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Neoplasm Recurrence, Local

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Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation.

Journal of cancer research and clinical oncology
PURPOSE: This study aims to develop an effective machine learning (ML)-based predictive model for the recurrence of borderline ovarian tumor (BOT), and provide the guidelines of accurate clinical diagnosis and precise treatment for patients.

Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review.

F1000Research
INTRODUCTION: Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies ...

Artificial intelligence in liver cancer surgery: Predicting success before the first incision.

World journal of gastroenterology
Advancements in machine learning have revolutionized preoperative risk assessment. In this article, we comment on the article by Huang , which presents a recent multicenter cohort study demonstrated that machine learning algorithms effectively strati...

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...

Identification of Recurrence-associated Gene Signatures and Machine Learning-based Prediction in IDH-Wildtype Histological Glioblastoma.

Journal of molecular neuroscience : MN
Glioblastoma (GBM) is a highly aggressive brain tumor with frequent recurrence, yet the molecular mechanisms driving recurrence remain poorly understood. Identifying recurrence-associated genes may improve prognosis and treatment strategies. We appli...

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.

Annals of medicine
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method fo...

Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.

Scientific reports
Acute myeloid leukemia (AML) is a severe hematological malignancy characterized by high recurrence rates, especially in pediatric patients, highlighting the need for reliable prognostic markers. This study proposes methylation signatures associated w...

Training, Validating, and Testing Machine Learning Prediction Models for Endometrial Cancer Recurrence.

JCO precision oncology
PURPOSE: Endometrial cancer (EC) is the most common gynecologic cancer in the United States with rising incidence and mortality. Despite optimal treatment, 15%-20% of all patients will recur. To better select patients for adjuvant therapy, it is impo...

Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction.

BMC medical informatics and decision making
BACKGROUND: Differentiated thyroid cancer (DTC) is a common endocrine malignancy with rising incidence and frequent recurrence, despite a generally favorable prognosis. Accurate recurrence prediction is critical for guiding post-treatment strategies....

Deep learning MRI-based radiomic models for predicting recurrence in locally advanced nasopharyngeal carcinoma after neoadjuvant chemoradiotherapy: a multi-center study.

Clinical & experimental metastasis
Local recurrence and distant metastasis were a common manifestation of locoregionally advanced nasopharyngeal carcinoma (LA-NPC) after neoadjuvant chemoradiotherapy (NACT). To validate the clinical value of MRI radiomic models based on deep learning ...