Associations of Parent–Child Anxious and Depressive Symptoms When a Caregiver Has a History of Depression

We examined the associations between parent and child anxious and depressive symptoms controlling for co-occurring symptoms in both. One hundred and four families participated, including 131 9–15 year old children considered at risk for anxiety and/or depression due to a history of depression in a parent. Parents and children completed questionnaires assessing depressive and anxious symptoms. Linear Mixed Models analyses controlling for the alternate parent and child symptoms indicated that both parent and child depressive symptoms and parent and child anxious symptoms were positively associated. Parental depressive symptoms were not positively associated with child anxious symptoms, and parental anxious symptoms were not positively associated with child depressive symptoms. The findings provide evidence for positive specific links between parent and child development of same-syndrome, but not cross-syndrome, symptoms when a caregiver has a history of depression

Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted MR imaging; a multichannel statistical classifier,”

A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance ͑MR͒ methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging ͑LSDI͒. From these MR sequences, four different sets of image intensities were obtained: T2-weighted ͑T2W͒ from T2-weighted imaging, Apparent Diffusion Coefficient ͑ADC͒ from LSDI, and proton density ͑PD͒ and T2 ͑T2 Map͒ from T2-mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor ''ground truth.'' Textural features were extracted from the images using co-occurrence matrix ͑CM͒ and discrete cosine transform ͑DCT͒. Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood ͑ML͒ classifiers were based on 1 of the 4 basic image intensities. Our multichannel classifiers: support vector machine ͑SVM͒ and Fisher linear discriminant ͑FLD͒, utilized five different sets of derived features. Each classifier generated a summary statistical map that indicated tumor likelihood in the peripheral zone ͑PZ͒ of the prostate gland. To assess classifier accuracy, the average areas under the receiver operator characteristic ͑ROC͒ curves over all subjects were compared. Our best FLD classifier achieved an average ROC area of 0.839(Ϯ0.064), and our best SVM classifier achieved an average ROC area of 0.761 (Ϯ0.043). The T2W ML classifier, our best single-channel classifier, only achieved an average ROC area of 0.599(Ϯ0.146). Compared to the best single-channel ML classifier, our best multichannel FLD and SVM classifiers have statistically superior ROC performance ( Pϭ0.0003 and 0.0017, respectively͒ from pairwise two-sided t-test. By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary statistical maps can potentially aid in image-guided prostate biopsy and assist in guiding and controlling delivery of localized therapy under image guidance