Currently, many studies build prognostic models based on genomic data but neglect the importance of clinical features. Pathological examination of histopathological slides is a routine procedure for tumour diagnosis and prognosis, including breast cancer, skin cancer, etc. Traditional radiomics models can generate prognostic imaging signatures for overall survival prediction and patient stratification for tumours. Therefore, identification of histopathological slides and radiomics models contributes to more accurate treatment.

Previous studies have identified multiple potential prognostic signatures with remarkable clinical efficacy in cancer management based on omics data such as transcriptomics, proteomics, and epigenomics. Comprehensive research which integrates biomarkers, pathological features, and imaging signatures can be significant. It is expected that the development of systematic prognostic models will enhance disease diagnostics and promote clinical management. Machine learning has been applied to multiple areas due to its ability to process large-scale data, identify common features of different classifications, and offer guidance of clinical decision. Therefore, building prognostic models based on machine learning has received increasing attention. Machine learning includes random forest, support vector machine, artificial neural networks, etc. Recently in medicine, machine learning can assist with alternative splicing prediction, drug sensitivity scrutiny, patients’ survival outcome prediction, tumour diagnosis, and tumour classification. Therefore, adopting machine learning to identify prognostic factors like omics data, histopathological slides, and radiomics, and calculate corresponding prognostic models is a more precise prognostic prediction.

This Special Issue will focus on prognostic models based on machine learning for cancer research. We welcome original research as well as review articles.

Potential topics include but are not limited to the following:

• Prognostic models based on clinical features such as histopathological slides, radiomics, or other factors
• Prognostic models involved in different type of tumour, pan-cancer analysis, or data from variety database
• Machine learning-related prognostic prediction and drug sensitivity prediction
• Prognostic models based on traditional omics data, including mRNA, non-coding RNA, DNA methylation, etc.
• Verifying results on clinical samples or cell lines, or adopting multiple cohorts including training and verification cohorts