ArticleJournal of digital imaging2011
Human-computer interaction in radiotherapy target volume delineation: a prospective, multi-institutional comparison of user input devices.
Article in Journal of digital imaging, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning.Journal of applied clinical medical physics · 2016Trial
- Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2014Trial
- Image-Based Search in Radiology: Identification of Brain Tumor Subtypes within Databases Using MRI-Based Radiomic Features.AJNR. American journal of neuroradiology · 2025Article
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation.The journal of machine learning for biomedical imaging · 2024Article
- Clinical assessment of a novel machine-learning automated contouring tool for radiotherapy planning.Journal of applied clinical medical physics · 2023Article
- A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy.Frontiers in oncology · 2023Article
- Article
- Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2021Review
- Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach.International journal of radiation oncology, biology, physics · 2021Article
- Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy.Physics and imaging in radiation oncology · 2020Article
- Stylus/tablet user input device for MRI heart wall segmentation: efficiency and ease of use.European radiology · 2018Article
- Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.International journal of radiation oncology, biology, physics · 2018Article
- Evaluation of user input methods for manipulating a tablet personal computer in sterile techniques.International journal of computer assisted radiology and surgery · 2017Article
- User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.Journal of digital imaging · 2016Article
- Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison.Journal of digital imaging · 2014Article
- Evaluation of Two Commercial Artificial Intelligence Segmentation Systems for Radiation Therapy.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
The purpose of this study was the prospective comparison of objective and subjective effects of target volume region of interest (ROI) delineation using mouse-keyboard and pen-tablet user input devices (UIDs). The study was designed as a prospective test/retest sequence, with Wilcoxon signed rank test for matched-pair comparison. Twenty-one physician-observers contoured target volume ROIs on four standardized cases (representative of brain, prostate, lung, and head and neck malignancies) twice: once using QWERTY keyboard/scroll-wheel mouse UID and once with pen-tablet UID (DTX2100, Wacom Technology Corporation, Vancouver, WA, USA). Active task time, ROI manipulation task data, and subjective survey data were collected. One hundred twenty-nine target volume ROI sets were collected, with 62 paired pen-tablet/mouse-keyboard sessions. Active contouring time was reduced using the pen-tablet UID, with mean ± SD active contouring time of 26 ± 23 min, compared with 32 ± 25 with the mouse (p ≤ 0.01). Subjective estimation of time spent was also reduced from 31 ± 26 with mouse to 27 ± 22 min with the pen (p = 0.02). Task analysis showed ROI correction task reduction (p = 0.045) and decreased panning and scrolling tasks (p < 0.01) with the pen-tablet; drawing, window/level changes, and zoom commands were unchanged (p = n.s.) Volumetric analysis demonstrated no detectable differences in ROI volume nor intra- or inter-observer volumetric coverage. Fifty-two of 62 (84%) users preferred the tablet for each contouring task; 5 of 62 (8%) denoted no preference, and 5 of 62 (8%) chose the mouse interface. The pen-tablet UID reduced active contouring time and reduced correction of ROIs, without substantially altering ROI volume/coverage.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.