Background: The emergence of two-dimensional MXenes has spurred their versatile applications in broad fields, but the exploring of novel MXene-based family members and their potential applications in theranostic nanomedicine (concurrent diagnostic imaging and therapy) have been rarely explored. In this work, we report the construction of a novel superparamagnetic MXene-based theranostic nanoplatform for efficient breast-cancer theranostics, which was based on intriguing tantalum carbide (Ta₄C₃) MXene and its further rational surface-superparamagnetic iron-oxide functionalization (Ta₄C₃-IONP-SPs composite MXenes) for efficient breast-cancer theranostic.

A functionalized strategy for the construction of superparamagnetic 2D Ti ₃ C ₂ MXene nanocomposites was developed for highly efficient cancer theranostics.

For stage-I lung adenocarcinoma, the 5-years disease-free survival (DFS) rates of non-invasive adenocarcinoma (non-IA) is different with invasive adenocarcinoma (IA). This study aims to develop CT image based artificial intelligence (AI) schemes to classify between non-IA and IA nodules, and incorporate deep learning (DL) and radiomics features to improve the classification performance. We collect 373 surgical pathological confirmed ground-glass nodules (GGNs) from 323 patients in two centers. It involves 205 non-IA (including 107 adenocarcinoma in situ and 98 minimally invasive adenocarcinoma), and 168 IA. We first propose a recurrent residual convolutional neural network based on U-Net to segment the GGNs. Then, we build two schemes to classify between non-IA and IA namely, DL scheme and radiomics scheme, respectively. Third, to improve the classification performance, we fuse the prediction scores of two schemes by applying an information fusion method. Finally, we conduct an observer study to compare our scheme performance with two radiologists by testing on an independent dataset. Comparing with DL scheme and radiomics scheme (the area under a receiver operating characteristic curve (AUC): 0.83 ± 0.05, 0.87 ± 0.04), our new fusion scheme (AUC: 0.90 ± 0.03) significant improves the risk classification performance (p < 0.05). In a comparison with two radiologists, our new model yields higher accuracy of 80.3%. The kappa value for inter-radiologist agreement is 0.6. It demonstrates that applying AI method is an effective way to improve the invasiveness risk prediction performance of GGNs. In future, fusion of DL and radiomics features may have a potential to handle the classification task with limited dataset in medical imaging.