“…The accuracy of the categorization of patients as long, short, and midsurvivors was initially tested [37,45]. Moreover, a pair-wise error analysis was performed for the regression model [40] between the predicted and actual OS (in days), with the use [33] RFR with five-fold cross-validation Miron et al [34] Extra trees with a depth of seven levels Chato et al [69] Simple NN Anand et al [32] RFR Parmar et al [30] RFC with multi-fold cross-validation Soltaninejad et al [40] RFC with 50 trees having a depth of 10, fivefold cross-validation Pang et al [43] RFR with fivefold cross-validation Ali et al [41] RFR with grid search Russo et al [45] GLM with Tweedie distribution Akbar et al [42] MobileNetV1/MobileNetV2 with Droput 0.1 Marti Asenjo et al [49] DT (ensembled by RUSBoost method), SVM with quadratic kernel function, ensembled of regression trees (Matlab ML models) Carmo et al [31] MultiATTUNet Han et al [46] FFNN Pei et al [39] RFR with grid search Suter et al [78] ARD, AdaBoost, DT, Extra Tree(s), Gaussian processes, linear, MLP, Nearest Neighbors, passive-aggressive, radius neighbours, RANSAC, RF, stochastic gradient descent, SVR and Theil-Sen regression Zhao et al [38] 3D ResNet50 Patel et al [35] Cox proportional hazards model Dai et al [36] Linear regression McKinley et al [37] Linear regression model and the RF classification model Zhang et al [95] Linear classification, linear regression models of metrics like mean square error (MSE), median square error (median SE), standard deviation of square errors (std SE), and Spearman correlation coefficient (Spearman R) [45]. The standard evaluation framework for tumour survival prediction were based on these metrics: Accuracy: The prediction performance was evaluated using the classification accuracy (i.e., the number of adequately categorised cases) as indicated in Eq.…”