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Article: Early stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model

TitleEarly stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model
Authors
Keywordscomputational biology
computed tomography
lung cancer
medical imaging
medicine
none
prognostic model
survival
systems biology
transformer cnn
Issue Date4-Oct-2022
PublishereLife Sciences Publications
Citation
eLife, 2022, v. 11 How to Cite?
AbstractBACKGROUND: We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. METHODS: The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was calculated for both OS and RFS prediction. The Kaplan-Meier method was used to generate prognostic and survival estimates for low- and high-risk groups, along with net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. An additional subanalysis was conducted to examine the relationship between clinical data and imaging features associated with risk prediction. RESULTS: Our model achieved AUC values of 0.785 (95\% confidence interval [CI]: 0.716-0.855) and 0.695 (95\% CI: 0.603-0.787) on the testing and external data sets for OS prediction, and 0.726 (95\% CI: 0.653-0.800) and 0.700 (95\% CI: 0.615-0.785) for RFS prediction. Additional survival analyses indicated that our model outperformed the present TNM and ResNet-Graph models in terms of net benefit for survival prediction. CONCLUSIONS: Our Transformer-Graph model was effective at predicting survival in patients with early stage lung cancer, which was constructed using both imaging and non-imaging clinical features. Some high-risk patients were distinguishable by using a similarity score function defined by non-imaging characteristics such as age, gender, histology type, and tumour location, while Transformer-generated features demonstrated additional benefits for patients whose non-imaging characteristics were non-discriminatory for survival outcomes. FUNDING: The study was supported by the National Natural Science Foundation of China (91959126, 8210071009), and Science and Technology Commission of Shanghai Municipality (20XD1403000, 21YF1438200).
Persistent Identifierhttp://hdl.handle.net/10722/337956
ISSN
2023 Impact Factor: 6.4
2023 SCImago Journal Rankings: 3.932
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLian, J-
dc.contributor.authorDeng, J-
dc.contributor.authorHui, ES-
dc.contributor.authorKoohi-Moghadam, M-
dc.contributor.authorShe, Y-
dc.contributor.authorChen, C-
dc.contributor.authorVardhanabhuti, V-
dc.date.accessioned2024-03-11T10:25:11Z-
dc.date.available2024-03-11T10:25:11Z-
dc.date.issued2022-10-04-
dc.identifier.citationeLife, 2022, v. 11-
dc.identifier.issn2050-084X-
dc.identifier.urihttp://hdl.handle.net/10722/337956-
dc.description.abstractBACKGROUND: We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. METHODS: The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was calculated for both OS and RFS prediction. The Kaplan-Meier method was used to generate prognostic and survival estimates for low- and high-risk groups, along with net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. An additional subanalysis was conducted to examine the relationship between clinical data and imaging features associated with risk prediction. RESULTS: Our model achieved AUC values of 0.785 (95\% confidence interval [CI]: 0.716-0.855) and 0.695 (95\% CI: 0.603-0.787) on the testing and external data sets for OS prediction, and 0.726 (95\% CI: 0.653-0.800) and 0.700 (95\% CI: 0.615-0.785) for RFS prediction. Additional survival analyses indicated that our model outperformed the present TNM and ResNet-Graph models in terms of net benefit for survival prediction. CONCLUSIONS: Our Transformer-Graph model was effective at predicting survival in patients with early stage lung cancer, which was constructed using both imaging and non-imaging clinical features. Some high-risk patients were distinguishable by using a similarity score function defined by non-imaging characteristics such as age, gender, histology type, and tumour location, while Transformer-generated features demonstrated additional benefits for patients whose non-imaging characteristics were non-discriminatory for survival outcomes. FUNDING: The study was supported by the National Natural Science Foundation of China (91959126, 8210071009), and Science and Technology Commission of Shanghai Municipality (20XD1403000, 21YF1438200).-
dc.languageeng-
dc.publishereLife Sciences Publications-
dc.relation.ispartofeLife-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectcomputational biology-
dc.subjectcomputed tomography-
dc.subjectlung cancer-
dc.subjectmedical imaging-
dc.subjectmedicine-
dc.subjectnone-
dc.subjectprognostic model-
dc.subjectsurvival-
dc.subjectsystems biology-
dc.subjecttransformer cnn-
dc.titleEarly stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.7554/eLife.80547-
dc.identifier.scopuseid_2-s2.0-85139572217-
dc.identifier.volume11-
dc.identifier.eissn2050-084X-
dc.identifier.isiWOS:000961539900001-
dc.identifier.issnl2050-084X-

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