ObjectivesTo develop and validate an efficient and automatically computational approach for stratifying glioma grades and predicting survival of lower-grade glioma (LGG) patients using an integration of state-of-the-art convolutional neural network (CNN) and radiomics.MethodThis retrospective study reviewed 470 preoperative MR images of glioma from BraTs public dataset (n=269) and Jinling hospital (n=201). A fully automated pipeline incorporating tumor segmentation and grading was developed, which can avoid variability and subjectivity of manual segmentations. First, an integrated approach by fusing CNN features and radiomics features was employed to stratify glioma grades. Then, a deep-radiomics signature based on the integrated approach for predicting survival of LGG patients was developed and subsequently validated in an independent cohort.ResultsThe performance of tumor segmentation achieved a Dice coefficient of 0.81. The intraclass correlation coefficients (ICCs) of the radiomics features between the segmentation network and physicians were all over 0.75. The performance of glioma grading based on integrated approach achieved the area under the curve (AUC) of 0.958, showing the effectiveness of the integrated approach. The multivariable Cox regression results demonstrated that the deep-radiomics signature remained an independent prognostic factor and the integrated nomogram showed significantly better performance than the clinical nomogram in predicting overall survival of LGG patients (C-index: 0.865 vs. 0.796, P=0.005).ConclusionThe proposed integrated approach can be noninvasively and efficiently applied in prediction of gliomas grade and survival. Moreover, our fully automated pipeline successfully achieved computerized segmentation instead of manual segmentation, which shows the potential to be a reproducible approach in clinical practice.

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BackgroundChoroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment.PurposeAccurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment.MethodsIn this paper, we develop a CNN‐based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD‐OCT) B‐scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co‐segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow‐up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder‐recurrent‐decoder framework is developed for CNV growth prediction, in which an attention‐based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations.ResultsThe registration network rigidly registers the follow‐up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state‐of‐the‐art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow‐up visit.ConclusionThe proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.

Keratoconus (KC) is a noninflammatory ectatic disease characterized by progressive thinning and an apical cone-shaped protrusion of the cornea. In recent years, more and more researchers have been committed to automatic and semi-automatic KC detection based on corneal topography. However, there are few studies about the severity grading of KC, which is particularly important for the treatment of KC. In this work, we propose a lightweight KC grading network (LKG-Net) for 4-level KC grading (Normal, Mild, Moderate, and Severe). First of all, we use depth-wise separable convolution to design a novel feature extraction block based on the self-attention mechanism, which can not only extract rich features but also reduce feature redundancy and greatly reduce the number of parameters. Then, to improve the model performance, a multi-level feature fusion module is proposed to fuse features from the upper and lower levels to obtain more abundant and effective features. The proposed LKG-Net was evaluated on the corneal topography of 488 eyes from 281 people with 4-fold cross-validation. Compared with other state-of-the-art classification methods, the proposed method achieves 89.55% for weighted recall (W_R), 89.98% for weighted precision (W_P), 89.50% for weighted F1 score (W_F1) and 94.38% for Kappa, respectively. In addition, the LKG-Net is also evaluated on KC screening, and the experimental results show the effectiveness.