Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases.

Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation.

Prognostic Factors of Gliosarcoma in the Real World: A Retrospective Cohort Study.

Purpose: Gliosarcoma is a histopathological variant of glioblastoma, which is characterized by a biphasic growth pattern consisting of glial and sarcoma components. Owing to its scarcity, data regarding the impact of available treatments on the clinical outcomes of gliosarcoma are inadequate. The purpose of this retrospective cohort study was to analyze the prognostic factors of gliosarcoma. Methods: By screening the clinical database of neurosurgical cases at a single center, patients with gliosarcoma diagnosed histologically from 2013 to 2021 were identified. Clinical, pathological, and molecular data were gathered founded on medical records and follow-up interviews. Prognostic factors were derived using the Cox proportional hazards model with backward stepwise regression analysis. Results: Forty-five GSM patients were included. Median overall survival was 25.6 months (95% CI 8.0-43.1), and median relapse-free survival was 15.2 months (95% CI 9.7-20.8). In multivariable analysis, total resection (p = 0.023, HR = 0.192, 95% CI 0.046-0.797) indicated an improved prognosis. And low expression of Ki-67 (p = 0.059, HR = 2.803, 95% CI 0.963-8.162) would be likely to show statistical significance. However, there might be no statistically significant survival benefit from radiotherapy with concurrent temozolomide (n = 33, 73.3%, log-rank p = 0.99) or adjuvant temozolomide (n = 32, 71.1%, log-rank p = 0.74). Conclusion: This single-center retrospective study with a limited cohort size has demonstrated the treatment of gross total resection and low expression of Ki-67 which are beneficial for patients with GSM, while radiotherapy or temozolomide is not.

Survival and prognostic factors in patients undergoing the resection of solitary brain metastasis from non-small cell lung cancer: a retrospective cohort study.

Background: Neurosurgery is the standard of care for resectable solitary brain metastasis (BM) from non-small cell lung cancer (NSCLC), but still with a poor outcome. Postoperative whole-brain radiotherapy (WBRT) was reported to reduce local recurrence, whether it could prolong survival was uncertain. In this study, we attempted to evaluate WBRT and other prognostic for overall survival (OS) in these patients. Methods: In this retrospective study, NSCLC patients with a solitary BM and controlled primary tumor who underwent neurosurgical resection were selected from the medical records database between January 2014 and December 2018. Clinical data, disease control/progression results and survival outcomes were obtained from the medical records, regular outpatient follow-up and telephone interviews. Univariable and multivariable Cox analyses of potential prognostic factors including patients' characteristics, BM features, tissue-based parameters and postoperative treatments were conducted. OS was illustrated using Kaplan-Meier curves, and group differences were assessed using the log-rank test. The subgroup analysis compared each variable between the WBRT group and the untreated control by the hazard ratio and its 95% confidence interval (CI). Results: A total of 94 patients were included, with a median OS of 812 days. Univariable analysis showed that postoperative WBRT and targeted therapy were associated with OS. Multivariable analysis demonstrated that postoperative WBRT [P<0.001, hazard ratio (HR) 0.357], chemotherapy (P=0.008, HR 0.512), targeted therapy (P<0.001, HR 0.265), and smaller tumor size (P=0.018, HR 0.553) were independent prognostic factors for prolonged OS. However, tissue-based parameters (Ki67 tumor cell proliferation index, epidermal growth factor receptor, and checkpoint levels) were identified as statistically insignificant factors. In the subgroup analysis, the beneficial effect of WBRT was only observed in patients that did not receive systematic treatments. Conclusions: Postoperative WBRT and systematic treatments after solitary BM resection improve the prognosis of NSCLC patients with a controlled primary tumor. Postoperative WBRT could be considered, especially for those who not receive systematic chemotherapy or targeted therapy treatments, as they might be more likely to benefit from it.

Construction of an immune-related gene signature for the prognosis and diagnosis of glioblastoma multiforme.

Background: Increasing evidence has suggested that inflammation is related to tumorigenesis and tumor progression. However, the roles of immune-related genes in the occurrence, development, and prognosis of glioblastoma multiforme (GBM) remain to be studied. Methods: The GBM-related RNA sequencing (RNA-seq), survival, and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases. Immune-related genes were obtained from the Molecular Signatures Database (MSigDB). Differently expressed immune-related genes (DE-IRGs) between GBM and normal samples were identified. Prognostic genes associated with GBM were selected by Kaplan-Meier survival analysis, Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and multivariate Cox analysis. An immune-related gene signature was developed and validated in TCGA and CGGA databases separately. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore biological functions of the signature. The correlation between immune cell infiltration and the signature was analyzed by single-sample gene set enrichment analysis (ssGSEA), and the diagnostic value was investigated. The gene set enrichment analysis (GSEA) was performed to explore the potential function of the signature genes in GBM, and the protein-protein interaction (PPI) network was constructed. Results: Three DE-IRGs [Pentraxin 3 (PTX3), TNFSF9, and bone morphogenetic protein 2 (BMP2)] were used to construct an immune-related gene signature. Receiver operating characteristic (ROC) curves and Cox analyses confirmed that the 3-gene-based prognostic signature was a good independent prognostic factor for GBM patients. We found that the signature was mainly involved in immune-related biological processes and pathways, and multiple immune cells were disordered between the high- and low-risk groups. GSEA suggested that PTX3 and TNFSF9 were mainly correlated with interleukin (IL)-17 signaling pathway, nuclear factor kappa B (NF-κB) signaling pathway, tumor necrosis factor (TNF) signaling pathway, and Toll-like receptor signaling pathway, and the PPI network indicated that they could interact directly or indirectly with inflammatory pathway proteins. Quantitative real-time PCR (qRT-PCR) indicated that the three genes were significantly different between target tissues. Conclusion: The signature with three immune-related genes might be an independent prognostic factor for GBM patients and could be associated with the immune cell infiltration of GBM patients.