Analysis of Progression Patterns and Failure Sites of Patients With Metastatic Lung Adenocarcinoma With EGFR Mutations Receiving First-line Treatment of Tyrosine Kinase Inhibitors.

BACKGROUND: Reliable prediction of progression patterns and failure sites for patients with stage IV lung adenocarcinoma is valuable for physicians to deliver personalized tyrosine kinase inhibitor (TKI) treatment. PATIENTS AND METHODS: We retrospectively enrolled 266 patients who had stage IV lung adenocarcinoma and received first-line TKI treatment from 2013 to 2017 in Shanghai Chest Hospital. The clinical characteristics at initial diagnosis, progression patterns, and failure sites were analyzed with the attempt to identify some predictive factors for progression patterns and failure sites. RESULTS: Among all patients, 62.4% developed systemic progression, and 37.6% developed oligoprogression. Both cohorts had a median progression-free survival (PFS) of 9 months. The percentage of patients who developed original and distant failure was 39.1% and 60.9%, respectively. Patients with oligometastasis at initial diagnosis were more prone to develop oligoprogression (odds ratio [OR], 4.370; 95% confidence interval [CI], 1.881-10.151; P = .001), whereas pulmonary metastasis was negatively correlated with oligoprogression (OR, 0.567; 95% CI, 0.330-0.974; P = .04). Both oligometastasis diagnosis (OR, 2.959; 95% CI, 1.347-6.500; P = .007) and the maximum diameter of the primary lung lesion (threshold 3.25 cm: OR, 3.646; 95% CI, 2.041-6.515; P = .0001) were strong predictive factors for original failures. Osseous metastasis at initial diagnosis might be an indication for distant failure (OR, 0.536; 95% CI, 0.316-0.909; P = .021). CONCLUSION: Over one-half of patients with stage IV lung adenocarcinoma receiving first-line TKI treatment developed systemic progression and distant failure. Metastasis patterns at initial diagnosis was the most important predictive factor for progression patterns and failure sites. The maximum diameter of the primary lung lesion and evidence of osseous metastasis were also found to be significant indicative factors for failure sites.

The positive role of vitronectin in radiation induced lung toxicity: the in vitro and in vivo mechanism study.

BACKGROUND: Radiation-induced lung toxicity (RILT) is a severe complication of radiotherapy in patients with thoracic tumors. Through proteomics, we have previously identified vitronectin (VTN) as a potential biomarker for patients with lung toxicity of grade ≥ 2 radiation. Herein, we explored the molecular mechanism of VTN in the process of RILT. METHODS: In this study, lentivirus encoding for VTN and VTN-specific siRNA were constructed and transfected into the cultured fibroblasts and C57BL mice. Real-time PCR, western blot and ELISA were used to examine expression of collagens and several potential proteins involved in lung fibrosis. Hematoxylin-eosin and immunohistochemical staining were used to assess the fibrosis scores of lung tissue from mice received irradiation. RESULTS: The expression of VTN was up-regulated by irradiation. The change trend of collagens, TGF-ß expression and p-ERK, p-AKT, and p-JNK expression levels were positively related with VTN mRNA level. Furthermore, overexpression of VTN significantly increased the expression level of α-SMA, as well as the degree of lung fibrosis in mice at 8 and 12 weeks post-irradiation. By contrast, siRNA VTN induced opposite results both in vitro and in vivo. CONCLUSIONS: VTN played a positive role in the lung fibrosis of RILT, possibly through modulation of fibrosis regulatory pathways and up-regulating the expression levels of fibrosis-related genes. Taken together, all the results suggested that VTN had a novel therapeutic potential for the treatment of RILT.

Detection of epithelial growth factor receptor (EGFR) mutations on CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks.

BACKGROUND: We aim to analyze the ability to detect epithelial growth factor receptor (EGFR) mutations on chest CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks (MCNNs). METHODS: We retrospectively collected 1,010 consecutive patients in Shanghai Chest Hospital from 2013 to 2017, among which 510 patients were EGFR-mutated and 500 patients were wild-type. The patients were randomly divided into a training set (810 patients) and a validation set (200 patients) according to a balanced distribution of clinical features. The CT images and the corresponding EGFR status measured by Amplification Refractory Mutation System (ARMS) method of the patients in the training set were utilized to construct both a radiomics-based model (MRadiomics) and MCNNs-based model (MMCNNs). The MRadiomics and MMCNNs were combined to build the ModelRadiomics+MCNNs (MRadiomics+MCNNs). Clinical data of gender and smoking history constructed the clinical features-based model (MClinical). MClinical was then added into MRadiomics, MMCNNs, and MRadiomics+MCNNs to establish the ModelRadiomics+Clinical (MRadiomics+Clinical), the ModelMCNNs+Clinical (MMCNNs+Clinical) and the ModelRadiomics+MCNNs+Clinical (MRadiomics+MCNNs+Clinical). All the seven models were tested in the validation set to ascertain whether they were competent to detect EGFR mutations. The detection efficiency of each model was also compared in terms of area under the curve (AUC), sensitivity and specificity. RESULTS: The AUC of the MRadiomics, MMCNNs and MRadiomics+MCNNs to predict EGFR mutations was 0.740, 0.810 and 0.811 respectively. The performance of MMCNNs was better than that of MRadiomics (P=0.0225). The addition of clinical features did not improve the AUC of the MRadiomics (P=0.623), the MMCNNs (P=0.114) and the MRadiomics+MCNNs (P=0.058). The MRadiomics+MCNNs+Clinical demonstrated the highest AUC value of 0.834. The MMCNNs did not demonstrate any inferiority when compared with the MRadiomics+MCNNs (P=0.742) and the MRadiomics+MCNNs+Clinical (P=0.056). CONCLUSIONS: Both of the MRadiomics and the MCNNs could predict EGFR mutations on CT images of patients with lung adenocarcinoma. The MMCNNs outperformed the MRadiomics in the detection of EGFR mutations. The combination of these two models, even added with clinical features, is not significantly more efficient than MMCNNs alone.