Colon and Rectal Cancer Management in Low-Resource Settings

Nalluri, Harika; Gaertner, Wolfgang B.

doi:10.1055/s-0042-1746189

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Colorectal cancer is the third most common cancer worldwide and the second leading cause of cancer-related deaths ( 1 , 2 ). The World Health Organization (WHO) estimates it kills more than 930,000 people yearly ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

Qiu,

Shen,

et al. 2023

Front. Oncol.

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BackgroundDistant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.MethodThe study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley’s Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer.ResultThe study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer.ConclusionThe study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation

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Section: Introductionmentioning

confidence: 99%

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

Qiu,

Shen,

et al. 2023

Front. Oncol.

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Colorectal Cancer Outcomes: A Comparative Review of Resource-Limited Settings in Low- and Middle-Income Countries and Rural America

Jacobson,

Harbaugh,

Agbedinu

et al. 2024

Cancers

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Background/Objectives: Colorectal cancer remains a significant global health challenge, particularly in resource-limited settings where patient-centered outcomes following surgery are often suboptimal. Although more prevalent in low- and middle-income countries (LMICs), segments of the United States have similarly limited healthcare resources, resulting in stark inequities even within close geographic proximity. Methods: This review compares and contrasts colorectal cancer outcomes in LMICs with those in resource-constrained communities in rural America, utilizing an established implementation science framework to identify key determinants of practice for delivering high-quality colorectal cancer care. Results: Barriers and innovative, community-based strategies aimed at improving patient-centered outcomes for colorectal cancer patients in low resource settings are identified. We explore innovative approaches and community-based strategies aimed at improving patient-centered outcomes, highlighting the newly developed colorectal surgery fellowship in Sub-Saharan Africa as a model of innovation in this field. Conclusions: By exploring these diverse contexts, this paper proposes actionable solutions and strategies to enhance surgical care of colorectal cancer and patient outcomes, ultimately aiming to inform global health practices, inspire collaboration between LMIC and rural communities, and improve care delivery across various resource settings.

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Colon and Rectal Cancer Management in Low-Resource Settings

Cited by 2 publications

References 39 publications

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

Colorectal Cancer Outcomes: A Comparative Review of Resource-Limited Settings in Low- and Middle-Income Countries and Rural America

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