Methylation-mediated silencing of protein kinase C zeta induces apoptosis avoidance through ATM/CHK2 inactivation in dedifferentiated chondrosarcoma

Cell polarity changes in cancer initiation and progression

Cell polarity, which consists of the morphological, structural, and functional organization of cells along a defined axis, is a feature of healthy cells and tissues. In contrast, abnormal polarity is a hallmark of cancer cells. At the molecular level, key evolutionarily conserved proteins that control polarity establishment and maintenance in various contexts are frequently altered in cancer, but the relevance of these molecular alterations in the oncogenic processes is not always clear. Here, we summarize the recent findings, shedding new light on the involvement of polarity players in cancer development, and discuss the possibility of harnessing cell polarity changes to better predict, diagnose, and cure cancers.

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.