Cancer is a multifaceted disease that can elude the natural defense mechanisms of the immune system. Due to the heterogeneity and complexity of cancer, the technical methods used for pre-treatment evaluation, prediction of treatment efficacy, and prognosis analysis still require further research. Immunotherapy has shown immense potential in the treatment of numerous types of cancer. Cancer immunotherapy aims to eliminate malignant cells based on their antigen composition and tumor-associated antigens. PD-1 and PD-L1 are crucial targets for cancer immunotherapy. Although various inflammatory factors and immune markers have been identified to aid in selecting appropriate treatment (chemotherapy or immunotherapy), monitoring treatment efficacy, and predicting prognosis, the combination of different markers in predictive models performs better than a single marker in enhancing the accuracy of treatment efficacy and clinical judgments. In the context of precise cancer treatment, novel diagnoses, predictive factors, and predictive models are essential for better comprehension of cancer treatment and prognosis.
The amalgamation of big data and artificial intelligence has been widely utilized in various cancer fields, including basic cancer research, particularly in molecular biological mechanisms, metabolic reprogramming, tumor biology, and clinical transformation research (such as cancer prediction, early diagnosis methods, and development of new treatment methods). The systematic and objective data provided by big data and artificial intelligence can guide diagnosis, optimize clinical treatment decisions, and have a far-reaching impact on clinical transformation.
This research topic aims to explore novel biomarkers and predictive models that predict prognosis, treatment efficacy, and toxic side effects in cancer patients. We welcome submissions including, but not limited to:
(1) Clinical research investigating novel biomarkers and their comprehensive predictive models for cancer treatment (including chemotherapy, radiation therapy, targeted therapy, and immunotherapy) and prognosis.
(2) Original research investigating inflammatory and immune factors associated with various types of cancer, particularly breast and gastrointestinal cancer.
(3) Reviews and meta-analyses of effective biomarkers and predictive models in cancer treatment and prognosis.
(4) Cancer-related basic research and clinical transformation research based on big data and artificial intelligence.
(5) Accurate detection and diagnosis of early cancer, intelligent prediction models of neoadjuvant treatment, and targeted treatment response of cancer.
Cancer is a multifaceted disease that can elude the natural defense mechanisms of the immune system. Due to the heterogeneity and complexity of cancer, the technical methods used for pre-treatment evaluation, prediction of treatment efficacy, and prognosis analysis still require further research. Immunotherapy has shown immense potential in the treatment of numerous types of cancer. Cancer immunotherapy aims to eliminate malignant cells based on their antigen composition and tumor-associated antigens. PD-1 and PD-L1 are crucial targets for cancer immunotherapy. Although various inflammatory factors and immune markers have been identified to aid in selecting appropriate treatment (chemotherapy or immunotherapy), monitoring treatment efficacy, and predicting prognosis, the combination of different markers in predictive models performs better than a single marker in enhancing the accuracy of treatment efficacy and clinical judgments. In the context of precise cancer treatment, novel diagnoses, predictive factors, and predictive models are essential for better comprehension of cancer treatment and prognosis.
The amalgamation of big data and artificial intelligence has been widely utilized in various cancer fields, including basic cancer research, particularly in molecular biological mechanisms, metabolic reprogramming, tumor biology, and clinical transformation research (such as cancer prediction, early diagnosis methods, and development of new treatment methods). The systematic and objective data provided by big data and artificial intelligence can guide diagnosis, optimize clinical treatment decisions, and have a far-reaching impact on clinical transformation.
This research topic aims to explore novel biomarkers and predictive models that predict prognosis, treatment efficacy, and toxic side effects in cancer patients. We welcome submissions including, but not limited to:
(1) Clinical research investigating novel biomarkers and their comprehensive predictive models for cancer treatment (including chemotherapy, radiation therapy, targeted therapy, and immunotherapy) and prognosis.
(2) Original research investigating inflammatory and immune factors associated with various types of cancer, particularly breast and gastrointestinal cancer.
(3) Reviews and meta-analyses of effective biomarkers and predictive models in cancer treatment and prognosis.
(4) Cancer-related basic research and clinical transformation research based on big data and artificial intelligence.
(5) Accurate detection and diagnosis of early cancer, intelligent prediction models of neoadjuvant treatment, and targeted treatment response of cancer.