A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.

OBJECTIVES: Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM. METHODS: A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC). RESULTS: The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model. CONCLUSIONS: The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans. CRITICAL RELEVANCE STATEMENT: The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans. KEY POINTS: Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.

Differential reinforcement of cGAS-STING pathway-involved immunotherapy by biomineralized bacterial outer membrane-sensitized EBRT and RNT.

Radiotherapy (RT), including external beam radiation therapy (EBRT) and radionuclide therapy (RNT), realizes physical killing of local tumors and activates systemic anti-tumor immunity. However, these effects need to be further strengthened and the difference between EBRT and RNT should be discovered. Herein, bacterial outer membrane (OM) was biomineralized with manganese oxide (MnO2) to obtain OM@MnO2-PEG nanoparticles for enhanced radio-immunotherapy via amplifying EBRT/RNT-induced immunogenic cell death (ICD) and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) activation. OM@MnO2-PEG can react with H2O2 and then gradually produce O2, Mn2+ and OM fragments in the tumor microenvironment. The relieved tumor hypoxia improves the radio-sensitivity of tumor cells, resulting in enhanced ICD and DNA damage. Mn2+ together with the DNA fragments in the cytoplasm activate the cGAS-STING pathway, further exhibiting a positive role in various aspects of innate immunity and adaptive immunity. Besides, OM fragments promote tumor antigen presentation and anti-tumor macrophages polarization. More importantly, our study reveals that OM@MnO2-PEG-mediated RNT triggers much stronger cGAS-STING pathway-involved immunotherapy than that of EBRT, owing to the duration difference of RT. Therefore, this study develops a powerful sensitizer of radio-immunotherapy and uncovers some differences between EBRT and RNT in the activation of cGAS-STING pathway-related anti-tumor immunity.