Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA)

Kirby, Justin; Prior, Fred; Petrick, Nicholas; Hadjiski, Lubomir; Farahani, Keyvan; Drukker, Karen; Kalpathy–Cramer, Jayashree; Glide-Hurst, Carri K; Naqa, Issam El

doi:10.1002/mp.14595

Cited by 11 publications

(17 citation statements)

References 14 publications

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“…Immunophenotype scores with a scale ranging from 0 to 10 was calculated using the expression of representative genes or immune manifestation of gene sets ( Xu et al, 2021 ). The IPS of LUAD patients were obtained from the Cancer Immunome Atlas (TCIA) framework ( Kirby et al, 2020 ). Furthermore, IPS z-score is regarded as an integration of the four phenotypes: MHC, CPs, SCs and ECs.…”

Section: Methodsmentioning

confidence: 99%

A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma

Ding

Zhang

et al. 2022

Front. Genet.

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Backgrounds: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis, and immune escape of cancers. We aim to investigate Long non-coding RNAs (lncRNAs) that are correlated to NETs to find some potentially useful biomarkers for lung adenocarcinoma (LUAD), and to explore their correlations with immunotherapy and chemotherapy, as well as the tumor microenvironment.Methods: Based on the The Cancer Genome Atlas (TCGA) database, we identified the prognosis-related lncRNAs which are associated with NETs using cox regression. The patients were then separated into two clusters based on the expression of NETs-associated lncRNAs to perform tumor microenvironment analysis and immune-checkpoint analysis. Least absolute shrinkage and selection operator (LASSO) regression was then performed to establish a prognostic signature. Furthermore, nomogram analysis, tumor mutation burden analysis, immune infiltration analysis, as well as drug sensitivity analysis were performed to test the signature.Results: Using univariate cox regression, we found 10 NETs-associated lncRNAs that are associated with the outcomes of LUAD patients. Also, further analysis which separated the patients into 2 clusters showed that the 10 lncRNAs had significant correlations with the tumor microenvironment. Using LASSO regression, we finally constructed a signature to predict the outcomes of the patients based on 4 NETs-associated lncRNAs. The 4 NETs-associated lncRNAs were namely SIRLNT, AL365181.3, FAM83A-AS1, and AJ003147.2. Using Kaplan-Meier (K-M) analysis, we found that the risk model was strongly associated with the survival outcomes of the patients both in the training group and in the validation group 1 and 2 (p < 0.001, p = 0.026, and p < 0.01). Using receiver operating characteristic (ROC) curve, we tested the sensitivity combined with the specificity of the model and found that the risk model had a satisfactory level of 1-year, 3-year, and 5-year concordance index (C-index) (C = 0.661 in the training group, C = 0.679 in validation group 1, C = 0.692 in validation group 2). We also explored the immune microenvironment and immune checkpoint correlation of the risk model and found some significant results.Conclusion: We constructed a NETs-associated lncRNA signature to predict the outcome of patients with LUAD, which is associated with immunephenoscores and immune checkpoint-gene expression.

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Section: Methodsmentioning

confidence: 99%

A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma

Ding

Zhang

et al. 2022

Front. Genet.

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“…The higher the score, the stronger the immunogenicity. The IPSs (including IPS, IPS-CTLA4, IPS-CTLA4/PD-L1/PD1/PD-L2, and IPS-PD1/PD-L1/PD-L2 scores), acquired from The Cancer Immunome Atlas [TCIA ( )] ( 28 ), were applied to assess the response of UCEC patients for ICI.…”

Section: Methodsmentioning

confidence: 99%

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

et al. 2021

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BackgroundUterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.MethodsCIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan–Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.ResultsRed module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.ConclusionWe developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

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“…Approaches that are driven by the data that are collected from patients can help us gain a better knowledge of cancer and how it affects individuals, given the growing number of patient records that are now available. It is a laudable project that will be altered how cancer care is delivered, even though ML poses several difficult technological and organizational hurdles [21][22][23]. Figure 2 shows the potential applications of machine learning in oncology.…”

Section: Potential Implications Of Machine Learning In Oncologymentioning

confidence: 99%

“…Cancer screening practices and policies might be improved with the help of these early warning systems. Most importantly, they may allow for quicker treatment and better health results for patients [21,22].…”

Section: Diagnosis and Early Detectionmentioning

confidence: 99%

The Evolution and Reliability of Machine Learning Techniques for Oncology

Owida¹,

Moh’d²,

Turab

et al. 2023

Int. J. Onl. Eng.

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It is no secret that the rise of the Internet and other digital technologies has sparked renewed interest in AI-based techniques, especially those that fall under the umbrella of the subset of algorithms known as "Machine Learning" (ML). These advancements in electronics have allowed us to comprehend the world beyond the bounds of human cognition. A high-dimensional dataset's complicated nature. Although these techniques have been regularly employed by the medical sciences, their adoption to enhance patient care has been a bit slow. The availability of curated diverse data sets for model development is all examples of the substantial hurdles that have delayed these efforts. The future clinical acceptance of each of these characteristics may be affected by a number of limiting conditions, such as the time and resources spent on data collection and model development, the cost of integration relative to the time and resources spent on translation, and the potential for patient damage. In order to preserve value and enhance medical care, the goal of this article is to evaluate all facets of the issue in light of the validity of using ML methods in cancer, to serve as a template for further research and the subfield of oncology that serves as a model for other parts of the discipline.

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Introduction to special issue on datasets hosted in The Cancer Imaging Archive (TCIA)

Cited by 11 publications

References 14 publications

A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma

A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

The Evolution and Reliability of Machine Learning Techniques for Oncology

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