ArticleIEEE journal of translational engineering in health and medicine2023
Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients.
Article in IEEE journal of translational engineering in health and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
- An efficient patient's response predicting system using multi-scale dilated ensemble network framework with optimization strategy.Scientific reports · 2025Article
- Integrating support vector machines and deep learning features for oral cancer histopathology analysis.Biology methods & protocols · 2025Article
- Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients.Cancer medicine · 2023Article
- A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging.Frontiers in artificial intelligence · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver a decade, tissues dissected adjacent to primary tumors have been considered "normal" or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers.
methodsThis paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs.
resultsTen-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods.
conclusionPreliminary results not only add objective evidence to recent findings of NATs' molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. CLINICAL IMPACT: The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.
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