“…Furthermore, among the clinical traits, risk score had the largest area under the curve value; thus, our model was also superior to other clinical traits for predicting the survival of patients (Fig. 5 A) [ 19 ]. Fig.…”
Background
Autophagy inhibits tumorigenesis by limiting inflammation. LncRNAs regulate gene expression at various levels as RNAs; thus, both autophagy and lncRNAs are closely related to the occurrence and development of tumours.
Methods
A total of 232 autophagy-related genes were used to construct a coexpression network to extract autophagy-related lncRNAs. A prognostic signature was constructed by multivariate regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was applied to analyse enrichment in cancer-related pathways. Immune infiltration analysis was used to analyse the relationship between the prognostic signature and the tumour microenvironment.
Results
Nine autophagy-related lncRNAs were used to construct a prognostic model for non-small-cell lung cancer. The median risk score was used to discriminate the high- and low-risk groups, and the low-risk group was found to have better survival. Because KEGG pathway analysis showed that the prognostic signature was enriched in some immune pathways, further analysis of immune infiltration was conducted, and it was found that the prognostic signature did play a unique role in the immune microenvironment. Additionally, the prognostic signature was associated with clinical factors.
Conclusion
We constructed a prognostic model of autophagy-related lncRNAs that can predict the prognosis of non-small-cell lung cancer.
“…Furthermore, among the clinical traits, risk score had the largest area under the curve value; thus, our model was also superior to other clinical traits for predicting the survival of patients (Fig. 5 A) [ 19 ]. Fig.…”
Background
Autophagy inhibits tumorigenesis by limiting inflammation. LncRNAs regulate gene expression at various levels as RNAs; thus, both autophagy and lncRNAs are closely related to the occurrence and development of tumours.
Methods
A total of 232 autophagy-related genes were used to construct a coexpression network to extract autophagy-related lncRNAs. A prognostic signature was constructed by multivariate regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was applied to analyse enrichment in cancer-related pathways. Immune infiltration analysis was used to analyse the relationship between the prognostic signature and the tumour microenvironment.
Results
Nine autophagy-related lncRNAs were used to construct a prognostic model for non-small-cell lung cancer. The median risk score was used to discriminate the high- and low-risk groups, and the low-risk group was found to have better survival. Because KEGG pathway analysis showed that the prognostic signature was enriched in some immune pathways, further analysis of immune infiltration was conducted, and it was found that the prognostic signature did play a unique role in the immune microenvironment. Additionally, the prognostic signature was associated with clinical factors.
Conclusion
We constructed a prognostic model of autophagy-related lncRNAs that can predict the prognosis of non-small-cell lung cancer.
“…Additionally, when we retrospectively reviewed 66 AI-detected cancers, 42 (63.6%) had indiscernible findings obscured by dense breast tissue, whereas the remaining 24 were considered interpretative errors. Previous studies have shown that fatigue [ 24 25 ], hours awake and asleep [ 26 ], and annual and cumulative reading volumes [ 27 ] affect radiologists’ reading performance. As the interpretation process involves search, perception, and decision-making [ 28 29 ], faults in any of these processes can result in interpretative errors [ 30 ].…”
Despite recent advances in artificial intelligence (AI) software with improved performance in mammography screening for breast cancer, insufficient data are available on its performance in detecting cancers that were initially missed on mammography. In this study, we aimed to determine whether AI software-aided mammography could provide additional value in identifying cancers detected through supplemental screening ultrasound. We searched our database from 2017 to 2018 and included 238 asymptomatic patients (median age, 50 years; interquartile range, 45–57 years) diagnosed with breast cancer using supplemental ultrasound. Two unblinded radiologists retrospectively reviewed the mammograms using commercially available AI software and identified the reasons for missed detection. Clinicopathological characteristics of AI-detected and AI-undetected cancers were compared using univariate and multivariate logistic regression analyses. A total of 253 cancers were detected in 238 patients using ultrasound. In an unblinded review, the AI software failed to detect 187 of the 253 (73.9%) mammography cases with negative findings in retrospective observations. The AI software detected 66 cancers (26.1%), of which 42 (63.6%) exhibited indiscernible findings obscured by overlapping dense breast tissues, even with the knowledge of magnetic resonance imaging and post-wire localization mammography. The remaining 24 cases (36.4%) were considered interpretive errors by the radiologists. Invasive tumor size was associated with AI detection after multivariable analysis (odds ratio, 2.2; 95% confidence intervals, 1.5–3.3;
p
< 0.001). In the control group of 160 women without cancer, the AI software identified 19 false positives (11.9%, 19/160). Although most ultrasound-detected cancers were not detected on mammography with the use of AI, the software proved valuable in identifying breast cancers with indiscernible abnormalities or those that clinicians may have overlooked.
“…The radiology community must continue to identify and implement solutions to reduce the negative impacts of fatigue on radiologist performance. Based on the findings of this study by Alshabibi et al [8], potential systematic solutions that could boost the performance of radiologists interpreting screening mammograms may include modifying schedules to prioritize the interpretation of screening mammograms to times other than the very early morning when sleep pressure may be highest. Maintaining adequate staffing and instigating thoughtful scheduling to avoid excessive workloads and long work hours may additionally promote adequate sleep, reduce radiologist fatigue, and lower the potential for fatigue-related imaging interpretation errors.…”
Section: Matthew F Covington MDmentioning
confidence: 99%
“…Important downstream consequences of burnout in radiologists include sleep problems and errors in patient care [5,7]. The accompanying article by Alshabibi et al [8], although not directly addressing burnout, does compare screening mammography performance between more and less experienced readers as a function of reduced sleep and interpretation of studies within 2 hours of waking when homeostatic sleep pressure is highest. The study authors found that more experienced readers are able to maintain lesion sensitivity while interpreting screening mammograms independent of these variables.…”
Section: Matthew F Covington MDmentioning
confidence: 99%
“…Despite progress, burnout remains a serious problem for the radiology profession. Given the mutually reinforcing relationship between burnout and fatigue, the findings of Alshabibi et al [8] that reduced waking hours and reduced night sleep duration lower the diagnostic accuracy of less experienced radiologists is important. The ultimate solutions to this problem may be challenging to implement and pertain to restoring physical balance, worktime off balance, mindfulness, and other factors already proposed by an expert panel of radiologists to overcome burnout [3].…”
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