In the past, all lung cancers were considered the same and therefore, all lung cancer patients were treated with the identical treatment plan; however, innovations in healthcare had led to the finding that “one size doesn’t fit all.” Luckily, due to these advancements and move towards personalized medicine, this approach has changed. In recent years, AI has been gaining popularity in healthcare, particularly in personalized medicine and in the oncology field.
AI uses computer programing and machines which utilize algorithms to extract and retain large volumes of data, such as clinical, genetic, genomic, and environmental information, and subsequently uses this data to help providers make the best possible decision for their patients (Jiang et al., 2017; Meiliana, Dewi, & Wijaya, 2016). In order to analyze large volumes of the memorized structured data (for instance imaging, genetic testing data, etc.), ML and DL are currently being used.
On the other hand, when analyzing large volumes of the retained unstructured data (for instance clinical notes, data extracted from medical journals, clinical laboratory reports, operative notes, discharge summaries, etc.), NLP is used to convert this data to a structured one, which is afterwards analyzed by ML (Jiang et al., 2017). Because of its capabilities, AI has great potential in pulmonary oncology where it has already provided some favorable results such as more precise identification of cancer-affected area, prediction of outcomes, cancer characterization, and detection of possible metastasis (Coccia, 2020; Wang et al., 2019).
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Early cancer detection and accurate cancer type and subtype identification are imperative for better patient outcomes and should increase the life span and improve the quality of life for some of these affected patients. In order to detect lung cancer, standard procedures such as the microscopic examination of tissue slides and pathology image analysis have been in practice for years.
However, these methods are not only time-consuming but also provide a lot of variabilities in result interpretation (Wang et al., 2019). To make this process more precise and reduce variations, AI should be applied. ML is a subfield of AI which uses machine algorithms for extracting, analyzing and interpreting unstructured data, and is used for classification and prediction of information that is not easily perceptible to humans (Bi et al., 2019; Jiang et al., 2017; Rabbani, Kanevsky, Kafi, Chandelier, & Giles, 2018).
One way that ML can be utilized in lung cancer diagnosis and treatment is the genomic classification of NSCLC, where microarrays are used to train ML algorithms to identify different lung cancer mutations. This is a vital step since personalized medicine can be of great help in this case, and new treatments could be applied based on the existing mutations (Rabanni et al., 2018).
This can be especially beneficial for patients with ADC since 20% of lung ADCs are caused by mutations such as those of the epidermal growth factor receptor (EGFR) gene or rearrangements of the anaplastic lymphoma kinase (ALK) gene which are already being used in personalized medicine as biomarkers for targeted treatments (Cagle, Raparia, & Portier, 2016; Mascaux, Tomasini, Greillier, & Barlesi, 2017). Being able to use ML to detect these biomarkers is beneficial for lung cancer patients who will be not only able to receive the greatest benefits from targeted treatments but will also receive treatment resulting in lower treatment toxicity (Mascaux et al., 2017).
In regard to SCC patients, targeted therapies are unfortunately still not available; however, ML is being used for detecting mutations that could possibly be used as biomarkers for targeted treatments. Besides being used in lung cancer diagnosis, ML is being implemented in determining personalized treatment plans in lung cancer as well as following the prognosis. When deciding on a personalized treatment plan and patient survival, it is vital to distinguish between the two main sub-types of NSCLCs, SCC and ADC, since patients with different NSCLC sub-types require distinctive treatment approaches and have different survival outcomes (Zappa & Mousa, 2016). In regard to the treatment plan, this characterization is vital since some chemotherapy treatments have been reported to be more successful in treating SCC while other treatments are more successful in treating ADC (Huang et al., 2016; Shoshan-Barmatz et al., 2017).
In addition, ML is used to extract quantitative features, such as cell size and shape, scattering of pixel intensity in the cells and nuclei, and texture of the cells and nuclei, from histological slides, which, in turn, will help with distinguishing between cancerous tissue and surrounding healthy tissue (Yu et al., 2016). Once extracted, these aforementioned quantitative image features in combination with the patient survival indices can be advantageous in determining patient survival outcomes. When predicting survival outcomes in patients with ADC, texture of the nuclei, Zernike shape decomposition of the nuclei, and Zernike shape decomposition of the cytoplasm are of a great interest, while when predicting survival outcomes in patients with SCC, Zernike shape in the tumor nuclei and cytoplasm are being considered (Yu et al., 2016).
Additionally, patient features such as genetic, biochemical, physiological, behavioral, as well as environmental exposure are all taken into consideration when AI and ML are used in predicting the survival outcome of lung cancer patients (Schork, 2019). DL is a subcategory of machine learning and one area of pulmonary oncology where DL application has great potential relates to the prediction of treatment response for NSCLC patients who were undergoing chemotherapy and radiation treatment.
Predictions can be achieved by analyzing time series CT images of this group of patients (Xu et al., 2019b). This can be of a great help for pulmonary oncologists who will be able, if necessary, to modify treatments for their patients to make them more personalized, which in turn can improve the outcome of those patients. NLP is a subcategory of AI which can be used for analyzing unstructured data in pulmonary oncology. Once unstructured data is extracted by computers and machines from clinical notes, clinical laboratory reports, pathology reports, surgical notes, and other clinical sources, NLP is used to convert this data to the structured one which can be then analyzed by ML (Jiang et al., 2017).
In pulmonary oncology, NLP is primarily being used in assessing lung cancer progression and patient response to given therapies based on the radiology reports (Kehl et al., 2019). However, NLP is not used as often as ML and DL as NLP use is still a new technology, but with the constant advancements in technology, NLP could provide more significant patient-related data that could be used in personalized medicine. All this evidence demonstrates the potential that ML, DL, and NLP could have in personalized medicine, and especially in pulmonary oncology.
