 46 women (mean age, 42 years ± 14). Differences in initial enhancement time (P < .001), enhancement intensity (P < .001), and dynamic changes in enhancement (P < .001) between benign and malignant lesions were observed in the pilot group. Contrast-enhanced US scores were calculated using these features. The O-RADS risk stratification was upgraded one level for contrast-enhanced US scores of 4 or more and downgraded one level for contrast-enhanced US scores of 2 or less. In the validation group, the diagnostic performance of O-RADS plus contrast-enhanced US score was higher (area under the receiver operating characteristic curve [AUC] = 0.93) than O-RADS (AUC = 0.71, P < .001). Conclusion Contrast-enhanced US improved the diagnostic performance for malignancy of the O-RADS categories 3-5. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grant in this issue.

Surveillance for malignant progression of LI-RADS version 2017 category 3/4 nodules using contrast-enhanced ultrasound.

OBJECTIVES: To identify the risk factors for predicting the malignant progression of LR-3/4 observations on the baseline and contrast-enhanced ultrasound (CEUS). METHODS: In total, 245 liver nodules assigned to LR-3/4 in 192 patients from January 2010 to December 2016 were followed up by baseline US and CEUS. The differences in the rate and time of progression to hepatocellular carcinoma (HCC) among subcategories (defined as P1-P7) of LR-3/4 in CEUS Liver Imaging Reporting and Data System (LI-RADS) were analyzed. The risk factors to predict progression to HCC were analyzed by univariate and multivariate Cox proportional hazard model analysis. RESULTS: A total of 40.3% of LR-3 nodules and 78.9% of LR-4 nodules eventually progressed to HCC. The cumulative incidence of progression was significantly higher for LR-4 than LR-3 (p < 0.001). The rate of progression was 81.2% in nodules with arterial phase hyperenhancement (APHE), 64.7% in nodules with late and mild washout, and 100% in nodules with both characteristics. The overall progression rate and median progression time of subcategory P1 nodules (LR-3a) were lower (38.0% vs. 47.6-100.0%) and later (25.1 months vs. 2.0-16.3 months) than those of other subcategories. The cumulative incidence of progression of LR-3a (P1), LR-3b (P2/3/4), and LR-4 (P5/6/7) categories were 38.0%, 52.9%, and 78.9%. The risk factors of HCC progression were Visualization score B/C, CEUS characteristics (APHE, washout), LR-4 classification, echo changes, and definite growth. CONCLUSION: CEUS is a useful surveillance tool for nodules at risk of HCC. CEUS characteristics, LI-RADS classification, and changes in nodules provide useful information for the progress of LR-3/4 nodules. CLINICAL RELEVANCE STATEMENT: CEUS characteristics, LI-RADS classification, and nodule changes provide important predictions for LR-3/4 nodule progression to HCC, which may stratify the risk of malignant progression to provide a more optimized and refined, more cost-effective, and time-efficient management strategy for patients. KEY POINTS: • CEUS is a useful surveillance tool for nodules at risk of HCC, CEUS LI-RADS successfully stratified the risks that progress to HCC. • CEUS characteristics, LI-RADS classification, and changes in nodules can provide important information on the progression of LR-3/4 nodules, which may be helpful for a more optimized and refined management strategy.

US LI-RADS in surveillance for recurrent hepatocellular carcinoma after curative treatment.

OBJECTIVES: To investigate the performance of US LI-RADS in surveillance for recurrent hepatocellular carcinoma (RHCC) after curative treatment. MATERIALS AND METHODS: This study enrolled 644 patients between January 2018 and August 2018 as a derivation cohort, and 397 patients from September 2018 to December 2018 as a validation cohort. The US surveillance after HCC curative treatment was performed. The US LI-RADS observation categories and visualization scores were analyzed. Four criteria using US LI-RADS or Alpha-fetoprotein (AFP) as the surveillance algorithm were evaluated. The sensitivity, specificity, and negative predictive value (NPV) were calculated. RESULTS: A total of 212 (32.9%) patients in derivation cohort and 158 (39.8%) patients in validation cohort were detected to have RHCCs. The criterion of US-2/3 or AFP ≥ 20 µg/L had higher sensitivity (derivation, 96.7% vs 92.9% vs 81.1% vs 90.6%; validation, 96.2% vs 90.5% vs 80.4% vs 89.9%) and NPV (derivation, 95.7% vs 93.3% vs 88.0% vs 91.8%; validation, 94.6% vs 89.4% vs 83.6% vs 89.0%), but lower specificity (derivation, 35.9% vs 48.2% vs 67.6% vs 51.9%; validation, 43.5% vs 52.7% vs 66.1% vs 54.0%) than criterion of US-2/3, US-3, and US-3 or AFP ≥ 20 µg/L. Analysis of the visualization score subgroups confirmed that the sensitivity (89.2-97.6% vs 81.0-83.3%) and NPV(88.4-98.0% vs 80.0-83.3%) of score A and score B groups were higher than score C group in criterion of US-2/3 in both two cohorts. CONCLUSIONS: In the surveillance for RHCC, US LI-RADS with AFP had a high sensitivity and NPV when US-2/3 or AFP ≥ 20 µg/L was considered a criterion. CLINICAL RELEVANCE STATEMENT: The criterion of US-2/3 or AFP ≥ 20 µg/L improves sensitivity and NPV for RHCC surveillance, which provides a valuable reference for patients in RHCC surveillance after curative treatment. KEY POINTS: • US LI-RADS with AFP had high sensitivity and NPV in surveillance for RHCC when considering US-2/3 or AFP ≥ 20 µg/L as a criterion. • After US with AFP surveillance, patients with US-2/3 or AFP ≥ 20 µg/L should perform enhanced imaging for confirmative diagnosis. Patients with US-1 or AFP < 20 µg/L continue to repeat US with AFP surveillance. • Patients with risk factors for poor visualization scores limited the sensitivity of US surveillance in RHCC.

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer.

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher's exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis.

Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management.

Importance: To optimize the integration of artificial intelligence (AI) decision aids and reduce workload in thyroid nodule management, it is critical to incorporate personalized AI into the decision-making processes of radiologists with varying levels of expertise. Objective: To develop an optimized integration of AI decision aids for reducing radiologists' workload while maintaining diagnostic performance compared with traditional AI-assisted strategy. Design, Setting, and Participants: In this diagnostic study, a retrospective set of 1754 ultrasonographic images of 1048 patients with 1754 thyroid nodules from July 1, 2018, to July 31, 2019, was used to build an optimized strategy based on how 16 junior and senior radiologists incorporated AI-assisted diagnosis results with different image features. In the prospective set of this diagnostic study, 300 ultrasonographic images of 268 patients with 300 thyroid nodules from May 1 to December 31, 2021, were used to compare the optimized strategy with the traditional all-AI strategy in terms of diagnostic performance and workload reduction. Data analyses were completed in September 2022. Main Outcomes and Measures: The retrospective set of images was used to develop an optimized integration of AI decision aids for junior and senior radiologists based on the selection of AI-assisted significant or nonsignificant features. In the prospective set of images, the diagnostic performance, time-based cost, and assisted diagnosis were compared between the optimized strategy and the traditional all-AI strategy. Results: The retrospective set included 1754 ultrasonographic images from 1048 patients (mean [SD] age, 42.1 [13.2] years; 749 women [71.5%]) with 1754 thyroid nodules (mean [SD] size, 16.4 [10.6] mm); 748 nodules (42.6%) were benign, and 1006 (57.4%) were malignant. The prospective set included 300 ultrasonographic images from 268 patients (mean [SD] age, 41.7 [14.1] years; 194 women [72.4%]) with 300 thyroid nodules (mean [SD] size, 17.2 [6.8] mm); 125 nodules (41.7%) were benign, and 175 (58.3%) were malignant. For junior radiologists, the ultrasonographic features that were not improved by AI assistance included cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, and nodules smaller than 5 mm, whereas for senior radiologists the features that were not improved by AI assistance were cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, very hypoechoic nodules, nodules taller than wide, lobulated or irregular nodules, and extrathyroidal extension. Compared with the traditional all-AI strategy, the optimized strategy was associated with increased mean task completion times for junior radiologists (reader 11, from 15.2 seconds [95% CI, 13.2-17.2 seconds] to 19.4 seconds [95% CI, 15.6-23.3 seconds]; reader 12, from 12.7 seconds [95% CI, 11.4-13.9 seconds] to 15.6 seconds [95% CI, 13.6-17.7 seconds]), but shorter times for senior radiologists (reader 14, from 19.4 seconds [95% CI, 18.1-20.7 seconds] to 16.8 seconds [95% CI, 15.3-18.3 seconds]; reader 16, from 12.5 seconds [95% CI, 12.1-12.9 seconds] to 10.0 seconds [95% CI, 9.5-10.5 seconds]). There was no significant difference in sensitivity (range, 91%-100%) or specificity (range, 94%-98%) between the 2 strategies for readers 11 to 16. Conclusions and Relevance: This diagnostic study suggests that an optimized AI strategy in thyroid nodule management may reduce diagnostic time-based costs without sacrificing diagnostic accuracy for senior radiologists, while the traditional all-AI strategy may still be more beneficial for junior radiologists.

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer.

Dysregulation of signaling pathways plays an essential role in cancer. However, there is not a comprehensive understanding on how oncogenic signaling pathways affect the occurrence and development with a common molecular mechanism of pan-cancer. Here, we investigated the oncogenic signaling pathway dysregulation by using multi-omics data on patients from TCGA from a pan-cancer perspective to identify commonalities across different cancer types. First, the pathway dysregulation profile was constructed by integrating typical oncogenic signaling pathways and the gene expression of TCGA samples, and four molecular subtypes with significant phenotypic and clinical differences induced by different oncogenic signaling pathways were identified: TGF-ß+ subtype; cell cycle, MYC, and NF2- subtype; cell cycle and TP53+ subtype; and TGF-ß and TP53- subtype. Patients in the TGF-ß+ subtype have the best prognosis; meanwhile, the TGF-ß+ subtype is associated with hypomethylation. Moreover, there is a higher level of immune cell infiltration but a slightly worse survival prognosis in the cell cycle, MYC, and NF2- subtype patients due to the effect of T-cell dysfunction. Then, the prognosis and subtype classifiers constructed by differential genes on a multi-omics level show great performance, indicating that these genes can be considered as biomarkers with potential therapeutic and prognostic significance for cancers. In summary, our study identified four oncogenic signaling pathway-driven patterns presented as molecular subtypes and their related potential prognostic biomarkers by integrating multiple omics data. Our discovery provides a perspective for understanding the role of oncogenic signaling pathways in pan-cancer.

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To understand the influence of roots of understory plant entering litter layer on litter decomposition in forest ecosystems, we examined the effects of different treatments of Lolium multiflorum root biomass on microorganisms and enzyme activities during leaf litter decomposition of Symplocos setchuensis, a dominant species in a mid-subtropical evergreen broad-leaved forest, through a litter bag simulation experiment. Results showed that diversity index of bacterial and fungal communities of leaf litter surface under three treatments, i.e. no root (N), less roots (L), more roots (M), in a 240-day decomposition process showed the following pattern: M > L > N. The effects of these different root biomass treatments on the composition and quantity of fungal community were more significant than those on bacterial community. The biomass of living roots growing in the litter bag gradually decreased at the end of the growing season of L. multiflorum. The impacts of root growth on the composition of the fungal community gradually decreased during decomposition. At the same decomposition stage, the activities of acid phosphatase, ß-glucosidase, polyphenol oxidase, and peroxidase on the litter surface were higher in the treatments with roots than that without roots. These results indicated that root growth could change the composition and quantity of microbial communities and increase the extracellular enzyme activities of microbes, and thus stimulating litter decomposition.