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![Multiparametric MRI histogram analysis in HCC. Example shows a 54-year-old male patient with cirrhosis secondary to chronic hepatitis B virus infection and HCC. a Representative magnified parametric maps of dynamic contrast-enhanced MRI (DCE-MRI, top row) and blood blood-oxygenation level-dependent (BOLD) and tissue-oxygenation level-dependent MRI (TOLD; bottom row) in a large (8.3 cm) HCC. Location of the tumor within the liver is indicated by the white arrow on the T2-weighted image (bottom row, right). Substantial intratumor heterogeneity was observed, in particular on the arterial, portal, and total flow (Fa, Fp, and Ft) maps of DCE-MRI and R2* maps of BOLD. b Histograms of Fa, R2* pre O2, R1 pre O2, and ADC in the same lesion. The extensive heterogeneity observed in the parameter maps is also reflected in the histograms, as illustrated by the fat tails and pronounced skewness. ADC apparent diffusion coefficient, ART arterial fraction, DV distribution volume, Fa arterial flow, Fp portal flow, Ft total flow, MTT mean transit time, R1 longitudinal relaxation rate, R2* transverse relaxation rate
Adapted from Hectors et al. [69]](publication/338515858/figure/fig5/AS:941402989015072@1601459371932/Multiparametric-MRI-histogram-analysis-in-HCC-Example-shows-a-54-year-old-male-patient.png)
Multiparametric MRI histogram analysis in HCC. Example shows a 54-year-old male patient with cirrhosis secondary to chronic hepatitis B virus infection and HCC. a Representative magnified parametric maps of dynamic contrast-enhanced MRI (DCE-MRI, top row) and blood blood-oxygenation level-dependent (BOLD) and tissue-oxygenation level-dependent MRI (TOLD; bottom row) in a large (8.3 cm) HCC. Location of the tumor within the liver is indicated by the white arrow on the T2-weighted image (bottom row, right). Substantial intratumor heterogeneity was observed, in particular on the arterial, portal, and total flow (Fa, Fp, and Ft) maps of DCE-MRI and R2* maps of BOLD. b Histograms of Fa, R2* pre O2, R1 pre O2, and ADC in the same lesion. The extensive heterogeneity observed in the parameter maps is also reflected in the histograms, as illustrated by the fat tails and pronounced skewness. ADC apparent diffusion coefficient, ART arterial fraction, DV distribution volume, Fa arterial flow, Fp portal flow, Ft total flow, MTT mean transit time, R1 longitudinal relaxation rate, R2* transverse relaxation rate Adapted from Hectors et al. [69]
Source publication
The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of...
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Citations
... Radiomics has emerged as a tool to diagnose, risk stratify, and predict prognosis among patients with HCC [45,46] . For instance, several studies have focused on developing preoperative models to predict microvascular invasion, an important prognostic factor that is traditionally identified only after surgery on pathological examination [47,48] . ...
The rapid evolution of modern technology has made artificial intelligence (AI) an important emerging tool in healthcare. AI, which is a broad field of computer science, can be used to develop systems or machines equipped with the ability to tackle tasks that traditionally necessitate human intelligence. AI can be used to perform multifaceted tasks that involve the synthesis of large amounts of data with the generation of solutions, algorithms, and decision support tools. Various AI approaches, including machine learning (ML) and natural language processing (NLP), are increasingly being used to analyze vast healthcare datasets. In addition, visual AI has the potential to revolutionize surgery and the intraoperative experience for surgeons through augmented reality enhancing surgical navigation in real-time. Specific applications of AI in hepatobiliary tumors such as hepatocellular carcinoma and biliary tract cancer can improve patient diagnosis, prognostic risk stratification, as well as treatment allocation based on ML-based models. The integration of radiomics data and AI models can also improve clinical decision making. We herein review how AI may be of particular interest in the care of patients with complex cancers, such as hepatobiliary tumors, as these patients often require a multimodal treatment approach.
... Radiomics, first proposed by Lambin et al. [11], allows for in-depth tumor phenotyping and quantification of lesion heterogeneity, providing high-throughput quantitative data from radiological images that are invisible to the human eye. Several studies have explored the potential value of radiomics in abdominal oncology, with promising results for lesion characterization, assessment of therapeutic response, and patient survival [12][13][14]. The multi-phase enhanced CT images show similarities between HP and GISTs, making it difficult for radiologists to visually differentiate. ...
Background
To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the model’s performance.
Methods
Multi-phase CT radiological data were retrospectively collected from 94 patients. Of these, 40 with HP and 54 with GISTs were enrolled between April 2017 and November 2021. One experienced radiologist manually delineated the volume of interest and then resampled the voxel size of the images to 0.5 × 0.5 × 0.5 mm³, 1 × 1 × 1 mm³, and 2 × 2 × 2 mm³, respectively. Radiomics features were extracted using PyRadiomics, resulting in 1218 features from each phase image. The datasets were randomly divided into training set (n = 66) and validation set (n = 28) at a 7:3 ratio. After applying multiple feature selection methods, the optimal features were screened. Radial basis kernel function-based support vector machine (RBF-SVM) was used as the classifier, and model performance was evaluated using the area under the receiver operating curve (AUC) analysis, as well as accuracy, sensitivity, and specificity.
Results
The combined phase model performed better than the other phase models, and the resampling method of 0.5 × 0.5 × 0.5 mm³ achieved the highest performance with an AUC of 0.953 (0.881-1), accuracy of 0.929, sensitivity of 0.938, and specificity of 0.917 in the validation set. The Delong test showed no significant difference in AUCs among the three resampling methods, with p > 0.05.
Conclusions
Radiomics can effectively differentiate between HP and GISTs on CT images, and the diagnostic performance of radiomics is minimally affected by different resampling methods.
... The process typically involves identifying and segmenting a region of interest (ROI), which can be done manually or using automated algorithms (17). From these segmented regions, high-dimensional features are extracted, falling into two main categories: semantic features, which describe morphological aspects of lesions, and agnostic features, which are mathematical (18,19). Functioning in diverse capacities such as tumor classification, survival prediction, and therapy response assessment, radiomic signatures are pivotal in crafting imaging biomarkers for personalized therapy (12,20). ...
Background
Radiomics, an emerging field, presents a promising avenue for the accurate prediction of biomarkers in different solid cancers. Lung cancer remains a significant global health challenge, contributing substantially to cancer-related mortality. Accurate assessment of Ki-67, a marker reflecting cellular proliferation, is crucial for evaluating tumor aggressiveness and treatment responsiveness, particularly in non-small cell lung cancer (NSCLC).
Methods
A systematic review and meta-analysis conducted following the preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Two authors independently conducted a literature search until September 23, 2023, in PubMed, Embase, and Web of Science. The focus was on identifying radiomics studies that predict Ki-67 expression in lung cancer. We evaluated quality using both Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. For statistical analysis in the meta-analysis, we used STATA 14.2 to assess sensitivity, specificity, heterogeneity, and diagnostic values.
Results
Ten retrospective studies were pooled in the meta-analysis. The findings demonstrated that the use of computed tomography (CT) scan-based radiomics for predicting Ki-67 expression in lung cancer exhibited encouraging diagnostic performance. Pooled sensitivity, specificity, and area under the curve (AUC) in training cohorts were 0.78, 0.81, and 0.85, respectively. In validation cohorts, these values were 0.78, 0.70, and 0.81. Quality assessment using QUADAS-2 and RQS indicated generally acceptable study quality. Heterogeneity in training cohorts, attributed to factors like contrast-enhanced CT scans and specific Ki-67 thresholds, was observed. Notably, publication bias was detected in the training cohort, indicating that positive results are more likely to be published than non-significant or negative results. Thus, journals are encouraged to publish negative results as well.
Conclusion
In summary, CT-based radiomics exhibit promise in predicting Ki-67 expression in lung cancer. While the results suggest potential clinical utility, additional research efforts should concentrate on enhancing diagnostic accuracy. This could pave the way for the integration of radiomics methods as a less invasive alternative to current procedures like biopsy and surgery in the assessment of Ki-67 expression.
... The utilization of radiomics has expanded significantly over the years, particularly within the domain of cancer research, such as in the liver [11], prostate [12], breast [13], lung [14], and bone metastasis [15]. The use of CT-based radiomics-a quantitative approach for tissue characterization based on histogram analysis and textural features-has increased [16][17][18]. Many reports have demonstrated the potential of radiomics-based techniques for analyzing malignancies [16][17][18]. ...
... The use of CT-based radiomics-a quantitative approach for tissue characterization based on histogram analysis and textural features-has increased [16][17][18]. Many reports have demonstrated the potential of radiomics-based techniques for analyzing malignancies [16][17][18]. One recent study reported a feasible method for detecting diffuse splenic infiltration in lymphoma through the use of spleen-to-liver attenuation ratio in addition to splenic volume [7]. ...
Background: We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. Methods: We retrospectively analyzed CT studies from 139 patients (age range 26–93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. Results: Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. Conclusions: CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.
... Radiomics is the emerging field involving the extraction of quantitative features and subsequent analysis of radiological images using AI models. 40 The process of training a radiomics model has been extensively described in literature [41][42][43][44][45] and can be summarised into five steps: image requisition and preprocessing, segmentation, feature extraction, model training and model validation. 44 Image requisition involves selecting imaging data and identifying potential features causing variability when training the AI model. ...
... 40 The process of training a radiomics model has been extensively described in literature [41][42][43][44][45] and can be summarised into five steps: image requisition and preprocessing, segmentation, feature extraction, model training and model validation. 44 Image requisition involves selecting imaging data and identifying potential features causing variability when training the AI model. Crucially, developing a protocol for image requisition requires a balance of standardisation (to reduce noise and confounding) and variability (to ensure generalisability of the model in a clinical setting). ...
Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. This review explores the recent progress in the application of artificial intelligence (AI) in radiological diagnosis of HCC. The Barcelona Classification of Liver Cancer criteria guides treatment decisions based on tumour characteristics and liver function indicators, but HCC often remains undetected until intermediate or advanced stages, limiting treatment options and patient outcomes. Timely and accurate diagnostic methods are crucial for enabling curative therapies and improving patient outcomes. AI, particularly deep learning and neural network models, has shown promise in the radiological detection of HCC. AI offers several advantages in HCC diagnosis, including reducing diagnostic variability, optimising data analysis and reallocating healthcare resources. By providing objective and consistent analysis of imaging data, AI can overcome the limitations of human interpretation and enhance the accuracy of HCC diagnosis. Furthermore, AI systems can assist healthcare professionals in managing the increasing workload by serving as a reliable diagnostic tool. Integration of AI with information systems enables comprehensive analysis of patient data, facilitating more informed and reliable diagnoses. The advancements in AI-based radiological diagnosis hold significant potential to improve early detection, treatment selection and patient outcomes in HCC. Further research and clinical implementation of AI models in routine practice are necessary to harness the full potential of this technology in HCC management.
... In radiomic analysis, a statistical model is constructed to predict clinical events, such as prognosis or response. In clinical practice, radiomics technology is widely used to determine the prognosis of HCC [33,34]. One prediction model was established using preoperative enhanced CT images and machine learning, which can accurately predict the preoperative pathological grade of liver cancer [35,36]. ...
Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan-Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136-2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127-1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979-1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.
... Thus, the perspectives for establishing a relationship between previously defined imaging criteria and various HCC phenotypes are less encouraging using conventional imaging methods, leaving the field wide open for other emerging techniques. One such approach might be through radiomics, which could quantify tumor heterogeneity and predict tumor biology, molecular profiles, post-therapy response, and outcomes [31]. A multifaceted diagnostic approach using both imaging and biopsy when required appears sensible, with the ultimate goal of gathering as much consequential information as possible to increase the odds of a favorable outcome. ...
The field of hepatocellular carcinoma (HCC) has faced significant change on multiple levels in the past few years. The increasing emphasis on the various HCC phenotypes and the emergence of novel, specific therapies have slowly paved the way for a personalized approach to primary liver cancer. In this light, the role of percutaneous liver biopsy of focal lesions has shifted from a purely confirmatory method to a technique capable of providing an in-depth characterization of any nodule. Cancer subtype, gene expression, the mutational profile, and tissue biomarkers might soon become widely available through biopsy. However, indications, expectations, and techniques might suffer changes as the aim of the biopsy evolves from providing minimal proof of the disease to high-quality specimens for extensive analysis. Consequently, a revamped position of tissue biopsy is expected in HCC, following the reign of non-invasive imaging-only diagnosis. Moreover, given the advances in techniques that have recently reached the spotlight, such as liquid biopsy, concomitant use of all the available methods might gather just enough data to improve therapy selection and, ultimately, outcomes. The current review aims to discuss the changing role of liver biopsy and provide an evidence-based rationale for its use in the era of precision medicine in HCC.
... Radiomics is an emerging method for the extraction of quantitative imaging features from conventional imaging modalities that are not visible to the naked eye, and correlating these features with clinical endpoints, such as pathology and therapeutic response [50,51]. Radiomics workflow can be divided into five phases: data selection, segmentation into volumes or regions of interest, feature extraction (such as lesion size, shape, and location; histogram analysis; and texture analysis), exploratory analysis, and modeling [52]. ...
... Radiomics largely remains in the research setting, but the technique has the potential to play a pivotal role in the diagnosis, staging, and prognosis of liver disease [52]. Most effort to date has focused on liver malignancies and diffuse liver diseases. ...
The 10th Global Forum for Liver Magnetic Resonance Imaging (MRI) was held as a virtual 2-day meeting in October 2021, attended by delegates from North and South America, Asia, Australia, and Europe. Most delegates were radiologists with experience in liver MRI, with representation also from specialists in liver surgery, oncology, and hepatology.
Presentations, discussions, and working groups at the Forum focused on the following themes:
• Gadoxetic acid in clinical practice: Eastern and Western perspectives on current uses and challenges in hepatocellular carcinoma (HCC) screening/surveillance, diagnosis, and management
• Economics and outcomes of HCC imaging
• Radiomics, artificial intelligence (AI) and deep learning (DL) applications of MRI in HCC.
These themes are the subject of the current manuscript. A second manuscript discusses multidisciplinary tumor board perspectives: how to approach early-, mid-, and late-stage HCC management from the perspectives of a liver surgeon, interventional radiologist, and oncologist (Taouli et al, 2023).
Delegates voted on consensus statements that were developed by working groups on these meeting themes. A consensus was considered to be reached if at least 80% of the voting delegates agreed on the statements.
Clinical relevance statement
This review highlights the clinical applications of gadoxetic acid–enhanced MRI for liver cancer screening and diagnosis, as well as its cost-effectiveness and the applications of radiomics and AI in patients with liver cancer.
Key Points
• Interpretation of gadoxetic acid–enhanced MRI differs slightly between Eastern and Western guidelines, reflecting different regional requirements for sensitivity vs specificity.
• Emerging data are encouraging for the cost-effectiveness of gadoxetic acid–enhanced MRI in HCC screening and diagnosis, but more studies are required.
• Radiomics and artificial intelligence are likely, in the future, to contribute to the detection, staging, assessment of treatment response and prediction of prognosis of HCC—reducing the burden on radiologists and other specialists and supporting timely and targeted treatment for patients.
... The commonly used classifiers included random forest and support vector machine. Compared with the conventional methods, these algorithms are more robust tools to mine a large amount of radiomics data and grasp the data patterns to make the prediction [34,35]. In addition, before the radiomics features were subject to these machine learning algorithms, many pre-processing strategies were applied to select a subgroup of reproducible, nonredundant, and informative radiomics features, such as LASSO (4/11). ...
Objective:
To systematically review the efficacy of radiomics models derived from computed tomography (CT) or magnetic resonance imaging (MRI) in preoperative prediction of the histopathological grade of hepatocellular carcinoma (HCC).
Methods:
Systematic literature search was performed at databases of PubMed, Web of Science, Embase, and Cochrane Library up to 30 December 2022. Studies that developed a radiomics model using preoperative CT/MRI for predicting the histopathological grade of HCC were regarded as eligible. A pre-defined table was used to extract the data related to study and patient characteristics, characteristics of radiomics modelling workflow, and the model performance metrics. Radiomics quality score and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were applied for research quality evaluation.
Results:
Eleven eligible studies were included in this review, consisting of 2245 patients (range 53-494, median 165). No studies were prospectively designed and only two studies had an external test cohort. Half of the studies (five) used CT images and the other half MRI. The median number of extracted radiomics features was 328 (range: 40-1688), which was reduced to 11 (range: 1-50) after feature selection. The commonly used classifiers were logistic regression and support vector machine (both 4/11). When evaluated on the two external test cohorts, the area under the curve of the radiomics models was 0.70 and 0.77. The median radiomics quality score was 10 (range 2-13), corresponding to 28% (range 6-36%) of the full scale. Most studies showed an unclear risk of bias as evaluated by QUADAS-2.
Conclusion:
Radiomics models based on preoperative CT or MRI have the potential to be used as an imaging biomarker for prediction of HCC histopathological grade. However, improved research and reporting quality is required to ensure sufficient reliability and reproducibility prior to implementation into clinical practice.
... Recently, numerous radiomics analyses have been conducted to predict the ER of HCC after hepatectomy [7][8][9]. Most radiomics studies derived from computed tomography (CT) [10-15] and magnetic resonance imaging (MRI) with tumor features [16][17][18][19] and peritumoral features [20,21] yielded satisfactory efficacy with AUCs of 0.742-0.873. ...
Background:
It is of great importance to predict the early recurrence (ER) of hepatocellular carcinoma (HCC) after hepatectomy using preoperative imaging modalities. Nevertheless, no comparative studies have been conducted to determine which modality, CT or MRI with radiomics analysis, is more effective.
Methods:
We retrospectively enrolled 119 HCC patients who underwent preoperative CT and MRI. A total of 3776 CT features and 4720 MRI features were extracted from the whole tumor. The minimum redundancy and maximum relevance algorithm (MRMR) and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection, then support vector machines (SVMs) were applied for model construction. Multivariable logistic regression analysis was employed to construct combined models that integrate clinical-radiological-pathological (CRP) traits and radscore. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to compare the efficacy of CT, MRI, and CT and MRI models in the test cohort.
Results:
The CT model and MRI model showed no significant difference in the prediction of ER in HCC patients (p = 0.911). RadiomicsCT&MRI demonstrated a superior predictive performance than either RadiomicsCT or RadiomicsMRI alone (p = 0.032, 0.039). The combined CT and MRI model can significantly stratify patients at high risk of ER (area under the curve (AUC) of 0.951 in the training set and 0.955 in the test set) than the CT model (AUC of 0.894 and 0.784) and the MRI model (AUC of 0.856 and 0.787). DCA demonstrated that the CT and MRI model provided a greater net benefit than the models without radiomics analysis.
Conclusions:
No significant difference was found in predicting the ER of HCC between CT models and MRI models. However, the multimodal radiomics model derived from CT and MRI can significantly improve the prediction of ER in HCC patients after resection.
