Background: Radiomics and deep learning have been widely investigated in the quantitative analysis of medical images. Deep learning radiomics (DLR), combining the strengths of both methods, is increasingly used in head and neck cancer (HNC). This systematic review was aimed at evaluating existing studies and assessing the potential application of DLR in HNC prognosis. Materials and methods: The PubMed, Embase, Scopus, Web of Science, and Cochrane databases were searched for articles published in the past 10 years with the keywords “radiomics,” “deep learning,” and “head and neck cancer” (and synonyms). Two independent reviewers searched, screened, and reviewed the English literature. The methodological quality of each article was evaluated with the Radiomics Quality Score (RQS). Data from the studies were extracted and collected in tables. A systematic review of radiomics prognostic prediction models for HNC incorporating deep learning techniques is presented. Result: A total of eight studies, published in 2012–2022, with a varying number of patients (59–707 cases), were included. Each study used deep learning; three studies performed automatic segmentation of regions of interest (ROI), and the Dice score range for automatic segmentation was 0.75–0.81. Four studies involved extraction of deep learning features, one study combined different modality features, and two studies performed predictive model building. The range of the area under the curve (AUC) was 0.84–0.96, the range of the concordance index (C-index) was 0.72–0.82, and the range of model accuracy (ACC) was 0.72–0.96. The median total RQS for these studies was 13 (10–15), corresponding to a percentage of 36.11% (27.78%–41.67). Low scores were due to a lack of prospective design, cost-effectiveness analysis, detection and discussion of biologically relevant factors, and external validation. Conclusion: DLR has potential to improve model performance in HNC prognosis.

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In order to improve the traditional common space pattern (CSP) algorithm pattern in EEG feature extraction, this study proposes a feature extraction method of EEG signals based on permutation conditional mutual information common space pattern (PCMICSP), which used the sum of the permutation condition mutual information matrices of each lead to replacing the mixed spatial covariance matrix in the traditional CSP algorithm, and its eigenvectors and eigenvalues are used to construct a new spatial filter. Then the spatial features in the different time domains and frequency domains are combined to construct the two-dimensional pixel map, Finally, a convolutional neural network (CNN) is used for binary classification. The EEG signals of 7 community elderly before and after spatial cognitive training in virtual reality (VR) scenes were used as the test data set. The average classification accuracy of the PCMICSP algorithm for pre-test and post-test EEG signals is 98%, which was higher than that of CSP based on CMI (conditional mutual information), CSP based on MI (mutual

Background: Bone was the most common site of metastasis in prostate cancer(PCa) patients and was correlated with poor prognosis and increasing economicalburden. Studies were limited on the prognostic prediction for metastatic PCapatients with the assistance of neural network. Methods: Four convolutional neural network (CNN) models were developed andevaluated to predict overall survival (OS) of PCa patients with bone metastasis.All the CNN models were first trained with 64 samples and evaluated with 10samples, two models used only bone scan images and two models used both bonescan images and clinical parameters (CPs). Predictions of the best models werecompared with those of two urology surgeons on 20 test samples. Results: Our best models could predict OS of PCa patients with bone metastasiswith AUC = 0.8022 by using only bone scan images and AUC = 0.8132 by usingboth bone scan image and CPs on 20 test samples. When the sensitivities(specificities) set equal to average level of urology surgeons, their specificities(sensitivities) were 0%(7.2%) and 30.77%(7.7%) higher, which showedsignificant advantages of CNN models. Conclusion: The CNN models were suitable to predict OS in PCa patients withbone metastasis using bone scan images and CPs. Our models showed betterperformance in terms of accuracy and stability than urology surgeons. Keywords: Bone Metastasis; Bone Scan; Convolutional Neural Network;Prostate Cancer; Overall Survival

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