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Could Ovarian Cancer Prediction Models Improve the Triage of Symptomatic Women in Primary Care? A Modelling Study Using Routinely Collected DataCA125 is widely used as an initial investigation in women presenting with symptoms of possible ovarian cancer. We sought to develop CA125-based diagnostic prediction models and to explore potential implications of implementing model-based thresholds for further investigation in primary care. This retrospective cohort study used routinely collected primary care and cancer registry data from symptomatic, CA125-tested women in England (2011–2014). A total of 29,962 women were included, of whom 279 were diagnosed with ovarian cancer. Logistic regression was used to develop two models to estimate ovarian cancer probability: Model 1 consisted of age and CA125 level; Model 2 incorporated further risk factors. Model discrimination (AUC) was evaluated using 10-fold cross-validation. The sensitivity and specificity of various model risk thresholds (≥1% to ≥3%) were compared with that of the current CA125 cut-off (≥35 U/mL). Model 1 exhibited excellent discrimination (AUC: 0.94) on cross-validation. The inclusion of additional variables (Model 2) did not improve performance. At a risk threshold of ≥1%, Model 1 exhibited greater sensitivity (86.4% vs. 78.5%) but lower specificity (89.1% vs. 94.5%) than CA125 (≥35 U/mL). Applying the ≥1% model threshold to the cohort in place of the current CA125 cut-off, 1 in every 74 additional women identified had ovarian cancer. Following external validation, Model 1 could be used as part of a ‘risk-based triage’ system in which women at high risk of undiagnosed ovarian cancer are selected for urgent specialist investigation, while women at ‘low risk but not no risk’ are offered non-urgent investigation or interval CA125 re-testing. Such an approach has the potential to expedite ovarian cancer diagnosis, but further research is needed to evaluate the clinical impact and health–economic implications.
Detection of Morphological Abnormalities in Schizophrenia: An Important Step to Identify Associated Genetic Disorders or Etiologic SubtypesCurrent research suggests that alterations in neurodevelopmental processes, involving gene X environment interactions during key stages of brain development (prenatal period and adolescence), are a major risk for schizophrenia. First, epidemiological studies supporting a genetic contribution to schizophrenia are presented in this article, including family, twin, and adoption studies. Then, an extensive literature review on genetic disorders associated with schizophrenia is reviewed. These epidemiological findings and clinical observations led researchers to conduct studies on genetic associations in schizophrenia, and more specifically on genomics (CNV: copy-number variant, and SNP: single nucleotide polymorphism). The main structural (CNV) and sequence (SNP) variants found in individuals with schizophrenia are reported here. Evidence of genetic contributions to schizophrenia and current knowledge on genetic syndromes associated with this psychiatric disorder highlight the importance of a clinical genetic examination to detect minor physical anomalies in individuals with ultra-high risk of schizophrenia. Several dysmorphic features have been described in schizophrenia, especially in early onset schizophrenia, and can be viewed as neurodevelopmental markers of vulnerability. Early detection of individuals with neurodevelopmental abnormalities is a fundamental issue to develop prevention and diagnostic strategies, therapeutic intervention and follow-up, and to ascertain better the underlying mechanisms involved in the pathophysiology of schizophrenia.