Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model

Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics

OBJECTIVE

To investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics for predicting the response of primary lesions to neoadjuvant chemotherapy in hepatoblastoma.

METHODS

Clinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical-radiomics model incorporated both clinical and radiomics features.

RESULTS

The area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563-0.845), 0.830 (95% CI: 0.704-0.959), and 0.874 (95% CI: 0.768-0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616-0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively.

CONCLUSION

CECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.

Promoting the application of pediatric radiomics via an integrated medical engineering approach

Radiomics is widely used in adult tumors but has been rarely applied to the field of pediatrics. Promoting the application of radiomics in pediatric diseases, especially in the early diagnosis and stratified treatment of tumors, is of great value to the realization of the WHO 2030 “Global Initiative for Childhood Cancer.” This paper discusses the general characteristics of radiomics, the particularity of its application to pediatric diseases, and the current status and prospects of pediatric radiomics. Radiomics is a data‐driven science, and the combination of medicine and engineering plays a decisive role in improving data quality, data diversity, and sample size. Compared with adult radiomics, pediatric radiomics is significantly different in data type, disease spectrum, disease staging, and progression. Some progress has been made in the identification, classification, stratification, survival prediction, and prognosis of tumor diseases. In the future, big data applications from multiple centers and cross‐talent training should be strengthened to improve the benefits for clinical workers and children.

Incidence trends and predictive model of hepatic malignant tumors in children: a population-based study

OBJECTIVE

To analyze the incidence trend and establish a model to predict the prognosis of hepatic malignant tumors in children (CHMTs).

METHODS

We analyzed the incidence data of CHMTs from 1975 to 2018 from the Surveillance, Epidemiology, and End Results (SEER) database, and evaluated the incidence trends based on different demographic and pathological features. We also analyzed clinicopathologic data from 2000 to 2018 from the SEER database. Univariate and multivariate Cox regression analyses were performed to explore prognostic factors related to overall survival (OS). Then, we established nomograms based on independent predictors and verified them using receiver operating characteristic curves, calibration plots, and decision curve analysis plots.

RESULTS

The incidence of CHMTs increased significantly, from 0.1 per 100,000 in 1975 to 0.4 per 100,000 in 2018. Incidences among different races and genders were increasing and converging. The incidence of hepatoblastoma (HB) increased, while that of hepatocellular carcinoma (HCC) was relatively stable. The 1-, 3-, 5-, and 10-year OS rates were 86.2%, 77.5%, 74.2%, and 70.2%, respectively. Being Spanish-Hispanic-Latino, HB, surgery, and systemic therapy were independent predictors of longer OS, whereas regional and distant stages were independent predictors of shorter OS. Nomograms with good predictive ability and clinical utility were established to evaluate the prognosis of children with HB or HCC.

CONCLUSION

The incidence of CHMTs is increasing, especially for HB and in younger children. This study identified independent predictors and developed nomograms that could provide a personalized and accurate prognosis for CHMTs.

MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer

OBJECTIVE

To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer.

METHODS

A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models.

RESULTS

T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773).

CONCLUSION

The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up.

ADVANCES IN KNOWLEDGE

A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.

Above and Beyond Robotic Surgery and 3D Modelling in Paediatric Cancer Surgery

Although the survival rates for children's cancers have more than doubled in the last few decades, the surgical practise has not significantly changed. Among the most recent innovations introduced in the clinic, robotic surgery and augmented reality are two of the most promising, even if they are not widespread. The increased flexibility of the motion, the magnification of the surgical field and the tremor reduction provided by robotic surgery have been beneficial to perform complex oncological procedures in children. Besides, augmented reality has been proven helpful in planning for tumour removal, facilitating early discrimination between cancer and healthy organs. Nowadays, research in the field of surgical oncology is moving fast, and new technologies and innovations wich will help to shape a new way to perform cancer surgery. Paediatric surgeons need to be ready to adopt these novel devices and intraoperative techniques to allow more radical tumour resections with fewer complications. This review aims to present the mechanism of action and indications of several novel technologies such as optical imaging surgery, high definition cameras, and intraoperative loco-regional treatments. We hope this will enhance early adoption and more research on how to employ technology for the benefit of children.

miR-126 in Extracellular Vesicles Derived from Hepatoblastoma Cells Promotes the Tumorigenesis of Hepatoblastoma through Inducing the Differentiation of BMSCs into Cancer Stem Cells

Background

Extracellular vesicles (EVs) can deliver miRNAs between cells and play a crucial role in hepatoblastoma progression. In this study, we explored the differentially expressed miRNAs related to tumor cell-derived EVs and the mechanism by which EVs regulate hepatoblastoma progression.

Methods

Bioinformatics analysis was performed to explore the differentially expressed miRNAs between the hepatoblastoma and adjacent normal tissues. TEM, NTA, and western blotting were conducted to identify EVs. The expression of miR-126-3p, miR-126-5p, miR-30b-3p, miR-30b-3p, SRY, IL-1α, IL-6, and TGF-β was detected by RT-qPCR. Immunofluorescence (IF) was used to analyze the expression of PKH67, and flow cytometry was applied to assess the ratio of CD44+ CD90+ CD133+ cells. ELISA was used to evaluate the levels of IL-6 and TGF-β. A xenograft mouse model was constructed to detect the function of EVs with downregulated miR-126. IHC was performed to calculate β-catenin levels in tumor tissues.

Results

miR-126 was upregulated in hepatoblastoma. EVs derived from hepatoblastoma cells significantly increased the ratio of CD44+ CD90+ CD133+ cells and increased the expression of IL-6, Oct4, SRY, and TGF-β in bone marrow mesenchymal stem cells (BMSCs), while EVs with downregulated miR-126 reversed these phenomena. miR-126 downregulation notably attenuated hepatoblastoma tumor growth and decreased the ratio of CD44+ CD90+ CD133+ cells and increased the expression of IL-6, Oct4, SRY, TGF-β, and β-catenin in tumor tissues of mice. Furthermore, EVs with downregulated miR-126 inhibited the differentiation of BMSCs into cancer stem cells.

Conclusions

Exosomal miR-126 derived from hepatoblastoma cells promoted the tumorigenesis of liver cancer through inducing the differentiation of BMSCs into cancer stem cells.