Evaluation of the best single-energy scanning in energy spectrum CT in lower extremity arteriography

Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians' detection rate was 74.60 percent lower than deep learning's detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images.

Functionalized Au@Cu-Sb-S Nanoparticles for Spectral CT/Photoacoustic Imaging-Guided Synergetic Photo-Radiotherapy in Breast Cancer

Background

Radiotherapy (RT) is clinically well-established cancer treatment. However, radioresistance remains a significant issue associated with failure of RT. Phototherapy-induced radiosensitization has recently attracted attention in translational cancer research.

Methods

Cu-Sb-S nanoparticles (NPs) coated with ultra-small Au nanocrystals (Au@Cu-Sb-S) were synthesized and characterized. The biosafety profiles, absorption of near-infrared (NIR) laser and radiation-enhancing effect of the NPs were evaluated. In vitro and in vivo spectral computed tomography (CT) imaging and photoacoustic (PA) imaging were performed in 4T1 breast cancer-bearing mice. The synergetic radio-phototherapy was assessed by in vivo tumor inhibition studies.

Results

Au@Cu-Sb-S NPs were prepared by in situ growth of Au NCs on the surface of Cu-Sb-S NPs. The cell viability experiments showed that the combination of Au@Cu-Sb-S+NIR+RT was significantly more cytotoxic to tumor cells than the other treatments at concentrations above 25 ppm Sb. In vitro and in vivo spectral CT imaging demonstrated that the X-ray attenuation ability of Au@Cu-Sb-S NPs was superior to that of the clinically used Iodine, particularly at lower KeV levels. Au@Cu-Sb-S NPs showed a concentration-dependent and remarkable PA signal brightening effect. In vivo tumor inhibition studies showed that the prepared Au@Cu-Sb-S NPs significantly suppressed tumor growth in 4T1 breast cancer-bearing mice treated with NIR laser irradiation and an intermediate X-ray dose (4 Gy).

Conclusion

These results indicate that Au@Cu-Sb-S integrated with spectral CT, PA imaging, and phototherapy-enhanced radiosensitization is a promising multifunctional theranostic nanoplatform for clinical applications.