NIRF Nanoprobes for Cancer Molecular Imaging: Approaching Clinic.

Near-IR fluorescence imaging (NIRFI) is a highly promising technique for improving cancer theranostics in the era of precision medicine. Through the combination with cutting-edge bionanotechnologies, the potential of NIRFI can be greatly broadened. A variety of novel NIRF nanoprobes has been developed with ultimate goals of addressing unmet medical needs. Here, we present recent breakthroughs on the fundamental aspects of NIRFI, such as imaging at long wavelengths (1000-1700 nm), and the use of new approaches (X-rays, chemiluminescence, radioluminescence, etc.) for the excitation of novel nanoprobes. Within two decades, research on NIRF nanoprobes has translated to clinical trials and it will further translate to cancer management.

Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy.

PURPOSE: To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC). METHODS: A total of 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller-Payne grading system was applied to assess the response to NAC. Grade 1-2 cases were classified as insensitive to NAC. We extracted 1941 features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation. RESULTS: Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1-2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848-1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis. CONCLUSION: The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.

Near infrared-emitting persistent luminescent nanoparticles for Hepatocellular Carcinoma imaging and luminescence-guided surgery.

Hepatocellular carcinoma (HCC), the fifth most common cancer worldwide, is increasing nowadays and poses a serious threat to human health. However, if treated effectively and timely, it is clinically manageable or curable. Therefore, accurate detection and complete surgical resection remain priorities for HCC with a high potential of improving both survival and quality of life. Lacking of real-time guide technology, traditional surgery are usually relied on the subjective experience of surgeon, which have the limitation of high sensitivity detection tumor. Here, we developed a contrast agent, ZnGa2O4Cr0.004 (ZGC), used for guided surgery during operation to accurate delineation of HCC. ZGC showed excellent long-lasting afterglow properties that lasted for hours, which can aid in real-time guided surgery. Meanwhile, ZGC display high spatial resolution and deep penetration during pre-operation for diagnostic computed tomography (CT). Interestingly, we observed reverse imaging in the tumor region, known as a "dark hole", which further improves the contrast for surgery. This new multi-modality nanoparticle has great potential for accurate liver cancer imaging and resection guidance.