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Scientific monitoring and assessment of ecosystem services (ESs) in national parks is an important path to strengthen the management and protection of national parks. In this study, we constructed a test scheme for the ESs protection effectiveness based on the InVEST model and the Propensity score matching model (PSM). Based on this scheme, we best-matched three groups of pairs inside and outside the national park, and analyzed the magnitude and significance of differences in ESs protection between the best-matched groups by t-test, and then assessed whether the establishment of Pudacuo National Park had a protective effect on ESs. The results show that: Except for soil conservation, all ESs within Pudacuo National Park and its external 0–10 km range were higher than those in the 10–20 km area outside the national park. Moreover, the ESs within the national park and its external 0–10 km showed the same increase and decrease, and the difference between the two was small. This indicates that the existence of Pudacuo National Park effectively protects ESs and also has a positive spillover effect on its outer 0–10 km range. In addition, this study found that the soil conservation protection effect of Pudacuo National Park was slightly deficient. It exhibits weaker soil conservation than areas outside the national park. It is recommended to further strengthen the investment in soil conservation services and develop relevant measures to curb soil erosion according to local conditions.

As a reliable preoperative predictor for microvascular invasion (MVI) and disease-free survival (DFS) is lacking, we developed a radiomics nomogram of [18F]FDG PET/CT to predict MVI status and DFS in patients with very-early- and early-stage (BCLC 0, BCLC A) hepatocellular carcinoma (HCC). Methods: Patients (N = 80) with BCLC0-A HCC who underwent [18F]FDG PET/CT before surgery were enrolled in this retrospective study and were randomized to a training cohort and a validation cohort. Texture features from patients obtained using Lifex software in the training cohort were subjected to LASSO regression to select the most useful predictive features of MVI and DFS. Then, the radiomics nomogram was constructed using the radiomics signature and clinical features and further validated. Results: To predict MVI, the [18F]FDG PET/CT radiomics signature consisted of five texture features from the PET and six texture features from CT. The signature was significantly associated with MVI status in the training cohort (P = 0.001). None of the clinical features was independent predictors for MVI status (P > 0.05). The area under the curve value of the M-PET/CT model was 0.891 (95% CI: 0.799–0.984) in the training cohort and showed good discrimination and calibration. To predict DFS, the [18F]FDG PET/CT radiomics nomogram (D-PET/CT model) and a clinicopathologic nomogram were built in the training cohort. The D-PET/CT model, which integrated the D-PET/CT radiomics signature with INR and TB, provided better predictive performance (C-index: 0.831, 95% CI: 0.761–0.900) and larger net benefits than the simple clinical model, as determined by decision curve analyses. Conclusion: The newly developed [18F]FDG PET/CT radiomics signature was an independent biomarker for the estimation of MVI and DFS in patients with very-early- and early-stage HCC. Moreover, PET/CT nomogram, which incorporated the radiomics signature of [18F]FDG PET/CT and clinical risk factors in patients with very-early- and early-stage HCC, performed better for individualized DFS estimation, which might enable a step forward in precise medicine. [ABSTRACT FROM AUTHOR]

As a reliable preoperative predictor for microvascular invasion (MVI) and disease-free survival (DFS) is lacking, we developed a radiomics nomogram of [ 18 F]FDG PET/CT to predict MVI status and DFS in patients with very-early- and early-stage (BCLC 0, BCLC A) hepatocellular carcinoma (HCC)., Methods: Patients (N = 80) with BCLC0-A HCC who underwent [ 18 F]FDG PET/CT before surgery were enrolled in this retrospective study and were randomized to a training cohort and a validation cohort. Texture features from patients obtained using Lifex software in the training cohort were subjected to LASSO regression to select the most useful predictive features of MVI and DFS. Then, the radiomics nomogram was constructed using the radiomics signature and clinical features and further validated., Results: To predict MVI, the [ 18 F]FDG PET/CT radiomics signature consisted of five texture features from the PET and six texture features from CT. The signature was significantly associated with MVI status in the training cohort (P = 0.001). None of the clinical features was independent predictors for MVI status (P > 0.05). The area under the curve value of the M-PET/CT model was 0.891 (95% CI: 0.799-0.984) in the training cohort and showed good discrimination and calibration. To predict DFS, the [ 18 F]FDG PET/CT radiomics nomogram (D-PET/CT model) and a clinicopathologic nomogram were built in the training cohort. The D-PET/CT model, which integrated the D-PET/CT radiomics signature with INR and TB, provided better predictive performance (C-index: 0.831, 95% CI: 0.761-0.900) and larger net benefits than the simple clinical model, as determined by decision curve analyses., Conclusion: The newly developed [ 18 F]FDG PET/CT radiomics signature was an independent biomarker for the estimation of MVI and DFS in patients with very-early- and early-stage HCC. Moreover, PET/CT nomogram, which incorporated the radiomics signature of [ 18 F]FDG PET/CT and clinical risk factors in patients with very-early- and early-stage HCC, performed better for individualized DFS estimation, which might enable a step forward in precise medicine.