Abstract
Simple SummaryLung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising.Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.
Highlights
Though there are still some obstacles, deploying artificial intelligence (AI) systems in the clinical workflow is vital for the foreseeable future
We present a narrative review of AI applications in lung cancer by introducing AI models first and reported applications according to the clinical workflow: screening, diagnosis, decision making, and prognosis prediction
The Multicentric Italian Lung Detection (MILD) trial showed that prolonged lowdose computed tomography (LDCT) screening for more than five years reduced lung cancer mortality and overall mortality at ten years
Summary
In addition to the late stage at diagnosis, the heterogeneity of imaging features and histopathology of lung cancer makes it a challenge for clinicians to choose the best treatment option. To find a model for disease detection, classification, or prediction This knowledge is based on clinical trials and the experience of doctors. To develop such a model, a large amount of computation is required. A large number of studies have reported the application in lung nodule detection, diagnostic application in histopathology, disease risk stratification, drug development, and even prognosis prediction. We present a narrative review of AI applications in lung cancer by introducing AI models first and reported applications according to the clinical workflow: screening, diagnosis, decision making, and prognosis prediction. CXR: Chest X-ray, LDCT: low-dose computed tomography, WSI: whole slide imaging
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