This study aimed to compare fixed‐field, intensity‐modulated radiotherapy (f‐IMRT) with intensity‐modulated arc therapy (IMAT) treatment plans in dosimetry and practical application for cervical esophageal carcinoma. For ten cervical esophageal carcinoma cases, f‐IMRT plan (seven fixed‐fields) and two IMAT plans, namely RA (coplanar 360° arcs) and RAx (coplanar 360° arcs without sectors from 80° to 110°, and 250° to 280°), were generated. DVHs were adopted for the statistics of above parameters, as well as conformal index (CI), homogeneity index (HI), dose‐volumetric parameters of normal tissues, total accelerator output MUs and total treatment time. There were differences between RAx and f‐IMRT, as well as RA in PTV parameters such as HI, and , but not in CI. RAx reduced lung from ( in f‐IMRT and ( in RA to ( in RAx . However, lung and MLD increased in RAx. There was no difference in the mean heart dose in three plans. Total MU was reduced from in f‐IMRT to in RA and in RAx . Compared with f‐IMRT, IMAT reduced low dose volumes of lung and total MU on the basis of meeting clinical requirements.PACS numbers: 87.55.D, 87.55.dk, 87.55.ne

Whole-brain radiotherapy (WBRT) plays an irreplaceable role in the treatment of brain metastases (BMs), but cognitive decline after WBRT seriously affects patients’ quality of life. The development of cognitive dysfunction is closely related to hippocampal injury, but standardized criteria for predicting hippocampal injury and dose limits for hippocampal protection have not yet been developed. This review systematically reviews the clinical efficacy of hippocampal avoidance - WBRT (HA-WBRT), the controversy over dose limits, common methods and characteristics of hippocampal imaging and segmentation, differences in hippocampal protection by common radiotherapy (RT) techniques, and the application of artificial intelligence (AI) and radiomic techniques for hippocampal protection. In the future, the application of new techniques and methods can improve the consistency of hippocampal dose limit determination and the prediction of the occurrence of cognitive dysfunction in WBRT patients, avoiding the occurrence of cognitive dysfunction in patients and thus benefiting more patients with BMs.

Dendritic river networks are fundamental elements in cartography, and the generalization of these river networks directly influences the quality of cartographic generalization. Automatic selection is a difficult and important process for river generalization that requires the consideration of semantic, geometric, topological, and structural characteristics. However, owing to a lack of effective use of river features, most existing methods lose important spatial distribution characteristics of rivers, thus affecting the selection result. Therefore, a hierarchical elimination selection method of dendritic river networks is proposed that consists of three steps. First, a directed topology tree (DTT) is investigated to realize the organization of river data and the intelligent identification of river structures. Second, based on the “180° hypothesis” and “acute angle hypothesis”, each river is traced in the upstream direction from its estuary to create the stroke connections of dendritic river networks based on a consideration of the river semantics, length, and angle features, and the hierarchical relationships of a dendritic river network are then determined. Finally, by determining the total number of selected rivers, a hierarchical elimination algorithm that accounts for density differences is proposed. The reliability of the proposed method was verified using sample data tests, and the rationality and validity of the method were demonstrated in experiments using actual data.

The aim of this study was to determine whether functional dose-volume histograms (FDVHs) are valuable for predicting radiation pneumonitis (RP), and to identify whether FDVHs have advantages over conventional dose-volume histograms (DVHs) for the prediction of RP in patients with locally advanced non-small cell lung cancer (LANSCLC). Fifty-seven patients with LANSCLC undergoing functional image-guided late-course accelerated hyperfractionated radiotherapy were enrolled. The grade of RP was evaluated according to the Common Toxicity Criteria 3.0. To identify predictive factors of RP, the FDVHs, including the volume of the functional lung receiving 5 Gy (FV5) through 50 Gy (FV50), mean perfusion-weighted lung dose (MPWLD) and functional normal tissue complication probability (FNTCP), were analyzed and compared to their counterparts [total lung receiving 5 Gy (V5) through 50 Gy (V50), mean lung dose (MLD) and normal tissue complication probability (NTCP)] derived from conventional DVHs. Univariate analysis revealed that V5-V40, MLD, NTCP and FV5-FV50, MPWLD, FNTCP were all statistically significant relative to the development of RP (all p<0.05). Multivariate analysis showed that only MLD and FV15 were associated with RP (p=0.001 and 0.044, respectively). Receiver operator characteristic curve anaysis indicated that almost all of the FDVHs had larger areas under the curve compared to the DVHs, although no statistically significant difference was observed (p-value ranged from 0.066 to 0.951). FDVHs are valuable for predicting RP with the predictive efficiency equivalent to or slightly advantageous over conventional DVHs. More homogeneous studies involving larger numbers of patients are required to further assess the value of FDVHs for predicting RP.

Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense junction areas, so a skeleton line extraction method considering stroke features has been proposed in this paper. Firstly, we put forward a long-edge adaptive node densification algorithm, which is used to construct boundary-constrained Delaunay triangulation to uniformly divide the polygon and extract the initial skeleton line. Secondly, we defined the triangles with three adjacent triangles (Type III) as the basic unit of junctions, then obtained the segmented areas with dense junctions on the basis of local width characteristics and correlation relationships of each Type III triangle. Finally, we concatenated the segments into strokes and corrected the initial skeleton lines based on the extension direction features of each stroke. The actual water network data of Jiangsu Province in China were used to verify the method. Experimental results show that the proposed method can better identify the areas with dense junctions and that the extracted skeleton line is naturally smooth and well-connected, which accurately reflects the main structure and extension characteristics of these areas.