[Targeting the spliceosome: A new therapeutic strategy to counteract chemotherapy resistance in lung cancer?] / Cibler le splicéosome : une nouvelle stratégie thérapeutique pour contrecarrer la résistance à la chimiothérapie dans les cancers du poumon ?

Lung cancer is the first cancer-related cause of death worldwide. This is in partially due to therapeutic resistance, which occurs in around 70% of patients, especially those receiving platinum salts, the gold-standard chemotherapy. The massive deregulation of alternative transcript splicing processes observed in many cancers has led to the development of a new class of pharmacological agents aimed at inhibiting the activity of the splicing machinery (spliceosome). The molecular mechanisms by which these inhibitors act remain largely unknown, as do the benefits of using them in combination with other therapies. In this context, our work is focused on an inhibitor of the SRPK1 kinase, a major regulator of the spliceosome.

Quality control of postoperative radiotherapy for non-small cell lung cancer: A study of mediastinal shift.

PURPOSE: This study aimed to assess the shifting patterns of the mediastinum, including the target volume and the isocenter point during the postoperative radiotherapy (PORT) process of non-small cell lung cancer (NSCLC), and to observe the occurrence of radiation injury. Additionally, we investigated the significance of mid-term assessment during the implementation of the PORT process. MATERIAL AND METHODS: We established coordinate axes based on bone anatomy and measured the mediastinum's three-dimensional direction and the shift of the isocenter point's shift in the PORT process. Statistical analysis was performed using Wilcoxon, Kruskal-Wallis, and the Chi-square test. P<0.05 was considered statistically significant. RESULTS: In this study, the analysis of patients revealed that the shift of anterior and posterior mediastinum (X), left and right mediastinum (Y), upper and lower mediastinum (Z), anterior and posterior isocenter point (Xi), and the left and right isocenter points (Yi) in the PORT process were 0.04-0.53, 0.00-0.84, 0.00-1.27, 0.01-0.86, and 0.00-0.66cm, respectively. The shift distance of the mediastinum was Z>Y>X, and the shift distance of the isocenter point was Xi>Yi. According to the ROC curve, the cut-off values were 0.263, 0.352, 0.405, 0.238, and 0.258, respectively, which were more significant than the cut-off values in 25 cases (25%), 30 cases (30%), 30 cases (30%), 17 cases (17%), and 15 cases (15%). In addition, there was a significant difference in the shift of the mediastinum and the isocenter point (all P=0.00). Kruskal-Wallis test showed no statistically significant difference between mediastinal shift and resection site in X, Y, and Z directions (P=0.355, P=0.239, P=0.256), surgical method (P=0.241, P=0.110, P=0.064). There was no significant difference in the incidence of RE and RP in PORT patients (P>0.05). No III-IV RP occurred. However, the incidence of ≥ grade III RE in the modified plan cases after M-S was significantly lower than in the original PORT patients, 0% and 7%, respectively (P=0.000). CONCLUSION: In conclusion, this study provides evidence that mediastinal shift is a potential complication during the PORT process for patients with N2 stage or R1-2 resection following radical resection of NSCLC. This shift affects about 20-30% of patients, manifesting as actual radiation damage to normal tissue and reducing the local control rate. Therefore, mid-term repositioning of the PORT and revision of the target volume and radiation therapy plan can aid in maintaining QA and QC during the treatment of NSCLC patients and may result in improved patient outcomes.

[The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. / Apport de l'intelligence artificielle dans le post-traitement de l'imagerie thoracique.

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.