Risk of Disease Recurrence and Mortality Varies by Type of Fat Consumed before Cancer Treatment in a Longitudinal Cohort of Head and Neck Squamous Cell Carcinoma Patients.

BACKGROUND: The associations between specific types of fat and head and neck squamous cell carcinoma (HNSCC) recurrence and mortality rates have not yet been examined. OBJECTIVES: The purpose of this study was to determine how intakes of various fat subtypes before cancer treatment are associated with recurrence and mortality in adults diagnosed with HNSCC. METHODS: This was a secondary analysis longitudinal cohort study of data collected from 476 newly diagnosed patients with HNSCC. Patients completed baseline FFQs and epidemiologic health surveys. Recurrence and mortality events were collected annually. Fat intakes examined included long-chain fatty acids (LCFAs), unsaturated fatty acids (FAs), PUFAs, ω-3 (n-3) PUFAs, ω-6 (n-6) PUFAs, MUFAs, animal fats, vegetable fats, saturated FAs, and trans fats. Associations between fat intake (categorized into tertiles) and time to event were tested using multivariable Cox proportional hazards models, adjusting for age, sex, smoking status, human papillomavirus status, tumor site, cancer stage, and total caloric intake. Intake of fats was compared with the lowest tertile. RESULTS: During the study period, there were 115 recurrent and 211 death events. High LCFA intake was associated with a reduced all-cause mortality risk (HR: 0.55; 95% CI: 0.34, 0.91; P-trend = 0.02). High unsaturated FA intake was associated with a reduced all-cause mortality risk (HR: 0.62; 95% CI: 0.40, 0.97; P-trend = 0.04) and HNSCC-specific mortality risk (HR: 0.51; 95% CI: 0.29, 0.90; P-trend = 0.02). High intakes of ω-3 PUFAs (HR: 0.56; 95% CI: 0.35, 0.91; P-trend = 0.02) and ω-6 PUFAs (HR: 0.57; 95% CI: 0.34, 0.94; P-trend = 0.02) were significantly associated with a reduced all-cause mortality risk. There were no significant associations between other fat types and recurrence or mortality risk. CONCLUSIONS: In this prospective survival cohort of 476 newly diagnosed patients with HNSCC, our data suggest that HNSCC prognosis may vary depending on the fat types consumed before cancer treatment. Clinical intervention trials should test these associations.

Diet quality indices and changes in cognition during chemotherapy.

PURPOSE: No evidence-based prevention strategies currently exist for cancer-related cognitive decline (CRCD). Although patients are often advised to engage in healthy lifestyle activities (e.g., nutritious diet), little is known about the impact of diet on preventing CRCD. This secondary analysis evaluated the association of pre-treatment diet quality indices on change in self-reported cognition during chemotherapy. METHODS: Study participants (n = 96) completed the Block Brief Food Frequency Questionnaire (FFQ) before receiving their first infusion and the PROMIS cognitive function and cognitive abilities questionnaires before infusion and again 5 days later (i.e., when symptoms were expected to be their worst). Diet quality indices included the Dietary Approaches to Stop Hypertension (DASH), Alternate Mediterranean Diet (aMED), and a low carbohydrate diet index and their components. Descriptive statistics were generated for demographic and clinical variables and diet indices. Residualized change models were computed to examine whether diet was associated with change in cognitive function and cognitive abilities, controlling for age, sex, cancer type, treatment type, depression, and fatigue. RESULTS: Study participants had a mean age of 59 ± 10.8 years and 69% were female. Although total diet index scores did not predict change in cognitive function or cognitive abilities, higher pre-treatment ratio of aMED monounsaturated/saturated fat was associated with less decline in cognitive function and cognitive abilities at 5-day post-infusion (P ≤ .001). CONCLUSIONS: Higher pre-treatment ratio of monounsaturated/saturated fat intake was associated with less CRCD early in chemotherapy. Results suggest greater monounsaturated fat and less saturated fat intake could be protective against CRCD during chemotherapy.

Automatic Cobb angle measurement method based on vertebra segmentation by deep learning.

The accuracy of the Cobb measurement is essential for the diagnosis and treatment of scoliosis. Manual measurement is however influenced by the observer variability hence affecting progression evaluation. In this paper, we propose a fully automatic Cobb measurement method to address the accuracy issue of manual measurement. We improve the U-shaped network based on the multi-scale feature fusion to segment each vertebra. To enable multi-scale feature extraction, the convolution kernel of the U-shaped network is substituted by the Inception Block. To solve the problem of gradient disappearance caused by the widening of the network structure from the Inception Block, we propose using Res Block. CBAM (Convolutional Block Attention Module) can help the network judges the importance of the feature map to modify learning weight. Also, to further enhance the accuracy of feature extraction, we add the CBAM to the U-shaped network bottleneck. Finally, based on the segmented vertebrae, the efficient automatic Cobb angle measurement method is proposed to estimate the Cobb angle. In the experiments, 75 spinal X-ray images are tested. We compare the proposed U-Shaped network with the state-of-the-art methods including DeepLabV3 + , FCN8S, SegNet, U-Net, U-Net + + , BASNet, and U2Net for vertebra segmentation. Our results show that compared to these methods, the Dice coefficient is improved by 32.03%, 33.58%, 12.42%, 5.65%, 4.55%, 4.42%, and 3.27%, respectively. The CMAE of the calculated Cobb measurement is 2.45°, which is lower than the average error of 5-7° of manual measurement. The experimental results indicate that the improved U-shaped network improves the accuracy of vertebra segmentation. The proposed efficient automatic Cobb measurement method can be used in clinics to reduce observer variability.

COVID-19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused hundreds of thousands of infections and deaths. Efficient diagnostic methods could help curb its global spread. The purpose of this study was to develop and evaluate a method for accurately diagnosing COVID-19 based on computed tomography (CT) scans in real time. METHODS: We propose an architecture named "concatenated feature pyramid network" ("Concat-FPN") with an attention mechanism, by concatenating feature maps of multiple. The proposed architecture is then used to form two networks, which we call COVID-CT-GAN and COVID-CT-DenseNet, the former for data augmentation and the latter for data classification. RESULTS: The proposed method is evaluated on 3 different numbers of magnitude of COVID-19 CT datasets. Compared with the method without GANs for data augmentation or the original network auxiliary classifier generative adversarial network, COVID-CT-GAN increases the accuracy by 2% to 3%, the recall by 2% to 4%, the precision by 1% to 3%, the F1-score by 1% to 3%, and the area under the curve by 1% to 4%. Compared with the original network DenseNet-201, COVID-CT-DenseNet increases the accuracy by 1% to 3%, the recall by 4% to 9%, the precision by 1%, the F1-score by 1% to 3%, and the area under the curve by 2%. CONCLUSION: The experimental results show that our method improves the efficiency of diagnosing COVID-19 on CT images, and helps overcome the problem of limited training data when using deep learning methods to diagnose COVID-19. SIGNIFICANCE: Our method can help clinicians build deep learning models using their private datasets to achieve automatic diagnosis of COVID-19 with a high precision.

Chronic Nutrition Impact Symptoms Are Associated with Decreased Functional Status, Quality of Life, and Diet Quality in a Pilot Study of Long-Term Post-Radiation Head and Neck Cancer Survivors.

BACKGROUND: As a result of tumor location and treatment that is aggressive, head and neck cancer (HNC) survivors experience an array of symptoms impacting the ability and desire to eat termed nutrition impact symptoms (NISs). Despite increasing cancer survival time, the majority of research studies examining the impact of NISs have been based on clinical samples of HNC patients during the acute phase of treatment. NISs are often chronic and persist beyond the completion of treatment or may develop as late side effects. Therefore, our research team examined chronic NIS complications on HNC survivors' functional status, quality of life, and diet quality. METHODS: This was a cross-sectional study of 42 HNC survivors who were at least 6 months post-radiation. Self-reported data on demographics, NISs, quality of life, and usual diet over the past year were obtained. Objective measures of functional status included the short physical performance battery and InBody© 270 body composition testing. NISs were coded so a lower score indicated lower symptom burden, (range 4-17) and dichotomized as ≤10 vs. >10, the median in the dataset. Wilcoxon rank sum tests were performed between the dichotomized NIS summary score and continuous quality of life and functional status outcomes. Diet quality for HNC survivors was calculated using the Healthy Eating Index 2015 (HEI-2015). Wilcoxon rank sum tests examined the difference between the HNC HEI-2015 as compared to the National Health and Nutrition Examination Survey (NHANES) data calculated using the population ratio method. RESULTS: A lower NIS score was statistically associated with higher posttreatment lean muscle mass (p = 0.002). A lower NIS score was associated with higher functional (p = 0.0006), physical (p = 0.0007), emotional (p = 0.007), and total (p < 0.0001) quality of life. Compared to NHANES controls, HNC survivors reported a significantly lower HEI-2015 diet quality score (p = 0.0001). CONCLUSIONS: Lower NIS burden was associated with higher lean muscle mass and functional, physical, emotional, and total quality of life in post-radiation HNC survivors. HNC survivors reported a significantly lower total HEI-2015 as compared to healthy NHANES controls, providing support for the hypothesis that chronic NIS burden impacts the desire and ability to eat. The effects of this pilot study were strong enough to be detected by straight forward statistical approaches and warrant a larger longitudinal study. For survivors most impacted by NIS burden, multidisciplinary post-radiation exercise and nutrition-based interventions to manage NISs and improve functional status, quality of life, and diet quality in this survivor population are needed.

A multiple-channel and atrous convolution network for ultrasound image segmentation.

PURPOSE: Ultrasound image segmentation is a challenging task due to a low signal-to-noise ratio and poor image quality. Although several approaches based on the convolutional neural network (CNN) have been applied to ultrasound image segmentation, they have weak generalization ability. We propose an end-to-end, multiple-channel and atrous CNN designed to extract a greater amount of semantic information for segmentation of ultrasound images. METHOD: A multiple-channel and atrous convolution network is developed, referred to as MA-Net. Similar to U-Net, MA-Net is based on an encoder-decoder architecture and includes five modules: the encoder, atrous convolution, pyramid pooling, decoder, and residual skip pathway modules. In the encoder module, we aim to capture more information with multiple-channel convolution and use large kernel convolution instead of small filters in each convolution operation. In the last layer, atrous convolution and pyramid pooling are used to extract multi-scale features. The architecture of the decoder is similar to that of the encoder module, except that up-sampling is used instead of down-sampling. Furthermore, the residual skip pathway module connects the subnetworks of the encoder and decoder to optimize learning from the deeper layer and improve the accuracy of segmentation. During the learning process, we adopt multi-task learning to enhance segmentation performance. Five types of datasets are used in our experiments. Because the original training data are limited, we apply data augmentation (e.g., horizontal and vertical flipping, random rotations, and random scaling) to our training data. We use the Dice score, precision, recall, Hausdorff distance (HD), average symmetric surface distance (ASD), and root mean square symmetric surface distance (RMSD) as the metrics for segmentation evaluation. Meanwhile, Friedman test was performed as the nonparametric statistical analysis to evaluate the algorithms. RESULTS: For the datasets of brachia plexus (BP), fetal head, and lymph node segmentations, MA-Net achieved average Dice scores of 0.776, 0.973, and 0.858, respectively; with average precisions of 0.787, 0.968, and 0.854, respectively; average recalls of 0.788, 0.978, and 0.885, respectively; average HDs (mm) of 13.591, 10.924, and 19.245, respectively; average ASDs (mm) of 4.822, 4.152, and 4.312, respectively; and average RMSDs (mm) of 4.979, 4.161, and 4.930, respectively. Compared with U-Net, U-Net++, M-Net, and Dilated U-Net, the average performance of the MA-Net increased by approximately 5.68%, 2.85%, 6.59%, 36.03%, 23.64%, and 31.71% for Dice, precision, recall, HD, ASD, and RMSD, respectively. Moreover, we verified the generalization of MA-Net segmentation to lower grade brain glioma MRI and lung CT images. In addition, the MA-Net achieved the highest mean rank in the Friedman test. CONCLUSION: The proposed MA-Net accurately segments ultrasound images with high generalization, and therefore, it offers a useful tool for diagnostic application in ultrasound images.

Dietary Fiber, Whole Grains, and Head and Neck Cancer Prognosis: Findings from a Prospective Cohort Study.

No studies, to date, have examined the relationship between dietary fiber and recurrence or survival after head and neck cancer diagnosis. The aim of this study was to determine whether pretreatment intake of dietary fiber or whole grains predicted recurrence and survival outcomes in newly diagnosed head and neck cancer (HNC) patients. This was a prospective cohort study of 463 participants baring a new head and neck cancer diagnosis who were recruited into the study prior to the initiation of any cancer therapy. Baseline (pre-treatment) dietary and clinical data were measured upon entry into the study cohort. Clinical outcomes were ascertained at annual medical reviews. Cox proportional hazard models were fit to examine the relationships between dietary fiber and whole grain intakes with recurrence and survival. There were 112 recurrence events, 121 deaths, and 77 cancer-related deaths during the study period. Pretreatment dietary fiber intake was inversely associated with risk of all-cause mortality (hazard ratio (HR): 0.37, 95% confidence interval (CI): 0.14-0.95, ptrend = 0.04). No statistically significant associations between whole grains and prognostic outcomes were found. We conclude that higher dietary fiber intake, prior to the initiation of treatment, may prolong survival time, in those with a new HNC diagnosis.

Pretreatment Dietary Patterns Are Associated with the Presence of Nutrition Impact Symptoms 1 Year after Diagnosis in Patients with Head and Neck Cancer.

BACKGROUND: Dietary inflammatory potential could impact the presence and severity of chronic adverse treatment effects among patients with head and neck cancer. The objective of this study was to determine whether pretreatment dietary patterns are associated with nutrition impact symptoms (NIS) as self-reported 1 year after diagnosis. METHODS: This was a longitudinal study of 336 patients with newly diagnosed head and neck cancer enrolled in the University of Michigan Head and Neck Specialized Program of Research Excellence. Principal component analysis was utilized to derive pretreatment dietary patterns from food frequency questionnaire data. Burden of seven NIS was self-reported 1 year after diagnosis. Associations between pretreatment dietary patterns and individual symptoms and a composite NIS summary score were examined with multivariable logistic regression models. RESULTS: The two dietary patterns that emerged were prudent and Western. After adjusting for age, smoking status, body mass index, tumor site, cancer stage, calories, and human papillomavirus status, significant inverse associations were observed between the prudent pattern and difficulty chewing [OR 0.44; 95% confidence interval (CI), 0.21-0.93; P = 0.03], dysphagia of liquids (OR 0.38; 95% CI, 0.18-0.79; P = 0.009), dysphagia of solid foods (OR 0.46; 95% CI, 0.22-0.96; P = 0.03), mucositis (OR 0.48; 95% CI, 0.24-0.96; P = 0.03), and the NIS summary score (OR 0.45; 95% CI, 0.22-0.94; P = 0.03). No significant associations were observed between the Western pattern and NIS. CONCLUSIONS: Consumption of a prudent diet before treatment may help reduce the risk of chronic NIS burden among head and neck cancer survivors. IMPACT: Dietary interventions are needed to test whether consumption of a prudent dietary pattern before and during head and neck cancer treatment results in reduced NIS burden.