Critical Reviews in Oncogenesis最新文献

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Neuroplasticity is characterized by the brain's ability to change its activity in response to extrinsic and intrinsic factors and is thought to be the mechanism behind all brain functions. Neuroplasticity causes structural and functional changes on a molecular level, specifically the growth of different regions in the brain and changes in synaptic and post-synaptic activities. The four types of neuroplasticity are homologous area adaption, compensatory masquerade, cross-modal reassignment, and map expansion. All of these help the brain work around injuries or new information inputs. In addition to baseline physical functions, neuroplasticity is thought to be the basis of emotional and mental regulations and the impairment of it can cause various mental illnesses. Concurrently, these mental illnesses further the damage of synaptic plasticity in the brain. Major depressive disorder (MDD) is one of the most common mental illnesses. It is affected by and accelerates the impairment of neuroplasticity. It is characterized by a chronically depressed state of mind that can impact the patient's daily life, including work life and interests. This review will focus on highlighting the physiological aspects of the disease and the role of neuroplasticity in the pathogenesis and pathology of the disorder. Moreover, the role of monoamine regulation and ketamine uptake will be discussed in terms of their antidepressant effects on the outcomes of MDD.

Oral cavity cancer remains a significant cause of morbidity and mortality globally, with a poor prognosis once the disease has metastasized to cervical lymph nodes. The anatomy of lymphatic drainage in the neck gives us a roadmap to follow when assessing for metastasis, although the predictive factors are still not well understood. The mainstay of treatment continues to be neck dissection. However, there is much debate on the management of the clinically negative neck. The necessity of elective neck dissection has been questioned in recent years, with other options such as sentinel lymph node biopsy gaining popularity. This review will explore the aspects of surgical management of the neck in oral cavity cancer and highlights the further research that needs to be done.

Squamous cell carcinoma of the oral cavity presents a significant global health burden, primarily due to risk factors such as tobacco smoking, smokeless tobacco use, heavy alcohol consumption, and betel quid chewing. Common clinical manifestations of oral cavity cancer include visible lesions and sores, often accompanied by pain in advanced stages. Diagnosis relies on a comprehensive assessment involving detailed history, physical examination, and biopsy. Ancillary imaging studies and functional evaluations aid in accurate staging and facilitate treatment planning. Prognostic information is obtained from histopathological factors, such as tumor grade, depth of invasion, lymphovascular invasion, and perineural invasion. Notably, lymph node metastasis, found in approximately half of the patients, carries significant prognostic implications. Effective management necessitates a multidisciplinary approach to optimize patient outcomes. Surgical resection is the backbone of treatment, aimed at complete tumor removal while preserving functional outcomes. Adjuvant therapies, including radiation and chemotherapy, are tailored according to pathological factors. Further work in risk stratification and treatment is necessary to optimize outcomes in squamous cell carcinoma of the oral cavity.

The prevalence of electronic cigarette use has been declared an epidemic by the U.S. Surgeon General in 2018, particularly among youth aged 18-24 years old. Little is known about the differential use of e-cigarettes by different groups. PubMed, Cochrane, and Google Scholar were used to find relevant articles. A total of 77 articles were included. The extant literature reveals disparities in e-cigarette use by race/ethnicity and sexuality/gender. There are conflicting conclusions regarding disparities by socioeconomic status.

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.

We have witnessed in the last decade new milestones in the treatment of various resistant cancers with new immunotherapeutic modalities. These advances have resulted in significant objective durable clinical responses in a subset of cancer patients. These findings strongly suggested that immunotherapy should be considered for the treatment of all subsets of cancer patients. Accordingly, the mechanisms underlying resistance to immunotherapy must be explored and develop new means to target these resistant factors. One of the pivotal resistance mechanisms in the tumor microenvironment (TME) is the high infiltration of tumor-associated macrophages (TAMs) that are highly immunosuppressive and responsible, in large part, of cancer immune evasion. Thus, various approaches have been investigated to target the TAMs to restore the anti-tumor immune response. One approach is to polarize the M2 TAMS to the M1 phenotype that participates in the activation of the anti-tumor response. In this review, we discuss the various and differential properties of the M1 and M2 phenotypes, the molecular signaling pathways that participate in the polarization, and various approaches used to target the polarization of the M2 TAMs into the M1 anti-tumor phenotype. These approaches include inhibitors of histone deacetylases, PI3K inhibitors, STAT3 inhibitors, TLR agonists, and metabolic reprogramming. Clearly, due to the distinct features of various cancers and their heterogeneities, a single approach outlined above might only be effective against some cancers and not others. In addition, targeting by itself may not be efficacious unless used in combination with other therapeutic modalities.

Given the radiobiological and physical properties of the proton, proton beam therapy has the potential to be advantageous for many patients compared with conventional radiotherapy by limiting toxicity and improving patient outcomes in specific breast cancer scenarios.

Recent advancements in cancer treatment have explored a variety of approaches to address the needs of patients. Recently, immunotherapy has evolved as an efficacious treatment for various cancers resistant to conventional therapies. Hence, significant milestones in immunotherapy were achieved clinically in a large subset of cancer patients. Unfortunately, some cancer types do not respond to treatment, and among the responsive cancers, some patients remain unresponsive to treatment. Consequently, there is a critical need to examine the mechanisms of immune resistance and devise strategies to target immune suppressor cells or factors, thereby allowing for tumor sensitivity to immune cytotoxic cells. M2 macrophages, also known as tumor-associated macrophages (TAMs), are of interest due to their role in suppressing the immune system and influencing antitumor immune responses through modulating T cell activity and immune checkpoint expression. TAMs are associated with signaling pathways that modulate the tumor microenvironment (TME), contributing to immune evasion. One approach targets TAMs, focusing on preventing the polarization of M1 macrophages into the protumoral M2 phenotype. Other strategies focus on direct or indirect targeting of M2 macrophages through understanding the interaction of TAMs with immune factors or signaling pathways. Clinically, biomarkers associated with TAMs' immune resistance in cancer patients have been identified, opening avenues for intervention using pharmacological agents or immunotherapeutic approaches. Ultimately, these multifaceted approaches are promising in overcoming immune resistance and improving cancer treatment outcomes.