Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Abstract

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients’ data (imaging, electronic health records, patients’ reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis

“…As shown in Figure 7A, the keyword cluster view indicates that "cell lung cancer" #0 and "deep learning" #1 are the largest clusters, suggesting that the application of deep learning in non-small cell lung cancer may be a mature and significant topic in this research field. Currently, deep learning has been widely applied in the clinical diagnosis (16,37,38), treatment (35,39) and prognosis prediction (34,40) of lung cancer. Simultaneously, the Timeline View analysis (Figure 7B) reveals that AI in the field of lung cancer has consistently focused on clinical applications.…”

Global research of artificial intelligence in lung Cancer: A bibliometric and visualized study

“…AI can complement the TNM staging of lung cancer and provide prognostic prediction information. It has been reported that AI-assisted prognosis prediction is more accurate and e cient than traditional prognosis prediction based on clinical features(52). Kun-Hsing et al(53) obtained histopathological images from TCGA of 2,186 patients with lung adenocarcinoma and Lung squamous cell carcinoma and used a ML approach to analyze 9,879 quantitative image features and select the top features, and distinguish between shortterm survivors and long-term survivors with stage I adenocarcinoma or squamous cell carcinoma in the TCGA dataset.…”