ArticleEuropean journal of nuclear medicine and molecular imaging2021
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.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment.Thoracic cancer · 2022Trial
- Noninvasive prediction of ALK fusion status in non-small cell lung cancer by a machine learning model combining CT images and clinical information.Journal of thoracic disease · 2026Article
- Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer.Translational lung cancer research · 2026Article
- Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma.BMC cancer · 2026Article
- Artificial Intelligence inCancers · 2026Review
- Artificial intelligence and machine learning in non-small cell lung cancer: the current state of the science on multi-omic applications.BMC medical research methodology · 2026Review
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
- Artificial intelligence in medical education for pulmonary nodule management: a narrative review.Translational lung cancer research · 2025Review
- Cytomorphological and histomorphological features of lung adenocarcinoma with epidermal growth factor receptor mutation and anaplastic lymphoma kinase gene rearrangement.Oncology letters · 2025Article
- An interpretableFrontiers in oncology · 2025Article
- Review
- [Advancements in Radiomics for Immunotherapy of Non-small Cell Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Review
- A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.Insights into imaging · 2024Article
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
- Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.European radiology · 2023Article
- Novel tools for early diagnosis and precision treatment based on artificial intelligence.Chinese medical journal pulmonary and critical care medicine · 2023Review
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Authors and funding
12 authors at 6 institutions in 1 country.
Funding
Abstract
purposeThis 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
methodsPreoperative 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.
resultsThe 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).
conclusionOur 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.