Technology in Cancer Research & Treatment最新文献

{"title":"Value Analysis of MRI Habitat Analysis Combined Model in the Diagnosis of Ovarian Tumors.","authors":"Junying Wang, Kang Liu, Yan Sui, Siyu Ma, Dewu Yang","doi":"10.1177/15330338261439633","DOIUrl":"10.1177/15330338261439633","url":null,"abstract":"<p><p>ObjectiveThis study aimed to investigate the clinical diagnostic performance of a combined classification model incorporating magnetic resonance imaging (T1WI-CE) habitat and human epididymis protein 4 (HE4) for differentiating borderline ovarian tumors (BOTs) from malignant epithelial ovarian tumors (MEOTs).MethodsA retrospective analysis was conducted on 127 patients with pathologically confirmed ovarian tumors, including 62 with BOTs and 65 with MEOTs, all of whom underwent preoperative magnetic resonance imaging examination. Twenty habitat features, including the original images, were extracted. T1WI-CE was used to extract 2395 radiomics features from two habitat subregions. Feature selection was performed using correlation analysis and least absolute shrinkage and selection operator regression.ResultsThe combined classification model had the highest area under the curve, 0.941 in the training group and 0.880 in the test group, thus outperforming the habitat area and clinical data classification model. The DeLong test demonstrated statistically significant differences between the combined classification model and the clinical classification model, with <i>P</i> values of 0.041 in the training group and 0.023 in the test group. Additionally, a statistically significant difference was observed in the DeLong test results between the cystic habitat subregion (H2) and the overall habitat region.ConclusionsThe combined classification model of habitat analysis and clinical data effectively improved the diagnostic efficacy of differentiating borderline from malignant ovarian tumors. The diagnostic efficacy of the habitat subregion (H1) dominated by solid components and the habitat overall region was superior to that of the habitat subregion dominated by cystic components.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261439633"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147609807","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":"Screening of Hepatocellular Carcinoma in Hepatic Cirrhosis Patients by a Novel Blood-Based Multi-Omics Test.","authors":"Lei Chen, Jun Liu, Mengcheng Long, Pinghai Hu","doi":"10.1177/15330338261435022","DOIUrl":"10.1177/15330338261435022","url":null,"abstract":"<p><p>IntroductionHepatocellular carcinoma (HCC) screening in patients with hepatic cirrhosis (HC) relies on ultrasound and alpha-fetoprotein (US + AFP), which has limitations in sensitivity, particularly for early-stage HCC detection. This study aims to evaluate the performance of a novel multi-omics blood test, HCCscreen, with its individual components (methylation, AFP, Des-γ-Carboxy Prothrombin (DCP), mutations) and the standard US + AFP for HCC screening in a hepatic cirrhotic population.MethodsA total of 5078 patients with known high-risk for HCC were recruited. A prospective screening study was conducted on 650 patients with hepatic cirrhosis identified by ultrasound. Blood samples were collected from all patients before the confirmation of diagnosis by imaging and/or pathological examinations. The performance of HCCscreen, individual markers and US + AFP were calculated and compared. Statistics was performed with Graphpad Prism 5.0.ResultsHCCscreen exhibited a sensitivity of 86.3% at a specificity of 81.3%, with a positive predictive value (PPV) of 28.2% and a negative predictive value (NPV) of 98.6%. The positive likelihood ratio (LR+) was 4.61 and the negative LR (LR-) was 0.17. The positive detection rate (PDR) for all markers increased with more advanced HCC stages, whether Barcelona Clinic Liver Cancer (BCLC) or clinical staging. Among the single-omics, methylation showed the highest PDR, followed by AFP, DCP and mutations. HCCscreen demonstrated superior overall performance with an AUC of 0.87, outperforming individual markers like methylation (AUC = 0.76), AFP (AUC = 0.83), and DCP (AUC = 0.77). Crucially, HCCscreen's PDR was significantly higher than US + AFP in early-stage HCC (BCLC-0 and clinical stage I). Furthermore, while AFP's PDR varied significantly by sex, HCCscreen's performance remained consistent across all demographics. Correlation analysis revealed a significant association only between the HCCscreen score and the methylation score.ConclusionsThe multi-omics approach of HCCscreen significantly enhances early HCC detection in patients with hepatic cirrhosis compared to both its individual components and the current standard of US + AFP. Its robust and consistent performance across patient demographics underscores its potential as a superior tool for population-wide early HCC screening.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261435022"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13009828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500124","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":"Hepatic Arterial Infusion Chemotherapy Plus Systemic Chemotherapy, Lenvatinib and PD-(L)1 Inhibitors Versus Systemic Chemotherapy Plus PD-(L)1 Inhibitors in Advanced Intrahepatic Cholangiocarcinoma.","authors":"Huazhong Guo, Yexing Huang, Na Liu, Minke He, Zhicheng Lai, Ming Shi, Qijiong Li","doi":"10.1177/15330338261444906","DOIUrl":"10.1177/15330338261444906","url":null,"abstract":"<p><p>IntroductionThe prognosis for unresectable intrahepatic cholangiocarcinoma (ICC) remains poor. This retrospective study aims to evaluate the efficacy and safety of HAIC combined therapy in advanced ICC.MethodsBetween April 2021 and July 2024, patients with advanced ICC treated with either HAIC of oxaliplatin plus systemic chemotherapy of gemcitabine, lenvatinib and PD-(L)1 inhibitors (HSLP) or systemic chemotherapy plus PD-(L)1 inhibitors (SP) were consecutively enrolled. Propensity score matching (PSM) was used to minimize selection bias. Primary endpoint was objective response rate (ORR) and secondary endpoints included overall survival (OS), progression-free survival (PFS) and safety.ResultsA total of 110 patients were recruited in this study with 53 in HSLP group and 57 in SP group. Prior to PSM, ORR was significantly higher (47.2% vs. 24.6%; P = 0.023) and both median OS and PFS were prolonged in HSLP group compared to SP group (OS: 15.2 vs. 12.3 months, P = 0.002; PFS: 11.3 vs. 7.3 months, P = 0.004). After 1:1 PSM, outcomes remained superior in the HSLP group, with a significantly higher ORR (43.2% vs. 18.9%; P=0.032) and longer median OS (15.4 vs. 11.8 months; P=0.018) and PFS (11.9 vs. 6.9 months; P=0.019). Multivariate analysis identified the treatment regimen as an independent prognostic factor for OS and PFS. All events were manageable with supportive care, and no additional toxicities were observed in the HSLP group.ConclusionHAIC plus systemic chemotherapy, lenvatinib and PD-(L)1 inhibitors represents a promising therapeutic regimen for advanced ICC patients, with tolerable toxicity.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261444906"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13150104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781774","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":"Corrigendum to \"Preoperative Ternary Classification of Pulmonary Ground-Glass Nodules (AIS/MIA/IAC): ResNet-10 Outperforms Radiomics and Clinicoradiographic Models in Multicenter Study\".","authors":"","doi":"10.1177/15330338261451115","DOIUrl":"10.1177/15330338261451115","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261451115"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13172714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147875787","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":"EPIDSeg-Net: A Multi-Modal Fusion Framework Based on DRR Guidance in Radiotherapy is Used for Precise Segmentation of MV-EPID Lung Targets.","authors":"Qianjia Huang, Heng Zhang, Lintao Song, Zhuqing Jiao, Xinye Ni","doi":"10.1177/15330338251414224","DOIUrl":"10.1177/15330338251414224","url":null,"abstract":"<p><p>BackgroundBy integrating Digitally Reconstructed Radiograph (DRR) images of pulmonary tumors with Electronic Portal Imaging Device (EPID) images to assist in target segmentation, and subsequently comparing morphological changes in segmented targets across different radiotherapy stages, this approach enables precise quantification of dynamic variations in target volume and shape. This methodological integration provides objective evidence for treatment response evaluation and dynamic optimization of treatment plans, thereby significantly enhancing the precision of radiotherapy delivery.MethodsThe proposed multimodal segmentation framework, named EPIDSeg-Net, comprises an encoder, a multi-scale feature layer, and a decoder. The encoder utilizes a dual-branch architecture: a CNN branch for extracting local texture features and a Swin-Transformer branch for capturing global semantic features. The model first calibrates multimodal input features through a Dual Attention Mechanism (DAM) to adaptively adjust modality-specific weights, thereby enhancing tolerance to missing image information in multi-sequence segmentation. Subsequently, two key modules are implemented within the multi-scale feature layer: a Large-Kernel Grouped Attention Gating (LKG-Gate) module to strengthen local contextual awareness, and a Multi-Path Feature Extraction (MPFE) module to improve feature robustness via a parallel structure. These designs enable the model to effectively focus on lung tumor target regions, optimize segmentation accuracy, and achieve high-performance reconstruction.ResultsThe framework effectively integrates multimodal features, enabling high-precision localization and sharp boundary delineation while preserving anatomical details. Quantitative evaluations demonstrate superior performance: DICE = 93.2 (92.4∼93.9), CE = 0.352, HD95 = 9.42 (6.03∼12.8), IOU = 86.0 (84.1∼87.9), and SENCE = 0.828. Overall, the model excels at preserving gradient information, regional integrity, and fine details; effectively suppresses feature loss; and reduces missed segmentation rates, leading to improvements in both subjective and objective performance metrics.ConclusionThe proposed segmentation method effectively integrates information from EPID and DRR images, enabling more precise localization and segmentation of lesion regions within EPID images while enhancing segmentation accuracy.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338251414224"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12868598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114434","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":"Whole-Field Continuous Wave Diffuse Reflectance Imaging for Breast Lesion Characterization: Clinical Results.","authors":"Nicolás A Carbone, Demián A Vera, M Victoria Waks-Serra, Héctor A García, Daniela I Iriarte, Juan A Pomarico, Nora Fuentes, María E Renati, Pablo H Capellino, Romina Osses, Pamela A Pardini, Inés Hope","doi":"10.1177/15330338261417262","DOIUrl":"10.1177/15330338261417262","url":null,"abstract":"<p><p>ObjectiveThis work introduces MamoRef, an innovative whole-field, near infrared spectroscopy based device for adjunctive breast examination, aiming to help classify benign and malignant lesions in women. Utilizing low-power, non-ionizing red and near-infrared lasers, it provides metabolic information to aid physicians in characterizing lesions in BI-RADS II to IV patients, offering a non-invasive screening alternative.ApproachClinical studies were conducted, benchmarking MamoRef against conventional imaging and core biopsies. The device generates 2D maps of relative oxyhemoglobin, deoxyhemoglobin, and oxygen saturation. NIRS-specialized professionals, with basic clinical training, independently scored MamoRef images using a 6-point scale analog to BI-RADS. Scores were averaged and normalized for biopsy comparison.Main resultsThe studied clinical cases show promising outcomes. For neoproliferative lesions, MamoRef images reveals high deoxygenated hemoglobin and diffuse high oxygenated/total hemoglobin, suggesting neovascularization around necrotic tissue. Preliminary receiver operating characteristic analysis yielded an area under the curve of 0.77. At a 0.6 threshold, MamoRef showed 70% accuracy and 74% specificity.SignificancePreliminary results suggest MamoRef can potentially differentiate benign from malignant lesions detected by standard imaging. Trained clinicians might detect and characterize lesions using these metabolic maps. Further larger-scale studies are needed to validate these findings and improve the technology, positioning MamoRef as a potential low-cost, accessible adjunctive screening tool.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261417262"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12881336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146132927","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":"Retraction: MiR-34a Regulates Nasopharyngeal Carcinoma Radiosensitivity by Targeting SIRT1.","authors":"","doi":"10.1177/15330338261432325","DOIUrl":"10.1177/15330338261432325","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261432325"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13009815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147499965","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":"Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and Meta-Analysis.","authors":"Qiong Li, Hongde Liu, Jinke Wang","doi":"10.1177/15330338261425328","DOIUrl":"10.1177/15330338261425328","url":null,"abstract":"<p><p>IntroductionMachine learning (ML)-based analysis of cell-free DNA (cfDNA) has emerged as a promising strategy for multi-cancer early detection (MCED). However, reported diagnostic performance varies widely across studies, and many estimates are derived from training or enriched cohorts, limiting their relevance to independent validation and real-world settings.MethodsWe conducted a systematic review and diagnostic accuracy meta-analysis of ML-based cfDNA assays for MCED. Four databases (PubMed, Embase, Web of Science, and the Cochrane Library) were searched from inception to February 2, 2025. Only independent validation or testing datasets were included; all training datasets were excluded. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves were estimated using a bivariate random-effects model. Subgroup analyses and meta-regression were performed to explore sources of heterogeneity.ResultsThirteen studies comprising 23 independent datasets and 14,892 participants were included. The pooled sensitivity was 0.78 (95% CI: 0.66-0.87), and the pooled specificity was 0.96 (95% CI: 0.90-0.98). The summary area under the curve (AUC) was 0.94, with a DOR of 76.6. Substantial between-study heterogeneity was observed (<i>I²</i> > 90%), with geographic region, sample size, and cfDNA biomarker type identified as major contributing factors.ConclusionML-based cfDNA assays demonstrate consistently high specificity and moderate-to-high sensitivity across independent validation datasets, supporting their potential role in multi-cancer early detection. However, diagnostic performance is highly context dependent and strongly influenced by study design, population characteristics, and analytical choices. These findings highlight the need for large-scale, prospective, population-based validation before widespread clinical implementation.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261425328"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12925023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146259299","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":"Evaluating a CBCT Correction Algorithm for Adaptive Radiotherapy.","authors":"Caleb Sawyer, Jihye Koo, Arash Naghavi, Jimmy J Caudell, Muqeem Qayyum, Gage Redler, Vladimir Feygelman, Jonathon Mueller, William Johansson, Kujtim Latifi","doi":"10.1177/15330338261428647","DOIUrl":"10.1177/15330338261428647","url":null,"abstract":"<p><p>IntroductionTo increase efficiency of adaptive radiotherapy (ART), we tested a cone beam computed tomography (CBCT) correction algorithm to evaluate the feasibility of utilizing daily CBCTs for treatment planning.MethodsA lung phantom was scanned with a CT and CBCT on two different linacs. The CBCTs were processed through a correction algorithm in the treatment planning system (TPS). The algorithm reduces artifacts and adjusts image intensity to more closely match the planning CT, to generate corrected CBCTs. Voxels outside the CBCT field of view (FOV) are replaced with voxels from the planning CT. A treatment plan was first generated on the CT, then recalculated on the corrected CBCTs. The same workflow was followed for seven previously adapted head and neck and seven sarcoma patients. Each patient's adaptive plan was recalculated on the corrected CBCTs. Dose differences were analyzed for these plans using a 3%/2 mm gamma analysis.ResultsBoth Ethos and TrueBeam CBCT plans on the phantom had high matching dose per voxel according to gamma analysis. After corrections of some registration errors, all 14 plans achieved gamma passing rate above 95% (3%/2 mm).ConclusionsThe CBCT correction algorithm demonstrates potential to reduce the need for re-simulation and enable faster offline adaptive planning without sacrificing dose calculation accuracy.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261428647"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310245","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":"Retraction: MiR-887 Promotes the Progression of Hepatocellular Carcinoma via Targeting VHL.","authors":"","doi":"10.1177/15330338261428254","DOIUrl":"10.1177/15330338261428254","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 ","pages":"15330338261428254"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12972548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147378650","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}