ஜர்னல் ஆஃப் இன்டகிரேடிவ் ஆன்காலஜி

Over the course of the last two decades, there has been a decrease in the incidence of head and neck cancers thanks to a decreasing prevalence of smoking. However, a new risk factor has been coming to the fore: Human Papillomavirus infection (HPV). HPV-positive Oropharyngeal Squamous cell Carcinoma (HPV+OPC) is more sensitive to chemotherapy and radiotherapy, which translates to a much better prognosis with conventional treatment protocols than tumors that are HPV-negative. Traditional therapeutic interventions are associated with substantial morbidity and have a great impact on patient quality of life. The main focus is on identifying an ideal group of HPV-positive patients that will receive de-intensification treatment regimens aimed at avoiding late toxicity of the administered treatment. Various strategies have been considered, such as reduction in radiotherapy dose following induction chemotherapy, radiotherapy alone, minimally invasive surgical techniques, and substituting platinum-based chemotherapy. The first generation of de-escalation randomized phase III trials have now been published. The following review summarizes the current knowledge and treatment of oropharyngeal carcinoma.

The treatment of metastatic stomach cancer has evolved over the last few years. Presently, the most common chemotherapy regimen comprises of Fluorouracil and platinum-based combinations. In the case of HER 2 positive disease, the treatment involves a combination of chemotherapy with trastuzumab. However, there have been cases of resistance to the above combination. Multiple underlying molecular mechanisms have been postulated for the resistance. One of the commonest pathways is CMET mutation. However, the presence of C MET and HER 2 is very rare. Here, we present such a rare case of metastatic stomach cancer who had abnormalities in both the pathways leading to resistance to Trastuzumab.

The topic of morphological analysis has received much attention with the increasing demands in different applications spatial in bioinformatics and biomedical applications. This paper summarizes the recent advances automated machine learning supervised suitable method for morphological Breast Cancer/masses Image Classification based on Non-Negative Matrix Factorization Algorithm. This scheme is making distinctions between all types roughly corresponding to Breast Cancer/masses types. Among many factors that morphological Classification of Breast Cancer and statistics have made great contributions for a radiologist and detection. Morphological (texture features, echogenicity, and homogenous). Breast masses analysis assists classification whether the mass is benign or malignant and finds the Breast Cancer shape. The Experimental results show that Breast Cancer images from the dataset can be classified automatically. With performance (average 94% accuracy) on a large-scale dataset, this demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.