Table 4.

Outcome measurements.

Reference	Effectiveness	Outcome
[45]	The mean of mPS = 24.22 (interquartile range [IQR] of 15.56–33.60).	(i) The MPS system is simple and cost-effective to apply and yet can reveal previously unrecognized heterogeneity among patient subpopulations in a platform-independent manner.
[64]	Radiomics model: AUC = 0.956, specificity = 0.928, sensitivity = 0.896. transcriptomics model: AUC specificity and sensitivity 0.948, 1, and 0.676.	(i) The integrative nomogram incorporated CECT radiomics, transcriptomics, and clinical features improved the PFI prediction in BLCA patients and is a feasible and practical reference for oncological precision medicine.
[27]	AUC, 0.837, p < 0.001; F1 score, 0.766.	(i) The radiomics signature model achieved a better classification performance than radiologists, which demonstrated the impressive prediction ability of radiomics signature.
[79]	Accuracy of 81% and AUC of 0.896 for the ROC curves.	(i) The model exhibited good interstage prognosis prediction performance. The genetic features could be used as biomarkers for effective LUAD prognosis prediction
[51]	Accuracy: decision tree:70.8% Discriminant analysis (Linear): 66.9% Linear SVM: 69.6% Weighted KNN: 73.5% ensemble classified (Subspace discriminant): 70.0%.	(i) The proposed methods were able to distinguish the metastatic sclerotic lesions with a complete response.
[70]	Accuracy = 0.9143.	(i) This synergy between liquid biopsy biotechnology and XAI will surely lead to personalized interpretable medicine, ensuring adequate and better diagnostic tools and treatments.
[60]	AUC = 0.97 − 0.98.	(i) A PLATYPUS model trained on the drug trial data can predict drug response for this patient without retraining.
[69]	Sensitivity = 97.1%, specificity = 98.8%, ROC curves = 0.99.	(i) This was concluded that this deep learning model provides an accurate and reproducible method for the prediction of BAP1 expression in uveal melanoma.
[25]	Sensitivity: upper than 84% in the training set but below 77% in the testing set.	(i) This study demonstrated that MRI-based radiomics features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.
[59]	The specificity, sensitivity and accuracy respectively: 0.861, 0.641, and 0.747.	(i) The TCPR model may benefit decision-making regarding total laryngectomy or larynx-preserving treatment. This TCPR model incorporating radiomics signature and T category reported by radiologists has good potential to be applied for individual accurate preoperative T category.
[63]	(MAE) 4.112E-06, (MSE) 4.318E-06.	(i) The proposed framework had demonstrated its capability and potential for mapping the gene and tumor status, it was effective for detecting association between gen information and the tumor growth regions.
[76]	AUC = 0.98.	(i) CDRscan is expected to allow the selection of the most effective anticancer drugs for the genomic profile of the individual patient.
[43]	The average correlation coefficient: 0.438-0.461.	(i) The result shows that GloNetDRP achieves comparable performance on the two-omics data for eight drugs collected from CCLE and GDSC. GloNetDRP globally calculated the responses of untested cell lines for the query drug by considering not only the neighbors but also other drugs and cell lines.
[26]	Precision = 0.98, recall = 0.99, and F − score = 0.98.	(i) Clinical or pathology notes alone or together provided the broadest cohort coverage and clinical notes alone provided the most precise measure of receptor status.
[58]	Concordance rate = 94.5% (95% CI, 92.7–96.0%) for gene mutations.	(i) WfG showed comparable analytical results for clinical genome sequencing. WfG demonstrated a significant improvement in mutation assignment from ver. 27 and 33. WfG may be useful in cases where large amounts of genomic data are available
[55]	Docetaxel and bortezomib with AUROCs of 0.74 and 0.71, respectively.	(i) The proposed was approach outperforms several state-of-the-art predictors in drug recommendation, if the training dataset is sparse, and generalizes to patient data.
[77]	Study1: the overall accuracies GEM 81.5%; 5-FU 81.7%; study 2: overall accuracy: 82.6%.	(i) ML-based models with validated high positive predictive values may provide physicians with a useful alternative to the traditional trial-and-error strategies.
[75]	AUC scores of RF: 0.66 XGB: 0.66 LR: 0.66 MV: 0.7	(i) Our results demonstrate the potential of multiview feature selection in integrative analyses and predictive modeling from multiomics data.
[28]	Accuracy (%) 97.06 AUC = 0.9929	(i) The experiments show that the OFSSVM is an appealing compromise between interpretability and classification accuracy, and is superior to other traditional methods in the sense of comprehensive evaluation.
[72]	FPR for DNT and DMT p values at α = .05 for Sc1: 0.04 and 0.208	(i) PANOPLY can be a tool to help clinicians in their decision-making process.
[56]	SCNN models median c index 0.745, p = 0.307 GSCNN models: 0.754 to 0.801.	(i) The proposed approach surpasses the prognostic accuracy of human experts for classifying brain tumors.
[44]	ROC curve of the Gemelli polyclinic's data set = 0.759. ROC curve of the Maastricht clinic = 0.881. ROC for the testing set was depicted 0.603 and 0.588 for each data set.	(i) Experimental results indicate that the system can generate a highly performant center-specific predictive model.
[73]	Accuracy across all pathways was 0.96 for a single dataset and 0.72 with multiple datasets	(i) GRAPE pathway scores provide researchers with a unique perspective of patient transcription profiles that may lead to improvements in the prediction performances of a wide range of personalized medicine applications.
[54]	For CTRP panel, the median was calculated for GBGFA, ENET 0.05, and 0.04. For CCLE panel, the median was calculated for GBGFA and ENET 0.06, 0.02.	(i) Current results show that the GBGFA model enables leveraging information from combinations of genomic data which improves the predictive performance and feature selection as compared to Elastic Net and BGFA.
[48]	Sensitivity = 0.82 and specificity = 0.82.	(i) The results suggested the effective therapies for the majority of cancer cells investigated in the dataset.
[57]	Recall, precision, and F2: 0.39, 0.21, and 0.33.	(i) This QA system can be effective for helping physicians in relevant knowledge. So, precision oncology can provide fewer toxic treatments in neoplasms.
[78]	More than 90% accuracy	(i) The analysis demonstrated that voting of the output categorical values for a given patient across distinct prognostic/classification methods could lead to a more robust, accurate, reproducible, and cost-efficient prognostic/ classification strategy for precision medicine.
[52]	The FNR and FPR values: 0.0512, 0.037.	(i) The proposed algorithm improves the cost efficiency and accuracy of the screening process compared to current clinical practice guidelines.
[50]	The best area under the ROC = 0.80 and the best PR (precision − recall) curve = 0.83.	(i) The proposed approach has the potential to enable the derivation of new hypotheses, improve drug selection, and lead to an improvement in patient genomics-tailored therapeutics for cancer.
[71]	Range of AUC 0.58-0.64	(i) Such studies are expected to contribute to precision medicine and better guide treatment for these deadly diseases
[74]	(≥0.80 % accuracy for 10 drugs, ≥75 % accuracy for 19 drugs	(i) This model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.
[29]	Sensitivity from 90% to 95%, specificity 67% to 93%).	(i) This study opens the way to further development for identification of new biomarker combinations in other applications such as prediction of treatment response.
[49]	Sensitivity = 43.8%, specificity = 100%, identical to qPCR on the same samples.	(i) The ability of Afirma BRAF to accurately detect V600E status may assist physicians in making these treatment decisions and potentially improve patient care.
[68]	Sensitivity = 96.77% on the training set. The model achieved 91. Specificity = 91.67%sensitivity = 100%. Test cases.	(i) In this study, the CANScript platform was versatile in its ability and capacity to predict the outcomes of both cytotoxic chemotherapy regimens and targeted therapeutics.
[95]	The accuracy of SMO, J48, RF, and CART was calculated respectively 76.56%, 75%, 75%, and 73%.	(i) The findings suggested that decision trees and support vector machines are engaged approaches for clinical decision support in the patient selection for targeted therapy in advanced NSCLC.
[53]	Accuracy = 0.99, sensitivity = 0.98, Jaccard index (stability) = 0.80	(i) The results have shown that MEFS improve the robustness and the accuracy of the signature and outperforms other methods in the literature
[18]	Accuracy = 0.84	(i) The proposed RWRF model can improve the prediction accuracy significantly. The method can facilitate using molecular signatures to predict the clinical outcomes of patients in prospective clinical studies.
[67]	Average accuracy for leukemia: 92.90%; breast 84.67%; colon cancer 86.53%;	(i) The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, the minimum number of classification markers, and computational time.
[65]	Pearson correlation Rp = 0.85; coefficient of determination R2 = 0.72, RMSE = 0.83	(i) This model had been shown that the prediction of drug response and mode of action by transcriptional profiling is significantly and effectively enhanced when paired with known a priori gene and protein networks.
[47]	The average training accuracy of 0.6995 and average testing accuracy of 0.6042.	(i) This investigation implicated XPD 751, XPD 312, and pack-years of smoking as significant predictors of bladder cancer susceptibility.
[46]	This algorithm performed better than simple metrics for variation in individual and multiple genes (R2 = 0.10; p < 0.05).	(i) This approach performed better than simple metrics for variation in individual and multiple genes
[62]	Average accuracy for SVM, NBC, and BCN was calculated respectively 85.7, 94.2, and 94.3.	(i) As this contribution, the experiments with lung cancer data prove that RPPA data can be used to profile patients for drug sensitivity prediction.
[61]	The confident predictability (CP), error in CP, the total error was respectively 98%, 4.23%, and 4.17 %, for the GCM dataset.	(i) The author believed that this method can be a useful tool for translating the gene expression signatures into clinical practice for personalized medicine.
[66]	Accuracy, sensitivity, specificity, PPV and NPV of respectively 0.872, 0.846, 0.882, 0.851 and 0.89.	(i) The C-T CERP algorithm appears to have a good potential and effective role for biomedical decision making in the assignment of patients to treatment therapies.
[80]	The CECT-QC algorithm reached an overall accuracy of 79.4% [95%CI = 75.2%, 82.9%].	(i) This study demonstrated that the CECT-QC algorithm is useful for radiomic-based precision diagnosis
[81]	The trained DL model classified patients into high-risk and low-risk groups in training cohort (p value < 0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (p value < 0.001, C-index: 0.78, HR: 11.76).	(i) DL model can provide CT-based prognostic risk scores related to the OS of GC patients, and the findings demonstrated higher prognostic value than clinical and radiomics models.
[82]	Average accuracy of 85% and AUC is 93%.	(i) The results show quite a high prediction accuracy, which proves the discriminative ability of the proposed model.
[83]	F1-measure of 0.8547 on TCGA dataset, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset.	(i) The proposed R-SNN maintains crucial features by using the residual connectivity from the encoder to the decoder, and it also uses only a few layers, which reduces the computational cost of the model.
[84]	The accuracies of training, validation, and test dataset were 93.5, 93.7 and 98.1%, respectively; AUROC value of 0.98 was observed for both the classes.	(i) The proposed omics integration strategy provides an effective way of extracting critical information from diverse omics data types enabling estimation of prognostic indicators.
[85]	The highest precision: 91% for true neutrals, 8% for false neutrals, 9% for false pathogenic, and 92% for true pathogenic.	(i) The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms.
[86]	On the GDSC dataset, the AUCROC of RefDNN were 0.891; the AUCROC of RefDNN were 0.071 on the CCLE dataset.	(i) As the proposed model can guarantee good prediction of drug responses to untrained drugs for given gene expression patterns, it may be of potential benefit in drug repositioning and personalized medicine
[87]	The median AUC value per target pathway ranges from 0.98 for hormone-related drugs to 0.73 for compounds targeting metabolism pathways.	(i) Appropriate feature selection strategies facilitate the development of interpretable models that are indicative for therapy design.
[31]	AUC value 0.98 and 0.99.	(i) The results demonstrate proposed framework improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.
[88]	Average accuracy of ECF-W and ECF-S is 74.25% and 77.25%, respectively.	(i) These two methods recommend the most suitable compounds and anticancer drugs for patients with NSCLC.
[30]	The highest AUC of RF, ELNET and SVM are 99.9%, 99.8%, and 85.0%, respectively.	(i) The protocols developed as a result of these comparisons provide valuable guidance on choosing ML workflows and their tuning to generate well-calibrated CP estimates for precision diagnostics using DNA methylation data.
[89]	The highest AUROC: 0.74	(i) The empirical results indicated that AITL achieved a significantly better performance compared with the baselines showing the benefits of addressing the discrepancies in both the input and output spaces.
[90]	The highest performance: 0.71	(i) REFINED-CNN improves the prediction performance as compared to the best single REFINED CNN model.
[91]	The highest performance: 0.84	(i) This method did not show ideal results when applied to an external set but it provided a valid proof of principle starting point, termed for future improvement.
[92]	Sensitivity: 95% Specificity: 83% AUC: 0.89	(i) This method provided more quantitative metrics for better characterization and complete picture of breast lesions.
[19]	Precision: 95%	(i) It might be very useful in new target recognition as well and proposing a potent drug for the newly identified target
[93]	The highest R2: 0.84	(i) This model provides a new method for the prediction of anticancer drugs in human tissues and can provide some reference value for the screening of anticancer drugs.
[20]	Accuracy: 96.9%	(i) Their results demonstrate the possibility of using stem-loop expression data for accurate cancer localization.
[94]	The highest AUC: 0.942	(i) The results showed that the proposed algorithm performed much better than the other two methods, warranting further studies in individual cancer patients to predict personalized cancer treatments.