Development and Validation of a Deep Learning and Radiomics Combined Model for Differentiating Complicated From Uncomplicated Acute Appendicitis.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a deep learning and radiomics combined model for differentiating complicated from uncomplicated acute appendicitis (AA). MATERIALS AND METHODS: This retrospective multicenter study included 1165 adult AA patients (training cohort, 700 patients; validation cohort, 465 patients) with available abdominal pelvic computed tomography (CT) images. The reference standard for complicated/uncomplicated AA was the surgery and pathology records. We developed our combined model with CatBoost based on the selected clinical characteristics, CT visual features, deep learning features, and radiomics features. We externally validated our combined model and compared its performance with that of the conventional combined model, the deep learning radiomics (DLR) model, and the radiologist's visual diagnosis using receiver operating characteristic (ROC) curve analysis. RESULTS: In the training cohort, the area under the ROC curve (AUC) of our combined model in distinguishing complicated from uncomplicated AA was 0.816 (95% confidence interval [CI]: 0.785-0.844). In the validation cohort, our combined model showed robust performance across the data from three centers, with AUCs of 0.836 (95% CI: 0.785-0.879), 0.793 (95% CI: 0.695-0.872), and 0.723 (95% CI: 0.632-0.802). In the total validation cohort, our combined model (AUC = 0.799) performed better than the conventional combined model, DLR model, and radiologist's visual diagnosis (AUC = 0.723, 0.755, and 0.679, respectively; all P < 0.05). Decision curve analysis showed that our combined model provided greater net benefit in predicting complicated AA than the other three models. CONCLUSION: Our combined model allows the accurate differentiation of complicated and uncomplicated AA.

Construction of Core-Shell CoMoO4@γ-FeOOH Nanosheets for Efficient Oxygen Evolution Reaction.

The oxygen evolution reaction (OER) occurs at the anode in numerous electrochemical reactions and plays an important role due to the nature of proton-coupled electron transfer. However, the high voltage requirement and low stability of the OER dramatically limits the total energy converting efficiency. Recently, electrocatalysts based on multi-metal oxyhydroxides have been reported as excellent substitutes for commercial noble metal catalysts due to their outstanding OER activities. However, normal synthesis routes lead to either the encapsulation of excessively active sites or aggregation during the electrolysis. To this end, we design a novel core-shell structure integrating CoMoO4 as support frameworks covered with two-dimensional γ-FeOOH nanosheets on the surface. By involving CoMoO4, the electrochemically active surface area is significantly enhanced. Additionally, Co atoms immerge into the γ-FeOOH nanosheet, tuning its electronic structure and providing additional active sites. More importantly, the catalysts exhibit excellent OER catalytic performance, reducing overpotentials to merely 243.1 mV a versus 10 mA cm-2. The current strategy contributes to advancing the frontiers of new types of OER electrocatalysts by applying a proper support as a multi-functional platform.

3D Lightweight Network for Simultaneous Registration and Segmentation of Organs-at-Risk in CT Images of Head and Neck Cancer.

Image-guided radiation therapy (IGRT) is the most effective treatment for head and neck cancer. The successful implementation of IGRT requires accurate delineation of organ-at-risk (OAR) in the computed tomography (CT) images. In routine clinical practice, OARs are manually segmented by oncologists, which is time-consuming, laborious, and subjective. To assist oncologists in OAR contouring, we proposed a three-dimensional (3D) lightweight framework for simultaneous OAR registration and segmentation. The registration network was designed to align a selected OAR template to a new image volume for OAR localization. A region of interest (ROI) selection layer then generated ROIs of OARs from the registration results, which were fed into a multiview segmentation network for accurate OAR segmentation. To improve the performance of registration and segmentation networks, a centre distance loss was designed for the registration network, an ROI classification branch was employed for the segmentation network, and further, context information was incorporated to iteratively promote both networks' performance. The segmentation results were further refined with shape information for final delineation. We evaluated registration and segmentation performances of the proposed framework using three datasets. On the internal dataset, the Dice similarity coefficient (DSC) of registration and segmentation was 69.7% and 79.6%, respectively. In addition, our framework was evaluated on two external datasets and gained satisfactory performance. These results showed that the 3D lightweight framework achieved fast, accurate and robust registration and segmentation of OARs in head and neck cancer. The proposed framework has the potential of assisting oncologists in OAR delineation.

Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.

Objective: The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Materials and methods: This retrospective study included 95 women (mean age, 48.1 years; range, 29-77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results: The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. Conclusion: The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.