Technology in Cancer Research & Treatment最新文献

Introductioncervical cancer ranks as the fourth most common cancer among females worldwide. Approximately 528,000 new cases of cervical cancer are reported annually, and about 85% of them occur in less-developed countries. The lack of skilled medical staff and pre-screening procedures is the main cause of the high fatality rate in these countries. Cervicography images are the gold standard procedure for the evaluation of cervical cancer; however, the high intra-class inconsistency makes the diagnosis process more challenging for skilled medical specialists.MethodIn this work, we propose a fully automated computer-aided diagnosis (CAD) system for classifying cervical cancer using Cervicography images. Data augmentation is performed in the initial phase to address dataset imbalance. Subsequently, we proposed two novel deep learning modules: the 11-Parallel Inverted Residual Bottleneck Blocks (11-PIRBnet) architecture and the 9-Parallel Inverted Residual blocks with Self-Attention Mechanism (9-PIRSANet). Both modules are fused at the network level via a depth concatenation layer to form a new network, 375NFNet. The proposed network is trained on the selected dataset, whereas the hyperparameters are initialized through Bayesian Optimization (BO). For feature extraction, a depth concatenation layer is used during testing to combine information from both deep learning modules. Finally, the extracted features are classified using a shallow neural network (SNN) to produce the final classification.ResultTo evaluate the model, experiments were conducted on a publicly available cervical screening dataset of Cervicography images, and results demonstrate an accuracy of 95.5%, a precision of 95.4%, and an area under the curve of 0.97. When compared with several pre-trained techniques, the proposed architecture achieved significant improvement in accuracy, precision, and number of trainable parameters.ConclusionThe proposed 375NFNet architecture demonstrates remarkable accuracy and efficiency in classifying cervical cancer through cervicography images, which shows its potential as a valuable tool in resource-constrained environments.

IntroductionLung particle therapy using pencil beam scanning achieves high dose conformity but remains vulnerable to geometric uncertainties from suboptimal initial setup. Surface-guided radiotherapy (SGRT) improves setup reproducibility in photon workflows, yet evidence in lung particle therapy remains limited. This study evaluates the clinical value of SGRT in improving six degrees of freedom (6-DOF) setup reproducibility in lung cancer particle therapy.MethodsThis retrospective cohort study analyzed 63 lung cancer patients receiving 1277 treatment fractions at our center from February 2023 to January 2024. Comparisons were made between conventional laser-based positioning, which included 983 fractions, and SGRT workflows, which included 294 fractions. Following patient positioning, therapists manually registered orthogonal kilovoltage (kV) x-ray images with planning digitally reconstructed radiographs (DRRs) to calculate 6-DOF correction parameters, including translational (lateral, longitudinal, vertical) and rotational (pitch, roll, yaw) components, and to quantify the pre-correction setup error . Absolute 6-DOF displacements and three-dimensional vector magnitudes (MAG) were measured. The analysis included 36 supine patients with 739 treatment fractions and 27 prone patients with 538 fractions.ResultsThe SGRT group exhibited statistically significant reductions in median shifts for lateral (0.25 cm to 0.21 cm, p = 0.021), longitudinal (0.25 cm to 0.21 cm, p = 0.014), pitch (1.0° to 0.8°, p = 0.001), and MAG (0.59 cm to 0.53 cm, p = 0.002) compared to conventional methods. These improvements in median values were more pronounced in supine-positioned patients, while no significant differences were observed in prone-positioned patients. Furthermore, substantial reductions were achieved in ninth decile deviations (1.09 cm to 1.03 cm), and the third quartile deviations (0.83 cm to 0.74 cm) in the overall cohort.ConclusionSGRT enhances setup precision for proton and carbon ion lung cancer radiotherapy, reduces pre-correction setup error, and provides clinical support for patient setup reproducibility.

IntroductionGlycolytic phenotype positively supports cancer cell migration and metastasis in various cancers including Triple negative breast cancers (TNBCs). In-depth understanding of molecular pathways associated with increased aerobic glycolysis in TNBCs could provide key insights into the drivers of TNBC progression.Methodsβ-catenin and glycolytic proteins (PFKP, LDHA, MCT1) were assessed by Immunohistochemistry (IHC) in TNBC patients (n = 98), with prognostic value evaluated by Kaplan-Meier and Cox regression. In vitro, the β-catenin inhibitor ie, XAV939 was tested for suppressing β-catenin-driven aerobic glycolysis in TNBC models using MTT for proliferation, Western blotting for protein expression, and wound healing, droplet invasion, and colony formation assays for physiological changes.Resultsβ-catenin and glycolytic markers (PFKP, LDHA, MCT1) were overexpressed in >50% of TNBCs. Kaplan-Meier and Cox regression analyses showed that combined expression of β-catenin with glycolytic markers correlated with reduced survival. In vitro, XAV939 suppressed β-catenin-driven aerobic glycolysis in TNBC cells, downregulating β-catenin and glycolytic proteins, reducing glycolytic activity, and impairing aggressive phenotypes (proliferation, migration, invasion, clonogenicity).ConclusionOverall, our results highlight the crucial role of β-catenin in controlling aerobic glycolysis via regulation of key glycolytic proteins, thereby positively driving the progression and metastasis of TNBCs. Additionally, our data strongly establish that XAV939 effectively inhibits glycolytic phenotype, thereby suggesting its therapeutic potential in TNBC patients.

Salvage treatment for locally recurrent prostate cancer after primary radiotherapy remains a clinical challenge, with multiple modalities- including stereotactic body radiotherapy (SBRT), high-dose-rate (HDR) brachytherapy, and low-dose-rate (LDR) brachytherapy-competing for optimal use. The recent UroGEC expert review in Radiotherapy & Oncology provides a timely synthesis of available evidence and underscores the potential role of brachytherapy in this setting. Here, we contextualize these findings with recently published meta-analyses that expand the evidence base and refine our understanding of salvage outcomes. Updated analyses highlight significant differences across modalities: HDR brachytherapy achieves favorable disease control with low gastrointestinal toxicity, whereas LDR appears to offer superior relapse- free survival in selected subgroups at the cost of higher late genitourinary morbidity. By contrast, SBRT, although attractive for its non-invasiveness, demonstrates lower long-term relapse-free survival when scrutinized in broader pooled cohorts, despite acceptable toxicity. Collectively, these findings emphasize that the "one-size-fits-all" paradigm is inadequate. Clinical decision-making must instead be individualized, integrating oncologic efficacy, toxicity risks, patient comorbidities, and personal preferences. Looking forward, prospective trials and harmonized outcome reporting will be essential to strengthen the comparative evidence. Until then, a nuanced, patient-centered approach-anchored in multidisciplinary discussion-remains the cornerstone of salvage treatment planning. This perspective complements and extends the UroGEC review, underscoring the need to balance efficacy with quality of life in managing radio- recurrent prostate cancer.

BackgroundDeep generative models can improve the generalization of deep learning in medical imaging by enriching limited training data with diverse, realistic synthetic images.PurposeTo assess whether Denoising Diffusion Probabilistic Models (DDPM) generated synthetic MRI, with and without mutual information (MI) regularization, enhances brain tumor classification across heterogeneous datasets.Study TypeRetrospective.PopulationA total of 559 patients with low and high grade brain tumors (LGG, HGG) were included from two datasets: public dataset (BraTS, n = 335) and clinical dataset (TASMC, n = 224), used exclusively to evaluate model generalization.Field Strength/Sequence1.5 T/3.0T-MR / T1WI, T1WI + C, T2WI, and FLAIR images.AssessmentDDPM models were trained to generate synthetic MR images of low grade glioma (LGG) and high grade glioma (HGG), with a variant incorporating MI. Image quality was assessed using Pearson-correlation, Frechet-Inception-Distance (FID) and Inception-Score (IS). For classification purposes. For classification, a 2D ResNet-152 was trained under four setups: (1) real images (baseline), (2) +augmentation, (3) +DDPM, and (4) +DDPM + MI. Performance was assessed by accuracy and F1-score. Robustness was tested through cross-dataset evaluation using a 5-fold ensemble.ResultsThe DDPM models, with and without MI, generated high-quality synthetic images, achieving FID = 31.47, 45.00, and IS = 1.50, 1.25, respectively. Lower FID and higher IS indicate enhanced realism and diversity, suggesting that MI improved both the quality and variability of the generated images. Cross-dataset evaluation demonstrated that DDPMs with MI achieved superior generalization performance in brain tumor classification task, with accuracies of 0.89 and 0.85 for BraTS-to-TAMSC and TAMSC-to-BraTS evaluations, respectively. These results outperform the baseline model (0.87, 0.80), traditional data augmentation (0.85, 0.78), and the standard DDPM without MI (0.82, 0.83).Data ConclusionDDPM + MI with ensemble learning significantly improves brain tumor generalization across diverse datasets, consistently outperforming baseline, traditional augmentation, and standard DDPM. This combination offers a robust solution for cross-institutional clinical applications.

IntroductionOwing to the limitation in the field size of the magnetic resonance (MR)-Linac, currently, tumors with a length of >20 cm cannot be treated. Thus, the present study aimed to develop an expanded magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) workflow for long, continuous planning target volumes (PTVs).MethodsThe PTVs were divided into two sub_target volumes (PTV_sub1 and PTV_sub2). We established two isocenters and defined a field overlap region. By adjusting the MR scan range, devising the online and offline adaptive procedures, synchronizing the online adaptive processes, and constructing a pretreatment dose evaluation, a new MRIgART workflow for long PTVs was established. The new workflow was validated using an in-house-made MR phantom. Additionally, the ArcherQA Monte Carlo-based method, ArcCHECK phantom, and ionization chamber measurement method were used for dose verification.ResultsTwo clinical scenarios were established: (1) both PTV_sub1 and PTV_sub2 followed the adapt-to-position (ATP) workflow, and (2) PTV_sub1 followed the adapt-to-shape (ATS) workflow, whereas PTV_sub2 followed the ATP workflow. The feasibility of the proposed MRIgART workflow for long, continuous PTVs was demonstrated through three independent rounds of testing and validation for each scenario. When field overlaps were utilized, the PTV length that can be treated is 40 cm minus the length of field overlap region. The average gamma pass rates for the PTV_sub1 and PTV_sub2 adaptive plans were 95.74% and 98.63%, respectively (ArcherQA vs TPS). For the field overlap region, the average gamma pass rate was 95.50% (ArcCHECK vs TPS). The difference between the ionization chamber measurements and calculated results was smaller than 2%.ConclusionThis study demonstrated the feasibility, safety, and accuracy of the MRIgART workflow for long PTVs. This workflow provides an effective solution for expanding the application of MRIgART to patients with long, continuous PTVs.

IntroductionColorectal cancer (CRC) remains a leading cause of cancer-related mortality globally, with drug resistance and poor prognosis significantly limiting treatment efficacy. To address this unmet clinical need, this study aimed to screen potential biomarkers for CRC drug resistance and prognosis through integrated bioinformatics analysis and clinical sample validation.MethodsWe analyzed Gene Expression Omnibus (GEO) database GSE153412 to screen differentially expressed genes (DEGs) between 5-fluorouracil (5-FU)-resistant and sensitive CRC cells (|log2FC| > 1.0, adj P < 0.05). Gene set enrichment analysis (GSEA) was used for pathway enrichment, Weighted gene co-expression network analysis (WGCNA) to identify resistance-related modules (correlation > 0.7, P < 0.01), and Protein-protein interaction (PPI) networks to screen hub genes. Their prognostic value was evaluated in TCGA-COAD, along with IC50 correlation. Finally, qPCR verified biomarker expression in clinical CRC samples.ResultsThere were altogether 1033 DEGs screened. Through GSEA, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms enriched by the DEGs were obtained. By PPI network construction, hub genes were screened. In TCGA-COAD datasets, CAV1 (P = 0.018), CDH1 (P = 0.049), CXCL8 (P = 0.00068), CD24 (P = 0.00017), NR3C1 (P = 0.016), and ZEB1 (P = 0.042) were also related to CRC prognosis. The correlation analysis of key genes and drug resistance suggested the emergence of CDH1, CAV1, NR3C1, and ZEB1, which was also examined by clinical data validation.ConclusionIntegrated bioinformatics and clinical validation analyses identified CDH1, CAV1, NR3C1, and ZEB1 as key biomarkers for CRC. These genes were significantly associated with 5-FU resistance and CRC prognosis, as supported by their dysregulated expression in clinical samples, highlighting their mechanistic roles in the CRC drug resistance pathways.

IntroductionNab-paclitaxel is a mainstay of treatment for a broad spectrum of cancers and is typically administered over multiple cycles. The anti-mitotic effects of nab-paclitaxel are well-established. However, the systemic impact of consecutive treatment cycles on host physiology remains largely unexplored. Of particular interest is the gut microbiota and its regulatory role in drug metabolism. This study aimed to investigate the effects of consecutive nab-paclitaxel chemotherapy cycles on gut microbiota composition, intestinal barrier function, and pharmacokinetic (PK) behavior in rats.MethodsTwenty-four Sprague-Dawley rats were randomly assigned to one-, two-, or three-cycle chemotherapy groups and received nab-paclitaxel via tail vein injection. Plasma drug concentrations were measured by Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS), gut microbial composition was analyzed using 16S Ribosomal RNA (16S rRNA) sequencing, and hepatic CYP3A and CYP2C expression was assessed by Western blot and Quantitative Polymerase Chain Reaction (qPCR).ResultsConsecutive nab-paclitaxel administration significantly altered the gut microbiota, decreasing Actinobacteriota and Firmicutes while increasing Proteobacteria and Cyanobacteria in a cycle-dependent manner. Microbial diversity indices, including Observed species and Rao's quadratic entropy, increased after multiple cycles. Pharmacokinetic analysis showed that clearance, mean residence time, and volume of distribution decreased, whereas Area Under the Curve (AUC) and Maximum Plasma Concentration (Cmax) increased significantly after repeated dosing. However, no significant differences were observed in CYP3A1 or CYP2C11 protein or Messenger RNA (mRNA) expression, suggesting that nab-paclitaxel may influence pharmacokinetics through non-CYP-dependent pathways potentially mediated by gut microbiota-host interactions.ConclusionIn conclusion, consecutive nab-paclitaxel chemotherapy cycles induce gut microbiota dysbiosis and alter pharmacokinetic profiles via non-CYP-dependent mechanisms, highlighting the critical role of the microbiota-gut-liver axis in chemotherapeutic drug disposition and providing a theoretical basis for microbiota-targeted interventions to optimize chemotherapy efficacy.

IntroductionLymphovascular invasion (LVI), an aggressive pathological manifestation of breast cancer, is closely associated with increased risk of distant metastasis and poor prognosis. This study proposes a novel modeling strategy that integrates MRI-derived microvascular atlas parameters with the TwinsSVT deep learning architecture to enable noninvasive prediction of LVI status in breast cancer patients and to explore its biological interpretability.Materials and MethodsA total of 436 breast cancer patients from two medical centers, all pathologically confirmed postoperatively, were retrospectively enrolled. All patients underwent high-resolution multi-b-value diffusion-weighted imaging (DWI) prior to surgery. From the MRI data, four types of microvascular simulation parameter maps were reconstructed within tumor regions: apparent diffusion coefficient (ADC), mean flow velocity (v_m), velocity dispersion (v_s), and angiographic branching index (ANB), aiming to characterize intratumoral microcirculation and vascular structural complexity. These functional parametric maps were individually input into separate encoder branches of the TwinsSVT model to extract multi-scale spatial features. A multi-layer Transformer fusion module was then employed to capture structural interactions across modalities, thereby constructing a multi-parametric fusion model. Model performance was evaluated using metrics including area under the curve (AUC) and F1 score.ResultsCompared with single-parameter models, the multi-parametric fusion model demonstrated significantly improved predictive performance, with AUCs of 0.881 (95% CI: 0.781-0.982) and 0.859 (95% CI: 0.764-0.953) in internal and external validation cohorts, respectively. Grad-CAM visualizations revealed that the model predominantly focused on tumor margins and regions of high vascular density, suggesting a strong correlation between the model's attention and actual pathological structures.ConclusionThe deep learning model constructed based on MRI-derived microvascular simulation atlases enables noninvasive preoperative prediction of LVI status in breast cancer patients. By effectively capturing structural information and offering biological interpretability, the model holds promise as a robust imaging-based tool for precision subtyping and clinical decision support.