Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Correction to: Psoriasis in systemic lupus erythematosus: a single-center experience

Clinical Rheumatology - Prof. Ari Polachek on of the author of the published version of this article missed to add his second affiliation which is the Department of Rheumatology, Tel Aviv Sourasky...     

Postoperative nomogram for disease-specific survival after an R0 resection for gastric carcinoma.

PURPOSE: Few published studies have addressed individual patient risk after R0 resection for gastric cancer. We developed and internally validated a nomogram that combines these factors to predict the probability of 5-year gastric cancer-specific survival on the basis of 1,039 patients treated at a single institution. METHODS: Nomogram predictor variables included age, sex, primary site (distal one-third, middle one-third, gastroesophageal junction, and proximal one-third), Lauren histotype (diffuse, intestinal, mixed), number of positive lymph nodes resected, number of negative lymph nodes resected, and depth of invasion. Death as a result of gastric cancer was the predicted end point. The concordance index was used as an accuracy measure, with bootstrapping to correct for optimistic bias. Calibration plots were constructed. RESULTS: Gastric cancer-specific survival at 5 years was 50%. A nomogram was constructed on the basis of a Cox regression model. The bootstrap-corrected concordance index was 0.80. When compared with the predictive ability of American Joint Committee on Cancer stage, the nomogram discrimination was superior (P <.001). Nomogram calibration appeared to be excellent. CONCLUSION: A nomogram was developed to predict 5-year disease-specific survival after R0 resection for gastric cancer. This tool should be useful for patient counseling, follow-up scheduling, and clinical trial eligibility determination.     

Development of the New Lung Allocation System in the United States

This article reviews the development of the new U.S. lung allocation system that took effect in spring 2005. In 1998, the Health Resources and Services Administration of the U.S. Department of Health and Human Services published the Organ Procurement and Transplantation Network (OPTN) Final Rule. Under the rule, which became effective in 2000, the OPTN had to demonstrate that existing allocation policies met certain conditions or change the policies to meet a range of criteria, including broader geographic sharing of organs, reducing the use of waiting time as an allocation criterion and creating equitable organ allocation systems using objective medical criteria and medical urgency to allocate donor organs for transplant. This mandate resulted in reviews of all organ allocation policies, and led to the creation of the Lung Allocation Subcommittee of the OPTN Thoracic Organ Transplantation Committee. This paper reviews the deliberations of the Subcommittee in identifying priorities for a new lung allocation system, the analyses undertaken by the OPTN and the Scientific Registry for Transplant Recipients and the evolution of a new lung allocation system that ranks candidates for lungs based on a Lung Allocation Score, incorporating waiting list and posttransplant survival probabilities.