No abstract

Significant effort has been devoted to the research of aggregation-induced emission (AIE); however, the discovery of new AIE materials is driven mainly by laborious trial-and-error. In this study, taking triphenylamine (TPA)-based luminophores as an example, we propose an efficient machine-learning scheme for predicting AIE-activity based on quantum mechanics.

The neoadjuvant nano-photothermal therapy used before operation could shrink the tumor in a short time and make the boundary clearer, thus greatly assisting subsequent surgery resection and improving the surgery effect finally.

Background Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach. Methods This retrospective cohort study included a total of 605 patients with intermediate-stage HCC who received TACE as their initial therapy. A fully automated framework (i.e., DSA-Net) contained a U-net model for automatic tumor segmentation (Model 1) and a ResNet model for the prediction of treatment response to the first TACE (Model 2). The two models were trained in 360 patients, internally validated in 124 patients, and externally validated in 121 patients. Dice coefficient and receiver operating characteristic curves were used to evaluate the performance of Models 1 and 2, respectively. Results Model 1 yielded a Dice coefficient of 0.75 (95% confidence interval [CI]: 0.73–0.78) and 0.73 (95% CI: 0.71–0.75) for the internal validation and external validation cohorts, respectively. Integrating the DSA videos, segmentation results, and clinical variables (mainly demographics and liver function parameters), Model 2 predicted treatment response to first TACE with an accuracy of 78.2% (95%CI: 74.2–82.3), sensitivity of 77.6% (95%CI: 70.7–84.0), and specificity of 78.7% (95%CI: 72.9–84.1) for the internal validation cohort, and accuracy of 75.1% (95% CI: 73.1–81.7), sensitivity of 50.5% (95%CI: 40.0–61.5), and specificity of 83.5% (95%CI: 79.2–87.7) for the external validation cohort. Kaplan-Meier curves showed a significant difference in progression-free survival between the responders and non-responders divided by Model 2 (p = 0.002). Conclusions Our multi-task deep learning framework provided a real-time effective approach for decoding DSA videos and can offer clinical-decision support for TACE treatment in intermediate-stage HCC patients in real-world settings.