INTRODUCTIONLung cancer remains the most frequently diagnosed cancer and a leading cause of cancer‐related mortality worldwide. Due to late presentation of clinical symptoms and limited screening programmes, as many as 57% of patients diagnosed with lung cancer present with metastasis. Histologically, approximately 85% of all new lung cancer cases are classified as nonsmall cell lung cancers (NSCLC), 10% are small cell lung cancer (SCLC) and 5% account for other variants.Most NSCLC can be grouped into 3 main categories: Squamous Cell Carcinoma, Adenocarcinoma and Large Cell Carcinoma histo‐subtypes. Besides histological, clinical and demographic information, a wide range of data ranging from genomics, proteomics, immunohistochemistry and imaging must be integrated by physicians when developing personalized treatment plans for patients which can be challenging and lead to unfavourable outcomes. Furthermore, there are a number of factors that limit timely access to these tests, including high cost and late availability in the patient care continuum.This has led to an interest in developing computational approaches such as machine learning (ML), a subfield of artificial intelligence (AI), to improve medical management by providing insights that improve patient outcomes and workflow throughout a patient's journey. ML is the analysis and interpretation of data by machine algorithms
European Journal of Clinical Investigation – Wiley
Published: Jan 1, 2018
Keywords: ; ; ; ; ;
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