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. 2024 Jul 3:7:1428501.
doi: 10.3389/frai.2024.1428501. eCollection 2024.

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Ahtisham Fazeel Abbasi  1   2 , Muhammad Nabeel Asim  2 , Sheraz Ahmed  2 , Sebastian Vollmer  1   2 , Andreas Dengel  1   2
Affiliations

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Ahtisham Fazeel Abbasi et al. Front Artif Intell. .

Abstract

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Keywords: artificial intelligence; cancer; machine learning; multiomics; survival prediction.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
An end-to-end survival prediction pipeline.
Figure 2
Figure 2
PRISMA flow diagram: a step-by-step process for articles search and their inclusion or exclusion criteria to generate a set of studies for further in-depth trends analysis. The included papers are collected from Jan 2020 to Jul 2023.
Figure 3
Figure 3
Cancer subtypes coverage based on pan-cancer or individual subtype settings.
Figure 4
Figure 4
Survival endpoint distribution across diverse studies.
Figure 5
Figure 5
Distribution of explored survival prediction streams from existing literature. DFS, disease-free survival; PFS, progression-free survival; OS, overall survival; BC, biochemical recurrence.
Figure 6
Figure 6
Distribution of omics data modalities across a diverse set of diseases. Bar heights represent the counts of each data modality with respect to disease specific published research papers. For instance, CNV has been used in six papers related to breast cancer, mRNA has been used in seven breast cancer papers and so on.
Figure 7
Figure 7
Distribution of different omics modalities with respect to survival endpoints.
Figure 8
Figure 8
Hierarchal illustration of survival prediction methods under three different categories.
Figure 9
Figure 9
Journal-wise distribution of articles.
Figure 10
Figure 10
Publisher-wise distribution of articles.
See this image and copyright information in PMC

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