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Background CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient response. In the era of personalised oncology, new approaches for developing predictive models of response are needed. Materials and methods Data derived from the electronic health records (EHRs) of real-world patients with HR+/HER2-negative advanced breast cancer were used to develop predictive models for early and late progression to first-line treatment. Two machine learning approaches were used: a classic approach using a data set of manually extracted features from reviewed (EHR) patients, and a second approach using natural language processing (NLP) of free-text clinical notes recorded during medical visits. Results Of the 610 patients included, there were 473 (77.5%) progressions to first-line treatment, of which 126 (20.6%) occurred within the first 6 months. There were 152 patients (24.9%) who showed no disease progression before 28 months from the onset of first-line treatment. The best predictive model for early progression using the manually extracted dataset achieved an area under the curve (AUC) of 0.734 (95% CI 0.687–0.782). Using the NLP free-text processing approach, the best model obtained an AUC of 0.758 (95% CI 0.714–0.800). The best model to predict long responders using manually extracted data obtained an AUC of 0.669 (95% CI 0.608–0.730). With NLP free-text processing, the best model attained an AUC of 0.752 (95% CI 0.705–0.799). Conclusions Using machine learning methods, we developed predictive models for early and late progression to first-line treatment of HR+/HER2-negative metastatic breast cancer, also finding that NLP-based machine learning models are slightly better than predictive models based on manually obtained data.

The COVID-19 pandemic has caused a profound change in health organizations at both the primary and hospital care levels. This cross-sectional study aims to investigate the impact of the COVID-19 pandemic in the annual rate of new cancer diagnosis in two university-affiliated hospitals. This study includes all the patients with a pathological diagnosis of cancer attended in two hospitals in Málaga (Spain) during the first year of pandemic. This study population was compared with the patients diagnosed during the previous year 2019. To analyze whether the possible differences in the annual rate of diagnoses were due to the pandemic or to other causes, the patients diagnosed during 2018 and 2017 were also compared. There were 2340 new cancer diagnosis compared to 2825 patients in 2019 which represented a decrease of −17.2% (p = 0.0001). Differences in the number of cancer patients diagnosed between 2018 and 2019 (2840 new cases, 0.5% increase) or 2017 and 2019 (2909 new cases, 3% increase) were not statistically significant. The highest number of patients lost from diagnosis in 2020 was in breast cancer (−26.1%), colorectal neoplasms (−16.9%), and head and neck tumors (−19.8%). The study of incidence rates throughout the first year of the COVID-19 pandemic shows that the diagnosis of new cancer patients has been significantly impaired. Health systems must take the necessary measures to restore pre-pandemic diagnostic procedures and to recover lost patients who have not been diagnosed.

Endometrial cancer is one of the most common gynaecological malignancies worldwide. Histologically, two types of endometrial cancer with morphological and molecular differences and also therapeutic implications have been identified. Type I endometrial cancer has an endometrioid morphology and is estrogen-dependent, while Type II appears with non-endometrioid differentiation and follows an estrogen-unrelated pathway. Understanding the molecular biology and genetics of endometrial cancer is crucial for its prognosis and the development of novel therapies for its treatment. However, until now, scant attention has been paid to environmental components like the microbiome. Recently, due to emerging evidence that the uterus is not a sterile cavity, some studies have begun to investigate the composition of the endometrial microbiome and its role in endometrial cancer. In this review, we summarize the current state of this line of investigation, focusing on the relationship between gut and endometrial microbiome and inflammation, estrogen metabolism, and different endometrial cancer therapies.