ArticleJournal of digital imaging2016
A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.
Article in Journal of digital imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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
42 citing papers in PubMed.
- Influence of CT harmonization in longitudinal radiomics for NSCLC immunotherapy response prediction.Computers in biology and medicine · 2026Article
- Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.Respiratory research · 2025Article
- Radiomics and artificial intelligence applications in pediatric brain tumors.World journal of pediatrics : WJP · 2024Review
- Tumor segmentation via enhanced area growth algorithm for lung CT images.BMC medical imaging · 2023Article
- Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis.Insights into imaging · 2023Review
- Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs.Cancers · 2023Article
- Multi-scale dense selective network based on border modeling for lung nodule segmentation.International journal of computer assisted radiology and surgery · 2023Article
- A Bi-FPN-Based Encoder-Decoder Model for Lung Nodule Image Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- What does radiomics do in PD-L1 blockade therapy of NSCLC patients?Thoracic cancer · 2022Review
- Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.The Lancet. Digital health · 2022Observational
- Reproducibility and Repeatability of Coronary Computed Tomography Angiography (CCTA) Image Segmentation in Detecting Atherosclerosis: A Radiomics Study.Diagnostics (Basel, Switzerland) · 2022Article
- A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation.European radiology · 2022Article
- Artificial intelligence and machine learning in cancer imaging.Communications medicine · 2022Review
- Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers.BJR open · 2022Review
- Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.IEEE transactions on medical imaging · 2021Article
- How does DICOM support big data management? Investigating its use in medical imaging community.Insights into imaging · 2021Article
- Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine.Cold Spring Harbor perspectives in medicine · 2021Review
- Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT.Quantitative imaging in medicine and surgery · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.
Indexed as
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.