ArticleEJNMMI research2022
A convolutional neural network for total tumor segmentation in [
Article in EJNMMI research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed.
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- PET/CT contouring techniques in neuroendocrine neoplasms: a state-of-the-art scoping review.EJNMMI reports · 2026Review
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- ARTEMIS: a pilot study comparing AI-based and expert therapeutic decisions in simulated clinical cases of neuroendocrine neoplasms.NPJ digital medicine · 2025Article
- Artificial Intelligence for Prognosis of Gastro-Entero-Pancreatic Neuroendocrine Neoplasms.Cancers · 2025Review
- FDA-approved drugs featuring macrocycles or medium-sized rings.Archiv der Pharmazie · 2025Review
- An automated pheochromocytoma and paraganglioma lesion segmentation AI-model at whole-bodyEJNMMI research · 2024Article
- Models using comprehensive, lesion-level, longitudinal [European journal of nuclear medicine and molecular imaging · 2024Article
- Retrospective evaluation of the predictive value of tumour burden at baseline [EJNMMI reports · 2024Article
- Application of Artificial Intelligence in Neuroendocrine Lung Cancer Diagnosis and Treatment: A Systematic Review.Cureus · 2024Review
- Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens.Diagnostics (Basel, Switzerland) · 2024Review
- Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.Medical physics · 2023Article
- AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT.European journal of hybrid imaging · 2023Article
- Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs).Life (Basel, Switzerland) · 2023Review
- Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundSegmentation of neuroendocrine neoplasms (NENs) in [
resultsCross-validation training was used to generate models and an ensemble model. The ensemble model performed best overall with a lesion-wise dice of 0.850 and pixel-wise dice, precision and sensitivity of 0.801, 0.786 and 0.872, respectively. Performance of the ensemble model was acceptable with some degree of manual adjustment in 35/41 (85%) patients. Final tumor segmentation could be obtained from the AI model with manual adjustments in 5 min versus 17 min for ground truth method, p < 0.01.
conclusionWe implemented and validated an AI model that achieved a high similarity with ground truth segmentation and resulted in faster tumor segmentation. With AI, total tumor segmentation may become feasible in the clinical routine.
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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.