Clinical practice of epidermal growth factor receptor-tyrosine kinase inhibitor targeted drugs combined with gadolinium oxide nanoparticles in the treatment of non-small cell lung cancer

Enhancing X-Ray Sensitization with Multifunctional Nanoparticles

In the progression of X-ray-based radiotherapy for the treatment of cancer, the incorporation of nanoparticles (NPs) has a transformative impact. This study investigates the potential of NPs, particularly those comprised of high atomic number elements, as radiosensitizers. This aims to optimize localized radiation doses within tumors, thereby maximizing therapeutic efficacy while preserving surrounding tissues. The multifaceted applications of NPs in radiotherapy encompass collaborative interactions with chemotherapeutic, immunotherapeutic, and targeted pharmaceuticals, along with contributions to photodynamic/photothermal therapy, imaging enhancement, and the integration of artificial intelligence technology. Despite promising preclinical outcomes, the paper acknowledges challenges in the clinical translation of these findings. The conclusion maintains an optimistic stance, emphasizing ongoing trials and technological advancements that bolster personalized treatment approaches. The paper advocates for continuous research and clinical validation, envisioning the integration of NPs as a revolutionary paradigm in cancer therapy, ultimately enhancing patient outcomes.

Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer

OBJECTIVE

To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC).

METHODS

106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness.

RESULTS

LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05).

CONCLUSION

The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.