ArticleMedical physics2016
Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach.
Article in Medical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.Frontiers in oncology · 2021Pooled it
- Diagnostic efficacy of [European journal of nuclear medicine and molecular imaging · 2024Trial
- Enhanced Lymphoma Subtype Classification and Prognosis Using Machine Learning withNuclear medicine and molecular imaging · 2026Article
- Evaluation of Bone Invasion in Gingival Squamous Cell Carcinoma Using PET/CT Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Reproducibility and Potential Prognostic Value of Spatial Metabolic PET Metrics in Oral Tongue Squamous Cell Carcinoma.Cancers · 2026Article
- One-stop evaluation using [Japanese journal of radiology · 2026Article
- Evaluating the impact of partial volume correction on FDG PET radiomics stability in lymphoma lesions.Physical and engineering sciences in medicine · 2026Article
- A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.Journal of imaging informatics in medicine · 2026Article
- Non-invasive assessment of PD-L1 status and histology in non-small cell lung cancer usingJournal of translational medicine · 2026Article
- ¹⁸F-FDG PET radiomics and machine learning for virtual biopsy and treatment decisions in lymphoma: a multicenter study.Physical and engineering sciences in medicine · 2026Article
- Non-invasive prediction of prognostic immune subtypes in lung adenocarcinoma using PET/CT-based radiomics.Frontiers in oncology · 2026Article
- 18F-FDG PET/CT metabolic network connectivity in clinical staging of diffuse large B-cell lymphoma.Frontiers in oncology · 2026Article
- Automatic Lugano staging for risk stratification in lymphoma: a multicenter PET radiomics and machine learning study with survival analysis.Nuclear medicine communications · 2025Article
- Automated segmentation of the primary tumor in nasopharyngeal carcinoma using a deep learning framework in positron emission tomography imaging: a comparative study.Quantitative imaging in medicine and surgery · 2025Article
- A prognostic PET radiomic model for risk stratification in non-small cell lung cancer: integrating radiogenomics and clinical features to predict survival and uncover tumor biology insights.Journal of cancer research and clinical oncology · 2025Article
- Comparison ofScientific reports · 2025Article
- Prediction of EGFR-TP53 genes co-mutations in patients with lung adenocarcinoma (LUAD) byClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.Journal of applied clinical medical physics · 2024Article
- Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma.BMC medical imaging · 2024Article
- Peri- and intra-nodular radiomic features based onFrontiers in medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
purposeThe purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans.
methodsA semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the "just-enough-interaction" principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts.
resultsSegmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach.
conclusionsLack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in clinical oncology decision-making. The properties of the authors approach make it well suited for applications in image-guided radiation oncology, response assessment, or treatment outcome prediction.
Identifiers
What OpenQuestion holds
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.