Design, Synthesis, and Evaluation of 18F-Labeling CYP1B1 PET Tracer Based on 2-Phenylquinazolin.

Cytochrome P450 (CYP)1B1 has been identified to be specifically overexpressed in several solid tumors, thus it's a potential target for the detection of tumors. Based on the 2-Phenylquinazolin CYP1B1 inhibitors, we designed and synthesized several positron emission computed tomography (PET) imaging probes targeting CYP1B1. Through IC50 determinations, most of these probes exhibited good affinity and selectivity to CYP1B1. Considering their affinity, solubility, and their 18F labeling methods, we chose compound 5c as the best candidate. The 18F radiolabeling of [18F] 5c was easy to handle with good radiolabeling yield and radiochemical purity. In vitro and in vivo stability study indicated that probe [18F]5c has good stability. In cell binding assay, [18F]5c could be specifically taken up by tumor cells, especially HCT-116 cells. Although the tumor-blood (T/B) and tumor-muscle (T/M) values and PET imaging results were unsatisfied, it is still possible to develop PET probes targeting CYP1B1 by structural modification on the basis of 5c in the future.

Flexible Fusion Network for Multi-Modal Brain Tumor Segmentation.

Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely adopted approach in the field of brain tumor segmentation that can provide different modality images. It is critical to leverage multi-modal images to boost brain tumor segmentation performance. Existing works commonly concentrate on generating a shared representation by fusing multi-modal data, while few methods take into account modality-specific characteristics. Besides, how to efficiently fuse arbitrary numbers of modalities is still a difficult task. In this study, we present a flexible fusion network (termed F 2Net) for multi-modal brain tumor segmentation, which can flexibly fuse arbitrary numbers of multi-modal information to explore complementary information while maintaining the specific characteristics of each modality. Our F 2Net is based on the encoder-decoder structure, which utilizes two Transformer-based feature learning streams and a cross-modal shared learning network to extract individual and shared feature representations. To effectively integrate the knowledge from the multi-modality data, we propose a cross-modal feature-enhanced module (CFM) and a multi-modal collaboration module (MCM), which aims at fusing the multi-modal features into the shared learning network and incorporating the features from encoders into the shared decoder, respectively. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our F 2Net over other state-of-the-art segmentation methods.