Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 144-156 http://medscipublisher.com/index.php/cge 146 Radiomics can also predict responses to other treatments. For instance, Chung et al. (2022) proposed an integrative clinical prediction model for triple-negative breast cancer patients receiving neoadjuvant chemotherapy. By integrating ultrasound findings with blood test results, they developed a comprehensive model that accurately predicted pathological complete response, guiding clinicians in treatment planning (Chung et al., 2022). Integrating imaging data with molecular analyses can significantly enhance the accuracy of treatment response predictions. Imaging techniques provide spatial and temporal dynamics of tumors, while molecular analyses reveal the genetic and proteomic characteristics. The combination of these approaches allows researchers to understand tumor biology from multiple perspectives.For example, Davis et al. (2020) emphasized the importance of integrating imaging and molecular data to improve predictive models. They found that combining imaging data with genomic and transcriptomic data resulted in more accurate predictions of treatment responses. This finding highlights the importance of utilizing multiple data sources in future cancer research (Davis et al., 2020). In practical applications, the integration of imaging and molecular data has shown great potential. Studies have demonstrated that combining these data types can enhance tumor staging accuracy, predict treatment responses, and monitor tumor changes during therapy. These advancements not only improve clinical decision-making precision but also provide new possibilities for personalized treatment. 2.3 Clinical data and patient characteristics Clinical variables such as patient demographics, tumor stage, and previous treatment history play a crucial role in predicting treatment responses. Davis et al. (2020) emphasized the importance of integrating clinical data with molecular and imaging information to improve predictive models. They found that the inclusion of clinical variables significantly enhanced the accuracy of treatment response predictions. For example, patient age, sex, tumor size, and location provided additional context for predictive models, increasing their precision (Davis et al., 2020). Patient lifestyle and comorbidities can also influence treatment responses. Factors such as smoking history, alcohol consumption, obesity, and diabetes may affect treatment outcomes. Integrating these clinical variables not only enhances predictive accuracy but also helps identify high-risk patients, guiding personalized treatment planning. Electronic Health Records (EHR) are digital systems that record patient health information, including medical histories, diagnoses, treatment plans, and laboratory test results. Integrating EHR with molecular and imaging data provides researchers with comprehensive patient information, facilitating more accurate predictions of treatment responses. Liu et al. (2023) demonstrated that combining clinical, molecular, and imaging data using machine learning algorithms significantly improved the accuracy of drug response predictions. They found that integrating EHR data with genomic, transcriptomic, and imaging data enhanced predictive model performance. This underscores the importance of leveraging multiple data sources in future personalized cancer treatments (Liu et al., 2023).Additionally, EHR integration enables real-time patient monitoring and dynamic treatment adjustments. By analyzing laboratory test results and imaging data recorded in EHRs, clinicians can promptly detect abnormalities during treatment, adjusting therapies to improve outcomes. 3 Integrative Approaches 3.1 Multimodal data integration Multimodal data integration is of great significance in precision oncology as it can provide a comprehensive understanding of cancer biology and improve the predictive ability of treatment response. This machine learning method can integrate high-dimensional biomedical data, including co expression analysis, survival analysis, and matrix factorization. These methods successfully predicted post treatment responses by extracting salient features from tissue pathological images and combining gene expression and clinical data. Research has shown that this comprehensive analysis method is superior to traditional models with a single data source and can more accurately identify key features related to treatment response.
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