Artificial intelligence to predict outcomes of head and neck radiotherapy.
Clin Transl Radiat Oncol
; 39: 100590, 2023 Mar.
Article
en En
| MEDLINE
| ID: mdl-36935854
ABSTRACT
Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.
ADASYN, adaptive synthetic sampling; AI, artificial intelligence; ANN, artificial neural network; AUC, Area Under the ROC Curve; Artificial intelligence; BMI, body mass index; C-Index, concordance index; CART, Classification and Regression Tree; CBCT, cone-beam computed tomography; CIFE, conditional informax feature extraction; CNN, convolutional neural network; CRT, chemoradiation; CT, computed tomography; Cancer outcomes; DL, deep learning; DM, distant metastasis; DSC, Dice Similarity Coefficient; DSS, clinical decision support systems; DT, Decision Tree; DVH, Dose-volume histogram; GANs, Generative Adversarial Networks; GB, Gradient boosting; GPU, graphical process units; HNC, head and neck cancer; HPV, human papillomavirus; HR, hazard ratio; Head and neck cancer; IAMB, incremental association Markov blanket; IBDM, image based data mining; IBMs, image biomarkers; IMRT, intensity-modulated RT; KNN, k nearest neighbor; LLR, Local linear forest; LR, logistic regression; LRR, loco-regional recurrence; MIFS, mutual information based feature selection; ML, machine learning; MRI, Magnetic resonance imaging; MRMR, Minimum redundancy feature selection; Machine learning; N-MLTR, Neural Multi-Task Logistic Regression; NPC, nasopharynx; NTCP, Normal Tissue Complication Probability; OPC, oropharyngeal cancer; ORN, osteoradionecrosis; OS, overall survival; PCA, Principal component analysis; PET, Positron emission tomography; PG, parotid glands; PLR, Positive likelihood ratio; PM, pharyngeal mucosa; PTV, Planning target volumes; PreSANet, deep preprocessor module and self-attention; Predictive modeling; QUANTEC, Quantitative Analyses of Normal Tissue Effects in the Clinic; RF, random forest; RFC, random forest classifier; RFS, recurrence free survival; RLR, Rigid logistic regression; RRF, Regularized random forest; RSF, random survival forest; RT, radiotherapy; RTLI, radiation-induced temporal lobe injury; Radiomic; SDM, shared decision making; SMG, submandibular glands; SMOTE, synthetic minority over-sampling technique; STIC, sticky saliva; SVC, support vector classifier; SVM, support vector machine; XGBoost, extreme gradient boosting
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Clin Transl Radiat Oncol
Año:
2023
Tipo del documento:
Article
País de afiliación:
Canadá