Suicide rates among patients with first and second primary cancer

Abstract Aims With advancements in cancer treatments, the survival rates of patients with their first primary cancer (FPC) have increased, resulting in a rise in the number of patients with second primary cancer (SPC). However, there has been no assessment on the incidence of suicide among patients with SPC. This study assessed the occurrence of suicide among patients with SPC and compared them with that in patients with FPC. Methods This was a retrospective, population-based cohort study that followed patients with FPC and SPC diagnosed from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) 17 registries database between 1 January 2000 and 31 December 2019. Results For patients with SPC, an age of 85+ years at diagnosis was associated with a higher incidence of suicide death (HR, 1.727; 95% CI, 1.075–2.774), while the suicide death was not considerably different in the chemotherapy group (P > 0.05). Female genital system cancers (HR, 3.042; 95% CI, 1.819–6.361) accounted for the highest suicide death among patients with SPC. The suicide death distribution of patients with SPC over time indicated that suicide events mainly occurred within 5 to 15 years of diagnosis. Compared with patients with FPC, patients with SPC in general had a lower risk of suicide, but increased year by year. Conclusion The risk of suicide was reduced in patients with SPC compared with patients with FPC, but increased year by year. Therefore, oncologists and related health professionals need to provide continuous psychological support to reduce the incidence of suicide. The highest suicide death was found among patients with female genital system cancer

Stress and Corrosion Defect Identification in Weak Magnetic Leakage Signals Using Multi-Graph Splitting and Fusion Graph Convolution Networks

Weak magnetic flux leak detection is one of the most important non-destructive testing and measurement methods for pipelines. Since different defects cause different damage, it is necessary to classify the different types of defects. Traditional machine learning methods of defect type identification mainly use feature analysis methods and rely on expert a priori knowledge and the ability of designers. These methods have the following weaknesses: a priori knowledge needs to be designed iteratively, and a priori knowledge design relies on expert experience. In recent years, the rapid development of deep learning methods in the field of machine vision has led to the development of defect analysis in the industry. However, most deep learning methods lack the ability to analyze both detailed information and the overall structure. In this paper, we propose graph convolution networks for splitting and fusing multiple graphs of detail graphs and a root graph. Detail information (detail graphs) provides detailed information for the detection of WMFLs. The structure information (root graph) provides structural information for the detection of WMFLs. This paper uses simulation data and experimental data to verify that the proposed method can identify stress defects and corrosion defects well. The paper explains the experimental results in detail to demonstrate the superiority of the method in industrial methods

Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features

Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect detection and inversion. Current methods, based on machine learning and deep learning have been able to classify signals with significant differences. However, the ultrasonic internal detection signal is interspersed with a large number of anomalous signals of an unknown origin and is affected by the time shift of echo features and noise interference, which leads to the low recognition accuracy of the ultrasonic internal detection signal, at this stage. To address the above problems, this paper proposes a two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES). In the first stage, a normal signal classification method, based on the envelope feature extraction and fusion is proposed to solve the problem of the low ultrasonic signal classification accuracy under the conditions of the echo feature time shift and noise interference. In the second stage, an abnormal signal detection method, based on the local similarity feature extraction and enhancement is proposed to solve the problem of detecting abnormal signals in ultrasound internal detection data. The experimental results show that the accuracy of the two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES) in this paper is 97.43%, and the abnormal signal detection accuracy and recall rate are as high as 99.7% and 97.81%

A Fast Globally Convergent Particle Swarm Optimization for Defect Profile Inversion Using MFL Detector

For the problem of defect inversion in magnetic flux leakage technology, a fast, globally convergent particle swarm optimization algorithm based on the finite-element forward model is introduced as an inverse iterative algorithm in this paper. Two aspects of the traditional particle swarm optimization algorithm have been improved: self-adaptive inertia weight and speed updating strategy. For the inertia weight, it can be adaptively adjusted according to the particle position. The speed update strategy mainly uses the best experience positions of other particles in a randomly selected population to realize the algorithm’s learning. At the same time, the learning factor of the position variable is designed to change with the number of iteration steps. The particle with a good position is added to jump out of the local minimum and accelerate the optimization process. Through the comparison experiment, the improved particle swarm optimization algorithm has a faster convergence speed compared with other traditional particle swarm optimization algorithms. It is more difficult for it to fall into the local minimum value and it is more easily converted to a higher precision

Abdominal DIBH reduces the cardiac dose even further: a prospective analysis

Abstract Background Deep inspiration breath hold (DIBH) can be performed using different breathing maneuvers, such as DIBH with a thoracic breathing maneuver (T-DIBH) and DIBH with an abdominal breathing maneuver (A-DIBH). Dosimetric benefits of A-DIBH were investigated in the treatment of left-sided breast cancer radiotherapy (RT) with both 3-Dimensional conformal radiation therapy (3D-CRT) and intensity-modulated radiotherapy (IMRT) techniques. Methods Twenty-two patients with left-sided breast cancer were enrolled in this study. 3D-CRT and IMRT plans were generated for each patient with three different CT scans of free breathing (FB), T-DIBH and A-DIBH. There were total of six treatment plans generated for each patient: FB_3D-CRT; TDIBH_3D-CRT; ADIBH_3D-CRT; FB-IMRT; TDIBH-IMRT; ADIBH-IMRT. Doses to the heart, left anterior descending coronary artery (LADCA), and ipsilateral lung were evaluated and compared using the Wilcoxon signed-rank test. Results The mean doses to the heart, LADCA and ipsilateral lung in 3D-CRT plans generated from 3D-CRT with FB, T-DIBH and A-DIBH were (2.89 ± 1.30), (1.67 ± 0.90) and (1.34 ± 0.43) Gy (all P  0.05), respectively, with A-DIBH. Conclusions This study indicates that both 3D-CRT and IMRT plans with A-DIBH achieved lower cardiac and LADCA doses than plans with FB and T-DIBH; 3D-CRT plans with A-DIBH achieved lower ipsilateral lung doses than plans with FB and T-DIBH; and IMRT plans with A-DIBH had better outcomes than 3D-CRT plans with A-DIBH with respect to the mean dose to the heart, LADCA and ipsilateral lung. IMRT plans with A-DIBH should be incorporated into the daily routine for left-sided breast RT

Postoperative radiotherapy for glioma: improved delineation of the clinical target volume using the geodesic distance calculation.

OBJECTS: To introduce a new method for generating the clinical target volume (CTV) from gross tumor volume (GTV) using the geodesic distance calculation for glioma. METHODS: One glioblastoma patient was enrolled. The GTV and natural barriers were contoured on each slice of the computer tomography (CT) simulation images. Then, a graphic processing unit based on a parallel Euclidean distance transform was used to generate the CTV considering natural barriers. Three-dimensional (3D) visualization technique was applied to show the delineation results. Speed of operation and precision were compared between this new delineation method and the traditional method. RESULTS: In considering spatial barriers, the shortest distance from the point sheltered from these barriers equals the sum of the distance along the shortest path between the two points; this consists of several segments and evades the spatial barriers, rather than being the direct Euclidean distance between two points. The CTV was generated irregularly rather than as a spherical shape. The time required to generate the CTV was greatly reduced. Moreover, this new method improved inter- and intra-observer variability in defining the CTV. CONCLUSIONS: Compared with the traditional CTV delineation, this new method using geodesic distance calculation not only greatly shortens the time to modify the CTV, but also has better reproducibility

Inter- and intra-observer variability.

<p>(A) Inter-observer variability in the multiple contours from five observers; (B) Intra-observer variability involving a single physician at different times.</p

The flow process diagram regarding calculation of the CTV.

<p><i>CTV: clinical target volume.</i></p

Schematic diagram of growth iterations.

<p>Schematic diagram of growth iterations.</p