Application of Deep Learning in Cancer Prognosis Prediction Model.

As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.

Nondeformed Ultrasound Image Production Method for Ultrasound-Guided Radiotherapy.

Purpose: During ultrasound (US)-guided radiotherapy, the tissue is deformed by probe pressure, and the US image is limited by changes in tissue and organ position and geometry when the US image is aligned with computed tomography (CT) image, leading to poor alignment. Accordingly, a pixel displacement-based nondeformed US image production method is proposed. Methods: The correction of US image deformation is achieved by calculating the pixel displacement of an image. The positioning CT image (CTstd) is used as the gold standard. The deformed US image (USdef) is inputted into the Harris algorithm to extract corner points for selecting feature points, and the displacement of adjacent pixels of feature points in the US video stream is calculated using the Lucas-Kanade optical flow algorithm. The moving least squares algorithm is used to correct USdef globally and locally in accordance with image pixel displacement to generate a nondeformed US image (USrev). In addition, USdef and USrev were separately aligned with CTstd to evaluate the improvement of alignment accuracy through deformation correction. Results: In the phantom experiment, the overall and local average correction errors of the US image under the optimal probe pressure were 1.0944 and 0.7388 mm, respectively, and the registration accuracy of USdef and USrev with CTstd was 0.6764 and 0.9016, respectively. During the volunteer experiment, the correction error of all 12 patients' data ranged from -1.7525 to 1.5685 mm, with a mean absolute error of 0.8612 mm. The improvement range of US and CT registration accuracy, before and after image deformation correction in the 12 patients evaluated by a normalized correlation coefficient, was 0.1232 to 0.2476. Conclusion: The pixel displacement-based deformation correction method can solve the limitation imposed by image deformation on image alignment in US-guided radiotherapy. Compared with USdef, the alignment results of USrev with CT were better.

Altered spontaneous brain activities in maintenance hemodialysis patients with cognitive impairment and the construction of cognitive function prediction models.

OBJECTIVE: The brain neuromechanism in maintenance hemodialysis patients (MHD) with cognitive impairment (CI) remains unclear. The study aimed to probe the relationship between spontaneous brain activity and CI by using resting-state functional magnetic resonance imaging (rs-fMRI) data. METHODS: Here, 55 MHD patients with CI and 28 healthy controls were recruited. For baseline data, qualitative data were compared between groups using the χ2 test; quantitative data were compared between groups using the independent samples t-test, ANOVA test, Mann-Whitney U-test, or Kruskal-Wallis test. Comparisons of ALFF/fALFF/ReHo values among the three groups were calculated by using the DPABI toolbox, and then analyzing the correlation with clinical variables. p < .05 was considered a statistically significant difference. Furthermore, back propagation neural network (BPNN) was utilized to predict cognitive function. RESULTS: Compared with the MHD-NCI group, the patients with MHD-CI had more severe anemia and higher urea nitrogen levels, lower mALFF values in the left postcentral gyrus, lower mfALFF values in the left inferior temporal gyrus, and greater mALFF values in the right caudate nucleus (p < .05). The above-altered indicators were correlated with MOCA scores. BPNN prediction models indicated that the diagnostic efficacy of the model which inputs were hemoglobin, urea nitrogen, and mALFF value in the left central posterior gyrus was optimal (R2 = 0.8054), validation cohort (R2 = 0.7328). CONCLUSION: The rs-fMRI can reveal the neurophysiological mechanism of cognitive impairment in MHD patients. In addition, it can serve as a neuroimaging marker for diagnosing and evaluating cognitive impairment in MHD patients.

Radiation-induced miR-208a increases the proliferation and radioresistance by targeting p21 in human lung cancer cells.

BACKGROUND: Lung cancer has long been the most dangerous malignant tumor among males in both well developed and poorly developed countries. Radiotherapy plays a critical role in the curative management of inoperable non-small cell lung cancer (NSCLC) and is also used as a post-surgical treatment in lung cancer patients. Radioresistance is an important factor that limits the efficacy of radiotherapy for NSCLC patients. Increasing evidence suggests that microRNAs (miRNAs) possess diverse cellular regulatory roles in radiation responses. METHODS: In this study, we used miRNA microarray technology to identify serum miRNAs that were differentially expressed before and after radiotherapy in lung cancer patients. We further examined the biological function of miR-208a on cell viability, apoptotic death and cell cycle distribution in human lung cancer cells and explored the probable mechanism. RESULTS: Nine miRNAs, including miR-29b-3p, miR-200a-3p, and miR-126-3p were significantly down-regulated, whereas miR-208a was the only miRNA that was up-regulated in the serum of the patients after radiation treatment (P < 0.05). The expression of miR-208a could be induced by X-ray irradiation in lung cancer cells. Forced expression of miR-208a promoted cell proliferation and induced radioresistance via targeting p21 with a corresponding activation of the AKT/mTOR pathway in lung cancer cells, whereas down-regulation of miR-208a resulted in the opposite effects. In addition, down-regulation of miR-208a increased the percentage of cells undergoing apoptosis and inhibited the G1 phase arrest in NSCLC cells. Moreover, miR-208a from the serum exosome fraction of lung cancer patients could shuttle to A549 cells in a time-dependent manner, which was likely to contribute to the subsequent biological effects. CONCLUSIONS: The present study provides evidence that miR-208a can affect the proliferation and radiosensitivity of human lung cancer cells by targeting p21 and can be transported by exosomes. Thus, miR-208a may serve as a potential therapeutic target for lung cancer patients.