# 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

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/the-deep-learning-model-combining-ct-image-and-clinicopathological-information/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Zhengbo Song,Tianchi Liu,Lei Shi,Zongyang Yu,Qing Shen,Mengdi Xu,Zhangzhou Huang,Zhijian Cai,Wenxian Wang,C. Xu,Jingjing Sun,Ming Chen |
| Citations | 52 |
| DOI | 10.1007/s00259-020-04986-6 |
| Fields | Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3049038887 |
| PMID | 32794105 |
| Type | article |
| Year | 2020 |

## Paper authors

- [C. Xu](https://scholariq.org/researchers/c-xu/)

## Paper primary topic

- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)

## Paper topics

- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [Lung Cancer Diagnosis and Treatment](https://scholariq.org/topics/lung-cancer-diagnosis-and-treatment/)
- [Lung Cancer Treatments and Mutations](https://scholariq.org/topics/lung-cancer-treatments-and-mutations/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.