Artificial intelligence assists precision medicine in cancer treatment

Abstract

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

“…Machine learning algorithms are also being used to integrate and analyze vast data sets, including genetic, molecular, clinical, and radiological information. , By identifying complex patterns within this data, machine learning can help clinicians predict patient prognosis, identify potential treatment targets, and tailor therapies to individual patients. These algorithms have the capacity to consider a wide range of variables and discover hidden correlations that may not be apparent through traditional methods, offering a more comprehensive and personalized approach to colorectal cancer treatment. , …”

“…These algorithms have the capacity to consider a wide range of variables and discover hidden correlations that may not be apparent through traditional methods, offering a more comprehensive and personalized approach to colorectal cancer treatment. 215,216 Furthermore, AI and machine learning can assist in the identification of new drug targets and development of novel therapies. By mining extensive databases of biological information, these technologies can uncover potential drug candidates and predict their efficacy in specific patient populations 12 (Figure 8).…”

Diagnostic Performance of Radiomics in Prediction of Ki-67 Index Status in Non-small Cell Lung Cancer: A Systematic Review and Meta-Analysis

“…We included studies with the following criteria: (1) Age ≥ 18; (2) Individuals diagnosed with lung cancers, irrespective of the specific histological subtype; (3) Studies utilizing CT scan-based radiomics analysis to predict Ki-67 index status, with reported diagnostic performance metrics like sensitivity and specificity and outcomes related to distinct Ki-67 index categories (e.g., low, high). Exclusion criteria encompassed: (1) Other study types (2) Studies involving pediatric populations (age below 18 years); (3) Studies exclusively focused on patients with tumors other than lung primary tumors; (4) Studies not utilizing radiomics analysis for predicting Ki-67 index status; (5) Studies employing imaging modalities other than CT scan for radiomics analysis; (6) Studies lacking relevant diagnostic performance metrics or outcomes related to Ki-67 index categories.…”

“…This approach provides novel insights into tumor heterogeneity and behavior through the automated extraction and analysis of detailed tumor features such as texture and shape (5). Building upon non-invasive and repeatable imaging modalities, radiomics provides insights into lung cancer progression and therapy response (6).…”

From cancer big data to treatment: Artificial intelligence in cancer research

“…). These predictive models can then be used to develop precise diagnostic tools and predict patient outcomes, and they are helpful in developing personalized treatment strategies 17–21 . The generation of new data contributes to the expansion of cancer big data resources, which are large‐scale datasets containing diverse information related to cancer 22,23 .…”