Abstract
The reconstruction algorithm based on the network of generative adversarial and contextual coding (RANGC) was proposed in this study to analyze the impacts on the prognosis of patients with giant thoracic tumors with CT imaging under artificial intelligence algorithms. The algorithms of Feldkamp–Davis–Kress (FDK) and the generative adversarial network (GAN) were introduced. The patients were divided into the test group with comfort care and the control group with conventional care. Three sets of indicators below were also compared between the patients in two groups, including the pain level and complication incidence, the self-rating anxiety scale (SAS), the self-rating depression scale (SDS), and the patients’ satisfaction and average duration of hospital stay. When the scanning range was [0°, 89°], the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) under the RANGC algorithm were 45.6 dB and 0.99, respectively. When the scanning range changed to [0°, 119°], the PSNR and SSIM were 39.21 dB and 0.98, respectively. The results were significantly higher than those under the FDK and GAN algorithms, and the difference was obviously of statistical significance P < 0.05 . The average postoperative pain level of the patients in the control group was 3.12 points, and the postoperative complication incidence was 36.13%, while those of the test group patients were 2.27 points and 20.02%, respectively, which was greatly lower than those of the control group patients, and such a difference was of statistical significance P < 0.05 . There was no statistically significant difference in the SDS and SAS scores between the patients in the two groups before surgery. However, the SAS and SDS scores of the test group patients were 41.23 and 43.25, respectively, after surgery, which are obviously lower than those of the control group patients, with a statistically significant difference P < 0.05 . The average duration of hospital stay of the test group patients was 6.31 days, which was lower than that of the control group patients, with a statistically significant difference P < 0.05 . The overall satisfaction of the test group patients was 83.33%, which was remarkably higher than that of the control group patients, and the difference had statistical significance P < 0.05 . All these showed that the performance of the RANGC algorithm was relatively better, and comfort care did good to improve the negative mood, satisfaction, and life quality of patients after surgery.
Highlights
A giant thoracic tumor is a kind of rare tumor clinically [1,2,3]
To verify the performance of the algorithm, peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) were compared when the FDK [20] and generative adversarial network (GAN) [21] algorithms were run in the scanning range of [0°, 89°] and [0°, 119°]. e results were shown in Figure 2 and Figure 3
Compared to the results in the scanning range of [0°, 89°], PSNR was reduced by 14.6%, and SSIM was reduced by 2%
Summary
A giant thoracic tumor is a kind of rare tumor clinically [1,2,3]. Because of its complicated pathological types and prognosis, there is no unified standard for the diagnosis and treatment, and surgical resection becomes the major clinical treatment means [4,5,6,7]. e specific location, origin, and adjacency relations of the tumor can be assessed accurately by an imaging examination. e tumor adhesion and blood supply can be identified, and the feasibility of complete surgical resection would be determined [8, 9]. Because of its complicated pathological types and prognosis, there is no unified standard for the diagnosis and treatment, and surgical resection becomes the major clinical treatment means [4,5,6,7]. E tumor adhesion and blood supply can be identified, and the feasibility of complete surgical resection would be determined [8, 9]. Comfort care is a holistic, creative, and personalized nursing model, aiming to make people pleasant at the psychological, physical, and social communication levels. Ye et al (2021) [10] explored the impact of comfort care on the recovery quality of patients with lung cancer. Comfort care reduced complications during the recovery effectively and was worthy of clinical application
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