BACKGROUND: Breast cancer is one of the most prevalent malignancies globally, imposing a substantial disease burden. Its inherent heterogeneity complicates prognosis and treatment, underscoring the need for accurate survival prediction models to guid...
OBJECTIVE: This study aims to explore the impact of different ROI delineation strategies on the axillary lymph nodes metastasis (ALNM) prediction model by analyzing two-dimensional ultrasound images of lymph nodes. In addition, we integrated clinical...
This study focuses on improving the detection of breast cancer at an early stage. The common approach for diagnosing breast cancer is mammography, but it is quite tedious as it is subject to subjective analysis. To address these challenges, the resea...
Accurate detection, localization, and staging of breast cancer lymph node metastases are critical for guiding treatment decisions and predicting patient outcomes. This study presents a selective neighborhood attention-based deep learning framework th...
The role of axillary lymph node dissection (ALND) in breast cancer patients with sentinel lymph node (SLN) micrometastases, particularly after neoadjuvant therapy, remains debated. The present study aimed to assess whether adding ALND provides a surv...
Cancer is a life-threatening disease that affects several human lives all over the world. The classification of cancer severities utilizing histopathological images is vital for effective and timely diagnosis. This always creates a demandable require...
Secondary cancers (SCs) following radiotherapy (RT) represent a significant long-term risk of cancer survivors, necessitating accurate predictive models for early intervention. This study developed a machine learning (ML) model integrating clinical, ...
Advances in radiotherapy have increased treatment plan complexity, making manual quality evaluation more subjective and variable. While deep learning approaches offer automation in planning, evaluation remains a manual bottleneck. Existing indices ev...
PURPOSE: To propose a multi-parametric ultrasound imaging-based deep learning method for accurately classifying metastatic and non-metastatic axillary lymph nodes in breast cancer patients.
Mammography is a routine imaging technique used by radiologists to detect breast lesions, such as tumors and lumps. Precise lesion detection is critical for early treatment and diagnosis planning. Lesion detection and segmentation are still problemat...
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