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From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images

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Abstract

Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.

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Acknowledgements

We extend our sincere appreciation to the Islamic World Educational, Scientific, and Cultural Organization (ICESCO) for their invaluable support in establishing the ICESCO Chair of Data Science and Analytics for Business at the National University of Sciences and Technology (NUST). This initiative has significantly contributed to the advancement of research and academic endeavors in the field, and we are grateful for their commitment to fostering excellence and innovation in Data Science and AI education.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Arshi Parvaiz, Esha Sadia Nasir & Muhammad Moazam Fraz

Authors
  1. Arshi Parvaiz
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  2. Esha Sadia Nasir
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  3. Muhammad Moazam Fraz
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Contributions

All authors contributed equally to the study’s conception, design, material preparation, data collection, and documentation.

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Correspondence to Muhammad Moazam Fraz.

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Parvaiz, A., Nasir, E.S. & Fraz, M.M. From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01049-2

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  • DOI: https://doi.org/10.1007/s10278-024-01049-2

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