2023
DOI: 10.3389/fneur.2023.1100933
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Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning

Abstract: BackgroundA deep learning (DL) model based on representative biopsy tissues can predict the recurrence and overall survival of patients with glioma, leading to optimized personalized medicine. This research aimed to develop a DL model based on hematoxylin-eosin (HE) stained pathological images and verify its diagnostic accuracy.MethodsOur study retrospectively collected 162 patients with glioma and randomly divided them into a training set (n = 113) and a validation set (n = 49) to build a DL model. The HE-sta… Show more

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Cited by 9 publications
(10 citation statements)
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“…Nine studies focussed on predicting patient prognosis directly from histopathological images. [70][71][72][73][74][75][76][77][78][79] Most studies adopted a multi-modal approach, integrating histological data with other modalities such as radiological, genomic or clinical data. Patients were stratified into survival probability groups or derived survival predictions through regression analysis.…”
Section: Goal 7: Survival and Outcome Predictionmentioning
confidence: 99%
See 3 more Smart Citations
“…Nine studies focussed on predicting patient prognosis directly from histopathological images. [70][71][72][73][74][75][76][77][78][79] Most studies adopted a multi-modal approach, integrating histological data with other modalities such as radiological, genomic or clinical data. Patients were stratified into survival probability groups or derived survival predictions through regression analysis.…”
Section: Goal 7: Survival and Outcome Predictionmentioning
confidence: 99%
“…AI models improved performance when considering data from multiple modalities compared to histopathological data alone. 72,[77][78][79] No studies explicitly showed that histopathology data alone performed better or similar to multimodal data.…”
Section: Goal 7: Survival and Outcome Predictionmentioning
confidence: 99%
See 2 more Smart Citations
“…With the advancement of digital imaging and computer technology in recent years, computational pathology based on whole slide images (WSI) for artificial intelligence (AI)-assisted analysis is rapidly developing ( 7 ). For example, computational pathology can predict tumor classification ( 8 , 9 ), prognosis ( 10 ), and molecular mutations based on histopathological images, etc. ( 11 , 12 ).…”
Section: Introductionmentioning
confidence: 99%