Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer DOI Open Access
Min Zhang, Ya Li, Yongxiang Hu

et al.

Translational Cancer Research, Journal Year: 2023, Volume and Issue: 0(0), P. 0 - 0

Published: Jan. 1, 2023

There are individualized differences in the prognosis of radiochemotherapy for non-small cell lung cancer (NSCLC), and accurate prediction is essential treatment. This study proposes to explore potential multiregional two-dimensional (2D) dosiomics combined with radiomics as a new imaging marker prognostic risk stratification NSCLC patients receiving radiochemotherapy. In this study, 365 histologically confirmed NSCLC, who had computed tomography (CT) scans before treatment, received standard radiochemotherapy, Karnofsky Performance Scale (KPS) scores ≥70 were included three medical institutions, 145 cases excluded due surgery, data accuracy, poor image quality, presence other tumors. Finally, 220 study. Efficacy evaluation criteria solid tumors used evaluate efficacy. Complete partial remission indicate radiochemotherapy-sensitive group, disease stability progression radiochemotherapy-resistant group. We all then randomised them into training cohort (154 cases) validation (66 7:3 ratio. Radiomics features extracted gross tumor volume (GTV), GTV-heat, 50 Gy-heat screened. 2D model (DMGTV DM50Gy), (RMGTV), radiomics-dosiomics (RDM), models constructed, predictive performances resistance compared. Subsequently, performance various was compared by receiver operating characteristic (ROC) curves calculating sensitivity specificity. The multi-omics clinical integrated patient stratification. DM50Gy better than RMGTV DMGTV, area under curve (AUC) ROC cohorts 0.764 [95% confidence interval (CI): 0.687-0.841] 0.729 (95% CI: 0.568-0.889). And RDM performed significantly single models, AUC 0.836 0.773-0.899) 0.748 0.617-0.879), respectively. Hemoglobin level T stage independent predictors model. containing further improved both cohorts, 0.844 0.781-0.907) 0.753 0.618-0.887). Grouping according critical value revealed significant progression-free survival (PFS) overall (OS) between high-risk low-risk groups (P<0.05). Compared traditional model, demonstrates superior performance. based on data, radiomics, has effectively Through precise assessment, doctors can understand which may develop treatment optimize plans accordingly.

Language: Английский

Caspase-independent cell death in lung cancer: from mechanisms to clinical applications DOI Creative Commons
Gaurav Gupta,

Vijaya Paul Samuel,

M M Rekha

et al.

Naunyn-Schmiedeberg s Archives of Pharmacology, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

Abstract Caspase-independent cell death (CICD) has recently become a very important mechanism in lung cancer, particular, to overcome critical failure apoptotic that is common disease progression and treatment failures. The pathways involved CICD span from necroptosis, ferroptosis, mitochondrial dysfunction, autophagy-mediated death. Its potential therapeutic applications have been highlighted. Glutathione peroxidase 4 (GPX4) inhibition-driven ferroptosis drug resistance non-small cancer (NSCLC). In addition, necroptosis involving RIPK1 RIPK3 causes tumor modulation of immune responses the microenvironment (TME). Mitochondrial are for through metabolic redox homeostasis. Ferroptosis amplified by reactive oxygen species (ROS) lipid peroxidation cells, depolarization induces oxidative stress leads mitochondria-mediated autophagy, or mitophagy, results clearance damaged organelles under conditions, while this function also linked when dysregulated. role autophagy regulated ATG proteins PI3K/AKT/mTOR pathway dual: suppress sensitize cells therapy. A promising approach enhancing outcomes involves targeting mechanisms CICD, including inducing SLC7A11 inhibition, modulating ROS generation, combining inhibition with chemotherapy. Here, we review molecular underpinnings particularly on their transform treatment.

Language: Английский

Citations

0
Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer DOI Open Access
Min Zhang, Ya Li, Yongxiang Hu

et al.

Translational Cancer Research, Journal Year: 2023, Volume and Issue: 0(0), P. 0 - 0

Published: Jan. 1, 2023

There are individualized differences in the prognosis of radiochemotherapy for non-small cell lung cancer (NSCLC), and accurate prediction is essential treatment. This study proposes to explore potential multiregional two-dimensional (2D) dosiomics combined with radiomics as a new imaging marker prognostic risk stratification NSCLC patients receiving radiochemotherapy. In this study, 365 histologically confirmed NSCLC, who had computed tomography (CT) scans before treatment, received standard radiochemotherapy, Karnofsky Performance Scale (KPS) scores ≥70 were included three medical institutions, 145 cases excluded due surgery, data accuracy, poor image quality, presence other tumors. Finally, 220 study. Efficacy evaluation criteria solid tumors used evaluate efficacy. Complete partial remission indicate radiochemotherapy-sensitive group, disease stability progression radiochemotherapy-resistant group. We all then randomised them into training cohort (154 cases) validation (66 7:3 ratio. Radiomics features extracted gross tumor volume (GTV), GTV-heat, 50 Gy-heat screened. 2D model (DMGTV DM50Gy), (RMGTV), radiomics-dosiomics (RDM), models constructed, predictive performances resistance compared. Subsequently, performance various was compared by receiver operating characteristic (ROC) curves calculating sensitivity specificity. The multi-omics clinical integrated patient stratification. DM50Gy better than RMGTV DMGTV, area under curve (AUC) ROC cohorts 0.764 [95% confidence interval (CI): 0.687-0.841] 0.729 (95% CI: 0.568-0.889). And RDM performed significantly single models, AUC 0.836 0.773-0.899) 0.748 0.617-0.879), respectively. Hemoglobin level T stage independent predictors model. containing further improved both cohorts, 0.844 0.781-0.907) 0.753 0.618-0.887). Grouping according critical value revealed significant progression-free survival (PFS) overall (OS) between high-risk low-risk groups (P<0.05). Compared traditional model, demonstrates superior performance. based on data, radiomics, has effectively Through precise assessment, doctors can understand which may develop treatment optimize plans accordingly.

Language: Английский

Citations

0