International Journal of Clinical Case Reports 2024, Vol.14, No.5, 290-298 http://medscipublisher.com/index.php/ijccr 294 human error. One key development is the integration of AI in magnetic resonance-guided radiotherapy (MRgRT), where machine learning models optimize dose distribution based on daily imaging, enabling highly personalized and adaptive treatments. Additionally, deep learning approaches have been employed to generate synthetic CT images from MR data, streamlining the radiotherapy planning process and further improving the adaptability of treatments. These AI-driven innovations offer the potential for real-time adaptation during treatment, promising better outcomes for patients. 7 Patient Selection and Personalized Radiotherapy 7.1 Biomarkers for predicting radiotherapy response Biomarkers that predict patient response to radiotherapy are becoming increasingly crucial in personalizing treatment. These biomarkers, such as DNA repair pathway genes, microRNAs, and radiosensitivity indexes, allow for stratifying patients based on their tumor biology. Studies have shown that mutations in DNA repair genes, such as NOTCH1 and CHEK2, are associated with better local control post-radiotherapy, while certain microRNAs (e.g., miR-98-5p and miR-613) can help predict radiotherapy response in lung cancer patients (Tang et al., 2022). Additionally, radiosensitivity indexes have shown promise as predictors in radiotherapy and immunotherapy combinations, allowing for the identification of optimal treatment plans for individual patients. 7.2 Genomic and molecular profiling in treatment planning Genomic profiling is critical for tailoring radiotherapy treatment. High-throughput sequencing and analysis of genomic alterations, such as those found in the PIK3CA gene, are used to predict tumor response to radiation. For example, patients with PIK3CA mutations in breast cancer have shown lower recurrence rates after radiotherapy, suggesting that this mutation may serve as a protective factor against radiation resistance. Furthermore, molecular profiling of circulating tumor DNA (ctDNA) has emerged as a non-invasive method for monitoring radiotherapy response, offering real-time insights into the dynamic genetic composition of tumors (He et al., 2019). 7.3 Tailoring radiotherapy based on tumor and patient characteristics Radiotherapy can be tailored by integrating patient-specific characteristics, including tumor genetics and molecular profiles. Personalized approaches involve using biomarkers such as microRNAs and genomic signatures to adjust radiation doses, ensuring optimal therapeutic effects while minimizing side effects. For instance, combining radiomic features with genomics has enabled the identification of patients likely to respond favorably to specific radiotherapy doses and techniques. This approach represents a significant advance in personalized radiotherapy, allowing for more precise and effective treatment plans that take into account individual tumor characteristics and patient variability. 8 Cost-Effectiveness and Accessibility of Advanced Radiotherapy 8.1 Economic considerations in implementing advanced radiotherapy The implementation of advanced radiotherapy techniques, such as intensity-modulated radiotherapy (IMRT), stereotactic body radiotherapy (SBRT), and carbon-ion radiotherapy (CIRT), has significant economic implications. Studies have shown that these techniques can be cost-effective by improving patient outcomes and reducing long-term treatment costs. For instance, a comparison of SBRT and conventional radiotherapy for early-stage non-small-cell lung cancer (NSCLC) demonstrated that SBRT was more cost-effective, with a favorable incremental cost-effectiveness ratio (ICER) relative to conventional methods, owing to reduced toxicity and fewer hospitalizations (Sun et al., 2022). CIRT, despite its higher upfront costs, has been found to provide excellent long-term survival outcomes and is considered cost-effective in certain patient populations when compared to SBRT (Okazaki et al., 2021). 8.2 Accessibility challenges in low and middle-income countries Access to advanced radiotherapy techniques is limited in low and middle-income countries (LMICs) due to high costs, lack of infrastructure, and limited trained personnel. In these regions, conventional radiotherapy is often the
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