ArticleMedical & biological engineering & computing2020
DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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31 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Deep Learning-Based Multimodal Fusion of Whole-Slide Images and RNA Sequencing Identifies Survival-Relevant Glioblastoma Clusters.Cancer medicine · 2026Article
- A unified multi modal transformer framework for breast cancer recurrence prediction and survival analysis.Scientific reports · 2026Article
- Multimodal deep learning for cancer prognosis prediction with clinical information prompts integration.NPJ digital medicine · 2025Article
- Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers.Scientific reports · 2025Article
- Fusion-Brain-Net: A Novel Deep Fusion Model for Brain Tumor Classification.Brain and behavior · 2025Article
- Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types.Journal of imaging informatics in medicine · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Deep learning and pathomics analyses predict prognosis of high-grade gliomas.Frontiers in neurology · 2025Article
- Scoping review of image-based overall survival prediction in glioma using machine learning.Polish journal of radiology · 2025Review
- Automatic ovarian follicle detection using object detection models.Scientific reports · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.BMC medical imaging · 2024Article
- Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.Scientific reports · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions.Machine learning with applications · 2024Article
- A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks.Scientific reports · 2024Article
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case.Journal of neurosurgery. Case lessons · 2023Article
- Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning.Cancers · 2022Article
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients' survival rate based on histopathological images (class I, 0-6 months; class II, 6-12 months; class III, 12-24 months; and class IV, >24 months survival after diagnosis). After training and testing of DeepSurvNet model on a public brain cancer dataset, The Cancer Genome Atlas, we have generalized it using independent testing on unseen samples. Using DeepSurvNet, we obtained precisions of 0.99 and 0.8 in the testing phases on the mentioned datasets, respectively, which shows DeepSurvNet is a reliable classifier for brain cancer patients' survival rate classification based on histopathological images. Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer. Graphical abstract A DCNN model was generated to accurately predict survival rates of brain cancer patients (classified in 4 different classes) accurately. After training the model using images from H&E stained tissue biopsies from The Cancer Genome Atlas database (TCGA, left), the model can predict for each patient, based on a histological image (top right), its survival class accurately (bottom right).
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