4.7 Article

Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network

Related references

Note: Only part of the references are listed.
Article Oncology

Prediction of cancer-specific survival and overall survival in middle-aged and older patients with rectal adenocarcinoma using a nomogram model

Hao Liu et al.

Summary: The study developed two prognostic models using nomograms to predict survival in middle-aged and elderly patients with rectal adenocarcinoma. The models showed good predictive performance and successfully discriminated high-, medium-, and low-risk patients for both overall survival and cancer-specific survival. Decision curve analysis demonstrated the usefulness of the nomograms in guiding treatment decisions.

TRANSLATIONAL ONCOLOGY (2021)

Article Oncology

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung et al.

Summary: The global cancer burden in 2020 saw an estimated 19.3 million new cancer cases and almost 10.0 million cancer deaths. Female breast cancer surpassed lung cancer as the most commonly diagnosed cancer, while lung cancer remained the leading cause of cancer death. These trends are expected to rise in 2040, with transitioning countries experiencing a larger increase compared to transitioned countries due to demographic changes and risk factors associated with globalization and a growing economy. Efforts to improve cancer prevention measures and provision of cancer care in transitioning countries will be crucial for global cancer control.

CA-A CANCER JOURNAL FOR CLINICIANS (2021)

Article Oncology

Pathologic-Based Nomograms for Predicting Overall Survival and Disease-Free Survival Among Patients with Locally Advanced Rectal Cancer

Shuai Liu et al.

Summary: The study aimed to develop and validate models based on pathological findings to predict overall survival (OS) and disease-free survival (DFS) in patients with locally advanced rectal cancer. The results showed significant differences in 3-year OS between high-risk and low-risk groups. Nomograms based on pathological findings are reliable tools for predicting patient outcomes.

CANCER MANAGEMENT AND RESEARCH (2021)

Article Gastroenterology & Hepatology

Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach

Beiqun Zhao et al.

JOURNAL OF GASTROINTESTINAL SURGERY (2020)

Article Health Care Sciences & Services

Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling

Kung-Jeng Wang et al.

JOURNAL OF MEDICAL SYSTEMS (2020)

Review Surgery

Management of Rectal Cancer

Neal Wilkinson

SURGICAL CLINICS OF NORTH AMERICA (2020)

Article Oncology

Adult Overweight and Survival from Breast and Colorectal Cancer in Swedish Women

Melina Arnold et al.

CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION (2019)

Review Medicine, General & Internal

Colorectal cancer

Evelien Dekker et al.

LANCET (2019)

Article Oncology

The impact of microsatellite stability status in colorectal cancer

Ruby Gupta et al.

CURRENT PROBLEMS IN CANCER (2018)

Article Orthopedics

Can a Bayesian Belief Network Be Used to Estimate 1-year Survival in Patients With Bone Sarcomas?

Rajpal Nandra et al.

CLINICAL ORTHOPAEDICS AND RELATED RESEARCH (2017)

Article Oncology

Prognostic and Oncologic Significance of Perineural Invasion in Sporadic Colorectal Cancer

Abdulrahman Muaod Alotaibi et al.

ANNALS OF SURGICAL ONCOLOGY (2017)

Article Computer Science, Artificial Intelligence

From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support

Anthony Costa Constantinou et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2016)

Article

Factors Affecting Survival in Patients with Colorectal Cancer in Shiraz, Iran

Mohammad Zare-Bandamiri et al.

Asian Pacific Journal of Cancer Prevention (2016)

Article Biochemical Research Methods

A primer on learning in Bayesian networks for computational biology

Chris J. Needham et al.

PLOS COMPUTATIONAL BIOLOGY (2007)