MATERIALS AND METHODS: A literature review was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases from the earliest record up to September 2022. Related studies on deep learning models for radiotherapy toxicity prediction were selected based on predefined PICOS criteria.
RESULTS: Fourteen studies of radiotherapy-treated patients on different types of cancer [prostate (n=2), HNC (n=4), liver (n=2), lung (n=4), cervical (n=1), and oesophagus (n=1)] were eligible for inclusion in the systematic review. Information regarding patient characteristics and model development was summarized. Several approaches, such as ensemble learning, data augmentation, and transfer learning, that were utilized by selected studies were discussed.
CONCLUSION: Deep learning techniques are able to produce a consistent performance for toxicity prediction. Future research using large and diverse datasets and standardization of the study methodologies are required to improve the consistency of the research output.
MATERIALS AND METHODS: Original research studies associating genetic features and normal tissue complications following radiotherapy were identified from PubMed. The use of dosimetric data was determined by mining the statement of prescription dose, dose fractionation, target volume selection or arrangement and dose distribution. The consideration of the dosimetric data as covariates was based on the statement mentioned in the statistical analysis section. The significance of these covariates was extracted from the results section. Descriptive analyses were performed to determine their completeness and inclusion as covariates.
RESULTS: A total of 174 studies were found to satisfy the inclusion criteria. Studies published ≥2010 showed increased use of dose distribution information (p = 0.07). 33% of studies did not include any dose features in the analysis of gene-toxicity associations. Only 29% included dose distribution features as covariates and reported the results. 59% of studies which included dose distribution features found significant associations to toxicity.
CONCLUSION: A large proportion of studies on the correlation of genetic markers with radiotherapy-related side effects considered no dosimetric parameters. Significance of dose distribution features was found in more than half of the studies including these features, emphasizing their importance. Completeness of radiation-specific clinical data may have increased in recent years which may improve gene-toxicity association studies.
MATERIALS & METHODS: Data were obtained retrospectively from all patients who underwent both CT examinations - brain (frontal bone), thorax (T7), abdomen (L3), spine (T7 & L3) or pelvis (left hip) - and DXA between 2014 and 2018 in our centre. To ensure comparability, the period between CT and DXA studies must not exceed one year. Correlations between HU values and t-scores were calculated using Pearson's correlation. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was used to determine threshold HU values for predicting osteoporosis.
RESULTS: The inclusion criteria were met by 1043 CT examinations (136 head, 537 thorax, 159 lumbar and 151 left hip). The left hip consistently provided the most robust correlations (r = 0.664-0.708, p 0.05.
CONCLUSION: HU values derived from the hip, T7 and L3 provided a good to moderate correlation to t-scores with a good prediction for osteoporosis. The suggested optimal thresholds may be used in clinical settings after external validations are performed.
METHODS: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source.
RESULTS: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively.
CONCLUSION: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.
METHODS: A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included.
RESULTS: Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively.
CONCLUSION: Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.