Machine Learning Algorithm Guiding Local Treatment Decisions to Reduce Pain for Lung Cancer Patients with Bone Metastases, a Prospective Cohort Study

Abstract

Introduction: As life expectancy increases for lung cancer patients with bone metastases, the need for personalized local treatment to reduce pain is expanding. Methods: Patients were treated by a multidisciplinary team (MDT), and local treatment including surgery, percutaneous osteoplasty, or radiation. Visual analog scale (VAS) and quality of life (QoL) scores were analyzed. VAS at 12 weeks after treatment was the main outcome. We developed and tested machine learning models to predict which patients should receive local treatment. Model discrimination was evaluated by the area under curve (AUC), and the best model was used for prospective decision-making accuracy validation. Results: Under the direction of MDT, 161 patients in the training set, 32 patients in the test set, and 36 patients in the validation set underwent local treatment. VAS in surgery, percutaneous osteoplasty, and radiation groups decreased significantly to 4.78 ± 1.28, 4.37 ± 1.36, and 5.39 ± 1.31 at 12 weeks, respectively (p \ 0.05), with no significant differences among the three datasets, and improved QoL was also observed (p \ 0.05). A decision tree (DT) model that included VAS, bone metastases character, Frankel classification, Mirels score, age, driver gene, aldehyde dehydrogenase 2, and enolase 1 expression had a best AUC in predicting whether patients would receive local treatment of 0.92 (95% CI 0.89-0.94) in the training set, 0.85 (95% CI 0.77-0.94) in the test set, and 0.88 (95% CI 0.81-0.96) in the validation set. Conclusion: Local treatment provided significant pain relief and improved QoL. There were no significant differences in reducing pain and improving QoL among training, test, and validation sets. The DT model was best at

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

“…A further four articles were identified through manually perusing the reference lists of the included articles. In total, 26 papers met the inclusion criteria and were used for this review [ 9 – 34 ]. These studies were published between 2015 and 2021.…”

“…In their study, Ahn et al used ML to demonstrate the effect of transcranial direct current stimulation (tDCS) in the management of pain [ 19 ]. In their study, Wang et al developed and tested ML models to predict which patients with lung cancer and pain due to bone metastases should receive local treatment and found that a decision tree (DT) model was the most effective and accurate method for that aim [ 34 ].…”

“…Bayes [17,19,20,22] Estimates the probability of data patterns belonging to a specific class Boosting: functional data boosting (FDboost) [13]; gradient boosting (GB) [24,28]; extreme gradient boosting regression (XGBoost) [27,31] Merges weak classifiers into strong ones Deep learning neural network (DLNN) [10,11,14,16,18,34,35] Similarly to multiple linear regression it contains layers of interconnected nodes. A subclass of NN is the convolutional neural network (CNN)…”

“…Decision trees (DT) [14,22,29,34] Gradually reject classes assigned into multistage decision systems to accept a final class. In pain medicine, decision trees algorithms such as classification and regression trees have been used…”

“…A subclass of NN is the convolutional neural network (CNN) Decision trees (DT) [ 14 , 22 , 29 , 34 ] Gradually reject classes assigned into multistage decision systems to accept a final class. In pain medicine, decision trees algorithms such as classification and regression trees have been used k-means clustering [ 14 ] Divides a number of data points into a number of clusters based on the nearest mean k-nearest neighbors (kNN) [ 10 , 22 , 24 , 27 , 29 , 32 ] Assigns data patterns to a class on the basis of the distance to the training patterns of a certain class Multilayer perceptron (MLP) [ 9 , 22 ] Trains on a set of input data patterns to predict/classify the output class Random forest (RF) [ 14 , 15 , 28 , 29 , 31 , 32 ] Builds and merges multiple decision trees to provide a more accurate prediction Regression: kernel ridge regression (KRR), [ 30 ]; elastic net (EN) [ 23 , 28 ]; generalized linear mixed-models (GLMMs) based on repeated data points, Lasso [ 15 , 24 ]; least square (LS) [ 28 ]; linear regression (LiR) [ 27 , 33 ]; logistic regression (LoR) [ 10 , 15 , 29 , 31 , 32 ]; ridge regression (RR) [ 28 ] Predicts the probability of agreement using continuous data points Support vector machine (SVM) [ 9 , ...…”