Archives of Surgical Oncology

Bone sarcomas are rare malignant tumors arising from the bone tissue, posing significant challenges in diagnosis and treatment. This paper explores the epidemiology, clinical presentation, diagnostic approaches, and treatment modalities for bone sarcomas. Key aspects include surgical resection, chemotherapy, and radiation therapy, aimed at achieving local control and improving survival outcomes. Despite advancements, managing bone sarcomas remains complex due to their rarity and heterogeneity. Future directions involve personalized medicine and innovative therapeutic strategies to enhance patient outcomes.

Surgical site infections are significant complications in gynecologic oncology surgeries, impacting patient outcomes and healthcare costs. This paper explores the epidemiology, risk factors, prevention strategies, management approaches, and implications of SSIs in the context of gynecologic oncology procedures.

One of the most common types of cancer is breast cancer. Pathological image processing of the breast has emerged as a significant method for early breast cancer diagnosis. In the field of medical image diagnosis, the use of medical image processing to help doctors detect potential breast cancer as soon as possible has always been a hot topic. In this paper, a bosom disease acknowledgment strategy in light of picture handling is efficiently explained from four perspectives: Image fusion, image segmentation, image registration, and breast cancer detection in the context of breast cancer examination, the accomplishments and application scope of supervised learning, unsupervised learning, deep learning, CNN, and other methods are discussed. The possibility of unaided learning and move learning for bosom malignant growth conclusion is prospected. Finally, patients with breast cancer should have their privacy protected.

Many people who have cancerous tumors can get better with surgery. Since multimodality treatment has been linked to promising outcomes in some types of cancer, more attention has been paid to the combination of surgery and chemotherapy. Despite these findings, there is still clinical disagreement regarding the ideal patient selection and timing for neo-adjuvant or adjuvant strategies. By assisting in the prediction of tumor behavior and response to therapy, the emerging field of radiomics, which involves the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and advance personalized therapy. Predicting prognosis, recurrence, survival, and therapeutic response for various cancer types using radiomics and machine learning in patients who have received neo-adjuvant and/or adjuvant chemotherapy is the focus of this review. Although neoadjuvant and adjuvant studies show above average accuracy in predicting progression free survival and overall survival, widespread application of this technology faces numerous obstacles. The inclusion and rapid adoption of radiomics in prospective clinical studies has been hampered by the absence of auto- segmentation, limited data sharing, and standardization of common procedures for analyzing radiomics.