Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. FUNDING: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

Acupoint selection for cancer pain: based on current evidence and Delphi method / 中国针灸

Based on literature research and Delphi expert consensus method, the important acupoints for cancer pain was summarized to provide evidence basis for the formulation of

Development of a fast and ultrasensitive black phosphorus-based colorimetric/photothermal dual-readout immunochromatography for determination of norfloxacin in tap water and river water.

A rapid and ultrasensitive method for colourimetric/photothermal dual-readout detection was developed using an 808 nm NIR laser and a thermal imaging app on mobile phone. Norfloxacin was used as a model contaminant to demonstrate this universal rapid detection method. It is innovatively, to use the advanced two-dimensional material black phosphorus as a colourimetric/photothermal reagent for the first time. The samples were added to the strip, and the analytes were selectively captured on the conjugate pad by monoclonal antibody-modified magnetic/upconversion nanocomposites. The samples flowed through the strips by capillary action until reaching the control line, where immune complex formation occurred due to the presence of secondary antibody. The added black phosphorus could be captured by the the antigens which were directly exposed to the test line and a brown band could be observed by naked eye. Upon illumination by NIR light for 1 min, the real-time temperature is obtained for quantitative analysis through the thermal imaging performed by mobile phone camera. This method can achieve the detection of norfloxacin in water samples within 20 min, and the detection limits of colorimetric and photothermal readout can reach 45 pg mL-1. Compared with conventional strips, this method provided an increased sensitivity by about two orders of magnitude, with a integrated portable laser and a mobile phone. It is a valuable method for rapid detection and can be applied to other environmental contaminants as well.

Fast-Acting Black-Phosphorus-Assisted Depression Therapy with Low Toxicity.

A black phosphorus (BP)-nanosheet-based drug-delivery system containing a therapeutic drug (Fluoxetine, Flu) is synthesized. According to subsequent behavioral, biochemical, and electrophysiological analysis, BP-Flu, after irradiated with near-infrared light (808 nm), can significantly reduce the therapy time of depression. Meanwhile, the inherent biotoxicity of Flu is also alleviated.