Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 144-156 http://medscipublisher.com/index.php/cge 150 response. Through this comprehensive analysis method, researchers can gain a more comprehensive understanding of the biological characteristics and treatment response mechanisms of tumors. Figure 2 Predictive value of ctDNA (Adopted from Zhou et al., 2022) Image caption: A, Flow chart depicting patients with (ctDNAþ) and without (ctDNA ) detectable ctDNA at BL, MT, and EOT. NA, no plasma available. B, OR with 95% confidence intervals (CI) and P values for ctDNA detection at BL, MT, EOT, or all time points to predict tumor response (RCB 0/I vs. RCB II/III) calculated from univariate logistic regression models. C, Plotted are the fractions of patients stratified by response (RCB 0–III) with (ctDNAþ) and without (ctDNA ) detectable ctDNA at MT. D, Detection rates of ctDNA at BL, MT, and EOT stratified by RCB scores (E) same as in B but for pCR. NA, not analyzable (Adopted from Zhou et al., 2022) The study by Xu et al. (2019) demonstrated the application of deep learning based integration methods in predicting lung cancer treatment response. This study significantly improved the predictive ability of clinical outcomes by analyzing time series CT imaging data. The study used two datasets, dataset A included 179 stage III NSCLC patients who received radiotherapy and chemotherapy, and dataset B included 89 patients who underwent surgery after radiotherapy and chemotherapy. Research has found that as the number of follow-up scan data increases, the predictive performance of the model gradually improves. For example, in predicting the overall 2-year survival rate, the AUC values of the model after each follow-up scan were 0.58 (baseline scan), 0.64 (1-month follow-up), 0.69 (3-month follow-up), and 0.74 (6-month follow-up), respectively, demonstrating the importance of follow-up data in survival prediction (Figure 3) (Xu et al., 2019). The application of integration methods in the treatment of NSCLC is not limited to the analysis of imaging data, but also involves the comprehensive utilization of molecular markers and clinical data. For example, researchers can combine genomic sequencing data with radiomics characteristics of tumors to further improve the accuracy of predictive models. This multi omics integration analysis enables predictive models to not only recognize the physical characteristics of tumors, but also reveal their molecular mechanisms, providing more comprehensive information for personalized treatment. By combining baseline and follow-up CT imaging data, molecular markers, and clinical data, deep learning models can accurately predict the clinical outcomes of NSCLC patients, providing new tools and methods for personalized healthcare. This not only improves the accuracy and efficiency of image analysis, but also has a profound impact on future clinical practice. 5.3 Colorectal cancer In the treatment of colorectal cancer (CRC), comprehensive molecular analysis has become an important research method. By integrating genomic, transcriptome, and proteomic data, researchers can gain a more comprehensive understanding of the biological characteristics and treatment response mechanisms of tumors.
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