Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.
Konstantina Kourou,Konstantinos P. Exarchos,Costas Papaloukas,Prodromos Sakaloglou,Themis Exarchos,Dimitrios I. Fotiadis,Dimitrios I. Fotiadis +6 more
TLDR
In this article, the authors present the most indicative studies with respect to the ML algorithms and data used in cancer research and provide a thorough examination of the clinical scenarios with regards to disease diagnosis, patient classification and cancer prognosis and survival.Abstract:
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.read more
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Journal ArticleDOI
Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection
Alessandro Allegra,Alessandro Tonacci,Raffaele Sciaccotta,Sara Genovese,Caterina Musolino,Giovanni Pioggia,Sebastiano Gangemi +6 more
TL;DR: This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multipleMyeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment.
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Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease
TL;DR: A review of the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects is presented in this article , where the authors performed a review from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review.
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A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence
Sanjeevi Pandiyan,Li E. Wang +1 more
TL;DR: In this paper , the authors explored molecular docking and their interactions to recognize metabolic activities that support drug design and highlighted corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
Journal ArticleDOI
Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis
Valentina Russo,Eleonora Lallo,Armelle Munnia,Miriana Spedicato,Luca Messerini,Romina D'Aurizio,Elia Giuseppe Ceroni,Giulia Brunelli,Antonio Galvano,Antonio Russo,Ida Landini,Stefania Nobili,Marcello Ceppi,Marco Bruzzone,Fabio Cianchi,Fabio Staderini,Mario Roselli,Silvia Riondino,F. Ferroni,Fiorella Guadagni,Enrico Mini,Marco Peluso +21 more
TL;DR: This systematic review of the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 showed that all investigations conducted in this field provided generally promising results in predicting the response to therapy or toxic side-effects.
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Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models
TL;DR: A deep learning model using convolutional neural networks, which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans is proposed.
References
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