A Deep Learning Model Combining Multimodal Factors to Predict the Overall Survival of Transarterial Chemoembolization.

Background: To develop and validate an overall survival (OS) prediction model for transarterial chemoembolization (TACE). Methods: In this retrospective study, 301 patients with hepatocellular carcinoma (HCC) who received TACE from 2012 to 2015 were collected. The residual network was used to extract prognostic information from CT images, which was then combined with the clinical factors adjusted by COX regression to predict survival using a modified deep learning model (DLOPCombin). The DLOPCombin model was compared with the residual network model (DLOPCTR), multiple COX regression model (DLOPCox), Radiomic model (Radiomic), and clinical model. Results: In the validation cohort, DLOPCombin shows the highest TD AUC of all cohorts, which compared with Radiomic (TD AUC: 0.96vs 0.63) and clinical model (TD AUC: 0.96 vs 0.62) model. DLOPCombin showed significant difference in C index compared with DLOPCTR and DLOPCox models (P < 0.05). Moreover, the DLOPCombin showed good calibration and overall net benefit. Patients with DLOPCombin model score ≤ 0.902 had better OS (33 months vs 15.5 months, P < 0.0001). Conclusion: The deep learning model can effectively predict the patients' overall survival of TACE.

Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma.

BACKGROUND: According to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival. METHODS: A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves. RESULTS: Of all the models, the random forest (RF) model with peritumor (+10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoff value (0.34) was calculated according to the Youden's index. Patients were then divided into a high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤ 0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260-0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002-1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.001-1.005, P = 0.025), performance status (HR = 2.400, 95% CI: 1.200-4.800, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780-0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416-8.552, P = 0.007). CONCLUSIONS: The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.

Comparison of CalliSpheres® microspheres drug-eluting beads and conventional transarterial chemoembolization in hepatocellular carcinoma patients: a randomized controlled trial.

BACKGROUND: This trial aimed to compare the outcomes of drug-eluting beads transarterial chemoembolization (DEB-TACE) with CalliSpheres® microspheres (CSM) and conventional transarterial chemoembolization cTACE in the treatment of patients with unresectable hepatocellular carcinoma (HCC). PATIENTS AND METHODS: A total of 90 patients were divided into DEB-TACE group (n = 45) and cTACE group (n = 45). The treatment response, overall survival (OS), progression-free survival (PFS), and the safety were compared between the two groups. RESULTS: The objective response rate (ORR) in the DEB-TACE group was significantly higher than that in cTACE group at 1, 3, and 6 months of follow-up (P = 0.031, P = 0.003, P = 0.002). The complete response (CR) in DEB-TACE group was significantly higher than that in cTACE group at 3 months (P = 0.036). Survival analysis revealed that, DEB-TACE group had better survival benefits than cTACE group (median OS: 534 days vs. 367 days, P = 0.027; median PFS: 352 days vs. 278 days P = 0.004). The degree of liver function injury was more serious in DEB-TACE group at 1 week, but was similar between the two groups at 1 month. DEB-TACE with CSM caused a high incidence of fever and a severe abdominal pain (P = 0.031, P = 0.037). CONCLUSIONS: DEB-TACE with CSM showed better treatment response and survival benefits than cTACE group. Although a transient more severe liver damage, high incidence of fever and a severe abdominal pain occurred in the DEB-TACE group, it could be resolved through symptomatic treatment.

Contrast-Enhanced CT Imaging Features Combined with Clinical Factors to Predict the Efficacy and Prognosis for Transarterial Chemoembolization of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Accurate prediction of treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) is critical for precision treatment. This study aimed to develop a comprehensive model (DLRC) that incorporates contrast-enhanced computed tomography (CECT) images and clinical factors to predict the response to TACE in patients with HCC. MATERIALS AND METHODS: A total of 399 patients with intermediate-stage HCC were included in this retrospective study. Deep learning and radiomic signatures were established based on arterial phase CECT images, Correlation analysis and the least absolute shrinkage and selection (LASSO) regression analysis were applied for features selection. The DLRC model incorporating deep learning radiomic signatures and clinical factors was developed using multivariate logistic regression. The area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the models. Kaplan-Meier survival curves based on the DLRC were plotted to assess overall survival in the follow-up cohort (n = 261). RESULTS: The DLRC model was developed using 19 quantitative radiomic features, 10 deep learning features, and 3 clinical factors. The AUC of the DLRC model was 0.937 (95% confidence interval [CI], 0.912-0.962) and 0.909 (95% CI, 0.850-0.968) in the training and validation cohorts, respectively, outperforming models established with two signatures or a single signature (p < 0.05). Stratified analysis showed that the DLRC was not statistically different between subgroups (p > 0.05), and the DCA confirmed the greater net clinical benefit. In addition, multivariable cox regression revealed that DLRC model outputs were independent risk factors for the overall survival (hazard ratios: 1.20, 95% CI: 1.03-1.40; p = 0.019). CONCLUSION: The DLRC model exhibited a remarkable accuracy in predicting response to TACE, and it can be utilized as a potent tool for precision treatment.

Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation.

BACKGROUND: Radiofrequency ablation (RFA) is one of the effective therapeutic modalities in patients with hepatocellular carcinoma (HCC). However, there is no proper method to evaluate the HCC response to RFA. This study aimed to establish and validate a clinical prediction model based on dual-energy computed tomography (DECT) quantitative-imaging parameters, clinical variables, and CT texture parameters. METHODS: We enrolled 63 patients with small HCC. Two to four weeks after RFA, we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients' clinical baseline variables. DECT images were manually segmented, and 56 CT texture features were extracted. We used LASSO algorithm for feature selection and data dimensionality reduction; logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters; we then added texture features to build a clinical-texture model based on clinical model. RESULTS: A total of six optimal CT texture analysis (CTTA) features were selected, which were statistically different between patients with or without tumor progression (P < 0.05). When clinical variables and DECT-quantitative parameters were included, the clinical models showed that albumin-bilirubin grade (ALBI) [odds ratio (OR) = 2.77, 95% confidence interval (CI): 1.35-6.65, P = 0.010], λAP (40-100 keV) (OR = 3.21, 95% CI: 3.16-5.65, P = 0.045) and ICAP (OR = 1.25, 95% CI: 1.01-1.62, P = 0.028) were associated with tumor progression, while the clinical-texture models showed that ALBI (OR = 2.40, 95% CI: 1.19-5.68, P = 0.024), λAP (40-100 keV) (OR = 1.43, 95% CI: 1.10-2.07, P = 0.019), and CTTA-score (OR = 2.98, 95% CI: 1.68-6.66, P = 0.001) were independent risk factors for tumor progression. The clinical model, clinical-texture model, and CTTA-score all performed well in predicting tumor progression within 12 months after RFA (AUC = 0.917, 0.962, and 0.906, respectively), and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957, respectively. CONCLUSIONS: DECT-quantitative parameters, CTTA, and clinical variables were helpful in predicting HCC progression after RFA. The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.

CDCA2 Promotes HCC Cells Development via AKT-mTOR Pathway.

Background: Hepatocellular carcinoma (HCC) is a highly aggressive and solid malignancy with a poor prognosis. Cell division cycle associated 2 (CDCA2) is highly expressed in HCC and is considered to be closely related to the prognosis of patients with HCC. In this research, we aimed to investigate the function and potential mechanism of CDCA2 in HCC cells. Methods: Gain- and loss-of-function experiments were conducted to determine the biological function of CDCA2 in HCC cells. Quantitative reverse transcription-polymerase chain reaction and western blot were utilized to examine the Messenger RNA (mRNA) and protein levels of CDCA2 in HCC cells. The malignant behaviors of HCC cells were analyzed by several biological experiments including cell viability, cell colony formation, and transwell assays. Western blot was also implemented to examine the expression of : AKT, protein kinase B and mTOR, mammalian target of rapamycin (AKT-mTOR) pathway related proteins and Cyclin D1. Results: A significant increase of CDCA2 was observed in HCC cell lines. Upregulation of CDCA2 resulted in the enhancement of the growth, migration, and invasion of HCC cells. Inversely, depletion of CDCA2 displayed the opposite results. Furthermore, the protein levels of p-AKT, p-mTOR, and Cyclin D1 were elevated with CDCA2 upregulation and reduced with CDCA2 depletion in HCC cells. Conclusion: Our observations revealed that CDCA2 promoted the malignant development of HCC cells, and AKT-mTOR pathway might involve in the underlying mechanism.

Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) ranks the second most lethal tumor globally and is the fourth leading cause of cancer-related death worldwide. Unfortunately, HCC is commonly at intermediate tumor stage or advanced tumor stage, in which only some palliative treatment can be used to offer a limited overall survival. Due to the high heterogeneity of the genetic, molecular, and histological levels, HCC makes the prediction of preoperative transarterial chemoembolization (TACE) efficacy and the development of personalized regimens challenging. In this study, a new multi-modal point-of-care system is employed to predict the response of TACE in HCC by a concept of integrating multi-modal large-scale data of clinical index and computed tomography (CT) images. This multi-modal point-of-care predicting system opens new possibilities for predicting the response of TACE treatment and can help clinicians select the optimal patients with HCC who can benefit from the interventional therapy.

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer.

PURPOSE: To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer. METHODS: A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram. RESULTS: The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835-0.930) in the training dataset and 0.846 (95% CI 0.757-0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730-0.987). CONCLUSION: A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

Preparation of Poly[lactic-co-glycolic] Acid Nanospheres and Its Role in Hepatoma Cells.

Poly[lactic-co-glycolic] acid (PLGA) targeting nanoparticles AFP/PLGA/Dt386, loaded with Dt386 plasmid of diphtheria toxin gene, modified by Alpha fetoprotein (AFP) monoclonal antibody, is prepared. Its physical and chemical properties and its effect on HepG2 cells are studied. Firstly, Dt386 expression plasmid pET11a/Dt386 is constructed and PLGA nanoparticles are prepared by emulsion solvent evaporation (ESE). Scanning electron microscope (SEM) is used to observe its morphology. Laser Particle Sizer is used to measure the particle size. In addition, the encapsulation efficiency, drug loading and in vitro release rate of PLGA nanoparticles are measured. Carboxy fluorescein and rhodamine fluorescein are used to double label IgG/PLGA/Dt386 and AFP/PLGA/Dt386 nanospheres, respectively, the entry of nanospheres into HepG2 cells are observed at 3 h and 12 h. The effect of AFP/PLGA/Dt386 nanospheres on the migration of HepG2 cells is examined by wounding healing assay. Transwell chamber experiment is used to detect the effect of AFP/PLGA/Dt386 nanospheres on the invasion of HepG2 cells. MTT method is utilized to determine the inhibitory activity of nanoparticles on HepG2 cell proliferation. After treated with IgG/PLGA/Dt386 and AFP/PLGA/Dt386 nanoparticles for 48 hours, flow cytometry is used to detect the apoptosis rate and cell cycle of HepG2 cells in each group. The results show that the prepared nanospheres have regular morphology, flat surface, average particle size of 265.72±12.46 nm, zeta potential of -18.15 mV. The average entrapment efficiency and drug loading are 78.48±1.71% and 3.16±0.35%, respectively. The nanoparticles release slowly and stably in vitro. At the 10th day, the release rate reaches 75.13%. PLGA nanospheres can effectively protect DNA from nuclease degradation. The results show that AFP/PLGA/Dt386 nanospheres have biological targeting effect and can be enriched in cells. AFP/PLGA/Dt386 nanoparticles can significantly inhibit the migration, invasion and proliferation of HepG2 cells, and promote apoptosis.

Development of "water-suitable" agriculture based on a statistical analysis of factors affecting irrigation water demand.

Water shortage has become a serious problem for the sustainable development of irrigated agriculture in arid regions. In these areas, the scale and planting structure of agriculture suitable for local water resources is particularly important. Irrigation water demand is a crucial indicator of water requirement in irrigation districts. In this study, Mann-Kendall method was used to analyze the temporal changes of climatic factors of the past 50 years and ArcGis to determine spatial changes in human activities. The path analysis was used to quantitative characterize direct and indirect effects of these factors on irrigation water demand and suggest how human activity can be altered to reduce irrigation water demand. The results show that temperature has risen significantly since the completion of the second-stage irrigation district, wind speed has dropped since the completion of the first-stage irrigation district, and cultivated land area has greatly expanded. The direct impact and comprehensive effect of planting area on irrigation water demand is the largest. Controlling for the total water intake, the maximum agricultural planting scale is 40,133 ha. Through adjustment of the planting structure, the scale of irrigated agriculture could increase by as much as 25.8%. Therefore, agricultural planting structures and planting scales suitable for local water resources should be put into action for future sustainable development of agriculture.

Clinical imaging characteristics of inpatients with coronavirus disease-2019 in Heilongjiang Province, China: a retrospective study.

OBJECTIVE: To investigate the clinical, laboratory, and radiological characteristics of patients with coronavirus disease-2019 (COVID-19) in Heilongjiang Province. RESULTS: Patients in the ICU group were older and their incidence of cardiovascular disease was higher than those in the non-ICU group. Lymphocyte levels were lower and neutrophil and D-dimer levels were higher in the ICU than that in the non-ICU group. Compared to the non-ICU group, the incidence of pulmonary consolidation and ground-glass opacity with consolidation was significantly higher in the ICU group, all lung lobes were more likely to be involved, with higher number of lung lobes and areas surrounding the bronchi. Of the 59 patients with COVID-19 in this group, 15 received mechanical ventilation. All intubated patients involved lung lobes, and a large number of lesions were observed in the area around the bronchial vessels. CONCLUSION: Significant differences were observed in clinical symptoms, laboratory tests, and computed tomography features between the ICU and non-ICU groups. METHODS: A total of 59 patients with COVID-19, comprising 44 patients in the intensive care unit (ICU) and 15 in the non-ICU, were retrospectively analyzed. Characteristics of the two groups of patients were compared.