Anti-EGFR Peptide-Conjugated Triangular Gold Nanoplates for Computed Tomography/Photoacoustic Imaging-Guided Photothermal Therapy of Non-Small Cell Lung Cancer

2018 ◽  
Vol 10 (20) ◽  
pp. 16992-17003 ◽  
Author(s):  
Ying Zhao ◽  
Wenfei Liu ◽  
Ying Tian ◽  
Zhenlu Yang ◽  
Xiaofen Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Photothermal Therapy ◽  
Photoacoustic Imaging ◽  
Small Cell ◽  
Small Cell Lung

scholarly journals P07.07 Computed Tomography of Lymph Nodes to Predict Occult pN2 Disease in Non-Small-Cell Lung Cancer

2021 ◽  
Vol 16 (3) ◽  
pp. S280
Author(s):  
H. Onozawa ◽  
D. Nemoto ◽  
J. Miura ◽  
D. Eriguchi ◽  
H. Adachi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Lymph Nodes ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung

Comparison of bronchoscopy and computed tomography-guided needle biopsy for re-biopsy in non-small cell lung cancer patients

2021 ◽  
Vol 59 (2) ◽  
pp. 240-246
Author(s):  
Hirohisa Kano ◽  
Toshio Kubo ◽  
Kiichiro Ninomiya ◽  
Eiki Ichihara ◽  
Kadoaki Ohashi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Needle Biopsy ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancer Patients

scholarly journals Histological transformation of lung adenocarcinoma to small cell lung cancer with mutant C797S conferring acquired resistance to osimertinib

2020 ◽  
Vol 48 (6) ◽  
pp. 030006052092791 ◽  
Author(s):  
Xiaohui Ren ◽  
Xinfeng Cai ◽  
Jing Li ◽  
Xia Zhang ◽  
Jianfei Yu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Acquired Resistance ◽  
Small Cell ◽  
Small Cell Lung ◽  
T790m Mutation ◽  
Exon 19 Deletion ◽  
Exon 19

Epidermal growth factor receptor (EGFR) gene-mutated non-small cell lung cancer may initially respond to EGFR tyrosine kinase inhibitors (TKIs), but may subsequently become resistant; however, the resistance mechanisms remain unclear. We report a rare case of acquired resistance to osimertinib associated with transformation to small cell lung cancer (SCLC) with cis-C797S mutation. A man with recurrent lung adenocarcinoma harboring an EGFR exon 19 deletion received erlotinib for 10 months following curative surgery and adjuvant chemotherapy. However, he switched to osimertinib after repeat biopsy showed EGFR exon 19 deletion and T790M mutation leading to erlotinib resistance. His disease progressed after 15 months and repeat biopsy showed SCLC. Next-generation sequencing of peripheral blood detected EGFR exon 19 deletion, T790M mutation, cis-C797S mutation, and RB1 inactivation. The tumor was reduced after four cycles of etoposide and cisplatin and his respiratory symptoms improved. However, computed tomography after six cycles of chemotherapy showed multiple bilateral lung lesions, and single-photon emission computed tomography showed bone metastasis. The patient received paclitaxel plus cisplatin for two cycles with partial response. Because heterogeneous genetic and phenotypic mechanisms of TKI-resistance may occur at different times and locations, histopathological and molecular testing both provide evidence to support appropriate treatment.

scholarly journals Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet

2020 ◽  
Vol 19 ◽  
pp. 153303382094748
Author(s):  
Fuli Zhang ◽  
Qiusheng Wang ◽  
Haipeng Li
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Automatic Segmentation ◽  
Target Volume ◽  
Small Cell ◽  
Small Cell Lung ◽  
Computed Tomography Images ◽  
Positive Rate

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

A prognostic analysis method for non-small cell lung cancer based on the computed tomography radiomics

2020 ◽  
Vol 65 (4) ◽  
pp. 045006 ◽  
Author(s):  
Xu Wang ◽  
Huihong Duan ◽  
Xiaobing Li ◽  
Xiaodan Ye ◽  
Gang Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Analysis Method ◽  
Small Cell Lung ◽  
Prognostic Analysis
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