Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy.

Objective: To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods: A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results: Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion: We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.

Proximity-Induced Interfacial Room-Temperature Ferromagnetism in Semiconducting Fe3GeTe2.

The discoveries of two-dimensional ferromagnetism and magnetic semiconductors highly enrich the magnetic material family for constructing spin-based electronic devices, but with an acknowledged challenge that the Curie temperature (Tc) is usually far below room temperature. Many efforts such as voltage control and magnetic ion doping are currently underway to enhance the functional temperature, in which the involvement of additional electrodes or extra magnetic ions limits their application in practical devices. Here we demonstrate that the magnetic proximity, a robust effect but with elusive mechanisms, can induce room-temperature ferromagnetism at the interface between sputtered Pt and semiconducting Fe3GeTe2, both of which do not show ferromagnetism at 300 K. The independent electrical and magnetization measurements, structure analysis, and control samples with Ta highlighting the role of Pt confirm that the ferromagnetism with the Tc of above 400 K arises from the Fe3GeTe2/Pt interfaces, rather than Fe aggregation or other artificial effects. Moreover, contrary to conventional ferromagnet/Pt structures, the spin current generated by the Pt layer is enhanced more than two times at the Fe3GeTe2/Pt interfaces, indicating the potential applications of the unique proximity effect in building highly efficient spintronic devices. These results may pave a new avenue to create room-temperature functional spin devices based on low-Tc materials and provide clear evidence of magnetic proximity effects by using nonferromagnetic materials.

Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy.

Objective: To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy. Methods: Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2nd-3rd immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS. Results: The C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05), with a C-index of 0.83 and 0.70, respectively. Additionally, the delta radiomics model (C-index of 0.86) had a higher predictive accuracy compared to PD-L1 expression (C-index of 0.50) (P<0.0001). Conclusions: The combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

OBJECTIVES: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-mean were utilized for performance evaluation. RESULTS: In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM-SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD, and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS: The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

Nature-Inspired Capillary-Driven Welding Process for Boosting Metal-Oxide Nanofiber Electronics.

Recently, semiconducting nanofiber networks (NFNs) have been considered as one of the most promising platforms for large-area and low-cost electronics applications. However, the high contact resistance among stacking nanofibers remained to be a major challenge, leading to poor device performance and parasitic energy consumption. In this report, a controllable welding technique for NFNs was successfully demonstrated via a bioinspired capillary-driven process. The interfiber connections were well-achieved via a cooperative concept, combining localized capillary condensation and curvature-induced surface diffusion. With the improvements of the interfiber connections, the welded NFNs exhibited enhanced mechanical property and high electrical performance. The field-effect transistors (FETs) based on the welded Hf-doped In2O3 (InHfO) NFNs were demonstrated for the first time. Meanwhile, the mechanisms involved in the grain-boundary modulation for polycrystalline metal-oxide nanofibers were discussed. When the high-k ZrO x dielectric thin films were integrated into the FETs, the field-effect mobility and operating voltage were further improved to be 25 cm2 V-1 s-1 and 3 V, respectively. This is one of the best device performances among the reported nanofibers-based FETs. These results demonstrated the potencies of the capillary-driven welding process and grain-boundary modulation mechanism for metal-oxide NFNs, which could be applicable for high-performance, large-scale, and low-power functional electronics.