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Neoplasm Staging

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T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.

Frontiers in immunology
BACKGROUND: T-cell receptor (TCR) repertoires provide insights into tumor immunology, yet their variations across digestive system cancers are not well understood. Characterizing TCR differences between colorectal cancer (CRC) and gastric cancer (GC)...

Impact of Tumor Location on Predicting Early-Stage Breast Cancer Patient Survivability Using Explainable Machine Learning Models.

JCO clinical cancer informatics
PURPOSE: This study aims to investigate the impact of tumor quadrant location on the 5-year early-stage breast cancer survivability prediction using explainable machine learning (ML) models. By integrating these predictive models with Shapley Additiv...

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model.

Journal of visualized experiments : JoVE
Lymph node status is a critical prognostic predictor for patients; however, the prognosis of colorectal signet-ring cell carcinoma (SRCC) has garnered limited attention. This study investigates the prognostic predictive capacity of the log odds of po...

Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.

BMC cancer
BACKGROUND: Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most impo...

A comparative analysis of three graph neural network models for predicting axillary lymph node metastasis in early-stage breast cancer.

Scientific reports
The presence of axillary lymph node metastasis (ALNM) in breast cancer patients is an important factor in deciding whether to have axillary surgery or pursue alternative treatments. Based on axillary ultrasound (US) and histopathologic data, three gr...

Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: Recurrences after curative resection in early-stage and locoregionally advanced non-small cell lung cancer (NSCLC) are common, necessitating a nuanced understanding of associated risk factors. This study aimed to establish a natural language...

Machine learning-based reconstruction of prognostic staging for gastric cancer patients with different differentiation grades: A multicenter retrospective study.

World journal of gastroenterology
BACKGROUND: The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic status before treatment and determine appropriate treatment strategies.

A machine learning approach to differentiate stage IV from stage I colorectal cancer.

Computers in biology and medicine
BACKGROUND AND AIM: The stage at which Colorectal cancer (CRC) diagnosed is a crucial prognostic factor. Our study proposed a novel approach to aid in the diagnosis of stage IV CRC by utilizing supervised machine learning, analyzing clinical history,...

MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer.

Clinical and translational science
Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgent for improving therapeutic planning and patients' risk stratification in clinical practice. This st...

Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.

Scientific reports
The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to ...