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Lymph Nodes

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Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.

The Journal of pathology
The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomar...

Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and validated a deep learning (DPL) model for predicting lymph node metastasis (LNM) in CRC.

Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography.

Single-Port Robotic Inguinal Lymph Node Dissection for Penile Cancer.

Urology
OBJECTIVE: Inguinal lymph node dissection (ILND) is an essential component in the diagnosis, management, and treatment of penile cancer. Recent advances in minimally invasive surgery may play an important role in decreasing the adverse effects and co...

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors.

MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status.

Academic radiology
RATIONALE AND OBJECTIVES: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in pat...

Characterizing impact of positive lymph node number in endometrial cancer using machine-learning: A better prognostic indicator than FIGO staging?

Gynecologic oncology
BACKGROUND: Number of involved lymph nodes (LNs) is a crucial stratification factor in staging of numerous disease sites, but has not been incorporated for endometrial cancer. We evaluated whether number of involved LNs provide improved prognostic va...

Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking.

IEEE/ACM transactions on computational biology and bioinformatics
Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases i...

Transhiatal robot-assisted minimally invasive esophagectomy: unclear benefits compared to traditional transhiatal esophagectomy.

Journal of robotic surgery
Esophagectomy is a high-risk operation, regardless of technique. Minimally invasive transthoracic esophagectomy could reduce length of stay and pulmonary complications compared to traditional open approaches, but the benefits of minimally invasive tr...