Technology in Cancer Research & Treatment最新文献

{"title":"Retraction: FGF23 is a potential prognostic biomarker in uterine sarcoma.","authors":"","doi":"10.1177/15330338261417025","DOIUrl":"10.1177/15330338261417025","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261417025"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12819966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146012300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Portable Electrical Impedance Prescreening for Breast tissue suspicious for malignancy: Model Optimization and Clinical Performance of the Julieta Device in a Multicenter Cross-Sectional Study in Colombia.","authors":"Maria Andrea Negret, Valentina González, David Grajales, Maria Alejandra Velez, Maria Alejandra Yepez, Valentina Agudelo, Sergio Lopez, Clara Piedrahita","doi":"10.1177/15330338261422902","DOIUrl":"10.1177/15330338261422902","url":null,"abstract":"<p><p>IntroductionBreast cancer remains a leading cause of cancer mortality despite being potentially curable when detected early, particularly in low- and middle-income countries where access to screening is limited. This is largely driven by operational gaps, including limited access to screening and delays in diagnosis and treatment. JULIETA is a portable bioimpedance spectroscopy device designed to identify electrical tissue patterns associated with potentially malignant findings and to prioritize women for further diagnostic evaluation. This study assessed the performance of a hierarchical algorithm integrated into JULIETA to distinguish findings without malignant potential (BI-RADS 1-2) from those with malignant potential (BI-RADS ≥3), using mammography as the reference standard.MethodsA cross-sectional observational study with prospective data collection was conducted between May and July 2024 in four Colombian cities. Adult women undergoing screening or follow-up mammography were evaluated with JULIETA prior to imaging. Impedance-derived features, breast density estimates, and individual risk scores were used to retrain a hierarchical classifier combining Random Forest and SVM-RBF models, using an 80/20 stratified split and cross-validation.ResultsA total of 1350 women were recruited (mean age 56.5 ± 8.0 years); 67% were BI-RADS 1-2 and 21% BI-RADS 4. After data cleaning, 673 breasts (469 women) were included. The model achieved 73% sensitivity, 76% specificity, 65.5% positive predictive value, and 82.1% negative predictive value.ConclusionJULIETA is a feasible, safe, and reproducible noninvasive bioimpedance pre-screening tool that may enable scalable triage and support earlier detection and improved equity when integrated into public health pathways.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261422902"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12901910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146166761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative Ternary Classification of Pulmonary Ground-Glass Nodules (AIS/MIA/IAC): ResNet-10 Outperforms Radiomics and Clinicoradiographic Models in Multicenter Study.","authors":"Dan Long, Zhichao Zuo, Huchuan Zhou, Wanyin Qi, Sanhong Zhang, Jinqiu Deng, Ziqiang Yang","doi":"10.1177/15330338261423265","DOIUrl":"10.1177/15330338261423265","url":null,"abstract":"<p><p>IntroductionPreoperative differentiation among adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) is crucial for guiding ground-glass nodule (GGN) management. This multicenter study evaluated the comparative utility of deep learning (DL), radiomics, and conventional machine learning (cML)-based clinicoradiographic models for this ternary classification.MethodsWe developed four DL models (DenseNet-121, ResNet-10, ResNet-18, and VGG-13) for the ternary classification of AIS, MIA, and IAC using multicenter CT datasets. For comparative analysis, we constructed two additional classification models: (1) a radiomics model employing feature engineering through analysis of variance, recursive feature elimination with cross-validation, and least absolute shrinkage and selection operator, and (2) the cML-based clinicoradiographic model utilizing 12 different classifiers. The performance of all models was evaluated using the macro area under the curve (Macro-AUC) metric.Results847 GGNs postoperatively confirmed as lung adenocarcinoma were included in this multicenter study, which were randomly split into a training set (70%, n=592) and a validation set (30%, n=255). The DL model ResNet-10 demonstrated superior performance, achieving a Macro-AUC of 0.8055 (95% CI: 0.7723-0.8387), an accuracy of 0.6300 (95% CI: 0.5541-0.6764), and an F1-score of 0.4206 (95% CI: 0.3821-0.4598). This performance surpassed that of the radiomics model, which had a Macro-AUC of 0.7801 (95% CI: 0.7432-0.8170), an accuracy of 0.6100 (95% CI: 0.5276-0.6204), and an F1-score of 0.5505 (95% CI: 0.4983-0.6017), and the cML-based clinicoradiographic model, which achieved a Macro-AUC of 0.7770 (95% CI: 0.708-0.846), an accuracy of 0.6000 (95% CI: 0.5376-0.6604), and an F1-score of 0.4438 (95% CI: 0.3925-0.4961).ConclusionThe ResNet-10 network established a novel ternary classification model for predicting the invasiveness of GGNs. This approach provides clinically actionable insights that support surgical planning and facilitate risk-adapted management.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261423265"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12909756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146207796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Prognostic Value of SERPINE1 in Clinical Outcomes in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.","authors":"Shifeng Yan, Xinyu Li, Changyu Zhu, Wei Li","doi":"10.1177/15330338261435460","DOIUrl":"https://doi.org/10.1177/15330338261435460","url":null,"abstract":"<p><p>BackgroundSERPINE1 has attracted considerable attention in tumor biology, but its clinical importance in head and neck squamous cell carcinoma (HNSCC) is not yet clear. We therefore examined whether SERPINE1 expression is related to survival in patients with HNSCC.MethodsWe searched three major databases (PubMed, EMBASE, and the Cochrane Library) and identified observational studies reporting survival outcomes in relation to SERPINE1 expression through November 11, 2024. From eligible reports we extracted data on progression-free survival (PFS), overall survival (OS), disease-specific survival (DSS) and disease-free survival (DFS), and calculated pooled hazard ratios (HRs) using random-effects models.ResultsEleven studies including 733 individuals with HNSCC met the inclusion criteria. Across these cohorts, higher SERPINE1 expression was consistently linked with shorter OS (HR 2.81, <i>P</i> = 0.003) and shorter DFS (HR 1.57, <i>P</i> = 0.004). In contrast, no clear associations were observed for PFS or DSS (<i>P</i> ≥ 0.05).ConclusionCurrent evidence suggests that increased SERPINE1 expression is associated with an unfavorable prognosis in HNSCC, particularly for OS and DFS. Larger prospective studies are needed to confirm these findings and to determine how SERPINE1 assessment might be incorporated into risk stratification and treatment planning for patients with HNSCC.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261435460"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147609757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully Automated Stain Quantification Framework for IHC Whole Slide Images in Breast Cancer.","authors":"Tuo Yin, Frédéric Lifrange, Zoë Denis, Alex de Caluwé, Laurence Buisseret, Xavier Catteau, Clara Legros, Nick Reynaert, Jennifer Dhont","doi":"10.1177/15330338251407734","DOIUrl":"10.1177/15330338251407734","url":null,"abstract":"<p><p>IntroductionImmunohistochemistry (IHC) plays a crucial role in breast cancer diagnosis, treatment selection, and research. However, manual scoring of IHC whole slide images (WSIs) is time-consuming and suffers from inter- and intra-observer variability.MethodsTo help address these challenges, we present and publicly release a fully automated, compartment-specific (ie, tumor and stroma) H-scoring framework for IHC analysis. The framework consists of three deep learning modules: tumor-stroma segmentation, nuclei segmentation, and H-score estimation for tumor and stroma. It processes WSIs in minutes, delivering consistent and reproducible H-scores with accuracy comparable to expert pathologists. The modular design also allows flexibility for use in other IHC tasks such as cellularity quantification, and supports configuration options to balance accuracy and computational efficiency.ResultsFine-tuned on 87 expert-annotated patches, the framework achieved a Spearman's rank correlation (<i>ρ</i>) in internal validation of 0.84 (95% confidence interval [CI]: 0.77-0.89) across 100 expert-annotated WSIs, outperforming state-of-the-art (<i>ρ</i> = 0.78, 95% CI: 0.68-0.85) and matching the inter-observer variability between two expert pathologists (<i>ρ</i> = 0.84, 95% CI: 0.63-0.94). In external validation, it achieved 86% accuracy in HER2 classification (0-3+) and a mean absolute error of 21 ± 10 (range: [5-46]) in CD73 scoring, where ground truth H-scores were all 0.ConclusionThe framework achieves agreement comparable to that of expert pathologists, underscoring its clinical utility in providing reproducible IHC scores that can reduce diagnostic variability and support consistent treatment decisions. The code is available at https://github.com/YinTuo/AutoIHC.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338251407734"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147609752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Interfractional Variations of Bladder and Rectal Filling on Vaginal Cuff Movement and the Role of Rectal Balloons in Gynecological Pelvic Proton Beam Therapy.","authors":"Lars Wessel, Friderike K Longarino, Natalia Sycheva, Jan-Hendrik Bolten, Hanna Waldsperger, Katharina Kozyra, Philipp Schroeter, Julia Bauer, Fabian Weykamp, Eva Meixner, Kristin Lang, Laila König, Jürgen Debus, Nathalie Arians","doi":"10.1177/15330338261438327","DOIUrl":"https://doi.org/10.1177/15330338261438327","url":null,"abstract":"<p><p>IntroductionPostoperative active raster-scanning intensity modulated proton beam therapy (IMPT) for gynecological cancers requires precise target coverage, yet interfractional motion of the vaginal cuff and adjacent pelvic organs may compromise dosimetric robustness. This study retrospectively assessed interfractional organ movement and evaluated the clinical need for rectal balloon (RB) use to ensure adequate target coverage.Methods23 patients, 17/6 with/without RB, received postoperative IMPT between 2017 and 2020 at Heidelberg Ion-beam Therapy Center (HIT). Positioning verification computed tomography (pv-CT) and treatment planning CT (tp-CT) images were retrieved and rectum, bladder and the vaginal cuff (VC) were contoured. The clinical target volume (CTV) and planning target volume (PTV) were mapped from tp-CT to the pv-CT images and forward dose calculation was performed. To assess the volume of the VC not covered by the CTV or PTV, the region of interest (ROI), VC outside of CTV (VC-CTV) and outside of PTV (VC-PTV) were created. Volume differences (Δ) to the tp-CT images and dose parameters for each ROI were evaluated.Results139 pv-CTs were analysed. The use of RB significantly reduced VC displacements, resulting in fewer pv-CTs with the VC located outside the CTV (40% vs. 91%, p = 0.0252) and PTV (28% vs. 68%, p = 0.0362). CTV/PTV coverage and ROI doses remained stable across all fractions and there was no significant difference between groups. The applied PTV margins ensured robust dose coverage despite interfractional anatomical variations.ConclusionRB application effectively reduced interfractional VC motion and there was no significant Δ in target coverage or ROI doses. Using tp-CT images with full and empty bladder for definition of the CTV and standardized PTV margins contributed to stable dosimetry outcomes, confirming the robustness of the used IMPT treatment protocol. However, the use of RB may be beneficial, especially in patients with known gastrointestinal comorbidities or trapped air in the rectum.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261438327"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13111882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lightweight Skeletal Muscle Intelligent Segmentation Network Based on Planning CT for Cervical Cancer Radiotherapy.","authors":"Liming Lu, Xiwei Chen, Jing Liu, Gongfu Chen, Junyue Shi, Zhe Wu, Gaokui He","doi":"10.1177/15330338261446535","DOIUrl":"10.1177/15330338261446535","url":null,"abstract":"<p><p>PurposeA lightweight deep learning network SMA-Net was proposed to intelligently segment the skeletal muscle of the third lumbar (L3) level in patients with cervical cancer radiotherapy, and the segmentation performance of the network was evaluated.Methods and MaterialsA total of 160 eligible patients with cervical cancer admitted to the oncology department of our hospital from September 2021 to June 2024 were randomly divided into training set (N = 112), validation set (N = 16) and test set (N = 32) according to 7 : 1 : 2. The lightweight Mamba architecture is introduced into the UNet network, and the SAB and CAB attention mechanisms are introduced on the skip connection. The attention mechanism is used to suppress the irrelevant information in the image and highlight the important local features. The trained network is geometrically evaluated on the test set for segmentation performance, comparison of manual segmentation and predicted skeletal muscle area (SMA). Compare the parameters and computations of SMA-Net with existing networks.ResultsThe dice similarity coefficient of SMA-Net network for skeletal muscle segmentation was 89.16%, the sensitivity SEN was 88.21%, the positive predictive value PPV was 90.13% and the 95% Hausdorff distance was 5.30mm. Manual segmentation is basically close to the predicted SMA. Our proposed network for cervical cancer patients predicted sarcopenia with 87.5% accuracy, 92.31% precision, 80% recall, 85.72% F1-Score, and 0.871 AUC. The calculation amount of SMA-UNet network is 1.50 GFLOPS, and the parameter amount is 1.24 M. The radiologist's scores show that minor and no revision accounted for 93.75% on manual revision of skeletal muscle.ConclusionThe lightweight SMA-Net proposed in this study can accurately segment L3 skeletal muscle and quickly calculate its area, which basically meets the clinical application and is convenient for clinical deployment. It is helpful for clinicians to quickly diagnose sarcopenia in patients with cervical cancer, save medical resources, reduce the workload of physicians, and improve diagnostic efficiency.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261446535"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13133491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individualized Prediction of Radiation Pneumonitis Using RP-GAN: Leveraging Global Lung Features and Explainable Artificial Intelligence.","authors":"Yang-Wei Hsieh, Pei-Ju Chao, Yi-Lun Liao, Wen-Ping Yun, Ling-Chuan Chang-Chien, Cheng-Shie Wuu, Yu-Wei Lin, Tsair-Fwu Lee","doi":"10.1177/15330338261450065","DOIUrl":"https://doi.org/10.1177/15330338261450065","url":null,"abstract":"<p><p>IntroductionThis study aims to develop an individualized risk prediction model for radiation pneumonitis (RP) based on unsupervised image feature learning. A deep convolutional generative adversarial network (DCGAN) was utilized to automatically extract features from computed tomography (CT) images.MethodsA retrospective analysis was conducted on 180 lung cancer patients treated with volumetric modulated arc therapy (VMAT) at Kaohsiung Veterans General Hospital between 2015 and 2022. To mitigate clinical sample size limitations, rotation-based augmentation was employed to expand the training dataset. The pretreatment CT images were processed into three input configurations: whole-lung, V_5Gy dose regions, and V_20Gy dose regions. An unsupervised feature extraction model, designated RP-GAN, was constructed to capture latent representations associated with RP risk. High-dimensional features were refined via least absolute shrinkage and selection operator (LASSO) and integrated into a stacking ensemble learning framework (including RF, SVM, KNN, XGBoost, and LR). Model stability and generalization were validated through 10-fold cross-validation alongside an independent test set, while clinical interpretability was ensured using Grad-CAM and LIME.ResultsThe whole-lung input model demonstrated superior performance, achieving an AUC of 0.856 and an accuracy of 0.861, with a recall of 0.778. In contrast, models restricted to V_20Gy dose regions showed a significant decline in sensitivity, with the recall decreasing to 0.273. XAI visualization confirmed that the model focused not only on the tumor bed but also on the peritumoral parenchyma and contralateral lung.ConclusionThe proposed RP-GAN architecture effectively captures subtle textural changes across the whole lung microenvironment without requiring manual annotations. This framework provides a robust tool for individualized RP risk assessment, facilitating the optimization of radiation therapy plans.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261450065"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Perspective in Tumor Therapy: Targeting M2-Type Tumor-Associated Macrophages.","authors":"Qiang Zhang, Yungang Sun, Yu Zhuang, Mengxu Yao, Siyang Jiao, Qi Wang, Xiaoying Zhang, Feng Shao","doi":"10.1177/15330338261448345","DOIUrl":"https://doi.org/10.1177/15330338261448345","url":null,"abstract":"<p><p>Uncontrolled proliferation is not the only factor driving tumor growth; the tumor microenvironment (TME) has a significant impact. Tissue homeostasis and pathogen removal depend on macrophages, which are important innate immune effector cells. Nevertheless, there is growing evidence that tumor-associated macrophages (TAMs) do not consistently suppress cancer and are functionally diverse. Specifically, M2-polarized TAMs (M2-TAMs) build up in multiple solid tumors, where they promote angiogenesis, metastasis, and immunosuppression, hastening the course of the disease. Here, we critically assess the clinical translatability of TAM-targeted treatment approaches, outline the molecular circuits underpinning M2-TAMs-tumor cell interaction, and extensively explore the phenotypic spectrum and functional diversity of macrophages in cancer. Our objective is to offer a theoretical foundation for upcoming immunotherapeutic interventions.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261448345"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Preliminary Study on the Auto-Segmentation of Targets and Organs at Risk in Pediatric Total Marrow and Lymphoid Irradiation.","authors":"Zhihua Xie, Jinfeng Liu, Lixia Hou, Haoran Feng, Fuli Zhang, Na Lu","doi":"10.1177/15330338261426261","DOIUrl":"10.1177/15330338261426261","url":null,"abstract":"<p><p>IntroductionLeukemia is one of the most prevalent cancers in children. The use of total marrow and lymphoid irradiation (TMLI) via helical tomotherapy (TOMO) as a conditioning regimen prior to bone marrow transplant (BMT) has been widely adopted in clinical practice. Accurate and efficient segmentation of target volumes and organs at risk (OARs) is a prerequisite for precise TMLI. The purpose of this study was to investigate the feasibility of deep learning-based auto-segmentation technology (using 2D U-net and 3D V-net models) for target volumes (bone marrow and lymphatic drainage regions) and organs at risk (OARs) in pediatric total marrow and lymphoid irradiation (TMLI).MethodsThis study was designed as a retrospective study. Thirty-six pediatric patients treated with TMLI between 2018 and 2024 were included. Target volumes and OARs were manually segmented and refined. The CT images and corresponding contours were imported into the AccuLearning workstation (Manteia Company, Xiamen, China) to train, validate, and test based on 2D U-net and 3D V-net deep learning models. The auto-segmentation performance was evaluated on 6 test cases using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Average Surface Distance (ASD).ResultsFinally, analysis revealed DSC values >0.7 for all OARs except lenses segmented by the 3D V-net model. For target volumes, bone structures achieved high segmentation accuracy.ConclusionThe 3D V-net model demonstrated superior performance compared to the 2D U-net model. Auto-segmented contours generated by the 2D U-net and 3D V-net models, with minor manual adjustments, are clinically applicable for TMLI radiotherapy planning.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261426261"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13150095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}