The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

Prognostic Modeling for Liver Cirrhosis Mortality Prediction and Real-Time Health Monitoring from Electronic Health Data

Liver cirrhosis stands as a prominent contributor to mortality, impacting millions across the United States. Enabling health care providers to predict early mortality among patients with cirrhosis holds the potential to enhance treatment efficacy significantly. Our hypothesis centers on the correlation between mortality and laboratory test results along with relevant diagnoses in this patient cohort. Additionally, we posit that a deep learning model could surpass the predictive capabilities of the existing Model for End-Stage Liver Disease score. This research seeks to advance prognostic accuracy and refine approaches to address the critical challenges posed by cirrhosis-related mortality. This study evaluates the performance of an artificial neural network model for liver disease classification using various training dataset sizes. Through meticulous experimentation, three distinct training proportions were analyzed: 70%, 80%, and 90%. The model's efficacy was assessed using precision, recall, F1-score, accuracy, and support metrics, alongside receiver operating characteristic (ROC) and precision-recall (PR) curves. The ROC curves were quantified using the area under the curve (AUC) metric. Results indicated that the model's performance improved with an increased size of the training dataset. Specifically, the 80% training data model achieved the highest AUC, suggesting superior classification ability over the models trained with 70% and 90% data. PR analysis revealed a steep trade-off between precision and recall across all datasets, with 80% training data again demonstrating a slightly better balance. This is indicative of the challenges faced in achieving high precision with a concurrently high recall, a common issue in imbalanced datasets such as those found in medical diagnostics.

Integrated single-cell and bulk RNA sequencing reveals immune-related SPP1+ macrophages as a potential strategy for predicting the prognosis and treatment of liver fibrosis and hepatocellular carcinoma

Background

Liver fibrosis is a pathological response to liver damage induced by multiple etiologies including NASH and CCl4, which may further lead to cirrhosis and hepatocellular carcinoma (HCC). Despite the increasing understanding of liver fibrosis and HCC, clinical prognosis and targeted therapy remain challenging.

Methods

This study integrated single-cell sequencing analysis, bulk sequencing analysis, and mouse models to identify highly expressed genes, cell subsets, and signaling pathways associated with liver fibrosis and HCC. Clinical prediction models and prognostic genes were established and verified through machine learning, survival analysis, as well as the utilization of clinical data and tissue samples from HCC patients. The expression heterogeneity of the core prognostic gene, along with its correlation with the tumor microenvironment and prognostic outcomes, was analyzed through single-cell analysis and immune infiltration analysis. In addition, the cAMP database and molecular docking techniques were employed to screen potential small molecule drugs for the treatment of liver fibrosis and HCC.

Result

We identified 40 pathogenic genes, 15 critical cell subsets (especially Macrophages), and regulatory signaling pathways related to cell adhesion and the actin cytoskeleton that promote the development of liver fibrosis and HCC. In addition, 7 specific prognostic genes (CCR7, COL3A1, FMNL2, HP, PFN1, SPP1 and TENM4) were identified and evaluated, and expression heterogeneity of core gene SPP1 and its positive correlation with immune infiltration and prognostic development were interpreted. Moreover, 6 potential small molecule drugs for the treatment of liver fibrosis and HCC were provided.

Conclusion

The comprehensive investigation, based on a bioinformatics and mouse model strategy, may identify pathogenic genes, cell subsets, regulatory mechanisms, prognostic genes, and potential small molecule drugs, thereby providing valuable insights into the clinical prognosis and targeted treatment of liver fibrosis and HCC.

MRI texture analysis of acetabular cancellous bone can discriminate between normal, cam positive, and cam-FAI hips

OBJECTIVES

To compare the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive, and symptomatic cam-FAI hips and determine the accuracy of a machine learning model for discriminating between the three hip classes.

METHODS

A case-control, retrospective study was performed including 68 subjects (19 normal, 26 asymptomatic cam, 23 symptomatic cam-FAI). Acetabular subchondral bone of unilateral hip was contoured on 1.5 T MR images. Nine first-order 3D histogram and 16 s-order texture features were evaluated using specialized texture analysis software. Between-group differences were assessed using Kruskal-Wallis and Mann-Whitney U tests, and differences in proportions compared using chi-square and Fisher's exact tests. Gradient-boosted ensemble methods of decision trees were created and trained to discriminate between the three groups of hips, with percent accuracy calculated.

RESULTS

Sixty-eight subjects (median age 32 (28-40), 60 male) were evaluated. Significant differences among all three groups were identified with first-order (4 features, all p ≤ 0.002) and second-order (11 features, all p ≤ 0.002) texture analyses. First-order texture analysis could differentiate between control and cam positive hip groups (4 features, all p ≤ 0.002). Second-order texture analysis could additionally differentiate between asymptomatic cam and symptomatic cam-FAI groups (10 features, all p ≤ 0.02). Machine learning models demonstrated high classification accuracy of 79% (SD 16) for discriminating among all three groups.

CONCLUSION

Normal, asymptomatic cam positive, and cam-FAI hips can be discriminated based on their MRI texture profile of subchondral bone using descriptive statistics and machine learning algorithms.

CLINICAL RELEVANCE STATEMENT

Texture analysis can be performed on routine MR images of the hip and used to identify early changes in bone architecture, differentiating morphologically abnormal from normal hips, prior to onset of symptoms.

KEY POINTS

• MRI texture analysis is a technique for extracting quantitative data from routine MRI images. • MRI texture analysis demonstrates that there are different bone profiles between normal hips and those with femoroacetabular impingement. • Machine learning models can be used in conjunction with MRI texture analysis to accurately differentiate between normal hips and those with femoroacetabular impingement.

Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients

BACKGROUND

Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC).

METHODS

Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups.

RESULTS

We used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134).

CONCLUSIONS

Parameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.

Development and validation of MRI-based model for the preoperative prediction of macrotrabecular hepatocellular carcinoma subtype

BACKGROUND

Macrotrabecular hepatocellular carcinoma (MTHCC) has a poor prognosis and is difficult to diagnose preoperatively. The purpose is to build and validate MRI-based models to predict the MTHCC subtype.

METHODS

Two hundred eight patients with confirmed HCC were enrolled. Three models (model 1: clinicoradiologic model; model 2: fusion radiomics signature; model 3: combined model 1 and model 2) were built based on their clinical data and MR images to predict MTHCC in training and validation cohorts. The performance of the models was assessed using the area under the curve (AUC). The clinical utility of the models was estimated by decision curve analysis (DCA). A nomogram was constructed, and its calibration was evaluated.

RESULTS

Model 1 is easier to build than models 2 and 3, with a good AUC of 0.773 (95% CI 0.696-0.838) and 0.801 (95% CI 0.681-0.891) in predicting MTHCC in training and validation cohorts, respectively. It performed slightly superior to model 2 in both training (AUC 0.747; 95% CI 0.689-0.806; p = 0.548) and validation (AUC 0.718; 95% CI 0.618-0.810; p = 0.089) cohorts and was similar to model 3 in the validation (AUC 0.866; 95% CI 0.801-0.928; p = 0.321) but inferior in the training (AUC 0.889; 95% CI 0.851-0.926; p = 0.001) cohorts. The DCA of model 1 had a higher net benefit than the treat-all and treat-none strategy at a threshold probability of 10%. The calibration curves of model 1 closely aligned with the true MTHCC rates in the training (p = 0.355) and validation sets (p = 0.364).

CONCLUSION

The clinicoradiologic model has a good performance in diagnosing MTHCC, and it is simpler and easier to implement, making it a valuable tool for pretherapeutic decision-making in patients.

Essential amino acids as diagnostic biomarkers of hepatocellular carcinoma based on metabolic analysis

Metabolomics, defined as the comprehensive identification of all small metabolites in a biological sample, has the power to shed light on phenotypic changes associated with various diseases, including cancer. To discover potential metabolomic biomarkers of hepatocellular carcinoma (HCC), we investigated the metabolomes of tumor and non-tumor tissue in 20 patients with primary HCC using capillary electrophoresis-time-of-flight mass spectrometry. We also analyzed blood samples taken immediately before and 14 days after hepatectomy to identify associated changes in the serum metabolome. Marked changes were detected in the different quantity of 61 metabolites that could discriminate between HCC tumor and paired non-tumor tissue and additionally between HCC primary tumors and colorectal liver metastases. Among the 30 metabolites significantly upregulated in HCC tumors compared with non-tumor tissues, 10 were amino acids, and 7 were essential amino acids (leucine, valine, tryptophan, isoleucine, methionine, lysine, and phenylalanine). Similarly, the serum metabolomes of HCC patients before hepatectomy revealed a significant increase in 16 metabolites, including leucine, valine, and tryptophan. Our results reveal striking differences in the metabolomes of HCC tumor tissue compared with non-tumor tissue, and identify the essential amino acids leucine, valine, and tryptophan as potential metabolic biomarkers for HCC.

Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms

The objectives of our study were to (a) evaluate the feasibility of using 3D printed phantoms in magnetic resonance imaging (MR) in assessing the robustness and repeatability of radiomic parameters and (b) to compare the results obtained from the 3D printed phantoms to metrics obtained in biological phantoms. To this end, three different 3D phantoms were printed: a Hilbert cube (5 × 5 × 5 cm³) and two cubic quick response (QR) code phantoms (a large phantom (large QR) (5 × 5 × 4 cm³) and a small phantom (small QR) (4 × 4 × 3 cm³)). All 3D printed and biological phantoms (kiwis, tomatoes, and onions) were scanned thrice on clinical 1.5 T and 3 T MR with 1 mm and 2 mm isotropic resolution. Subsequent analyses included analyses of several radiomics indices (RI), their repeatability and reliability were calculated using the coefficient of variation (CV), the relative percentage difference (RPD), and the interclass coefficient (ICC) parameters. Additionally, the readability of QR codes obtained from the MR images was examined with several mobile phones and algorithms. The best repeatability (CV ≤ 10%) is reported for the acquisition protocols with the highest spatial resolution. In general, the repeatability and reliability of RI were better in data obtained at 1.5 T (CV = 1.9) than at 3 T (CV = 2.11). Furthermore, we report good agreements between results obtained for the 3D phantoms and biological phantoms. Finally, analyses of the read-out rate of the QR code revealed better texture analyses for images with a spatial resolution of 1 mm than 2 mm. In conclusion, 3D printing techniques offer a unique solution to create textures for analyzing the reliability of radiomic data from MR scans.

Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI

Objectives

We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences.

Methods

We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model.

Results

The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration.

Conclusions

The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00855-w.

Correlation of CT Perfusion Parameters and Vascular Endothelial Growth Factor (VEGF) and Basic Fibroblast Growth Factor (BFGF) in Patients with Primary Liver Cancer

Objective

To investigate the correlation of CT perfusion-related parameters with serum vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (BFGF) in patients with primary liver cancer.

Methods

A total of 100 patients with primary liver cancer who were treated in our hospital from June 2019 to June 2021 were selected as the observation group, and 90 patients with benign liver lesions during the same period were selected as the control group. The CT perfusion-related parameters (perfusion volume and perfusion index) and serum VEGF and BFGF levels were compared between the two groups. Pearson correlation was used to analyze the correlation between CT perfusion-related parameters and serum VEGF and BFGF levels.

Results

Compared to the control group, significantly higher HAP and lower HPP and TLP were observed in the observation group. The perfusion volume indexes of patients with different stages of liver cancer in the observation group were statistically different (P < 0.05). Compared to the control group, the observation group witnessed significantly higher HAPI and lower HPPI. There were statistically significant differences in the perfusion index of patients with different stages of primary liver cancer in the observation group (P < 0.05). The serum VEGF and BFGF levels in the observation group were significantly higher than those in the control group, and the serum VEGF and BFGF levels in patients with different stages of primary liver cancer in the observation group were statistically different (P < 0.05). Pearson correlation analysis showed that HAP and HAPI were positively correlated with VEGF and BFGF (r = 0.986, P ≤ 0.001; r = 0.983, P ≤ 0.001), and HPP, TLP, and HPPI were negatively correlated with VEGF and BFGF (r = −0.992, P ≤ 0.001; r = -0.993, P ≤ 0.001; r = −0.995, P ≤ 0.001).

Conclusion

CT perfusion-related parameters and serum VEGF and BFGF levels in patients with primary liver cancer are abnormally expressed, and there is a strong correlation between the two, which might aid clinical diagnosis and treatment.

MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contrast images. Radiomic features from T2 images and arterial, portal and tardive post-contrast images from DCE-MRI were extracted by using Pyradiomics. Feature selection was performed using correlation filter, Wilcoxon-rank sum test and mutual information. Predictive models were constructed for HCC differentiation with respect to HT and HCC histopathologic grading used at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. Promising results were obtained from radiomic prediction models, with best AUCs ranging from 71% to 96%. Radiomics MRI based on T2 and DCE-MRI revealed promising results concerning both HCC detection and grading. It may be a suitable tool for personalized treatment of HCC patients and could also be used to develop new prognostic biomarkers useful for HCC assessment without the need for invasive procedures.

A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature

Objectives

The objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation.

Methods

202 patients with proven single HCC were enlisted and stochastically distributed into a training set (n = 142) and a test set (n = 60). Arterial phase, portal venous phase, balanced phase, delayed phase, and hepatobiliary phase images were used to radiomics features extraction. We retrieved 1906 radiomic features from each phase of every participant’s MRI images. The F-test was applied to choose the crucial features. A logistic regression model was adopted to generate a radiomics signature. By combining independent risk indicators from the fusion radiomics signature and clinico-radiological features, we developed a multivariable logistic regression model that could predict the VEGF status preoperatively through calculating the area under the curve (AUC).

Results

The entire group comprised 108 VEGF-positive individuals and 94 VEGF-negative patients. AUCs of 0.892 (95% confidence interval [CI]: 0.839 - 0.945) in the training dataset and 0.800 (95% CI: 0.682 - 0.918) in the test dataset were achieved by utilizing radiomics features from two phase images (8 features from the portal venous phase and 5 features from the hepatobiliary phase). Furthermore, the nomogram relying on a combined model that included the clinical factors α-fetoprotein (AFP), irregular tumor margin, and the fusion radiomics signature performed well in both the training (AUC = 0.936, 95% CI: 0.898-0.974) and test (AUC = 0.836, 95% CI: 0.728-0.944) datasets.

Conclusions

The combined model acquired from two phase (portal venous and hepatobiliary phase) pictures of gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI could be considered as a credible prognostic marker for the level of VEGF in HCC.

Radiomics of Patients with Locally Advanced Rectal Cancer: Effect of Preprocessing on Features Estimation from Computed Tomography Imaging

The purpose of this study was to investigate the effect of image preprocessing on radiomic features estimation from computed tomography (CT) imaging of locally advanced rectal cancer (LARC). CT images of 20 patients with LARC were used to estimate 105 radiomic features of 7 classes (shape, first-order, GLCM, GLDM, GLRLM, GLSZM, and NGTDM). Radiomic features were estimated for 6 different isotropic resampling voxel sizes, using 10 interpolation algorithms (at fixed bin width) and 6 different bin widths (at fixed interpolation algorithm). The intraclass correlation coefficient (ICC) and the coefficient of variation (CV) were calculated to assess the variability in radiomic features estimation due to preprocessing. A repeated measures correlation analysis was performed to assess any linear correlation between radiomic feature estimate and resampling voxel size or bin width. Reproducibility of radiomic feature estimate, when assessed through ICC analysis, was nominally excellent (ICC > 0.9) for shape features, good (0.75 < ICC ≤ 0.9) or moderate (0.5 < ICC ≤ 0.75) for first-order features, and moderate or poor (0 ≤ ICC ≤ 0.5) for textural features. A number of radiomic features characterized by good or excellent reproducibility in terms of ICC showed however median CV values greater than 15%. For most textural features, a significant (p < 0.05) correlation between their estimate and resampling voxel size or bin width was found. In CT imaging of patients with LARC, the estimate of textural features, as well as of first-order features to a lesser extent, is appreciably biased by preprocessing. Accordingly, this should be taken into account when planning clinical or research studies, as well as when comparing results from different studies and performing multicenter studies.

Progress of MRI Radiomics in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.

OBJECTIVE

This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.

METHODS

A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.

RESULTS

Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.

CONCLUSION

Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

Toward Personalized Medicine in Radiotherapy of Hepatocellular Carcinoma: Emerging Radiomic Biomarker Candidates of Response and Toxicity

Radiology and radiotherapy are currently undergoing radical transformation with use of biomarkers and digital technologies such as artificial intelligence. These current and upcoming changes in radiology speak of an overarching new vision for personalized medicine. This is particularly evident in the case of radiotherapy of cancers, and of liver cancer in particular. The development of modern radiotherapy with stereotactic body radiotherapy allows targeted treatments to be delivered to the tumor site, limiting the dose to surrounding healthy organs, thus becoming a new therapeutic alternative for hepatocellular carcinoma and other liver tumors. However, not all patients have the same response to radiotherapy or display the same side-effect profile. Biomarkers of response and toxicity in liver radiotherapy would facilitate the vision and practice of personalized medicine. This expert review examines the available molecular, radiomic, and radiogenomic biomarker candidates for acute liver toxicity with potential use for prediction of radiotherapy-induced liver toxicity. To this end, I highlight for oncologists and life scientists that radiomics allows diagnostic images to be analyzed using computer algorithms to extract information imperceptible to the human eye and of relevance to forecasting clinical outcomes. This article underscores particularly (1) the microRNA-based biomarker candidates as among the most promising predictors of radiation-induced liver toxicity and (2) the texture features in radiomic analyses for response prediction. Radiotherapy of hepatocellular carcinoma is edging toward personalized medicine with emerging radiomic biomarker candidates. Future large-scale biomarker studies are called for to enable personalized medicine in liver cancers.

The Emerging Factors and Treatment Options for NAFLD-Related Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, followed by cholangiocarcinoma (CCA). HCC is the third most common cause of cancer death worldwide, and its incidence is rising, associated with an increased prevalence of obesity and nonalcoholic fatty liver disease (NAFLD). However, current treatment options are limited. Genetic factors and epigenetic factors, influenced by age and environment, significantly impact the initiation and progression of NAFLD-related HCC. In addition, both transcriptional factors and post-transcriptional modification are critically important for the development of HCC in the fatty liver under inflammatory and fibrotic conditions. The early diagnosis of liver cancer predicts curative treatment and longer survival. However, clinical HCC cases are commonly found in a very late stage due to the asymptomatic nature of the early stage of NAFLD-related HCC. The development of diagnostic methods and novel biomarkers, as well as the combined evaluation algorithm and artificial intelligence, support the early and precise diagnosis of NAFLD-related HCC, and timely monitoring during its progression. Treatment options for HCC and NAFLD-related HCC include immunotherapy, CAR T cell therapy, peptide treatment, bariatric surgery, anti-fibrotic treatment, and so on. Overall, the incidence of NAFLD-related HCC is increasing, and a better understanding of the underlying mechanism implicated in the progression of NAFLD-related HCC is essential for improving treatment and prognosis.

Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment

The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx^®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.

Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features

(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 (p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03-2.35) using texture features, of 1.7 (95% CI: 1.54-1.9) using clinical data and of 4.61 (95% CI: 4.24-5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.

Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study

Background

Multiple guidelines for pancreatic ductal adenocarcinoma (PDAC) suggest that all stages of patients need to receive postoperative adjuvant chemotherapy. S-1 is a recently emerged oral antitumour agent recommended by the guidelines. However, which population would benefit from S-1 needs to be determined, and predictors of chemotherapy response are needed for personalized precision medicine. This pilot study aimed to initially identify whether whole-tumour evaluation with MRI and radiomics features could be used for predicting the efficacy of S-1 and to find potential predictors of the efficacy of S-1 as evidence to assist personalized precision treatment.

Methods

Forty-six patients with PDAC (31 in the primary cohort and 15 in the validation cohort) who underwent curative resection and subsequently adjuvant chemotherapy with S-1 were included. Pre-operative abdominal contrast-enhanced MRI was performed, and radiomics features of the whole PDAC were extracted from the primary cohort. After univariable analysis and radiomics features selection, a multivariable Cox regression model for survival analysis was subsequently used to select statistically significant factors associated with postoperative disease-free survival (DFS). Predictive capacities of the factors were tested on the validation cohort by using Kaplan–Meier method.

Results

Multivariable Cox regression analysis identified the probability of T₁WI_NGTDM_Strength and tumour location as independent predictors of the efficacy of S-1 for adjuvant chemotherapy of PDAC (p = 0.005 and 0.013) in the primary cohort, with hazard ratios (HRs) of 0.289 and 0.293, respectively. Further survival analysis showed that patients in the low-T₁WI_NGTDM_Strength group had shorter DFS (median = 5.1 m) than those in the high-T₁WI_NGTDM_Strength group (median = 13.0 m) (p = 0.006), and patients with PDAC on the pancreatic head exhibited shorter DFS (median = 7.0 m) than patients with tumours in other locations (median = 20.0 m) (p = 0.016). In the validation cohort, the difference in DFS between patients with low-T₁WI_NGTDM_Strength and high-T₁WI_NGTDM_Strength and the difference between patients with PDAC on the pancreatic head and that in other locations were approved, with marginally significant (p = 0.073 and 0.050), respectively.

Conclusions

Whole-tumour radiomics feature of T₁WI_NGTDM_Strength and tumour location were potential predictors of the efficacy of S-1 and for the precision selection of S-1 as adjuvant chemotherapy regimen for PDAC.