Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

The NCCN Guidelines for Prostate Cancer include recommendations for staging and risk
assessment after a prostate cancer diagnosis and for the care of patients with localized …

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant
treatment. The current gold standard of combined pathological and molecular profiling is …

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to
integration into clinical practice. This review describes the current state of the field, with a …

Background Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with
localized prostate cancer. However, ADT can negatively impact quality of life, and there …

Pathologic grading of laryngeal squamous cell carcinoma (LSCC) plays a crucial role in
diagnosis, prognosis, and migration. However, the grading performance and interpretability …

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare
delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology …

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is
crucial for clinical decision-making, but traditional pathology review is labor intensive and …

Prostate cancer, the most common cancer in men, is influenced by age, family history,
genetics, and lifestyle factors. Early detection of prostate cancer using screening methods …