The Lived Experience of Older Mexcian American Adults with Type 2, Non-Insulin Dependent Diabetes Mellitus

Purpose: The purpose of this study was to explore the lived experience of Mexican American older adults with type 2, non-insulin dependent diabetes. Research design and results: A phenomenological research approach was used when interviewing a sample of ten English speaking Mexican American older adults in Santa Clara County, California. Three collective themes were identified: Emotions prevalent in living with diabetes, diabetes\u27 impact on life, and cultural factors affecting diabetes self care. Discussion/conclusions/implications: A duality of thoughts and experiences in living with diabetes, and the strong influence of culture and its beliefs characterize the life-world of Mexican American non-insulin dependent diabetics. Health care practitioners can generate plans of care that address these findings to effectively provide culturally congruent care in daily practice

Non-small cell lung cancer with a single metastasis, the new stage M1b; does the site matter?

Non-small cell lung cancer (NSCLC) patients with a solitary metastasis are considered to have a more favourable prognosis compared to those with multiple metastases. This is also shown in the 8th tumor, node, metastases edition for lung cancer (TNM8): patients with M1b (single extrapulmonary metastasis) have a superior prognosis than those with M1c disease (multiple metastases). Although not described in the TNM8, site of single metastatic disease may reflect tumour biology and may be of important prognostic value. We report a case of a patient with squamous cell NSCLC and a single skeletal muscle metastasis with a remarkably aggressive disease course

Immunotherapy in small cell lung cancer:One step at a time: A narrative review

Chemotherapy with or without radiotherapy has been the standard of care for many years for patients with small cell lung cancer (SCLC). Despite exceptionally high responses (up to 80%) with chemotherapy, the majority of patients relapse rapidly within weeks to months after treatment completion. Therefore, new and better treatment options are necessary. Recently, synergistic activity has been reported for the addition of immune checkpoint inhibitors (ICI) to standard platinum-based chemotherapy in the therapeutic strategy of advanced SCLC. For the first time after several decades, a significant survival improvement was achieved for this population. However, the overwhelming majority of patients do not respond to ICI, or relapse rapidly. There is need for better knowledge about the biology, histopathologic features, and molecular pathways of SCLC. This can probably help to identify the optimal predictive biomarkers, which are warranted to develop an individual therapeutic strategy including the rational use of a combination of immunotherapeutic agents. Here, we provide an overview of the rationale for and clinical results of the completed and ongoing trials using different strategies of immunotherapy in SCLC. In addition, opportunities for further improvement of therapies will be discussed, including the addition of radiotherapy, co-stimulatory antibodies, and other immune modifying agents.</p

Management of stage I and II nonsmall cell lung cancer

The incidence of stage I and II nonsmall cell lung cancer is likely to increase with the ageing population and introduction of screening for high-risk individuals. Optimal management requires multidisciplinary collaboration. Local treatments include surgery and radiotherapy and these are currently combined with (neo)adjuvant chemotherapy in specific cases to improve long-term outcome. Targeted therapies and immunotherapy may also become important therapeutic modalities in this patient group. For resectable disease in patients with low cardiopulmonary risk, complete surgical resection with lobectomy remains the gold standard. Minimally invasive techniques, conservative and sublobar resections are suitable for a subset of patients. Data are emerging that radiotherapy, especially stereotactic body radiation therapy, is a valid alternative in compromised patients who are high-risk candidates for surgery. Whether this is also true for good surgical candidates remains to be evaluated in randomised trials. In specific subgroups adjuvant chemotherapy has been shown to prolong survival; however, patient selection remains important. Neoadjuvant chemotherapy may yield similar results as adjuvant chemotherapy. The role of targeted therapies and immunotherapy in early stage nonsmall cell lung cancer has not yet been determined and results of randomised trials are awaited

Current management of limited-stage SCLC and CONVERT trial impact:Results of the EORTC Lung Cancer Group survey

Objectives: The CONVERT trial showed that twice-daily (BD) concurrent chemoradiotherapy should continue tobe considered the standard of care in localised LS-SCLC. A survey was conducted to assess the impact of theCONVERT trial in clinical practice and to identify any relevant research questions for future trials in this setting.Methods and materials: An EORTC Group online survey of LS-SCLC practice was distributed to the EORTC LCGand to members of several European thoracic oncology societies between April and December 2018.Results: 198 responses were analysed. The majority of respondents (88%, n=174) were aware of the CONVERTtrial. Radiation oncologists comprised 56% of all respondents. Once-daily (OD) radiotherapy is still the mostcommonly used regimen, however the use of concurrent BD radiotherapy increased after the publication ofCONVERT (n=59/186, 32% prior to and n=78/187, 42% after the publication, p=0.053). The main reasonsfor not implementing BD after the CONVERT publication were logistical issues (n=88, 44%), inconvenience forpatients (n=56, 28%), and the absence of a statistical survival difference between the two arms in CONVERT(n=38, 19%). Brain MRI was used by 28% during staging but more than half (60%) of the respondents did notroutinely image the brain during follow-up. The main research questions of interest in LS-SCLC were 1) integratingnovel targeted therapies-immunotherapies (n=160, 81%), 2) PCI (+/- hippocampal sparing) vs. MRIsurveillance (n=140, 71%) and, 3) biomarker driven trials (n=92, 46%).Conclusion: Once daily radiotherapy (60–66 Gy in 30–33 fractions) remains the most prescribed radiotherapyfractionation, despite the findings suggested by the CONVERT trial.info:eu-repo/semantics/publishe

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Introduction There is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT. Methods Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated. Results The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient. Conclusion Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies