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. 2024 Dec;68(6):e462-e490.
doi: 10.1016/j.jpainsymman.2024.07.025. Epub 2024 Aug 3.

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

Vivian Salama  1 , Brandon Godinich  2 , Yimin Geng  3 , Laia Humbert-Vidan  4 , Laura Maule  4 , Kareem A Wahid  4 , Mohamed A Naser  4 , Renjie He  4 , Abdallah S R Mohamed  4 , Clifton D Fuller  4 , Amy C Moreno  4
Affiliations

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

Vivian Salama et al. J Pain Symptom Manage. 2024 Dec.

Abstract

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).

Conclusion: Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Keywords: Cancer pain; artificial intelligence; cancer pain management; machine learning.

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Conflict of interest statement

Conflict of interest:

Authors declare that they have no known competing commercial, financial interests or personal relationships that could be constructed as potential conflict of interest.

Figures

Figure 1:
Figure 1:
Types and clinical applications of Artificial Intelligence (AI) models.
Figure 2:
Figure 2:
PRISMA Flow Diagram for systematic reviews of AI and ML in cancer pain research.
Figure 3:
Figure 3:
a. Publications trends for AI/ML models used for cancer pain research between 2006-2023. b. Types of studies and the number of articles per each type.
Figure 4:
Figure 4:
a. Distribution of articles by AI/ML algorithm type. b. 'Frequency of ML algorithm applied in multiple model articles' (M; multiple).
Figure 5:
Figure 5:
a. Median area under the receiver operating curve (AUC) across all included studies by subgroups. b. Median area under the receiver operating curve (AUC) across all included studies by AI/ML model.
Figure 6:
Figure 6:
Frequency of adherence on included studies to reporting checklist of Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines.
Figure 7:
Figure 7:
Frequency of risk-of-bias assessment of included studies using Prediction Model Risk of Bias Assessment Tool (PROBAST).
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