Objectives
To construct a preoperative model for survival prediction in intrahepatic cholangiocarcinoma (ICC) patients using ultrasound (US) based radiographic‐radiomics signatures.
Methods
Between ...April 2010 and September 2015, 170 patients with ICC who underwent curative resection were retrospectively recruited. Overall survival (OS)‐related radiographic signatures and radiomics signatures based on preoperative US were built and assessed through a time‐dependent receiver operating characteristic curve analysis. A nomogram was developed based on the selected predictors from the radiographic‐radiomics signatures and clinical and laboratory results of the training cohort (n = 127), validated in an independent testing cohort (n = 43) by the concordance index (C‐index), and compared with the Tumor Node Metastasis (TNM) cancer staging system as well as the radiographic and radiomics nomograms.
Results
The median areas under the curve of the radiomics signature and radiographic signature were higher than that of the TNM staging system in the testing cohort, although the values were not significantly different (0.76–0.82 versus 0.62, P = .485 and .264). The preoperative nomogram with CA 19‐9, sex, ascites, radiomics signature, and radiographic signature had C‐indexes of 0.72 and 0.75 in the training and testing cohorts, respectively, and it had significantly higher predictive performance than the 8th TNM staging system in the testing cohort (C‐index: 0.75 versus 0.67, P = .004) and a higher C‐index than the radiomics nomograms (0.75 versus 0.68, P = .044).
Conclusions
The preoperative nomogram integrated with the radiographic‐radiomics signature demonstrated good predictive performance for OS in ICC and was superior to the 8th TNM staging system.
Purpose
To develop an ultrasound (US)-based radiomics score for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).
Methods
Between January 1, 2012, and October ...31, 2017, a total of 482 HCC patients who underwent contrast-enhanced ultrasound (CEUS) were retrospectively reviewed. The study population was divided into a training cohort (
n
= 341) and a validation cohort (
n
= 141) based on a cutoff time of January 1, 2016. Radiomics features were extracted from the grayscale US images of HCC. After features selection, a radiomics score was developed from the training cohort. The incremental value of the radiomics score to the clinic-pathological factors for MVI prediction was assessed in the validation cohort with respect to discrimination, calibration, and clinical usefulness.
Results
The US-based radiomics score consisted of six selected features. Multivariate logistic regression analysis showed that the radiomics score, alpha-fetoprotein (AFP), and tumor size were independent predictors of MVI. The radiomics nomogram (based on the three factors) showed better performance for MVI detection (area under the curve AUC 0.7310.647, 0.815 than the clinical nomogram (based on AFP and tumor size) (0.634 0.543, 0.724) (
p
= 0.015). Both nomograms showed good calibration. Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram.
Conclusion
The US-based radiomics score was an independent predictor of MVI in HCC. Combining the radiomics score with clinical factors improved the prediction efficacy.
Key points
• Radiomics can be applied in US images.
• US-based radiomics score was an independent predictor of MVI.
• Radiomics nomogram incorporated with the radiomics score showed good performance for MVI prediction.
Lesion localization and tracking are critical for accurate, automated medical imaging analysis. Contrast-enhanced ultrasound (CEUS) significantly enriches traditional B-mode ultrasound with contrast ...agents to provide high-resolution, real-time images of blood flow in tissues and organs. However, many trackers, designed primarily for natural RGB or B-mode ultrasound images, underutilize the extensive data from dual-screen enhanced images and fail to account for respiratory motion, thus facing challenges in achieving accurate target tracking. To address the existing challenges, we propose an adaptive-weighted dual mapping (ADMNet), an online tracking framework tailored for CEUS. Firstly, we introduced a novel Multimodal Atrous Attention Fusion (MAAF) module, innovatively designed to adapt the weightage between B-mode and enhanced images in dual-screen CEUS, reflecting the clinician's dynamic focus shifts between screens. Secondly, we proposed a Respiratory Motion Compensation (RMC) module to correct motion trajectory interferences due to respiratory motion, effectively leveraging temporal information. We utilized two newly established CEUS datasets, totaling 35,082 frames, to benchmark the ADMNet against various advanced B-mode ultrasound trackers. Our extensive experiments revealed that ADMNet achieves new state-of-the-art performance in CEUS tracking. Ablation studies and visualizations further underline the effectiveness of MAAF and RMC modules, demonstrating the promising potential of ADMNet in clinical CEUS tracing, thus providing novel research avenues in this field.
Materials exhibiting excitation wavelength‐dependent photoluminescence (Ex‐De PL) in the visible region have potential applications in bioimaging, optoelectronics and anti‐counterfeiting. Two ...multifunctional, chiral Au(NHC)2Au(CN)2 (NHC=(4R,5R)/(4S,5S)‐1,3‐dimethyl‐4,5‐diphenyl‐4,5‐dihydro‐imidazolin‐2‐ylidene) complex double salts display Ex‐De circularly polarized luminescence (CPL) in doped polymer films and in ground powder. Emission maxima can be dynamically tuned from 440 to 530 nm by changing the excitation wavelength. The continuously tunable photoluminescence is proposed to originate from multiple emissive excited states as a result of the existence of varied AuI⋅⋅⋅AuI distances in ground state. The steric properties of the NHC ligand are crucial to the tuning of AuI⋅⋅⋅AuI distances. An anti‐counterfeiting application using these two salts is demonstrated.
Ex‐De CPL: By modulating the strength of AuI⋅⋅⋅AuI and Coulombic interactions by adjusting steric hindrance of the ligand, together with the materials processing, the two chiral enantiomers of Au(NHC)2Au(CN)2 double salts in PMMA film or as ground powder exhibit mirror‐image excitation wavelength‐dependent photoluminescence (Ex‐De CPL).
Objective
To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.
Materials and Methods
This prospective study consisted of 144 patients with chronic ...hepatitis B. Ultrasomics—high-throughput quantitative data from ultrasound imaging of liver fibrosis—were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering. Features were selected using diagnostic tests for fibrosis, activity and steatosis stage, with the histopathological results as the reference. The fibrosis staging performance of ultrasomics models with combinations of the selected features was evaluated with machine-learning algorithms by calculating the area under the receiver-operator characteristic curve (AUC).
Results
ORF and CEMF features had better predictive power than conventional radiomics for liver fibrosis stage (both
p
< 0.01). CEMF features exhibited the highest diagnostic value for activity stage (both
p
< 0.05), and ORF had the best diagnostic value for steatosis stage (both
p
< 0.01). The machine-learning classifiers of adaptive boosting, random forest and support vector machine were found to be optimal algorithms with better (all mean AUCs = 0.85) and more stable performance (coefficient of variation = 0.01–0.02) for fibrosis staging than decision tree, logistic regression and neural network (mean AUC = 0.61–0.72, CV = 0.07–0.08). The multiparametric ultrasomics model achieved much better performance (mean AUC values of 0.78–0.85) than the features from a single modality in discriminating significant fibrosis (≥ F2).
Conclusion
Machine-learning-based analysis of multiparametric ultrasomics can help improve the discrimination of significant fibrosis compared with mono or dual modalities.
Key Points
• Multiparametric ultrasomics has achieved much better performance in the discrimination of significant fibrosis (≥ F2) than the single modality of conventional radiomics, original radiofrequency and contrast-enhanced micro-flow.
• Adaptive boosting, random forest and support vector machine are the optimal algorithms for machine learning.
To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after ...curative treatment.
In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI).
By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E.
The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.
Background and Aim
This study aims to construct a strategy that uses assistance from artificial intelligence (AI) to assist radiologists in the identification of malignant versus benign focal liver ...lesions (FLLs) using contrast‐enhanced ultrasound (CEUS).
Methods
A training set (patients = 363) and a testing set (patients = 211) were collected from our institute. On four‐phase CEUS images in the training set, a composite deep learning architecture was trained and tuned for differentiating malignant and benign FLLs. In the test dataset, AI performance was evaluated by comparison with radiologists with varied levels of experience. Based on the comparison, an AI assistance strategy was constructed, and its usefulness in reducing CEUS interobserver heterogeneity was further tested.
Results
In the test set, to identify malignant versus benign FLLs, AI achieved an area under the curve of 0.934 (95% CI 0.890–0.978) with an accuracy of 91.0%. Comparing with radiologists reviewing videos along with complementary patient information, AI outperformed residents (82.9–84.4%, P = 0.038) and matched the performance of experts (87.2–88.2%, P = 0.438). Due to the higher positive predictive value (PPV) (AI: 95.6% vs residents: 88.6–89.7%, P = 0.056), an AI strategy was defined to improve the malignant diagnosis. With the assistance of AI, radiologists exhibited a sensitivity improvement of 97.0–99.4% (P < 0.05) and an accuracy of 91.0–92.9% (P = 0.008–0.189), which was comparable with that of the experts (P = 0.904).
Conclusions
The CEUS‐based AI strategy improved the performance of residents and reduced CEUS's interobserver heterogeneity in the differentiation of benign and malignant FLLs.
Contrast-enhanced ultrasound (CEUS) LI-RADS assigns category LR-M to observations that are definitely or probably malignant but that on imaging are not specific for hepatocellular carcinoma (HCC). A ...high percentage of LR-M observations represent HCC.
The purpose of this study was to retrospectively evaluate the utility of additional features, beyond conventional LI-RADS major features, for detecting HCC among LR-M observations on CEUS.
This retrospective study included 174 patients (145 men, 29 women; mean age, 53 years) at high risk of HCC who underwent CEUS from August 2014 to June 2016 that showed an LR-M observation according to CEUS LI-RADS version 2017. Two radiologists independently assessed CEUS images for major features and four additional features (chaotic vessels, peripheral circular artery, clear boundary of tumor enhancement, clear boundary of intratumoral unenhanced area). The diagnostic performance of four proposed criteria for the detection of HCC among LR-M observations was assessed. The impact of criteria based on the additional findings on detection of HCC was further explored. Histology or composite imaging and clinical follow-up were the reference standards.
The 174 LR-M observations included 142 HCCs and 32 non-HCC lesions (20 intrahepatic cholangiocarcinomas, five combined hepatocellular-cholangiocarcinomas, seven benign lesions). Interreader agreement on the additional features ranged from κ = 0.65 to κ = 0.88. Two of the additional features had excellent PPV for HCC: chaotic vessels (94.8%) and peripheral circular arteries (98.1%). The presence of either of these two additional features had sensitivity of 50.7%, specificity of 90.6%, PPV of 96.0%, and NPV of 29.3% for HCC. Three other criteria incorporating variations of major LI-RADS features but not the additional features had sensitivities of 55.6-96.5%, specificities of 49.6-68.8%, PPVs of 87.8-90.6%, and NPVs of 25.0-75.0%. On the basis of criteria that included additional features, 75 of 174 LR-M observations were recategorized LR-5; 72 of the 75 were HCC.
The presence of chaotic vessels and/or peripheral circular artery had high specificity and PPV for HCC among LR-M observations. Other explored criteria based on major features did not have higher specificity or PPV.
Clinical adoption of the additional CEUS features could help establish the diagnosis of HCC noninvasively and avoid the need for biopsy of LR-M observations.
Purpose
To test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC).
Methods
We included primary HCC patient ...undergone curative therapies, using early recurrence as endpoint. Four datasets were constructed: 109 images from hospital #1 for training (set 1: 1-mm image slice thickness), 47 images from hospital #1 for internal validation (sets 2 and 3: 1-mm and 10-mm image slice thicknesses, respectively), and 47 images from hospital #2 for external validation (set 4: vastly different from training dataset). A radiomics model was constructed. Radiomics technical reproducibility was measured by overfitting and calibration deviation in external validation dataset. The influence of slice thickness on reproducibility was evaluated in two internal validation datasets.
Results
Compared with set 1, the model in set 2 indicated favorable prediction efficiency (the area under the curve 0.79 vs. 0.80,
P
= 0.47) and good calibration (unreliability statistic
U
:
P
= 0.33). However, in set 4, significant overfitting (0.63 vs. 0.80,
P
< 0.01) and calibration deviation (
U
:
P
< 0.01) were observed. Similar poor performance was also observed in set 3 (0.56 vs. 0.80,
P
= 0.02;
U
:
P
< 0.01).
Conclusions
CT-based radiomics has poor reproducibility between centers. Image heterogeneity, such as slice thickness, can be a significant influencing factor.
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