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Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges

Marwa Afnouch, Fares Bougourzi, Olfa Gaddour, Fadi Dornaika, Abdelmalik Taleb-Ahmed

TL;DR

This review addresses the problem of analyzing bone metastases (BM) with artificial intelligence by surveying studies across imaging modalities (bone scintigraphy, SPECT, CT, MRI, and hybrids) and tasks (classification, segmentation, detection, and multi-task analyses). It adopts a PRISMA-guided methodology to synthesize recent work (2012–2024), compare against prior surveys, and highlight public/private datasets, model architectures, and performance trends. Key contributions include a comprehensive mapping of BM AI research, critical discussion of data limitations and methodological gaps, and forward-looking directions such as semi/self-supervised learning, transformer-based models, and multi-task systems for clinical translation. The practical impact lies in guiding researchers and clinicians toward robust, data-efficient, and interpretable AI tools that can assist in BM detection, characterization, and treatment planning, while underscoring the need for rigorous validation and data sharing. The review ultimately provides a roadmap for advancing BM AI from research to routine clinical use.

Abstract

In recent years, Artificial Intelligence (AI) has been widely used in medicine, particularly in the analysis of medical imaging, which has been driven by advances in computer vision and deep learning methods. This is particularly important in overcoming the challenges posed by diseases such as Bone Metastases (BM), a common and complex malignancy of the bones. Indeed, there have been an increasing interest in developing Machine Learning (ML) techniques into oncologic imaging for BM analysis. In order to provide a comprehensive overview of the current state-of-the-art and advancements for BM analysis using artificial intelligence, this review is conducted with the accordance with PRISMA guidelines. Firstly, this review highlights the clinical and oncologic perspectives of BM and the used medical imaging modalities, with discussing their advantages and limitations. Then the review focuses on modern approaches with considering the main BM analysis tasks, which includes: classification, detection and segmentation. The results analysis show that ML technologies can achieve promising performance for BM analysis and have significant potential to improve clinician efficiency and cope with time and cost limitations. Furthermore, there are requirements for further research to validate the clinical performance of ML tools and facilitate their integration into routine clinical practice.

Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges

TL;DR

This review addresses the problem of analyzing bone metastases (BM) with artificial intelligence by surveying studies across imaging modalities (bone scintigraphy, SPECT, CT, MRI, and hybrids) and tasks (classification, segmentation, detection, and multi-task analyses). It adopts a PRISMA-guided methodology to synthesize recent work (2012–2024), compare against prior surveys, and highlight public/private datasets, model architectures, and performance trends. Key contributions include a comprehensive mapping of BM AI research, critical discussion of data limitations and methodological gaps, and forward-looking directions such as semi/self-supervised learning, transformer-based models, and multi-task systems for clinical translation. The practical impact lies in guiding researchers and clinicians toward robust, data-efficient, and interpretable AI tools that can assist in BM detection, characterization, and treatment planning, while underscoring the need for rigorous validation and data sharing. The review ultimately provides a roadmap for advancing BM AI from research to routine clinical use.

Abstract

In recent years, Artificial Intelligence (AI) has been widely used in medicine, particularly in the analysis of medical imaging, which has been driven by advances in computer vision and deep learning methods. This is particularly important in overcoming the challenges posed by diseases such as Bone Metastases (BM), a common and complex malignancy of the bones. Indeed, there have been an increasing interest in developing Machine Learning (ML) techniques into oncologic imaging for BM analysis. In order to provide a comprehensive overview of the current state-of-the-art and advancements for BM analysis using artificial intelligence, this review is conducted with the accordance with PRISMA guidelines. Firstly, this review highlights the clinical and oncologic perspectives of BM and the used medical imaging modalities, with discussing their advantages and limitations. Then the review focuses on modern approaches with considering the main BM analysis tasks, which includes: classification, detection and segmentation. The results analysis show that ML technologies can achieve promising performance for BM analysis and have significant potential to improve clinician efficiency and cope with time and cost limitations. Furthermore, there are requirements for further research to validate the clinical performance of ML tools and facilitate their integration into routine clinical practice.
Paper Structure (28 sections, 19 figures, 5 tables)

This paper contains 28 sections, 19 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Survey Methodology
  3. Related Surveys
  4. Papers Selection
  5. Papers Distribution
  6. Bone Metastasis: Overview
  7. Bone Metastases in Medical Oncology
  8. Common Primary Cancers Leading to Bone Metastases
  9. Imaging Modalities for Bone Metastasis Diagnosis
  10. Datasets
  11. Public Datasets
  12. Private Datasets
  13. Machine Learning Tasks in Bone Metastasis
  14. Machine Learning for BM Classification
  15. BM Classification using Bone Scintigraphy
  16. ...and 13 more sections

Figures (19)

  • Figure 1: Survey Search Strategy
  • Figure 2: A chronological distribution of artificial intelligence research publications in bone metastasis analytic
  • Figure 3: Anatomic localization of skeletal metastases from lung cancer sugiura2008predictors
  • Figure 4: Metastatic Bone Lesions Jinnah_2018
  • Figure 5: Distribution of Medical Imaging Modalities in Reviewed Articles
  • ...and 14 more figures