[Clinical value of a differentiation prediction model for invasive lung adenocarcinoma]

Objective: To investigate the value of predicting the degree of differentiation of pulmonary invasive adenocarcinoma (IAC) based on CT image radiomics model and the expression difference of immunohistochemical factors between different degrees of differentiation of lesions. Methods: The clinicopathological data of patients with pulmonary IAC confirmed by surgical pathology in the Affiliated Huai'an First People's Hospital to Nanjing Medical University from December 2017 to September 2018 were collected. High-throughput feature acquisition was performed for all outlined regions of interest, and prediction models were constructed after dimensionality reduction by the minimum absolute shrinkage operator. Receiver operating characteristic curve was used to assess the predictive efficacy of clinical characteristic model, radiomics model and individualized prediction model combined with both to identify the degree of pulmonary IAC differentiation, and immunohistochemical expressions of Ki-67, NapsinA and TTF-1 were compared between groups with different degrees of IAC differentiation using rank sum test. Results: A total of 396 high-throughput features were extracted from all IAC lesions, and 10 features with high generalization ability and correlation with the degree of IAC differentiation were screened. The mean radiomics score of poorly differentiated IAC in the training group (1.206) was higher than that of patients with high and medium differentiation (0.969, P=0.001), and the mean radiomics score of poorly differentiated IAC in the test group (1.545) was higher than that of patients with high and medium differentiation (-0.815, P<0.001). The differences in gender (P<0.001), pleural stretch sign (P=0.005), and burr sign (P=0.033) were statistically significant between patients in the well and poorly differentiated IAC groups. Multifactorial logistic regression analysis showed that gender and pleural stretch sign were related to the degree of IAC differentiation (P<0.05). The clinical feature model consisted of age, gender, pleural stretch sign, burr sign, tumor vessel sign, and vacuolar sign, and the individualized prediction model consisted of gender, pleural stretch sign, and radiomic score, and was represented by a nomogram. The Akaike information standard values of the radiomics model, clinical feature model and individualized prediction model were 54.756, 82.214 and 53.282, respectively. The individualized prediction model was most effective in identifying the degree of differentiation of pulmonary IAC, and the area under the curves (AUC) of the individualized prediction model in the training group and the test group were 0.92 (95% CI: 0.86-0.99) and 0.88 (95% CI: 0.74-1.00, respectively). The AUCs of the radiomics group model for predicting the degree of differentiation of pulmonary IAC in the training group and the test group were 0.91 (95% CI: 0.83-0.98) and 0.87 (95% CI: 0.72-1.00), respectively. The AUCs of the clinical characteristics model for predicting the degree of differentiation of pulmonary IACs in the training and test groups were 0.75 (95% CI: 0.63-0.86) and 0.76 (95% CI: 0.59-0.94), respectively. The expression level of Ki-67 in poorly differentiated IAC was higher than that in well-differentiated IAC (P<0.001). The expression levels of NapsinA, TTF-1 in poorly differentiated IAC were higher than those in well-differentiated IAC (P<0.05). Conclusions: Individualized prediction model consisted of gender, pleural stretch sign and radiomics score can discriminate the differentiation degree of IAC with the best performance in comparison with clinical feature model and radiomics model. Ki-67, NapsinA and TTF-1 express differently in different degrees of differentiation of IAC.

Application of integrated GIS and multimedia modeling on NPS pollution evaluation

In Taiwan, nonpoint source (NPS) pollution is one of the major causes of the impairment of surface waters. I-Liao Creek, located in southern Taiwan, flows approximately 90 km and drains toward the Kaoping River. Field investigation results indicate that NPS pollution from agricultural activities is one of the main water pollution sources in the I-Liao Creek Basin. Assessing the potential of NPS pollution to assist in the planning of best management practice (BMP) is significant for improving pollution prevention and control in the I-Liao Creek Basin. In this study, land use identification in the I-Liao Creek Basin was performed by properly integrating the skills of geographic information system (GIS) and global positioning system (GPS). In this analysis, 35 types of land use patterns in the watershed area of the basin are classified with the aid of Erdas Imagine process system and ArcView GIS system. Results indicate that betel palm farms, orchard farms, and tea gardens dominate the farmland areas in the basin, and are scattered around on both sides of the river corridor. An integrated watershed management model (IWMM) was applied for simulating the water quality and evaluating NPS pollutant loads to the I-Liao Creek. The model was calibrated and verified with collected water quality and soil data, and was used to investigate potential NPS pollution management plans. Simulated results indicate that NPS pollution has significant contributions to the nutrient loads to the I-Liao Creek during the wet season. Results also reveal that NPS pollution plays an important role in the deterioration of downstream water quality and caused significant increase in nutrient loads into the basin's water bodies. Simulated results show that source control, land use management, and grassy buffer strip are applicable and feasible BMPs for NPS nutrient loads reduction. GIS system is an important method for land use identification and waste load estimation in the basin. Linking the information of land utilization with the NPS pollution simulation model may further provide essential information of potential NPS pollution for all subregions in the river basin. Results and experience obtained from this study will be helpful in designing the watershed management and NPS pollution control strategies for other similar river basins.