Technology in Cancer Research & Treatment最新文献

Prostate cancer remains one of the most common malignancies in men, with its progression strongly influenced by androgen signaling. While genetic alterations are well-documented in prostate cancer, growing evidence highlights the contribution of environmental factors, particularly diet and the gut microbiome, in modulating disease risk and therapy response. The gut microbiota plays a crucial role in regulating host metabolism, immune responses, and hormone activity. Recent findings suggest that specific microbial communities influence androgen biosynthesis and metabolism through enzymes such as β-glucuronidase, altering systemic androgen availability and imp acting tumor progression. Additionally, microbial metabolites, including short-chain fatty acids, secondary bile acids, and bacterial genotoxins, can affect inflammatory pathways and cellular signaling relevant to prostate tumorigenesis. Experimental studies also indicate that modifying the gut microbiota through dietary interventions, probiotics, or fecal microbiota transplantation can influence tumor growth and improve responses to immunotherapy and hormone-based treatments. In this review we present the current knowledge on gut-prostate axis, examine the mechanistic links between microbial activity and prostate cancer biology, and discuss emerging microbiome-based strategies as potential therapies. A deeper understanding of this bidirectional crosstalk could pave the way for microbiome-informed approaches to prevention, diagnosis, and personalized treatment of prostate cancer.

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.

IntroductionTo explore the value of enhanced computed tomography (CT) -derived extracellular volume (ECV) combined with systemic immune-inflammation index (SII) in predicting tumor budding (TB) grading of rectal cancer.Materials and MethodsThe clinical and imaging data of 177 rectal cancer patients were retrospectively analyzed, and we divided them into a low-grade and medium-high group according to pathological TB count. ECV and SII values between the two groups were compared. Intra-class correlation coefficient (ICC) was used to detect the consistency of measurements among observers. Binary logistic regression was used to analyze the correlations between variables and TB grading of rectal cancer. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficiency of statistically significant parameters and their combination. Area under the curve (AUC), its 95% confidence interval, and the corresponding Youden index, sensitivity, and specificity were calculated.ResultsAmong the 177 rectal cancer patients, 108 were low-grade and 69 were medium-high grade. ECV values measured by two physicians showed good consistency (ICC = 0.98). ECV value of low-grade (21.76% ± 4.89%) was lower than that of medium-high grade TB group (27.91% ± 4.77%) (P < .001). SII value was lower in low-grade group (492.14 ± 239.56) than in medium-high grade TB group (825.02 ± 529.38). In the multivariate analysis, ECV value [odds ratio (OR): 1.339 (95% CI: 1.194-1.502)] and SII value [OR: 1.004 (95% CI: 1.002-1.005)] were independent risk factors for predicting TB grading. In the training set, AUCs of ECV, SII, and their combination in evaluating TB grading of rectal cancer were 0.838 (95% CI: 0.760-0.905), 0.755 (95% CI: 0.663-0.829), and 0.889 (95% CI: 0.832-0.943), respectively. In the test set, the corresponding AUCs were 0.741 (95% CI: 0.626-0.870), 0.716 (95% CI: 0.554-0.849), and 0.815 (95% CI: 0.711-0.913). Decision curve analysis (DCA) showed that the combination had higher clinical value than using ECV or SII alone.ConclusionThe combination of ECV and SII can non-invasively evaluate TB grading of rectal cancer before surgery, potentially providing a reference for preoperative risk stratification as a decision-support tool.

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.

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.

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 (I² > 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.

ObjectivesThis retrospective study presents an integrative transcriptomic approach for recurrent and/or metastatic head and neck squamous cell carcinoma (R/M HNSCC) by developing an immune response predictive score (IORPS) derived from tumor microenvironment (TME) transcriptomic profiles.MethodsA total of 30 R/M HNSCC patients treated with pembrolizumab or nivolumab, with available immune TME profiling data, were analyzed. IORPS was constructed based on the cumulative weighting of differentially expressed gene (DEG) expression levels. The predictive performance of conventional biomarkers, individual DEGs, and IORPS was evaluated for immunotherapy response and prognostic outcomes. The clinical relevance of IORPS was further validated using two external cohorts from the GEO database (CLB-IHN: GSE159067 and GHPS: GSE159141).ResultsBy comparing immune tumor microenvironment (TME) profiles between good and poor responders, GZMH, IFNG, and FASLG were identified as key DEGs with significantly higher expression in favorable immunotherapy responders. The IORPS, derived from transcriptomic profiling, demonstrated robust predictive accuracy for both immunotherapy response and survival outcomes in patients with R/M HNSCC.ConclusionCompared with the variable predictive performance of current biomarkers such as TPS and CPS, IORPS provides improved accuracy and reliability in identifying and stratifying patients most likely to benefit from immune checkpoint blockade therapy.