Accurate diagnostic information and prognostic information are vitally important for physicians and patients to make optimal treatment-related decisions. Gastrointestinal cancers pose great threat to human health globally, in which, colorectal cancer ranks third in terms of incidence, but second in terms of mortality, stomach cancer ranks fifth for incidence and fourth for mortality, and esophageal cancer ranks seventh in terms of incidence and sixth in mortality overall. Previous studies have built up numerous prediction models for gastrointestinal cancers but few of them are of satisfactory clinical usefulness. Thus, identifying new risk factors in gastrointestinal cancer diagnosis and prognosis, building models based on these factors and putting them into clinical practice are urgently needed. Machine learning with more sophisticated algorithm helps significantly in establishing prediction models. In the era of personalized medicine and precision oncology, we believe this research trend of prediction models along with artificial intelligence-assisted models will improve the model performance in predicting gastrointestinal cancer diagnosis and prognosis. Thus, it can better guide doctors to make decisions in clinical practice soon afterward. The goal of this Research Topic is to focus on exceptional and multidisciplinary scientific contributions to the advancing field of clinically useful prediction models for gastrointestinal cancer diagnosis and prognosis in the era of precision oncology.
We welcome authors from all disciplines involved in this topic to submit manuscripts and thank all authors in advance for their future contributions in helping identify risk factors for gastrointestinal cancer diagnosis and prognosis, enhancing clinical use of prediction models, and improving the diagnosis and prognosis of gastrointestinal cancer. We welcome content covering, but not limited to, the following subtopics:
• Identification of new risk factors for gastrointestinal cancer diagnosis.
• Determination of new influential factors for gastrointestinal cancer prognosis.
• Risk stratification for diagnosis of gastrointestinal cancer.
• Prognostic stratification for gastrointestinal cancer.
• Prediction model in gastrointestinal cancer diagnosis and prognosis.
• Clinical nomogram or user-friendly calculator in gastrointestinal cancer diagnosis and prognosis.
• Machine learning or artificial intelligence assisted model for improving diagnosis and prediction of prognosis in gastrointestinal cancers.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Accurate diagnostic information and prognostic information are vitally important for physicians and patients to make optimal treatment-related decisions. Gastrointestinal cancers pose great threat to human health globally, in which, colorectal cancer ranks third in terms of incidence, but second in terms of mortality, stomach cancer ranks fifth for incidence and fourth for mortality, and esophageal cancer ranks seventh in terms of incidence and sixth in mortality overall. Previous studies have built up numerous prediction models for gastrointestinal cancers but few of them are of satisfactory clinical usefulness. Thus, identifying new risk factors in gastrointestinal cancer diagnosis and prognosis, building models based on these factors and putting them into clinical practice are urgently needed. Machine learning with more sophisticated algorithm helps significantly in establishing prediction models. In the era of personalized medicine and precision oncology, we believe this research trend of prediction models along with artificial intelligence-assisted models will improve the model performance in predicting gastrointestinal cancer diagnosis and prognosis. Thus, it can better guide doctors to make decisions in clinical practice soon afterward. The goal of this Research Topic is to focus on exceptional and multidisciplinary scientific contributions to the advancing field of clinically useful prediction models for gastrointestinal cancer diagnosis and prognosis in the era of precision oncology.
We welcome authors from all disciplines involved in this topic to submit manuscripts and thank all authors in advance for their future contributions in helping identify risk factors for gastrointestinal cancer diagnosis and prognosis, enhancing clinical use of prediction models, and improving the diagnosis and prognosis of gastrointestinal cancer. We welcome content covering, but not limited to, the following subtopics:
• Identification of new risk factors for gastrointestinal cancer diagnosis.
• Determination of new influential factors for gastrointestinal cancer prognosis.
• Risk stratification for diagnosis of gastrointestinal cancer.
• Prognostic stratification for gastrointestinal cancer.
• Prediction model in gastrointestinal cancer diagnosis and prognosis.
• Clinical nomogram or user-friendly calculator in gastrointestinal cancer diagnosis and prognosis.
• Machine learning or artificial intelligence assisted model for improving diagnosis and prediction of prognosis in gastrointestinal cancers.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.