Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study

Wang, Shuo; He, Yu; Gan, Yuncui; Wu, Zhangjie; Li, Encheng; Li, Xiaohu; Cao, Jingxue; Zhu, Yongbei; Wang, Liusu; Deng, Hui; Xie, Mei; Wang, Yuanyong; Ma, Xidong; Liú, Dan; Chen, Bojiang; Tian, Panwen; Qiu, Zhixin; Xian, Jinghong; Ren, Jing; Wang, Kun; Wei, Wei; Xie, Fei; Li, Zhenhui; Wang, Qi; Xue, Xinying; Liu, Zaiyi; Shi, Jingyun; Liu, Weimin; Tian, Jie

doi:10.1016/s2589-7500(22)00024-3

Cited by 75 publications

(51 citation statements)

References 29 publications

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“…A prediction model combining pretreatment radiomics tumor parameters with immune parameters such as PDL-1 expression and density of tumor-infiltrating lymphocytes and CD3 expression identified a favorable outcome group characterized by a favorable immune-activated state [ 106 ]. A CT-based DL system predicted progression-free survival and identified features associated with the TKI-resistant EGFR genotype [ 107 ]. A model with DL radiomics features and integrated circulating tumor cell count could predict the recurrence of early-stage NSCLC patients treated with stereotactic body radiation therapy [ 108 ].…”

Section: Predictionmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

et al. 2022

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Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

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Section: Predictionmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

et al. 2022

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“…The prosperity of AI applied to the medical field, especially in respiratory system, has attracted substantial attention with promising results, such as detection of pulmonary nodules (21) and prediction of treatment response or outcome of lung cancer (22,23). Meanwhile, we have made excellent achievements, including diagnosis and discrimination of 2019 novel coronavirus pneumonia (24), predetermination of epidermal growth factor receptor (EGFR) gene mutation status, programmed death ligand-1 (PD-L1) expression level, and target therapy effect in patients with lung cancer (25)(26)(27).…”

Section: Artificial Intelligence In a Nutshellmentioning

confidence: 99%

The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis

Liang

Ma²,

Wang

et al. 2022

Front. Med.

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With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.

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“…The analysis of cancer omics data through AI revealed tumour pathogenesis at the molecular level, thus providing a very convenient method for non-invasive identification of patients who are at high risk of drug resistance. 10 Even though a molecular marker was not identified, this recent paper shed new light on the importance of using biomarkers to accurately match patients to the most appropriate treatment for them, thereby improving outcomes by optimising disease control and minimising drug toxicity. Unquestionably, this is the right way to go towards personalised medicine in highly refractory UC patients.…”

Section: N V I T E D E D I T O R I a L Editorial: Stat3 Phosphorylati...mentioning

confidence: 99%

“…We believe this approach can be transferred to IBD. The analysis of cancer omics data through AI revealed tumour pathogenesis at the molecular level, thus providing a very convenient method for non‐invasive identification of patients who are at high risk of drug resistance 10 …”

mentioning

confidence: 99%