Technology in Cancer Research & Treatment最新文献

{"title":"A Clinical-Radiomics Nomogram Predicts Early Tumor Necrosis After Transarterial Chemoembolization for Hepatocellular Carcinoma.","authors":"Xiang-Ling Wu, Bing-Zhi Duan, Zhi-Hua Jiang, Shan-Shan Zeng, Guo-Cheng Lin, Zhong-Liao Fang","doi":"10.1177/15330338261444986","DOIUrl":"10.1177/15330338261444986","url":null,"abstract":"<p><p>IntroductionWe introduce a standardized necrosis rate-percent reduction in enhancing tumor diameter normalized by baseline tumor diameter-with a threshold of ≥30%. This endpoint is derived from the mRECIST partial response criteria but is normalized to mitigate tumor size-dependent bias. A clinical-radiomics model was developed to assess necrosis in hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE).MethodsRetrospectively, 95 HCC patients undergoing TACE were included. Radiomics features were selected via LASSO regression, and clinical variables via logistic regression. Separate radiomics and clinical models were developed, and a combined model was constructed using multivariable logistic regression. The cohort was randomly split into training (70%) and validation (30%) sets, with all preprocessing, feature selection, and model training confined to the training set to prevent data leakage. Model performance was evaluated using discrimination (AUC), calibration, clinical utility (decision curve analysis), and a nomogram.ResultsFrom 1,316 extracted radiomics features, six were retained for Rad-score calculation. Key clinical predictors included hepatitis group, standardized viable tumor ratio, and vascular invasion. The integrated model achieved AUCs of 0.865 (95% CI: 0.768-0.961) in training and 0.853 (95% CI: 0.716-0.990) in validation (n=29), outperforming the clinical model (AUCs: 0.808 (95% CI: 0.695-0.922) and 0.666 (95% CI: 0.465-0.866), respectively). Decision curve analysis and calibration plots confirmed the combined model's superior performance.ConclusionThe radiomics-clinical nomogram, based on a standardized necrosis rate, may enable early prediction of TACE response, offering potential insights for therapeutic decision-making, risk stratification, and liver transplantation management. External validation is warranted before clinical application.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261444986"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718282","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":"Preparing for the Era of Multiple Primary Cancers: A Prevention-Centered Model for Survivorship Medicine.","authors":"Tessho Maruyama","doi":"10.1177/15330338261488983","DOIUrl":"10.1177/15330338261488983","url":null,"abstract":"<p><p>The number of cancer survivors is increasing rapidly, and the development of multiple primary cancers (MPCs) is becoming a foreseeable consequence of long-term survival. However, MPC detection remains fragmented. Clinical information is often scattered across institutions, and long-term follow-up is not always consistent. Early warning signs that appear outside oncology settings are also not always linked to a patient's prior cancer history. As the number of cancer survivors increases, these gaps can have important clinical consequences. This Perspective presents a prevention-centered framework that complements existing survivorship care by recognizing the development of MPCs as an expected event during long-term survivorship. I outline a framework integrating longitudinal genetic and multi-omics risk stratification with artificial intelligence-assisted risk modeling and interoperable health records for continuous monitoring. Extending surveillance beyond oncology clinics to settings such as emergency medicine, primary care, and dentistry, may help detect secondary malignancies earlier. Survivorship medicine should shift from episodic detection to more coordinated, lifelong prevention. Further studies are needed to evaluate this framework across diverse healthcare settings and to determine its potential to improve earlier detection and long-term outcomes for cancer survivors.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261488983"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921132","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 Comparative Analysis of Tissue and Digestive Microbiota Composition in Patients With Oral Squamous Cell Carcinoma.","authors":"Zhongyuan Yang, Xiujiao Shen, Yulian Lai, Tongjing Liu, Zhenjie Yue, Lizhen Huang, Shuaishuai Sun, Yunzhu Chen, Xinya Qiao, Tong Wu, Mingjie Jiang, Zan Jiao, Ankui Yang, Yu Miao, Wenlin Liu, Cuifang Chen","doi":"10.1177/15330338261465540","DOIUrl":"https://doi.org/10.1177/15330338261465540","url":null,"abstract":"<p><p>IntroductionAs the most prevalent malignant tumor in oral cancer, oral squamous cell carcinoma (OSCC) remains incompletely understood, especially its complex interactions with microbiota. This study investigated microbial signatures across tumor tissue and digestive tract niches in patients with OSCC.MethodsThis retrospective cohort study included 28 patients with OSCC who underwent surgical treatment at a tertiary cancer center between 2019 and 2022, along with 14 healthy controls. 16S rRNA gene sequencing was performed to characterize microbial profiles in tumor tissues, as well as oral and gut samples.ResultsTumor tissues exhibited largely similar microbial compositions to adjacent tissues and lymph nodes. However, tumor tissues showed a relatively higher abundance of <i>Fusobacterium</i> and a depletion of <i>Streptococcus</i>. Gut microbiota in patients exhibited dysbiosis, characterized by enrichment of <i>Prevotella</i>, <i>Klebsiella</i>, <i>Lactobacillus</i>, and <i>Streptococcus</i>, while oral microbiota showed enrichment of <i>Streptococcus</i> and <i>Haemophilus</i>. In addition, increased abundance of <i>Lactobacillus</i> in the gut and Bacteroidales in the oral cavity was associated with adverse clinical outcomes (ACO) in OSCC patients.ConclusionThe tissues and digestive microbiota of OSCC patients exhibit heterogeneity. These findings suggest that specific microbial signatures may serve as potential biomarkers for disease detection and progression; however, these associations should be interpreted cautiously and require validation in larger cohorts and mechanistic studies.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261465540"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149031393","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":"Time-to-Event Machine Learning Model Incorporating MRI-Derived Intratumoral Heterogeneity Score for Predicting Invasive Breast Cancer Recurrence: A Dual-Center Study.","authors":"Yunhua Li, Xuehong Long, Jingyi Zuo, Xiuqi Yang, Yi Liu, Zhuo Chen, Hu Tang","doi":"10.1177/15330338261473041","DOIUrl":"10.1177/15330338261473041","url":null,"abstract":"<p><p>IntroductionPostoperative recurrence remains a major clinical challenge in invasive breast cancer (IBC), and conventional clinicopathologic factors and routine imaging assessment may not fully capture intratumoral spatial heterogeneity or nonlinear recurrence patterns. This study aimed to evaluate the prognostic value of a magnetic resonance imaging (MRI)-derived intratumoral heterogeneity (ITH) score incorporated into time-to-event machine learning models for predicting postoperative recurrence in patients with IBC.MethodsThis retrospective dual-center prognostic prediction-model study included 428 consecutive patients with IBC from two centers. Patients were randomly assigned to a training cohort (n = 256), validation cohort (n = 86), and independent testing cohort (n = 86). ITH scores were calculated from texture features and pixel intensity distributions on contrast-enhanced T1-weighted MRI using k-means-based intratumoral subregion quantification. Multiple time-to-event machine learning (ML) models were developed to predict recurrence-free survival (RFS), and model performance was assessed using Harrell's concordance index (C-index), time-dependent receiver operating characteristic curves, calibration curves, and SHapley Additive exPlanations (SHAP).ResultsAmong the evaluated models, the random survival forest (RSF) achieved the best predictive performance, yielding a C-index of 0.826 (95% confidence interval [CI], 0.735-0.894) in the validation cohort and 0.814 (95% CI, 0.722-0.905) in the testing cohort. SHAP identified the ITH score as the most critical predictor. Higher ITH scores were significantly associated with shorter RFS and upregulation of tumor proliferation-related pathways.ConclusionsIntegrating an MRI-derived ITH score with an RSF model provides a noninvasive and interpretable framework for recurrence prediction in IBC. This approach may support individualized risk stratification, postoperative surveillance planning, and tailored adjuvant management while requiring further prospective validation before routine clinical implementation.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261473041"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13408075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148607793","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 Feasibility Study of Hybrid Deep-Learning Prediction with Online Adaptation of Breathing Irregularities for Long-Term Internal Organ Motion During Radiotherapy.","authors":"Andrew Milewski, Uddin Fayed, Vyas Gupta, Xingyu Nie, Guang Li","doi":"10.1177/15330338261420917","DOIUrl":"10.1177/15330338261420917","url":null,"abstract":"&lt;p&gt;&lt;p&gt;IntroductionAlthough long short-term memory (LSTM) networks have been tested to predict short-term respiratory motion, their performance in long-term forecasting under breathing irregularities must be assessed. We aim to evaluate and enhance the long-term prediction of internal motion from an external surrogate using subject-specific LSTM models through a hybrid, adaptive approach.MethodsConcurrent internal navigator and external bellows respiratory-motion waveforms were acquired for ten volunteers during two four-dimensional magnetic resonance imaging (4DMRI) scans lasting 3-10 min each. Approximately 20 min intervened between the first (mid-term) and second (long-term) scan. After training on the first half of the mid-term data, subject-specific LSTM models were applied to the remaining mid-term and entire long-term datasets to predict internal waveforms. The accuracy of a model's prediction was assessed with Pearson's correlation (&lt;i&gt;C&lt;/i&gt;), referenced to the native waveforms, maximized through the time-domain cross-correlation (TCC), and enhanced by correcting residual phase shifts in the LSTM models using a hybrid (LSTM-TCC) approach. Hyperparameter selection by minimizing the root mean square error (RMSE) to identify high-performance (&lt;i&gt;C&lt;/i&gt; ≥ 0.8) LSTM models was evaluated by the area under the receiver operating characteristic curve (AUROC). The temporal accuracy of inspiratory-peak predictions was characterized.ResultsCompared to the native waveforms (&lt;i&gt;C&lt;/i&gt; = 0.42 ± 0.28) and TCC method (&lt;i&gt;C&lt;/i&gt; = 0.77 ± 0.09), the LSTM models yielded more accurate predictions (&lt;i&gt;C&lt;/i&gt; = 0.89 ± 0.07) in the mid-term scans. Over 20-30 min, LSTM predictions faltered (&lt;i&gt;C&lt;/i&gt; &lt; 0.80) in two subjects but were rescued by LSTM-TCC (&lt;i&gt;C&lt;/i&gt; = 0.90 ± 0.09). The temporal error in predicting inspiratory peaks was smaller for LSTM-TCC (Δ&lt;i&gt;t&lt;/i&gt; = 0.15 ± 0.11sec) than LSTM (Δ&lt;i&gt;t&lt;/i&gt; = 0.18 ± 0.15sec). RMSE reliably identified high-performance models: &lt;math&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mi&gt;U&lt;/mi&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;msubsup&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0.82&lt;/mn&gt;&lt;/math&gt;, &lt;math&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mi&gt;U&lt;/mi&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;msubsup&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mi&gt;M&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0.74&lt;/mn&gt;&lt;/math&gt;, and &lt;math&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;mi&gt;U&lt;/mi&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;msubsup&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0.83&lt;/mn&gt;&lt;/math&gt;.ConclusionThe feasibility of a novel adaptive subject-specific LSTM-TCC modeling was tested in 10 subjects, demonstrating that high accuracy of external-to-internal motion predictions in 3-10 min can be extended to 3","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261420917"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692243","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":"RNI Precision Trial Protocol: Optimization of Regional Node Irradiation for Sentinel Lymph Node-positive Breast Cancer Omitting Axillary Dissection Based on Clinical and Genomic Risk Assessment: A Prospective Clinical Trial.","authors":"Wei-Xiang Qi, Lu Cao, Siyue Zheng, Shuyan Li, Feifei Xu, Cheng Xu, Rong Cai, Gang Cai, Jiayi Chen","doi":"10.1177/15330338261450066","DOIUrl":"10.1177/15330338261450066","url":null,"abstract":"<p><p>BackgroundSentinel lymph node biopsy (SLNB) has been the standard treatment procedure for clinically node-negative (cN0) breast cancer (BC). Three randomized trials (Z0011, AMAROS and SENOMAC) have shown that patients with low-burden sentinel lymph node metastasis can be safely omitted axillary lymph node dissection who receive adjuvant radiotherapy. However, these published studies have been insufficient to accurately assess the recurrence risk in this patient population, leading to variability in adjuvant radiotherapy volume across different studies.ObjectiveThis study evaluates whether an integrated axillary management strategy combining SLNB with individualized regional nodal irradiation (RNI) based on recurrence risk reduces 2-year lymphedema compared to ALND followed by comprehensive RNI.Materials and MethodsThis trial is a single institute, open-labeled, non-randomized cohort trial. Participants are stratified into two cohorts based on the extent of axillary surgery after enrollment. For SLNB cohort, patients are divided into three groups according to clinical and genomic risk assessment. Clinically high-risk patients are defined as having at least two factors (tumor size≥2cm, percent of positive SLNs>30%, LVI positive and SLN macro-metastases), who have a predicted risk of n-SLN involvement greater than 30%. Clinically high-risk n-SLN involvement patients are further detected by using RecurIndex test. For pathological node positive (pN+) after ALND cohort, patients are treated with WBI/chest wall irradiation combined with comprehensive RNI excluding the dissected axillary region; A total of 205 patients will be enrolled, with 68 patients in SLNB cohort and 137 patients in ALND cohort with a 1:2 ratio assignment. The RNI precision trial uses a clinical-genomic model to accurately stratify the recurrence risk and provides the individualized RNI volume for early-stage BC with low-burden sentinel lymph node metastasis, and we anticipate that our study will provide high-quality data to potentially support individualized RNI in this patient population.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261450066"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13157548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842691","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 Multidimensional CT-Based Tumor Location Metric for Predicting Lymph Node Metastasis and Occult Lymph Node Metastasis in Lung Adenocarcinoma.","authors":"Licheng Wu, Haoran Shang, Chengyi Mao, Zhi Wei, Fuqiang Dai, Longyong Mei, Yonggeng Feng, Zhipeng Hao, Jingge Zhang, Dongdong Wu, Yi Wang, Bo Deng","doi":"10.1177/15330338261494765","DOIUrl":"https://doi.org/10.1177/15330338261494765","url":null,"abstract":"<p><p>IntroductionLymph node (LN) metastasis of lung adenocarcinoma, particularly occult lymph node metastasis (OLNM), remains difficult to predict using conventional imaging and basic positional parameters, adversely affecting prognosis. This study aimed to: (1) evaluate a new CT index (a/A + b/B) for predicting OLNM in cN0 patients, and (2) create and validate a nomogram for predicting LN metastasis probability in CT-suspected cases.MethodsThis single-center retrospective study involved 387 lung adenocarcinoma patients (225 non-LN, 84 OLNM, and 78 with CT-suspected/pathologically confirmed LN metastasis) who had surgery from 2016 to 2024. A spatial index (a/A + b/B) was developed to combine two perpendicular anatomical axes, vertical proximity to the interlobar fissure and horizontal proximity to the mediastinum/hilum, into one standardized metric.ResultsThe combined ratio a/A + b/B was significantly lower in the OLNM and metastatic groups. A lower a/A + b/B was significantly associated with OLNM (OR = 0.175, 95% CI: 0.048-0.638, P = 0.007), suggesting a 0.1-unit decrease in the a/A + b/B ratio increased OLNM risk by 19%. A nomogram utilizing a/A + b/B differentiates CT-suspected LN metastasis from node-negative cases, showing AUCs of 0.83 (95% CI: 0.76-0.90) in training, 0.85(95% CI: 0.76-0.94) in internal validation, and 0.70(95% CI: 0.50-0.91) in external validation.ConclusionA ratio of a/A + b/B < 0.6 indicates a high OLNM risk in cN0 patients. This index can predict OLNM when LNs are small on CT, and a nomogram can differentiate CT-suspected LN metastasis from node-negative cases, meriting further study.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261494765"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115104","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":"Efficacy and Safety of CyberKnife-Based SBRT and Linear Accelerator-Based IMRT/VMAT for Thoracic Radiotherapy in Non-Small Cell Lung Cancer: A Propensity Score-Matched Real-World Analysis.","authors":"Shuo Sun, Yi Duan, Guannan Yang, He Xiao, Xiuqiong Chen, Zhengbo Wang, Jungang Ma, Kunpu Su, Chuan Chen, Xueling Tong, Yanli Xiong, Xiao Yang, Mengxia Li","doi":"10.1177/15330338261494457","DOIUrl":"https://doi.org/10.1177/15330338261494457","url":null,"abstract":"<p><p>IntroductionCyberKnife-based stereotactic body radiotherapy (SBRT) has demonstrated favorable outcomes in multiple tumors. This study aimed to compare the efficacy and safety of CyberKnife-based SBRT versus linear accelerator (LINAC)-based IMRT/VMAT for thoracic radiotherapy in patients with non-small cell lung cancer (NSCLC) in a retrospective study.MethodsIn this retrospective cohort study, we retrospectively analyzed 461 NSCLC patients treated in our hospital. After propensity score matching (PSM), 138 patients (CyberKnife: 46; LINAC: 92) were matched. Median follow-up was 16.4 months (by reverse Kaplan-Meier method), and the short-term local control and early toxicity were compared.ResultsAfter PSM, baseline characteristics were well-balanced. No significant differences were observed in objective response rate (<i>P</i>=0.838), disease control rate (<i>P</i>=0.832), distribution of response categories (<i>P</i>=0.972), or local progression-free survival (Log-Rank <i>P</i>=0.450). However, the CyberKnife group demonstrated superior safety. Symptomatic radiation pneumonitis (RP) (Grade≥2) was significantly lower (6.5% vs. 27.2%; OR=0.214, <i>P</i>=0.004). Notably, no Grade≥2 radiation esophagitis (RE) occurred in the CyberKnife group versus 18.5% with LINAC (<i>P</i><0.001). Multivariate analyses further confirmed CyberKnife treatment as an independent protective factor against both symptomatic RP (OR=0.208, <i>P</i>=0.021) and symptomatic RE (OR=0.051, <i>P</i>=0.002). Dosimetrically, CyberKnife achieved superior sparing of lung tissue and esophageal tissue (all <i>P</i><0.001). CyberKnife remained the sole independent predictor for symptomatic RP, while for RE, treatment group (OR=0.068, <i>P</i>=0.041) and esophagus V50_EQD2 (OR=1.073, <i>P</i>=0.019) were independent prognostic factors.ConclusionIn this PSM cohort, CyberKnife and LINAC demonstrated comparable short-term tumor control and local progression outcomes for thoracic radiotherapy in NSCLC. However, CyberKnife was associated with a significantly lower risk of severe radiation-induced toxicities, suggesting a clinically meaningful safety advantage.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261494457"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169908","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":"IGF2BP3 Promotes Head and Neck Squamous Cell Carcinoma Progression via the M6A-LDHA-Lactate-H4K8la Axis and E2F2 Activation.","authors":"Fan Wu, Yulu Zeng, Yunlong Hu, Zihui Xie, Rong Shen, Jiaxin Zhou, Pingting Zhou, Shanshan Tong, Yehai Liu","doi":"10.1177/15330338261447338","DOIUrl":"10.1177/15330338261447338","url":null,"abstract":"<p><p>IntroductionHead and neck squamous cell carcinoma (HNSCC) is characterized by metabolic reprogramming and poor prognosis. While lactate accumulates in HNSCC, how upstream RNA regulation coordinates lactate-associated epigenetic alterations during tumor progression remains unclear.MethodsWe integrated TCGA-HNSCC analyses, paired clinical specimens, and in vitro functional assays with mechanistic readouts including RIP-qPCR and LDHA 3'UTR luciferase reporters. Lactate was quantified in culture supernatants, H4K8la was assessed by immunoblotting and tissue IF, and H4K8la CUT&Tag was performed as an exploratory chromatin profiling assay.ResultsIGF2BP3 was upregulated in HNSCC and associated with adverse survival in public datasets. IGF2BP3 silencing inhibited proliferation, migration, and invasion. Mechanistically, IGF2BP3 bound LDHA mRNA and promoted LDHA expression via an m⁶A-site-dependent LDHA 3'UTR mechanism, increasing lactate and H4K8la; exogenous lactate partially restored H4K8la under pH-matched conditions. Exploratory H4K8la CUT&Tag suggested increased H4K8la signal at the E2F2 locus with enrichment of cell-cycle programs upon lactate treatment.ConclusionThese findings support an IGF2BP3-LDHA-lactate-H4K8la axis linking post-transcriptional regulation to metabolic and chromatin remodeling in HNSCC.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261447338"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13129289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781789","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 Comparative Study of Computed Tomography-Guided Steerable Needle and Freehand Biopsy for Diagnosing Small Pulmonary Nodules: Accuracy and Complications for Greater Clarity and Precision.","authors":"Miaomiao Hu, Xusheng Zhang, Qianqian Yuan, Sen Yang, Baohu Wang, Peishun Li, Chao Xing, Minghui Lv, Wanying Yang, Kaixian Zhang","doi":"10.1177/15330338261423057","DOIUrl":"10.1177/15330338261423057","url":null,"abstract":"<p><p>IntroductionThis study aimed to retrospectively evaluate and compare the accuracy, complication rate, and feasibility of computed tomography (CT)-guided introducer-assisted percutaneous lung biopsy with those of freehand percutaneous lung biopsy for the diagnosis of pulmonary nodules ≤ 2 cm.MethodsWe retrospectively analysed clinical data of 307 patients with pulmonary nodules ≤ 2 cm who underwent percutaneous lung biopsy between January 2015 and August 2024. The patients were divided into two groups: A, 153 patients undergoing freehand puncture, and B, 154 patients in whom the procedure was assisted by a guidance device. A statistical analysis was performed to assess the success rate, diagnostic accuracy, and complication rate of the two techniques and to evaluate the feasibility of using a guide device-assisted puncture biopsy for small pulmonary nodules.ResultsAll 307 patients successfully completed the percutaneous biopsy procedure (100% technical success rate). Diagnostic accuracy was 95.4% and 96.8% in Groups A and B, respectively (<i>p</i> = .161). Groups A and B required an average of 9.04 ± 2.58 and 8.14 ± 1.72 intraoperative CT scans, respectively (<i>p</i> <i><</i> .001). Mean procedural durations for Groups A and B were 12.77 ± 4.51 and 10.83 ± 2.51 min, respectively (<i>p</i> <i><</i> .001). In Groups A and B, the immediate need for closed thoracic drainage was 4.6% (7/153) and 1.9% (3/154), respectively (<i>p</i> <i>=</i> .165), and incidence of haemoptysis was 10.5% (16/153) and 9.7% (15/154), respectively (<i>p</i> <i>=</i> .492). Neither group experienced complications such as air embolism or needle tract seeding metastasis.ConclusionBoth CT-guided needle biopsy performed using a guidance device and freehand biopsy demonstrated high diagnostic accuracy for pulmonary nodules ≤ 2 cm. Procedures conducted with a guidance device contributed to shorter operation times, fewer CT scans, and lower radiation exposure.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261423057"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12901863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182512","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}