The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.
Purpose: This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information for predicting anaplastic lymphoma kinase (ALK) fusion status in non-small cell lung cancer (NSCLC) patients. Materials and methods: Preoperative CT im...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 2; pp. 361 - 372 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148321595&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148321595 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: 2021 vid: 48 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148321595 145103593 10.1007/s00259-020-04986-6 148321595 ppf: 361 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients. aug: au: Song, Zhengbo Liu, Tianchi Shi, Lei Yu, Zongyang Shen, Qing Xu, Mengdi Huang, Zhangzhou Cai, Zhijian Wang, Wenxian Xu, Chunwei Sun, Jingjing Chen, Ming affil: Department of Clinical Trial, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), 310022, Hangzhou, Zhejiang, China sug: ab: Purpose: This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information for predicting anaplastic lymphoma kinase (ALK) fusion status in non-small cell lung cancer (NSCLC) patients. Materials and methods: Preoperative CT images, clinicopathological information as well as the ALK fusion status from 937 patients in three hospitals were retrospectively collected to train and validate the DLM for the prediction of ALK fusion status in tumors. Another cohort of patients (n = 91) received ALK tyrosine kinase inhibitor (TKI) treatment was also included to evaluate the value of the DLM in predicting the clinical outcomes of the patients. Results: The performances of the DLM trained only by CT images in the primary and validation cohorts were AUC = 0.8046 (95% CI 0.7715–0.8378) and AUC = 0.7754 (95% CI 0.7199–0.8310), respectively, while the DLM trained by both CT images and clinicopathological information exhibited better performance for the prediction of ALK fusion status (AUC = 0.8540, 95% CI 0.8257–0.8823 in the primary cohort, p < 0.001; AUC = 0.8481, 95% CI 0.8036–0.8926 in the validation cohort, p < 0.001). In addition, the deep learning scores of the DLMs showed significant differences between the wild-type and ALK infusion tumors. In the ALK-target therapy cohort (n = 91), the patients predicted as ALK-positive by the DLM showed better performance of progression-free survival than the patients predicted as ALK-negative (16.8 vs. 7.5 months, p = 0.010). Conclusion: Our findings showed that the DLM trained by both CT images and clinicopathological information could effectively predict the ALK fusion status and treatment responses of patients. For the small size of the ALK-target therapy cohort, larger data sets would be collected to further validate the performance of the model for predicting the response to ALK-TKI treatment. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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