Pregnancy outcomes in Benghazi, Libya, before and during the armed conflict in 2011

Stressful life events experienced by pregnant women may lead to adverse obstetric outcomes. This study in Benghazi compared the rates of preterm, low-birth-weight and caesarean-section births at Al-Jamhouria hospital in the months before and during the armed conflict in Libya in 2011. Data were collected on all women admitted to the delivery ward during February to May 2011 (the months of the most active fighting in the city) (n = 7096), and October to December 2010 (the months immediately before the war) (n = 5935). Compared with the preceding months there was a significant rise during the conflict in the rate of deliveries involving preterm (3.6% versus 2.5%) and low-birth-weight (10.1% versus 8.5%) infants and caesarean sections (26.9% versus 25.3%). Psychosocial stress may have been a factor (among others) in an increase in negative pregnancy outcomes, and obstetric hospitals should be aware of these issues in times of war

Prognostic value of deep learning-mediated treatment monitoring in lung cancer patients receiving immunotherapy

BackgroundCheckpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.MethodsA cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.ResultsOur results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations.ConclusionsOur results demonstrate that deep learning can quantify tumor- and non-tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.Pathogenesis and treatment of chronic pulmonary disease

Radiomics in immuno-oncology

With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications

Radiogenomics: bridging imaging and genomics

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as radiogenomics. In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright

The effect of everolimus and low-dose cyclophosphamide on immune cell subsets in patients with metastatic renal cell carcinoma: results from a phase I clinical trial

Contains fulltext : 203064.pdf (publisher's version ) (Open Access)For the treatment of metastatic renal cell cancer several strategies are used among which the mTOR inhibitor everolimus. As mTOR plays an important role in the immune system, e.g., by controlling the expression of the transcription factor FoxP3 thereby regulating regulatory T cells (Tregs), it plays a key role in the balance between tolerance and inflammation. Previous reports showed stimulatory effects of mTOR inhibition on the expansion of Tregs, an effect that can be considered detrimental in terms of cancer control. Since metronomic cyclophosphamide (CTX) was shown to selectively deplete Tregs, a phase 1 clinical trial was conducted to comprehensively investigate the immune-modulating effects of several dosages and schedules of CTX in combination with the standard dose of everolimus, with the explicit aim to achieve selective Treg depletion. Our data show that 50 mg of CTX once daily and continuously administered, in combination with the standard dose of 10 mg everolimus once daily, not only results in depletion of Tregs, but also leads to a reduction in MDSC, a sustained level of the CD8(+) T-cell population accompanied by an increased effector to suppressor ratio, and reversal of negative effects on three peripheral blood DC subsets. These positive effects on the immune response may contribute to improved survival, and therefore this combination therapy is further evaluated in a phase II clinical trial