Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Background and purposeTo develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.Materials and methodsThis retrospective study included 60 subjects [30 Alzheimer’s disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.ResultsThe single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.ConclusionOur deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV

Effects of Smoking on Menopausal Age: Results From the Korea National Health and Nutrition Examination Survey, 2007 to 2012

Objectives: Decreased fertility and impaired health owing to early menopause are significant health issues. Smoking is a modifiable health-related behavior that influences menopausal age. We investigated the effects of smoking-associated characteristics on menopausal age in Korean women. Methods: This study used data from the Korea National Health and Nutrition Examination Survey from 2007 to 2012. Menopausal age in relation to smoking was analyzed as a Kaplan-Meier survival curve for 11 510 women (aged 30 to 65 years). The risk of entering menopause and experiencing early menopause (before age 48) related to smoking were assessed using a Cox proportional hazards model. Results: The menopausal age among smokers was 0.75 years lower than that among non-smokers (p<0.001). The results of the Cox proportional hazards model showed pre-correction and post-correction risk ratios for entering menopause related to smoking of 1.26 (95% confidence interval [CI], 1.09 to 1.46) and 1.27 (95% CI, 1.10 to 1.47), respectively, and pre-correction and post-correction risk ratios for experiencing early menopause related to smoking of 1.36 (95% CI, 1.03 to 1.80) and 1.40 (95% CI, 1.05 to 1.85), respectively. Conclusions: Smokers reached menopause earlier than non-smokers, and their risk for experiencing early menopause was higher

Three cases of pancreatic pseudocysts associated with dorsal pancreatic agenesis

Agenesis of the dorsal pancreas (ADP) is an extremely rare congenital anomaly. Human pancreas is formed by ventral and dorsal endodermal buds of the foregut endoderm. The dorsal bud forms the upper part of the head, neck, body, and tail of the pancreas and the ventral bud generates most of the head and uncinate process. ADP is derived from the embryologic failure of the dorsal pancreatic bud to form the pancreatic body and tail. ADP can be related to some diseases and conditions such as pancreatitis, hypoglycemia, and rarely pancreatic tumors. The association between cystic lesions with ADP has previously been reported. Three cases of cystic lesions of the pancreas with ADP were diagnosed clinically based on the imaging features and without any past history of pancreatitis. However, the pathologic diagnosis of resected lesions confirmed pseudocysts without pathologic evidence of tumor. We report 3 cases of pancreatic pseudocysts associated with ADP Keywords: Pseudocyst, Dorsal pancreatic agenesis, Pancreatic tumo

Curcumin and Cancer Cells: How Many Ways Can Curry Kill Tumor Cells Selectively?

Cancer is a hyperproliferative disorder that is usually treated by chemotherapeutic agents that are toxic not only to tumor cells but also to normal cells, so these agents produce major side effects. In addition, these agents are highly expensive and thus not affordable for most. Moreover, such agents cannot be used for cancer prevention. Traditional medicines are generally free of the deleterious side effects and usually inexpensive. Curcumin, a component of turmeric (Curcuma longa), is one such agent that is safe, affordable, and efficacious. How curcumin kills tumor cells is the focus of this review. We show that curcumin modulates growth of tumor cells through regulation of multiple cell signaling pathways including cell proliferation pathway (cyclin D1, c-myc), cell survival pathway (Bcl-2, Bcl-xL, cFLIP, XIAP, c-IAP1), caspase activation pathway (caspase-8, 3, 9), tumor suppressor pathway (p53, p21) death receptor pathway (DR4, DR5), mitochondrial pathways, and protein kinase pathway (JNK, Akt, and AMPK). How curcumin selectively kills tumor cells, and not normal cells, is also described in detail