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Lymphatic Metastasis

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Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.

Molecular imaging and biology
PURPOSE: This study aims to develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients.

Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images.

Cancer medicine
BACKGROUND: Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. Howev...

A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study.

International journal of surgery (London, England)
OBJECTIVES: Preoperative lymph node (LN) status is essential in formulating the treatment strategy among pancreatic cancer patients. However, it is still challenging to evaluate the preoperative LN status precisely now.

Post-chemotherapy robot-assisted retroperitoneal lymph node dissection for metastatic germ cell tumors: safety and perioperative outcomes.

World journal of urology
PURPOSE: To evaluate the feasibility, safety, and early oncologic outcomes after post-chemotherapy robot-assisted retroperitoneal lymph node dissection (PC-RARPLND) for metastatic germ cell tumors (mGCT).

Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

Future oncology (London, England)
To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model a deep neura...

Development of a deep learning-based model to diagnose mixed-type gastric cancer accurately.

The international journal of biochemistry & cell biology
OBJECTIVE: The accurate diagnosis of mixed-type gastric cancer from pathology images presents a formidable challenge for pathologists, given its intricate features and resemblance to other subtypes of gastric cancer. Artificial Intelligence has the p...

Fovea-UNet: detection and segmentation of lymph node metastases in colorectal cancer with deep learning.

Biomedical engineering online
BACKGROUND: Colorectal cancer is one of the most serious malignant tumors, and lymph node metastasis (LNM) from colorectal cancer is a major factor for patient management and prognosis. Accurate image detection of LNM is an important task to help cli...

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

European radiology
OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of...

Development of machine learning models to predict lymph node metastases in major salivary gland cancers.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Indications for elective treatment of the neck in patients with major salivary gland cancers are still debated. Our purpose was to develop a machine learning (ML) model able to generate a predictive algorithm to identify lymph node meta...