Dataset Information

Metallic Nanoislands on Graphene for Monitoring Swallowing Activity in Head and Neck Cancer Patients.

ABSTRACT: There is a need to monitor patients with cancer of the head and neck postradiation therapy, as diminished swallowing activity can result in disuse atrophy and fibrosis of the swallowing muscles. This paper describes a flexible strain sensor comprising palladium nanoislands on single-layer graphene. These piezoresistive sensors were tested on 14 disease-free head and neck cancer patients with various levels of swallowing function: from nondysphagic to severely dysphagic. The patch-like devices detected differences in (1) the consistencies of food boluses when swallowed and (2) dysphagic and nondysphagic swallows. When surface electromyography (sEMG) is obtained simultaneously with strain data, it is also possible to differentiate swallowing vs nonswallowing events. The plots of resistance vs time are correlated to specific events recorded by video X-ray fluoroscopy. Finally, we developed a machine-learning algorithm to automate the identification of bolus type being swallowed by a healthy subject (86.4%. accuracy). The algorithm was also able to discriminate between swallows of the same bolus from either the healthy subject or a dysphagic patient (94.7% accuracy). Taken together, these results may lead to noninvasive and home-based systems for monitoring of swallowing function and improved quality of life.

SUBMITTER: Ramirez J

PROVIDER: S-EPMC6286678 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Metallic Nanoislands on Graphene for Monitoring Swallowing Activity in Head and Neck Cancer Patients.

Ramírez Julian J Rodriquez Daniel D Qiao Fang F Warchall Julian J Rye Jasmine J Aklile Eden E S-C Chiang Andrew A Marin Brandon C BC Mercier Patrick P PP Cheng C K CK Hutcheson Katherine A KA Shinn Eileen H EH Lipomi Darren J DJ

ACS nano 20180608 6

There is a need to monitor patients with cancer of the head and neck postradiation therapy, as diminished swallowing activity can result in disuse atrophy and fibrosis of the swallowing muscles. This paper describes a flexible strain sensor comprising palladium nanoislands on single-layer graphene. These piezoresistive sensors were tested on 14 disease-free head and neck cancer patients with various levels of swallowing function: from nondysphagic to severely dysphagic. The patch-like devices de ...[more]

PMID: 29874030

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Project description:Background:Many patients undergoing treatment for head and neck cancer (HNC) experience significant swallowing difficulties, and there is some evidence that swallowing exercises may improve outcomes, including quality of life. This feasibility study developed an evidence-based, practical Swallowing Intervention Package (SiP) for patients undergoing chemoradiotherapy (CRT) for HNC. As part of the study, an electronic version of SiP (e-SiP) was concurrently developed to support patients to self-manage during treatment. This paper reports on the e-SiP component of this work. Objective:The objective of our study was to develop and conduct a preliminary evaluation of an electronic support system (e-SiP) for patients undergoing CRT for HNC. Methods:The study was conducted using a recognized mHealth development and evaluation framework and involved health professionals and patients who were undergoing CRT for HNC. The scoping stage of e-SiP development investigated the potential usefulness of the app, exploring how e-SiP would look and feel and what content would be appropriate to provide. Patient and carer focus groups and a health professionals' consensus day were used as means of data gathering around potential e-SiP content. A repeat focus group looked at an outline version of e-SiP and informed the next stage of its development with regard to refining the requirements for the tool. This was followed by further development and a testing stage of e-SiP that involved the coding of a prototype, which was then evaluated using a series of steering group meetings, semistructured interviews with both patients and health care professionals, and analysis of e-SiP log data. Results:Feedback from focus groups and health professional interviews was very positive, and it was felt e-SiP use would support and encourage patients in conducting their swallowing exercises. However, of the 10 patients who were offered e-SiP, only 2 opted to use it. For these patients, the aspects of the e-SiP app were considered useful, in particular, the ease of keeping a diary of exercises performed. Interviews with users and nonusers suggested significant barriers to its use. Most significantly, the lack of flexibility of the platform on which e-SiP could be accessed appeared a dominant factor in deterring e-SiP use. Conclusions:The results suggest that further research needs to be conducted around the implementation of e-SiP. This involves evaluating how e-SiP can be better integrated into usual care and through patient training and staff engagement, can be perceived as a beneficial tool to help support patients in conducting swallowing exercises.

Project description:Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose-volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.

Dataset Information

Metallic Nanoislands on Graphene for Monitoring Swallowing Activity in Head and Neck Cancer Patients.

Publications

Metallic Nanoislands on Graphene for Monitoring Swallowing Activity in Head and Neck Cancer Patients.

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