Study of three-dimensional dose distribution based-deep learning in predicting distant metastasis in head and neck cancer / 中华放射肿瘤学杂志

Objective:To investigate the role of three-dimensional dose distribution-based deep learning model in predicting distant metastasis of head and neck cancer.Methods:Radiotherapy and clinical follow-up data of 237 patients with head and neck cancer undergoing intensity-modulated radiotherapy (IMRT) from 4 different institutions were collected. Among them, 131 patients from HGJ and CHUS institutions were used as the training set, 65 patients from CHUM institution as the validation set, and 41 patients from HMR institution as the test set. Three-dimensional dose distribution and GTV contours of 131 patients in the training set were input into the DM-DOSE model for training and then validated with validation set data. Finally, the independent test set data were used for evaluation. The evaluation content included the area under receiver operating characteristic curve (AUC), balanced accuracy, sensitivity, specificity, concordance index and Kaplan-Meier survival curve analysis.Results:In terms of prognostic prediction of distant metastasis of head and neck cancer, the DM-DOSE model based on three-dimensional dose distribution and GTV contours achieved the optimal prognostic prediction performance, with an AUC of 0.924, and could significantly distinguish patients with high and low risk of distant metastasis (log-rank test, P<0.001). Conclusion:Three-dimensional dose distribution has good predictive value for distant metastasis in head and neck cancer patients treated with IMRT, and the constructed prediction model can effectively predict distant metastasis.

Pathologically triggered in situ aggregation of nanoparticles for inflammation-targeting amplification and therapeutic potentiation

Uncontrolled and persistent inflammation is closely related to numerous acute and chronic diseases. However, effective targeting delivery systems remain to be developed for precision therapy of inflammatory diseases. Herein we report a novel strategy for engineering inflammation-accumulation nanoparticles via phenolic functionalization. Different phenol-functionalized nanoparticles were first developed, which can undergo in situ aggregation upon triggering by the inflammatory/oxidative microenvironment. Phenolic compound-decorated poly (lactide-co-glycolide) nanoparticles, in particular tyramine (Tyr)-coated nanoparticles, showed significantly enhanced accumulation at inflammatory sites in mouse models of colitis, acute liver injury, and acute lung injury, mainly resulting from in situ cross-linking and tissue anchoring of nanoparticles triggered by local myeloperoxidase and reactive oxygen species. By combining a cyclodextrin-derived bioactive material with Tyr decoration, a multifunctional nanotherapy (TTN) was further developed, which displayed enhanced cellular uptake, anti-inflammatory activities, and inflammatory tissue accumulation, thereby affording amplified therapeutic effects in mice with colitis or acute liver injury. Moreover, TTN can serve as a bioactive and inflammation-targeting nanoplatform for site-specifically delivering a therapeutic peptide to the inflamed colon post oral administration, leading to considerably potentiated in vivo efficacies. Preliminary studies also revealed good safety of orally delivered TTN. Consequently, Tyr-based functionalization is promising for inflammation targeting amplification and therapeutic potentiation of nanotherapies.