Head and neck cancers are still one of the most common types of cancer in the world. They rank in the leading sixth place in terms of incidence globally, and the incidence continues to rise. The mortality rates remain at high levels. Pathological subclassification places squamous cell carcinoma of the head and neck (HNSCC) in the first place concerning the histological forms of head and neck cancers; a tumor with extremely aggressive behavior and high mortality rates. The tumor microenvironment is a very complex ecosystem of cellular and non-cellular components, characterized by unique features, that contribute to the appearance of immunosuppression and diminished anticancer immunity, impacting patient prognosis and treatment outcome. Despite many important advances in therapy, resistance to therapy represents a difficult challenge in HNSCC patients. Tumor progression, metastasis, and response to therapy are all influenced by the complex ecosystem represented by the tumor microenvironment and by the interactions between cellular and non-cellular components of this system. Therefore, the tumor microenvironment, in the light of recent data, is not an innocent bystander. In the last few years, there has been a sustained effort to characterize the tumor microenvironment, to identify targets of response and identify other mechanisms of tumor-specific immune responses, or to discover other biomarkers of response. There is an urgent need to understand how to properly select patients, the therapy sequence, and how to use feasible biomarkers that can help to identify the patient who may obtain the most benefit from available therapies.
We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ2 tests demonstrated the homogeneity of the sensitivity’s models (χ2 = 7.6987, df = 6, p-value = 0.261) and the specificities of the ML models (χ2 = 3.0151, df = 6, p-value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891–0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80–0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75–0.86) and 0.82 (95% CI: 0.76–0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy.
Even with the decreasing incidence of colorectal cancer (CRC), data showing that the rate of incidence of CRC is declining with 2.9% every year starting with 2005 until 2014, CRC remains one of the most frequent neoplasia all over the world. Almost a quarter of patients with CRC present with stage IV disease at diagnosis and nearly 30% of patients with localized disease will progress within 5 years. Our study included 129 patients with metastatic CRC that received chemotherapy � bevacizumab, from January 2017 until December 2018. Patients received fluropirimidine-based chemotherapy plus or minus bevacizumab. No significant differences was registered between groups with respects of age, sex, tumor localization, chemotherapy regimen used. Also no significant difference was found in our groups regarding risk factors for bleeding and medical history. No remarkable differences were registered between the two groups regarding common adverse reactions to chemotherapy, with the exception of physical asthenia which was found in a greater proportion of patients that received bevacizumab in combination with chemotherapy. In our study most frequent adverse events related to bevacizumab were grade 1 or 2, only few adverse events were grade 3 or 4 and lead to discontinuation of bevacizumab treatment, and these were mainly thromboembolic events and bleeding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.