{"doi":"10.1002/mp.16001","title":"Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network","abstract":"BACKGROUND: Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning. PURPOSE: In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images. METHODS: The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis. RESULTS: In fivefold cross-validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods. CONCLUSION: The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.","journal":"Medical Physics","year":2022,"id":261384,"datarank":0.41588830833596724,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.0,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9594,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":236221,"name":"Tonghe Wang","orcid":"0000-0001-9021-1204","position":1,"is_corresponding":false},{"id":348333,"name":"Jiwoong Jeong","orcid":"0000-0001-5630-9443","position":2,"is_corresponding":false},{"id":421856,"name":"James Janopaul‐Naylor","orcid":"0000-0002-3612-4852","position":3,"is_corresponding":false},{"id":250449,"name":"Aparna H. Kesarwala","orcid":"0000-0001-8297-9643","position":4,"is_corresponding":false},{"id":702655,"name":"Justin Roper","orcid":null,"position":5,"is_corresponding":false},{"id":426285,"name":"Sibo Tian","orcid":"0000-0001-5018-4854","position":6,"is_corresponding":false},{"id":267373,"name":"Jeffrey D. Bradley","orcid":"0000-0003-3047-3151","position":7,"is_corresponding":false},{"id":32544,"name":"Tian Liu","orcid":"0000-0002-5810-4318","position":8,"is_corresponding":false},{"id":267372,"name":"Kristin Higgins","orcid":"0000-0003-1496-9878","position":9,"is_corresponding":false},{"id":236226,"name":"Xiaofeng Yang","orcid":"0000-0001-9023-5855","position":10,"is_corresponding":false},{"id":236222,"name":"Yang Lei","orcid":"0000-0002-3572-0345","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:26:12.158801Z","pmid":"36203393","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}