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Background: Predicting the mutation status of the epidermal growth factor receptor (EGFR) gene based on an integrated positron emission tomography/computed tomography (PET/CT) image of non-small cell lung cancer (NSCLC) is a noninvasive, low-cost method which is valuable for targeted therapy. Although deep learning has been very successful in robotic vision, it is still challenging to predict gene mutations in PET/CT-derived studies because of the small amount of medical data and the different parameters of PET/CT devices. Methods: We used the advanced EfficientNet-V2 model to predict the EGFR mutation based on fused PET/CT images. First, we extracted 3-dimensional (3D) pulmonary nodules from PET and CT as regions of interest (ROIs). We then fused each single PET and CT image. The network model was used to predict the mutation status of lung nodules by the new data after fusion, and the model was weighted adaptively. The EfficientNet-V2 model used multiple channels to represent nodules comprehensively. Results: We trained the EfficientNet-V2 model through our PET/CT fusion algorithm using a dataset of 150 patients. The prediction accuracy of EGFR and non-EGFR mutations was 86.25% in the training dataset, and the accuracy rate was 81.92% in the validation set. Conclusions: Combined with experiments, the demonstrated PET/CT fusion algorithm outperformed radiomics methods in predicting EGFR and non-EGFR mutations in NSCLC.
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Malignant tumors is a serious public health threat. Among them, lung cancer, which has the highest fatality rate globally, has significantly endangered human health. With the development of artificial intelligence (AI) and its integration with medicine, AI research in malignant lung tumors has become critical. This article reviews the value of CAD, computer neural network deep learning, radiomics, molecular biomarkers, and digital pathology for the diagnosis, treatment, and prognosis of malignant lung tumors.
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To enlarge the linear detection range without sacrificing the sensitivity is one of the urgent problems in the development of high-performance piezoresistive flexible sensors. Inspired by a multilayer corrugated board, this study develops a new multilayer microspherical sensor in which conductive core-shell SiO2/Polyaniline (PANI) (PS) microspheres serve as active particles, while insulated silk fibroin (SF)/poly lactic-co-glycolic acid (PLGA) (SP) fibers are used as the support. The size of conductive microspheres attached to the insulated layer is controllable. The multiple layers of assembly endow the flexible sensor with a high sensitivity (0.071 kPa-1) and a wide linear detection (from 10 Pa to 380 kPa) simultaneously. This corrugated sensor also have a fast response time (145 ms) and an excellent durability (over 2000 cycles), and it can be used to detect human joint pressure signals and transmit encrypted information. Moreover, flexible keyboard, safety protection of machinery, as well as object position tracking can be achieved based on this sensor. Most importantly, the sensor encapsulated by biological polysaccharide kappa-carrageenan (KC) is skin-friendly and breathable, and it can be decomposed in 90 °C hot water. In conclusion, this multilayer microspherical sensor presents great potential for flexible wearable devices.
