ArticleTechnology in cancer research & treatment
Gross Tumor Volume Segmentation for Stage III NSCLC Radiotherapy Using 3D ResSE-Unet.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.Radiation oncology (London, England) · 2025Article
- AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluating deep learning auto-contouring for lung radiation therapy: A review of accuracy, variability, efficiency and dose, in target volumes and organs at risk.Physics and imaging in radiation oncology · 2025Review
- Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy.ArXiv · 2024Article
- A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.Journal of applied clinical medical physics · 2024Article
- MARes-Net: multi-scale attention residual network for jaw cyst image segmentation.Frontiers in bioengineering and biotechnology · 2024Article
- Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.Journal of clinical medicine · 2022Article
- Development and verification of radiomics framework for computed tomography image segmentation.Medical physics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionRadiotherapy is one of the most effective ways to treat lung cancer. Accurately delineating the gross target volume is a key step in the radiotherapy process. In current clinical practice, the target area is still delineated manually by radiologists, which is time-consuming and laborious. However, these problems can be better solved by deep learning-assisted automatic segmentation methods.
methodsIn this paper, a 3D CNN model named 3D ResSE-Unet is proposed for gross tumor volume segmentation for stage III NSCLC radiotherapy. This model is based on 3D Unet and combines residual connection and channel attention mechanisms. Three-dimensional convolution operation and encoding-decoding structure are used to mine three-dimensional spatial information of tumors from computed tomography data. Inspired by ResNet and SE-Net, residual connection and channel attention mechanisms are used to improve segmentation performance. A total of 214 patients with stage III NSCLC were collected selectively and 148 cases were randomly selected as the training set, 30 cases as the validation set, and 36 cases as the testing set. The segmentation performance of models was evaluated by the testing set. In addition, the segmentation results of different depths of 3D Unet were analyzed. And the performance of 3D ResSE-Unet was compared with 3D Unet, 3D Res-Unet, and 3D SE-Unet.
resultsCompared with other depths, 3D Unet with four downsampling depths is more suitable for our work. Compared with 3D Unet, 3D Res-Unet, and 3D SE-Unet, 3D ResSE-Unet can obtain superior results. Its dice similarity coefficient, 95th-percentile of Hausdorff distance, and average surface distance can reach 0.7367, 21.39mm, 4.962mm, respectively. And the average time cost of 3D ResSE-Unet to segment a patient is only about 10s.
conclusionThe method proposed in this study provides a new tool for GTV auto-segmentation and may be useful for lung cancer radiotherapy.
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.