Deep Learning vs. Atlas-Based Models for Fast Auto-Segmentation of the Masticatory Muscles on Head and Neck CT Images

BACKGROUND: Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images. MATERIAL AND METHODS: Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods. RESULTS: DLAS outperformed ABAS in delineating all MMs (p \u3c 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83 ± 0.03 to 0.89 ± 0.02, the ABAS mean DSC ranged from 0.79 ± 0.05 to 0.85 ± 0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ∆D98%, ∆D95%, ∆D50%, and ∆D2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P \u3e 0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS. CONCLUSIONS: DLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours

Honor-Based Abuse in England and Wales: Who Does What to Whom?

Key findings are presented from an empirical study profiling 1,474 cases of honor-based abuse (HBA) known to police and victim services in England and Wales. Thematic and quantitative (regression) analyses were used to investigate whether and how HBA differed from other forms of domestic abuse and forced marriage. A new typology of HBA is proposed, based principally on the relationship(s) between victim and perpetrator(s). Interpreted within an overarching lens of gender-based violence, it is argued that Type 1 (partner abuse) and Type 3 (partner plus family abuse) are culturally specific forms of domestic abuse, whereas Type 2 (family abuse) is distinct

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Surveillance imaging following definitive radiotherapy for non–small cell lung cancer: What is the clinical impact?

Lung cancer is the leading cause of cancer death worldwide. Recurrence rates at all stages are high, but evidence-based post-treatment surveillance imaging strategies to detect recurrence are poorly defined, and salvage options are frequently limited. A number of national and international oncology guidelines address post-treatment imaging, but are largely based on low-level, retrospective evidence because of a paucity of high-quality data, particularly in regard to cost-effectiveness and quality-of-life endpoints. Given the lack of randomized data addressing appropriate surveillance imaging modality and interval following definitive treatment of lung cancer, there remains an unmet clinical need. Meaningful surveillance endpoints should include the financial impact, patient quality-of-life outcomes, and access-to-care issues associated with intensive follow-up to ensure that guidelines reflect quality and sustainability. A need for prospective randomized data on the subject of imaging surveillance after definitive local therapy remains an unmet need, and an opportunity for collaboration and further research