This study presents a comprehensive systematic review focusing on the applications of deep learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 scientific publications following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we selected 153 papers for an in-depth analysis. These papers were categorized based on imaging modality, deep learning model type, and practical applications in lung cancer, such as detection and survival prediction. We specifically emphasized deep learning models and examined their strengths and limitations for each application and imaging modality. Furthermore, we identified potential limitations within the field and proposed future research directions. This study serves as a pioneering resource, being the first comprehensive and systematic review of deep learning techniques, specifically in the context of lung cancer-related applications. Our primary objective was to provide a reference for future research, encouraging the advancement of deep learning techniques in the diagnosis and treatment of lung cancer. By suggesting the most effective deep learning tools for specific application areas, we offer a benchmark for future studies. In summary, this study consolidates and expands existing knowledge on deep learning and radiomics applications in lung cancer. It provides a foundation for further research and serves as a guide for developing and evaluating deep learning models in lung cancer-related applications. 

Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques

An understanding of the neurocognitive profile underlying the use of social networking sites (SNSs) can help inform decisions about the classification of problematic SNS use as an addictive disorder and elucidate how/when ‘SNS addiction’ might develop. The present review aimed to synthesize structural and functional MRI research investigating problematic/compulsive forms of SNS use or regular (non-addicted) SNS use behaviours. We conducted a systematic search for research articles published in English using the Web of Science, PubMed, and Scopus databases up to October 2022. Studies meeting our inclusion criteria were assessed for quality and a narrative synthesis of the results was conducted. Twenty-eight relevant articles were identified comprising structural MRI (n = 9), resting-state fMRI (n = 6) and task-based fMRI studies (n = 13). Current evidence suggests that problematic SNS use might be characterised by (1) reduced volume of the ventral striatum, amygdala, subgenual anterior cingulate cortex, orbitofrontal cortex and posterior insula; (2) increased ventral striatum and precuneus activity in response to SNS cues; (3) abnormal functional connectivity involving the dorsal attention network; (4) inter-hemispheric communication deficits. Regular SNS use behaviours appear to recruit regions involved in the mentalising network, the self-referential cognition network, the salience network, the reward network and the default mode network. Such findings are at least partially consistent with observations from the substance addiction literature and provide some provisional support for the addictive potential of SNSs. Nonetheless, the present review is limited by the small number of eligible studies and large heterogeneity in the methods employed, and so our conclusions should remain tentative. Moreover, there is a lack of longitudinal evidence suggesting SNSs cause neuroadaptations and thus conclusions that problematic SNS use represents a disease process akin to substance use addictions are premature. More well-powered longitudinal research is needed to establish the neural consequences of excessive and problematic SNS use.

/pdf/a-systematic-review-of-structural-and-functional-mri-studies-1rdx7d7g.pdf

A Systematic Review of Structural and Functional MRI Studies Investigating Social Networking Site Use

Owing to the limitations of cross‐sectional studies, it is unclear whether social media induce brain changes, or if individuals with certain biological traits are more likely to use social media. Functional connectivity (FC) can reflect cerebral functional plasticity, and if social media can influence cerebral FC, then the FC of light social media users should be more similar to that of heavy users after they “heavily” used social media for a long period. We combined longitudinal study design and intersubject correlation (ISC) analysis to investigate this similarity. Thirty‐five heavy and 21 light social media users underwent cognitive tests and functional MRIs. The 21 light social media users underwent another functional MRI scan after completing an additional four‐week social media task. We conducted the ISC at the group, individual, and brain‐region levels to investigate the similarity of FC and locate the brain regions most affected by social media. The FC of light social media users was more similar to that of heavy social media users after they completed the four‐week social media task. Then, social media had an impact on half of the brain, involving almost all brain networks. Finally, cerebral FC that mostly affected by social media was associated with selective attention. We concluded that the impact of social media use on cerebral functional connectivity changes is revealed by ISC method and longitudinal design, which may provide guidance for clinical practice. The methods used in the current research could also be applied to similar domains.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.25786

Intersubject correlation analysis reveals the plasticity of cerebral functional connectivity in the long‐term use of social media

Wood elasticity and compressible wood-based materials: Functional design and applications

The design and application of sustainable structures are promising approaches to achieve energy conservation and emission reduction in construction and buildings. Cork stoppers have high recycling value and can be particularly used as the core of eco-friendly sandwich structures. The object of this paper is to assess the eco-mechanical performance of a more sustainable sandwich structure made of eco-friendly cores (natural or agglomerated cork stopper), aluminum alloy skins and resin adhesive. A set of sandwich panels with different packing arrangements are fabricated. Experimental tests are conducted to address the effects of packing topology and cork stoppers' materials on bending characteristics and failure modes. The life cycle assessment method is adopted to quantify the environmental impacts, and the eco-mechanical performances are further evaluated. The results show that this sandwich structure concept with cork stopper cores has good structural and resource-environment efficiency and could be a promising alternative in construction and buildings. 

Bending and environmental characteristics of an eco-friendly sandwich panel with cork stopper cores

This study aimed to establish and compare the radiomics machine learning (ML) models based on non-contrast enhanced computed tomography (NECT) and clinical features for predicting the simplified risk categorization of thymic epithelial tumors (TETs).A total of 509 patients with pathologically confirmed TETs from January 2009 to May 2018 were retrospectively enrolled, consisting of 238 low-risk thymoma (LRT), 232 high-risk thymoma (HRT), and 39 thymic carcinoma (TC), and were divided into training (n = 433) and testing cohorts (n = 76) according to the admission time. Volumes of interest (VOIs) covering the whole tumor were manually segmented on preoperative NECT images. A total of 1218 radiomic features were extracted from the VOIs, and 4 clinical variables were collected from the hospital database. Fourteen ML models, along with varied feature selection strategies, were used to establish triple-classification models using the radiomic features (radiomic models), while clinical-radiomic models were built after combining with the clinical variables. The diagnostic accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of radiologist assessment, the radiomic and clinical-radiomic models were evaluated on the testing cohort.The Support Vector Machine (SVM) clinical-radiomic model demonstrated the highest AUC of 0.841 (95% CI 0.820 to 0.861) on the cross-validation result and reached an AUC of 0.844 (95% CI 0.793 to 0.894) in the testing cohort. For the one-vs-rest question of LRT vs HRT + TC, the sensitivity, specificity, and accuracy reached 80.00%, 63.41%, and 71.05%, respectively. For HRT vs LRT + TC, they reached 60.53%, 78.95%, and 69.74%. For TC vs LRT + HRT they reached 33.33%, 98.63%, and 96.05%, respectively. Compared with the radiomic models, superior diagnostic efficacy was demonstrated for most clinical-radiomics models, and the AUC of the Bernoulli Naive Bayes model was significantly improved. Radiologist2's assessment achieved a higher AUC of 0.813 (95% CI: 0.756-0.8761) than other radiologists, which was slightly lower than the SVM clinical-radiomic model. Combined with other evaluation indicators, SVM, as the best ML model, demonstrated the potential of predicting the simplified risk categorization of TETs with superior predictive performance to that of radiologists' assessment.Most of the ML models are promising in predicting the simplified TETs risk categorization with superior efficacy to that of radiologists' assessment, especially the SVM models, demonstrated the integration of ML with NECT may be valuable in aiding the diagnosis and treatment planning. 

http://www.lungcancerjournal.info/article/S0169500222003762/pdf

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.

Modal characteristics of a sustainable sandwich structure with cork stopper cores

Predicting the risk grades of thymic epithelial tumors using T1 mapping and diffusion-weighted MRI.

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Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.

Intersubject correlation analysis reveals the plasticity of cerebral functional connectivity in the long‐term use of social media

Modal characteristics of a sustainable sandwich structure with cork stopper cores

Predicting the risk grades of thymic epithelial tumors using T1 mapping and diffusion-weighted MRI.