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. 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. 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%). 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.

Objectives: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetric factors. This study proposes the use of machine learning (ML) methods as prediction models for mandible ORN incidence. Methods: A total of 96 patients (ORN incidence ratio of 1:1) treated between 2011 and 2015 were selected from the local HNC toxicity database. Demographic, clinical and dosimetric data (based on the mandible dose–volume histogram) were considered as model variables. Prediction accuracy (measured using a stratified fivefold nested cross-validation), sensitivity, specificity, precision and negative predictive value were used to evaluate the prediction performance of a multivariate logistic regression (LR) model, a support vector machine (SVM) model, a random forest (RF) model, an adaptive boosting (AdaBoost) model and an artificial neural network (ANN) model. The different models were compared based on their prediction accuracy and using the McNemar’s hypothesis test. Results: The ANN model (77% accuracy), closely followed by the SVM (76%), AdaBoost (75%) and LR (75%) models, showed the highest overall prediction accuracy. The RF model (71%) showed the lowest prediction accuracy. However, based on the McNemar’s test applied to all model pair combinations, no statistically significant difference between the models was found. Conclusion: Based on our results, we encourage the use of ML-based prediction models for ORN incidence as has already been done for other HNC toxicity end points. Advances in knowledge: This research opens a new path towards personalised RT for HNC using ML to predict mandible ORN incidence.

Objectives: To analyze survival outcomes in patients with oropharygeal cancer treated with primary intensity modulated radiotherapy (IMRT) using decision tree algorithms. Methods: A total of 273 patients with newly diagnosed oropharyngeal cancer were identified between March 2010 and December 2016. The data set contained nine predictor variables and a dependent variable (overall survival (OS) status). The open-source R software was used. Survival outcomes were estimated by Kaplan–Meier method. Important explanatory variables were selected using the random forest approach. A classification tree that optimally partitioned patients with different OS rates was then built. Results: The 5 year OS for the entire population was 78.1%. The top three important variables identified were HPV status, N stage and early complete response to treatment. Patients were partitioned in five groups on the basis of these explanatory variables. Conclusion: The proposed classification tree could help to guide future research in oropharyngeal cancer field. Advances in knowledge: Decision tree method seems to be an appropriate tool to partition oropharyngeal cancer patients.

Objective. Normal tissue complication probability (NTCP) modelling is rapidly embracing deep learning (DL) methods, acknowledging the importance of spatial dose information. Finding effective ways to combine information from radiation dose distribution maps (dosiomics) and clinical data involves technical challenges and requires domain knowledge. We propose different multi-modality data fusion strategies to facilitate future DL-based NTCP studies. Approach. Early, joint and late DL multi-modality fusion strategies were compared using clinical and mandibular radiation dose distribution volumes. These were contrasted with single-modality models: a random forest trained on non-image data (clinical, demographic and dose-volume metrics) and a 3D DenseNet-40 trained on image data (mandibular dose distribution maps). The study involved a matched cohort of 92 osteoradionecrosis cases and 92 controls from a single institution. Main results. The late fusion model exhibited superior discrimination and calibration performance, while the join fusion achieved a more balanced distribution of the predicted probabilities. Discrimination performance did not significantly differ between strategies. Late fusion, though less technically complex, lacks crucial inter-modality interactions for NTCP modelling. In contrast, joint fusion, despite its complexity, resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation. Significance. This study is a pioneering effort in comparing different strategies for including image data into DL-based NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. Multiple data fusion strategies should be compared and reported in multi-modality NTCP modelling using DL.

Mandibular osteoradionecrosis (ORN) is a severe late complication affecting patients with head and neck cancer (HNC) treated with radiation therapy (RT) that significantly impacts patients' quality of life and can require costly interventions. Although radiation dose is a key factor, other clinical and demographic risk factors also influence ORN development. Previous predictive models have primarily been single-institutional, limiting their generalizability. In this first analysis from the PREDMORN Consortium, we have aimed to reproduce existing statistical association and modeling analyses on the largest and most diverse mandibular ORN cohort worldwide to allow comparison with previous studies. This retrospective multi-institutional study included 3928 patients with HNC (622 ORN cases) from 8 institutions. Clinical, demographic, and dosimetric variables were analyzed to develop a prediction model (any grade ORN vs no ORN) using forward stepwise logistic regression with correlation-based variable preselection. The ORN normal tissue complication probability (NTCP) model was developed on 80% of data from 6 institutions, tested on the remaining unseen 20%, and externally validated on a matched cohort (58 patients, 19 ORN cases) and a large population-based cohort (2687 patients, 215 ORN cases). Key predictors of ORN were D_30%, V_70Gy, pre-RT dental extractions, and smoking status. The ORN NTCP model demonstrated very good calibration on the population-based external cohort (Brier score, 0.077; Log Loss, 0.281). Model discrimination improved on a subcohort including oropharyngeal and locally advanced larynx/hypopharynx cancer cases only (AUC from 0.69 to 0.75 and from 0.65 to 0.67 on the matched and the population-based external cohorts, respectively). The PREDMORN NTCP model is the largest multi-institutional effort to date aimed at predicting ORN risk in patients with HNC using real-world data. The model demonstrated good generalizability when externally validated to a large population-based cohort. Our observations align with current guidelines and corroborate findings from smaller single-institution studies.