ABSTRACT
A significant reduction in carbon dioxide (CO2) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account. In this study, a mobile travel platform was constructed to collect both on-road navigation Street View Panoramas (OSVPs) and the corresponding CO2 concentrations. >100 thousand sample pairs that matched "street view-CO2 concentration" were obtained, covering 675.8 km of roads in Shenzhen, China. In addition, four ensemble learning (EL) models were utilized to establish nonlinear connections between the semantic and object features of streetscapes and CO2 concentrations. After performing EL fusion modeling, the predictive R2 in the test set exceeded 90 %, and the mean absolute error (MAE) was <3.2 ppm. The model was applied to Baidu Street View Panoramas (BSVPs) in Shenzhen to generate a map of average on-road CO2 with a 100 m resolution, and the Local Indicator of Spatial Association (LISA) was then used to identify high CO2 intensity spatial clusters. Additionally, the Light Gradient Boost-SHapley Additive exPlanation (LGB-SHAP) analysis revealed that vertically planted trees can reduce CO2 emissions from on-road sources. Moreover, the factors that affect on-road CO2 exhibit interaction and threshold effects. Street View Panoramas (SVPs) and Artificial Intelligence (AI) were adopted here to enhance the spatial measurement of on-road CO2 concentrations and the understanding of driving factors. Our approach facilitates the assessment and design of low-emission transportation in urban areas, which is critical for promoting sustainable traffic development.
ABSTRACT
BACKGROUND: The purpose of this study is to establish a nomogram and risk stratification system to predict OS in patients with low-grade HCC. RESEARCH DESIGN AND METHODS: Data were extracted from the SEER database. C-index, time-dependent AUCs, and calibration plots were used to evaluate the effective performance of the nomogram. NRI, IDI, and DCA curves were adopted to compare the clinical utility of nomogram with AJCC. RESULTS: 3415 patients with low-grade HCC were available. The C-indices for the training and validation cohorts were 0.773 and 0.772. The time-dependent AUCs in the training cohort were 0.821, 0.817, and 0.846 at 1, 3 and 5 years. Calibration plots for 1-, 3- and 5-year OS showed good consistency between actual observations and that predicted by the nomogram. The values of NRI at 1, 3, and 5 years were 0.37, 0.66, and 0.64. The IDI values at 1, 3, and 5 years were 0.11, 0.16, and 0.23 (P< 0.001). DCA curves demonstrated that the nomogram showed better ability of predicting 1-, 3-, and 5-year OS probabilities than AJCC. CONCLUSIONS: A nomogram and risk stratification system for predicting OS in patients with low-grade HCC were established and validated.
Subject(s)
Carcinoma, Hepatocellular , Liver Neoplasms , Humans , Carcinoma, Hepatocellular/diagnosis , Carcinoma, Hepatocellular/epidemiology , Liver Neoplasms/diagnosis , Liver Neoplasms/epidemiology , Nomograms , Area Under Curve , Risk Assessment , SEER ProgramABSTRACT
Background: The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS). Methods: 1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared. Results: Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division. Conclusions: A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.
ABSTRACT
Objective: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. This study aims to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in HCC patients with severe liver fibrosis. Methods: Data on 1,878 HCC patients with severe liver fibrosis in the period 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results database (SEER). Patients were block-randomized (1,316 training cohort, 562 validation cohort) by setting random seed. Univariate and multivariate COX regression analyses were employed to select variables for the nomogram. The consistency index (C-index), the area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves were used to evaluate the performance of the nomogram. Decision curve analysis (DCA), the C-index, the net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare the nomogram with the AJCC tumor staging system. We also compared the risk stratification of the nomogram with the American Joint Committee on Cancer (AJCC) staging system. Results: Seven variables were selected to establish the nomogram. The C-index (training cohort: 0.781, 95%CI: 0.767-0.793; validation cohort: 0.793, 95%CI = 95%CI: 0.779-0.798) and the time-dependent AUCs (the training cohort: the values of 1-, 3-, and 5 years were 0.845, 0.835, and 0.842, respectively; the validation cohort: the values of 1-, 3-, and 5 years were 0.861, 0.870, and 0.876, respectively) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. NRI (training cohort: 1-, 2-, and 3-year CSS: 0.42, 0.61, and 0.67; validation cohort: 1-, 2-, and 3-year CSS: 0.26, 0.52, and 0.72) and IDI (training cohort: 1-, 3-, and 5-year CSS:0.16, 0.20, and 0.22; validation cohort: 1-, 3-, and 5-year CSS: 0.17, 0.26, and 0.30) indicated that the established nomogram significantly outperformed the AJCC staging system (P < 0.001). Moreover, DCA also showed that the nomogram was more practical and had better recognition. Conclusion: A nomogram for predicting CSS for HCC patients with severe liver fibrosis was established and validated, which provided a new system of risk stratification as a practical tool for individualized treatment and management.
ABSTRACT
Background: Hepatocellular carcinoma (HCC) has the highest cancer-related mortality rate. This study aims to create a nomogram to predict the cancer-specific survival (CSS) in patients with advanced hepatocellular carcinoma. Methods: Patients diagnosed with advanced HCC (AJCC stage III and IV) during 1975 to 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Qualified patents were randomized into training cohort and validation cohort at a ratio of 7:3. The results of univariate and multivariate Cox regression analyses were used to construct the nomogram. Consistency index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve [time-dependent area under the curve (AUC)], and calibration plots were used to identify and calibrate the nomogram. The net reclassification index (NRI), integrated discrimination improvement (IDI), and C-index, and decision curve analysis DCA were adopted to compare the nomogram's clinical utility with the AJCC criteria. Results: The 3,103 patients with advanced hepatocellular carcinoma were selected (the training cohort: 2,175 patients and the validation cohort: 928 patients). The C-index in both training cohort and validation cohort were greater than 0.7. The AUC for ROC in the training cohort was 0.781, 0.771, and 0.791 at 1, 2, and 3 years CSS, respectively. Calibration plots showed good consistency between actual observations and the 1-, 2-, and 3-year CSS predicted by the nomogram. The 1-, 2-, and 3-year NRI were 0.77, 0.46, and 0.48, respectively. The 1-, 2-, and 3-year IDI values were 0.16, 0.15, and 0.12 (P < 0.001), respectively. DCA curves in both the training and validation cohorts demonstrated that the nomogram showed better predicted 1-, 2-, and 3-year CSS probabilities than AJCC criteria. Conclusions: This study established a practical nomogram for predicting CSS in patients with advanced HCC and a risk stratification system that provided an applicable tool for clinical management.
