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Objective: To investigate the clinical outcomes and toxicity in patients with locally advanced cervical cancer treated with supplementary applicator guided‐intensity modulated radiation therapy (IMRT) based on conventional intracavitary brachytherapy (IC/IMRT). Design: A retrospective cohort study. Setting: Sichuan Cancer Hospital & Institute, Sichuan Cancer Centre, China. Population Large high‐risk clinical target volume (HR‐CTV) volume (>40 ml) at the time of brachytherapy cervical cancer patients were recruited. Methods: This study is a retrospective analysis of 76 patients with locally advanced cervical cancer (FIGO IIB‐IVA) treated with concurrent chemoradiotherapy followed by IC/IMRT between June 2010 and October 2016. External radiotherapy (45 Gy in 25 fractions) was adminstered with cisplatin chemotherapy treatment before IC/IMRT. The IMRT plan was optimised using the ICBT plan base dose plan by an inverse dose optimisation tool which allows the use of DVH constraints on the total dose of ICBT. A seven‐field gantry angle IMRT plan was devised to avoid hotspots when optimising the boost plan. The prescription dose for HR‐CTV and IR‐CTV were 6 and 5 Gy per fraction for five fractions, respectively. Results: Mean HR‐CTV was 65.8 ± 23.6 ml at the time of brachytherapy. D90 for HR‐CTV and IR‐CTV were 88.7 ± 3.6 Gy and 78.1 ± 2.5 Gy. D2cc for bladder, rectum, sigmoid and small intestine were 71.8 ± 3.8, 64.6 ± 4.9, 63.9 ± 5.3 and 56.7 ± 8.7 Gy, respectively. Median follow‐up was 85 months (47.9–124.2 months). Five‐year local recurrence‐free survival rate, metastasis recurrence‐free survival rate, disease‐free survival rate and cancer‐special survival rate were 87.6, 82.4, 70.9 and 76.3%, respectively. The grade 1 + 2 gastrointestinal and urinary late toxicities were 15.8 and 21.1%, and grade 3 late toxicities were 3.9 and 5.2%, respectively. Neither acute nor late grade 4 gastrointestinal or urinary toxicities were seen. Conclusions: The combination of ICBT with an applicator‐guided supplementary IMRT boost achieved excellent local control and overall survival with low toxicity for bulky residual cervical tumour. [ABSTRACT FROM AUTHOR]

• DBN models are improved using SA, PSO and SSA for spatial landslide susceptibility assessment (LSA). • LSA obtained using SA-DBN, PSO-DBN, and SSA-DBN are compared with that of the DBN model using ROC curve and SCAI. • SSA-DBN model has the highest accuracy in spatial LSA. • Temporal LSA is analyzed using the effective rainfall model (ERM) and peak ground acceleration (PGA). • Spatiotemporal LSA map is generated by coupling the optimal spatial LSA map and temporal LSA map. Landslide is one of the most serious geo-hazards, and the landslide susceptibility assessment (LSA) is an existing effective method to efficiently mitigate the loss caused by landslides. This study develops the improved deep belief network (DBN) models by selecting the optimal model hyperparameter to improve the accuracy of LSA. Evaluation factors of the LSA are selected from fifteen influencing factors by chi-square test, multicollinearity test and out-of-bag error. 30% data of the study area are selected randomly as the training data to assess the landslide susceptibility of each grid in the study area. The spatial LSA is then obtained by integrating the DBN models with three optimization algorithms, namely the simulated annealing (SA), particle swarm optimization (PSO) and sparrow search algorithm (SSA). The assessment results obtained using DBN and improved DBN models are thus compared and verified using the receiver operating characteristic (ROC) curve and seed cell area index. It shows that the three improved DBN models outperform the DBN model, which demonstrates the ability of optimization algorithms to improve model performance, and the SSA-DBN model achieves the highest assessment accuracy, followed by the PSO-DBN and SA-DBN models. Meanwhile, the effective rainfall model and peak ground acceleration are respectively employed to evaluate the impact of two inducing factors, namely the rainfall and earthquake, and the temporal LSA is thus obtained. The spatiotemporal LSA map is then generated by coupling the optimal spatial LSA map and temporal LSA map. Therefore, the present study further explores the proposed improved methods and offers instructions for spatiotemporal LSA. [ABSTRACT FROM AUTHOR]

A dataset of landslides from Sichuan Province in China, containing 1551 historical individual landslides, is a result of two teams' effort in the past few years to map the susceptibility to landslides. Considering complex internal relations among the triggering factors, logistic regression (LR) and shallow neural networks, such as back-propagation neural network (BPNN), are often limited. In this paper, we make a straightforward development that the deep belief network (DBN) based on deep learning technology is introduced to map the regional susceptibility to landslides. Seven factors with respect to geomorphology, geology and hydrology are considered and verified through the collinearity test. A DBN model containing three pre-trained layers of restricted Boltzmann machines by stochastic gradient descent method is configured to obtain the susceptibility to landslides. Susceptibility results evaluated by DBN model are compared with those by LR and BPNN in the receive operator characteristic (ROC) analysis, showing that DBN has a better prediction precision, with a lower rate of false alarms and fake alarms. The case study also indicates different sensitivities of the triggering factors to the landslide susceptibility, that the factors of altitude, distance to drainage network and average annual rainfall have significant impact in mapping the susceptibility to landslides in the region. This research will contribute to a better-performance model for regional-scale mapping for the susceptibility to landslides, in particular, at the area where triggering factors show complex relations and relative independence. [ABSTRACT FROM AUTHOR]

Featured Application: Landslide susceptibility assessment and other geological disaster assessment issues. Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study. [ABSTRACT FROM AUTHOR]