Dataset Information

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

ABSTRACT: Objective: To explore a CT-based radiomics model for preoperative prediction of event-free survival (EFS) in patients with hepatoblastoma and to compare its performance with that of a clinicopathologic model. Patients and Methods: Eighty-eight patients with histologically confirmed hepatoblastoma (mean age: 2.28 ± 2.72 years) were recruited from two institutions between 2002 and 2019 for this retrospective study. They were divided into a training cohort (65 patients from institution A) and a validation cohort (23 patients from institution B). Radiomics features were extracted manually from pretreatment CT images in the portal venous (PV) phase. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to construct a "radiomics signature" and radiomics score (Rad-score) for EFS prediction. Then, a nomogram incorporating the Rad-score, updated staging system, and significant variables of clinicopathologic risk (age, alpha-fetoprotein (AFP) level, histology subtype, tumor diameter) as the radiomic model, clinicopathologic model, and combined clinicopathologic-radiomic model were built for EFS estimation in the training cohort, the performance of which was assessed in an external-validation cohort with respect to clinical usefulness, discrimination, and calibration. Results: Nine survival-relevant features were selected for a radiomics signature and Rad-score building. Multivariable analysis revealed that histology subtype (P = 0.01), PV (P = 0.001) invasion, and metastasis (P = 0.047) were independent risk factors of EFS. Patients were divided into low- and high-risk groups based on the Rad-score with a cutoff of 0.08 according to survival outcome. The radiomics signature-incorporated nomogram showed good performance (P < 0.001) for EFS estimation (C-Index: 0.810; 95% CI: 0.738-0.882), which was comparable with that of the clinicopathological model for EFS estimation (C-Index: 0.81 vs. 0.85). The radiomics-based nomogram failed to show incremental prognostic value compared with that using the clinicopathologic model. The combined model (radiomics signature plus clinicopathologic parameters) showed significant improvement in the discriminatory accuracy, along with good calibration and greater net clinical benefit, of EFS (C-Index: 0.88; 95% CI: 0.829-0.933). Conclusion: The radiomics signature can be used as a prognostic indicator for EFS in patients with hepatoblastoma. A combination of the radiomics signature and clinicopathologic risk factors showed better performance in terms of EFS prediction in patients with hepatoblastoma, which enabled precise clinical decision-making.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC8086552 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundHepatoblastoma (HB) is a prevalent form of liver cancer in pediatric patients, characterized by an embryonal malignant tumor. In the current study, a clinical prediction model was developed; that can effectively assess the likelihood of a patient's survival with HB.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) database for cases of HB between 2010 and 2019 were used in this retrospective research. Information on clinicopathologic characteristics, therapeutic interventions, and survival outcomes were included in the data. The HB patients were randomly assigned to the training or validation cohort in a 7:3 ratio. Using univariate and multivariate Cox proportional hazards regression models, the prognostic indicators for overall survival (OS) and cancer-specific survival (CSS) were identified. The area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and concordance index (C-index) were used to evaluate the accuracy and calibration of these models. The clinical utility of the models was examined using decision curve analysis (DCA).ResultsThe multivariate Cox regression analysis revealed multiple autonomous prognostic determinants for the OS and CSS, including age, surgical interventions, and chemotherapy administration. Significantly, tumor size was found to be a strong predictor of OS. AUC values of 0.915, 0.846, and 0.847 for 1-, 3-, and 5-year OS, respectively, indicated that the nomogram-based models were highly accurate at predicting outcomes. Similarly, the AUC values for CSS were 0.871, 0.814, and 0.825. The C-index measurements, which quantify the discriminatory performance of the models, produced CSS values of 0.836 and OS values of 0.864. Furthermore, the calibration plots accurately represented the actual survival rates. Concurrently, the DCA had validated the clinical relevance of the nomogram-based models.ConclusionsThe present study successfully developed and validated user-friendly nomogram-based models, allowing for accurate assessment of OS and CSS in pediatric HB patients. These tools enable personalized survival predictions, enhance risk stratification, and strengthen clinical decision-making for managing HB.

Project description:Purpose Children with acute myeloid leukemia (AML) whose disease is refractory to standard induction chemotherapy therapy or who experience relapse after initial response have dismal outcomes. We sought to comprehensively profile pediatric AML microRNA (miRNA) samples to identify dysregulated genes and assess the utility of miRNAs for improved outcome prediction. Patients and Methods To identify miRNA biomarkers that are associated with treatment failure, we performed a comprehensive sequence-based characterization of the pediatric AML miRNA landscape. miRNA sequencing was performed on 1,362 samples-1,303 primary, 22 refractory, and 37 relapse samples. One hundred sixty-four matched samples-127 primary and 37 relapse samples-were analyzed by using RNA sequencing. Results By using penalized lasso Cox proportional hazards regression, we identified 36 miRNAs the expression levels at diagnosis of which were highly associated with event-free survival. Combined expression of the 36 miRNAs was used to create a novel miRNA-based risk classification scheme (AMLmiR36). This new miRNA-based risk classifier identifies those patients who are at high risk (hazard ratio, 2.830; P ? .001) or low risk (hazard ratio, 0.323; P ? .001) of experiencing treatment failure, independent of conventional karyotype or mutation status. The performance of AMLmiR36 was independently assessed by using 878 patients from two different clinical trials (AAML0531 and AAML1031). Our analysis also revealed that miR-106a-363 was abundantly expressed in relapse and refractory samples, and several candidate targets of miR-106a-5p were involved in oxidative phosphorylation, a process that is suppressed in treatment-resistant leukemic cells. Conclusion To assess the utility of miRNAs for outcome prediction in patients with pediatric AML, we designed and validated a miRNA-based risk classification scheme. We also hypothesized that the abundant expression of miR-106a could increase treatment resistance via modulation of genes that are involved in oxidative phosphorylation.

Project description:Importance:The incidence of hepatoblastoma is increasing, and liver transplant (LT) provides a potential cure for pediatric patients with unresectable hepatoblastoma; however, the use of LT for hepatoblastoma has not been examined in a modern cohort. Moreover, data are lacking on the association between the type of surgical management received and overall risk of death among pediatric patients with hepatoblastoma. Objectives:To examine the receipt of LT among pediatric patients with hepatoblastoma and to assess overall survival of pediatric patients with hepatoblastoma who were treated with chemotherapy after LT or liver resection (LR) using data from a national cancer registry. Design, Setting, and Participants:This cohort study used data for 443 pediatric patients with histologically confirmed hepatoblastoma who received chemotherapy and surgical therapies, as documented in the Surveillance, Epidemiology, and End Results database of the National Cancer Institute, from 2004 to 2016, with follow-up through December 31, 2018. Multivariable logistic regression was used to determine factors associated with the use of LT. Cox proportional hazards models were used to assess factors associated with overall survival. Data analysis was performed from April 18, 2019, to July 25, 2019. Main Outcomes and Measures:Overall survival. Results:Among 443 patients receiving chemotherapy (mean [SD] age, 1.8 [2.6] years; 167 [37.7%] female), 350 (79%) underwent LR and 93 (21%) underwent LT. Multivariable analysis showed that patients with multiple lesions were more likely to undergo LT than LR (31% vs 13%; P < .001) and that patients with higher stage tumors were more likely to undergo LT than LR (local disease, 20% vs 58%; regional disease, 58% vs 24%; distant disease, 22% vs 18%; P < .001). There was a statistically significant 19% increase in the receipt of LT from 8% in 1998 to 27% 2016 (trend test, P = .02). Overall survival at 10 years was not significantly different for the 2 surgical management strategies (87.2% [95% CI, 78.3%-97.1%] for patients undergoing LT vs 87.8% [95% CI, 83.5%-92.4%] for those undergoing LR; P = .92). The overall risk of death was not significantly different for LT compared with LR (hazard ratio, 0.716; 95% CI, 0.309-1.657; P = .44). Conclusions and Relevance:The use of LT for the management of hepatoblastoma has increased significantly over time. Among pediatric patients with hepatoblastoma receiving chemotherapy, LT was not associated with improved overall survival compared with LR. There was no significant different between treatments with regard to the outcome variable, but this finding cannot be interpreted as indicating equivalence or lack of superiority.

Project description:Predicting the prognosis of patients in advance is conducive to providing personalized treatment for patients. Our aim was to predict the therapeutic efficacy and progression free survival (PFS) of patients with liver metastasis of colorectal cancer according to the changes of computed tomography (CT) radiomics before and after chemotherapy.MethodsThis retrospective study included 139 patients (397 lesions) with colorectal liver metastases who underwent neoadjuvant chemotherapy from April 2015 to April 2020. We divided the lesions into training cohort and testing cohort with a ratio of 7:3. Two - dimensional region of interest (ROI) was obtained by manually delineating the largest layers of each metastasis lesion. The expanded ROI (3 mm and 5 mm) were also included in the study to characterize microenvironment around tumor. For each of the ROI, 1,316 radiomics features were extracted from delineated plain scan, arterial, and venous phase CT images before and after neoadjuvant chemotherapy. Delta radiomics features were constructed by subtracting the radiomics features after treatment from the radiomics features before treatment. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were applied in the training cohort to select the valuable features. Based on clinical characteristics and radiomics features, 7 Cox proportional-hazards model were constructed to predict the PFS of patients. C-index value and Kaplan Meier (KM) analysis were used to evaluate the efficacy of predicting PFS of these models. Moreover, the prediction performance of one-year PFS was also evaluated by area under the curve (AUC).ResultsCompared with the PreRad (Radiomics form pre-treatment CT images; C-index [95% confidence interval (CI)] in testing cohort: 0.614(0.552-0.675) and PostRad models (Radiomics form post-treatment CT images; 0.642(0.578-0.707), the delta model has better PFS prediction performance (Delta radiomics; 0.688(0.627-0.749). By incorporating clinical characteristics, CombDeltaRad obtains the best performance in both training cohort [C-index (95% CI): 0.802(0.772-0.832)] and the testing cohort (0.744(0.686-0.803). For 1-year PFS prediction, CombDeltaRad model obtained the best performance with AUC (95% CI) of 0.871(0.828-0.914) and 0.745 (0.651-0.838) in training cohort and testing cohort, respectively.ConclusionCT radiomics features have the potential to predict PFS in patients with colorectal cancer and liver metastasis who undergo neoadjuvant chemotherapy. By combining pre-treatment radiomics features, post-treatment radiomics features, and clinical characteristics better prediction results can be achieved.

Project description:ObjectivesMicrosatellite instability (MSI) status is an important hallmark for prognosis prediction and treatment recommendation of colorectal cancer (CRC). To address issues due to the invasiveness of clinical preoperative evaluation of microsatellite status, we investigated the value of preoperative 18F-FDG PET/CT radiomics with machine learning for predicting the microsatellite status of colorectal cancer patients.MethodsA total of 173 patients that underwent 18F-FDG PET/CT scans before operations were retrospectively analyzed in this study. The microsatellite status for each patient was identified as microsatellite instability-high (MSI-H) or microsatellite stable (MSS), according to the test for mismatch repair gene proteins with immunohistochemical staining methods. There were 2,492 radiomic features in total extracted from 18F-FDG PET/CT imaging. Then, radiomic features were selected through multivariate random forest selection and univariate relevancy tests after handling the imbalanced dataset through the random under-sampling method. Based on the selected features, we constructed a BalancedBagging model based on Adaboost classifiers to identify the MSI status in patients with CRC. The model performance was evaluated by the area under the curve (AUC), sensitivity, specificity, and accuracy on the validation dataset.ResultsThe ensemble model was constructed based on two radiomic features and achieved an 82.8% AUC for predicting the MSI status of colorectal cancer patients. The sensitivity, specificity, and accuracy were 83.3, 76.3, and 76.8%, respectively. The significant correlation of the selected two radiomic features with multiple effective clinical features was identified (p < 0.05).Conclusion18F-FDG PET/CT radiomics analysis with the machine learning model provided a quantitative, efficient, and non-invasive mechanism for identifying the microsatellite status of colorectal cancer patients, which optimized the treatment decision support.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets