Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data

Bagante, Fabio; Spolverato, Gaya; Ruzzenente, Andrea; Luchini, Claudio; Tsilimigras, Diamantis I.; Campagnaro, Tommaso; Conci, Simone; Corbo, Vincenzo; Scarpa, Aldo; Guglielmi, Alfredo; Pawlik, Timothy M.

doi:10.1016/j.ejca.2021.01.049

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…Previous research has utilized machine learning (ML) and deep learning (DL) to predict leukemia cancer using single data [ 83 , 84 ]. To analyze data related to breast cancer and other types, prior investigations have preferred DL algorithms such as CNN, RNN, ANN, and VAE (Variational Autoencoder) with relu activation function and BSE as loss function [ [85] , [86] , [87] , [88] , [89] ]. Feature selection methods such as PCA, RF Recursive selection, and Chi-square have been widely used in earlier research.…”

Section: Resultsmentioning

confidence: 99%

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

Abbasi,

Deng,

Ali

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

Abbasi,

Deng,

Ali

et al. 2024

Heliyon

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“…Although the BCLC staging system is instrumental in guiding recommendations for guiding therapies, unfavorable outcomes remain higher than hoped [43]. In response to this problem, Bagante et al proposed a strategy for advancing staging system accuracy, which in theory would subsequently improve patient prognosis, treatment, and overall outcomes [44]. They advocated for the integration of molecular classifications and biomarkers with clinical data for staging purposes and illustrated the improved prognostic accuracy in comparison with standard staging systems.…”

Section: The Role Of Ai In Facilitating Biomarkers To Stage Liver Can...mentioning

confidence: 99%

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Mansur

Vrionis

Charles

et al. 2023

Cancers

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Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

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“…Bagante et al integrated whole-exome sequencing data with the help of artificial neural networks for cell-of-origin pattern prediction and molecular subtypes classification of hepato-pancreato-biliary cancers. Combining the clinical data and the above information, they also analyzed the prognosis of cancer patients using random survival forest and Cox analysis 260 .…”

Section: Other Applications Of Ai In Pcmentioning

confidence: 99%

Artificial intelligence in pancreatic cancer

Huang¹,

Huang²,

Zhang³

et al. 2022

Theranostics

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Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

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Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data

Cited by 6 publications

References 41 publications

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Artificial intelligence in pancreatic cancer

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