Topic Editors

Department of Mechanical and Automotive Engineering, School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Prof. Dr. Ali Hekmatnia
Radiology Department, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
Biosciences Institute , Newcastle University, Newcastle upon Tyne, UK
Dr. Ali Jamali
Department of Mechanical, Manufacturing and Mechatronic Engineering, RMIT, Melbourne, Australia

Artificial Intelligence in Cancer Pathology and Prognosis

Abstract submission deadline
28 February 2025
Manuscript submission deadline
1 May 2025
Viewed by
564

Topic Information

Dear Colleague,

Artificial Intelligence (AI) has transformed the landscape of cancer pathology and prognosis, presenting unparalleled opportunities for early detection, precise diagnosis, and tailored treatment strategies. This topic delves into the latest advancements in AI technologies, encompassing machine learning algorithms, deep learning models, and computer vision techniques, applied across various domains of cancer pathology. From the analysis of histopathological images to the identification of biomarkers and the prediction of patient outcomes, AI-driven approaches have exhibited remarkable efficacy in enhancing the accuracy and efficiency of both cancer diagnosis and prognosis. This topic elucidates key research findings, addresses prevailing challenges, and outlines future directions for leveraging AI to augment cancer care and management, ultimately resulting in improved patient outcomes and advancements in precision oncology. All papers from related journals are welcome for peer review.

Dr. Hamid Khayyam
Prof. Ali Hekmatnia
Dr. Rahele Kafieh
Dr. Ali Jamali
Topic Editors

Keywords

  • AI
  • machine learning
  • cancer pathology
  • prognosis
  • machine learning
  • deep learning
  • computer vision
  • precision oncology
  • histopathological images
  • biomarkers
  • patient outcomes

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Cancers
cancers
5.2 8.0 2009 17.9 Days CHF 2900 Submit
Current Oncology
curroncol
2.6 3.3 1994 18 Days CHF 2200 Submit
Diagnostics
diagnostics
3.6 4.7 2011 20.7 Days CHF 2600 Submit
Diseases
diseases
3.7 0.8 2013 18.8 Days CHF 1800 Submit
Onco
onco
- - 2021 18.3 Days CHF 1000 Submit

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Published Papers (1 paper)

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28 pages, 963 KiB  
Review
Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition
by Hamidreza Ashayeri, Navid Sobhi, Paweł Pławiak, Siamak Pedrammehr, Roohallah Alizadehsani and Ali Jafarizadeh
Cancers 2024, 16(11), 2138; https://doi.org/10.3390/cancers16112138 - 4 Jun 2024
Viewed by 371
Abstract
Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, [...] Read more.
Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions. Full article
(This article belongs to the Topic Artificial Intelligence in Cancer Pathology and Prognosis)
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