Machine Learning for Precision Oncology: From Bench to Bedside

Submission Deadline: 30 April 2026 View: 154 Submit to Special Issue

Guest Editors

Dr. Ramin Ranjbarzadeh

Email: ramin.ranjbarzadehkondrood2@mail.dcu.ie

Affiliation: School of Computing, Faculty of Engineering and Computing, Dublin City University, Dublin, Ireland

Homepage:

Research Interests: Machine Learning Deep Learning Brain tumor

Summary

Machine learning has emerged as a powerful tool in the field of precision oncology, revolutionizing the way cancer is diagnosed, treated, and managed. This special issue titled "Machine Learning for Precision Oncology: From Bench to Bedside" aims to explore the significant advancements and potential applications of machine learning in the context of personalized cancer care.

Machine learning algorithms have the ability to analyze vast amounts of diverse data, including genomic profiles, clinical data, imaging data, and electronic health records. By integrating these datasets, machine learning models can identify patterns, uncover hidden relationships, and make accurate predictions to assist in personalized cancer diagnosis, prognosis, and treatment selection. This special issue will provide a platform to showcase innovative research that applies machine learning techniques to improve precision oncology outcomes.

One of the key areas of focus for this special issue is the application of machine learning in cancer genomics. Machine learning algorithms can analyze genomic data to identify specific genetic alterations, classify tumor subtypes, and predict treatment responses. Additionally, these models can integrate multi-omics data, such as transcriptomics and proteomics, to gain a deeper understanding of the molecular mechanisms driving cancer progression and drug resistance. By leveraging machine learning in genomics, researchers can uncover novel biomarkers and therapeutic targets, paving the way for more targeted and effective cancer treatments.

Machine learning also plays a crucial role in medical imaging analysis for cancer diagnosis and treatment. Advanced imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), generate large volumes of complex data. Machine learning algorithms can extract meaningful features from these images, aid in tumor detection, segmentation, and quantification, and assist in the development of radiomic signatures for predicting treatment outcomes. Furthermore, machine learning models can integrate imaging data with other clinical and molecular data to enhance the accuracy of cancer diagnosis and monitoring.

Another important aspect to be explored in this special issue is the translation of machine learning models from bench to bedside. While the development of robust machine learning algorithms is crucial, the successful implementation of these models in clinical practice requires addressing various challenges, including interpretability, validation, and scalability. This special issue will provide a platform to discuss these challenges and explore novel approaches for the translation of machine learning into routine clinical decision-making.

In summary, this special issue aims to highlight the transformative role of machine learning in advancing precision oncology. By bringing together researchers, clinicians, and experts in the field, this special issue will foster discussions, showcase innovative methodologies, and facilitate collaborations to drive the integration of machine learning into routine cancer care, ultimately improving patient outcomes in the era of precision medicine.

Potential topics include but are not limited to the following:
· Development of machine learning models for personalized cancer diagnosis and prognosis.
· Integrating multi-omics data with machine learning algorithms to uncover molecular biomarkers for precision oncology.
· Application of machine learning in cancer genomics for identifying therapeutic targets and predicting treatment responses.
· Machine learning-based approaches for medical image analysis and radiomics in cancer diagnosis and treatment.
· Interpretability and explainability of machine learning models in precision oncology.
· Machine learning for prediction of treatment toxicity and adverse effects in cancer patients.
· Clinical implementation and validation of machine learning models for precision oncology.
· Machine learning for predicting therapeutic resistance and guiding treatment strategies.
· Deep learning models for analysis of histopathology images and improving cancer diagnosis accuracy.
· Ethical considerations and challenges in the application of machine learning in precision oncology.

Keywords