1
|
Salama V, Godinich B, Geng Y, Humbert-Vidan L, Maule L, Wahid KA, Naser MA, He R, Mohamed ASR, Fuller CD, Moreno AC. Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review. J Pain Symptom Manage 2024:S0885-3924(24)00908-4. [PMID: 39097246 DOI: 10.1016/j.jpainsymman.2024.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/22/2024] [Accepted: 07/23/2024] [Indexed: 08/05/2024]
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.
Collapse
Affiliation(s)
- Vivian Salama
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Brandon Godinich
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Medical Education (B.G.), Paul L. Foster School of Medicine, Texas Tech Health Sciences Center, El Paso, TX, USA
| | - Yimin Geng
- Research Medical Library (Y.G.), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laia Humbert-Vidan
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Maule
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kareem A Wahid
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mohamed A Naser
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renjie He
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Clifton D Fuller
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amy C Moreno
- Department of Radiation Oncology (V.S., B.G., L.H.V., L.M., K.A.W., M.A.N., R.H., A.S.R.M., C.D.F., A.C.M), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
2
|
Salama V, Godinich B, Geng Y, Humbert-Vidan L, Maule L, Wahid KA, Naser MA, He R, Mohamed ASR, Fuller CD, Moreno AC. Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299610. [PMID: 38105979 PMCID: PMC10723503 DOI: 10.1101/2023.12.06.23299610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background/objective Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). 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 of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.
Collapse
|