Technology in Cancer Research & Treatment最新文献

IntroductionThis study aims to analyze the impact of the surface dose at depth (1-5mm) in different planning techniques and immobilization devices by varying the dose-voxel size (DVS) and statistical uncertainty (SU) using Monte Carlo (MC) algorithm.MethodsThree Sets of computed tomography (CT) images were taken from an in-house developed chest phantom, which included an open phantom, a vaclok and a thermoplastic mask. The image sets were pushed to the Monaco planning station for registration and contouring. Six beams of 6 MV photon energy are used to plan an Intensity modulated radiotherapy (IMRT) technique, and a half arc beam is used for Volumetric Modulated arc therapy (VMAT). In each plan, recalculation is performed by changing only the grid size from 1 mm to 8 mm and the statistical uncertainty from 1% to 5% from the parameter control window, keeping the other dose constraints the same. A total of 240 plans were performed for all three image sets together for both the IMRT and VMAT techniques, and the dose at depth was compared and statistically analyzed via the Kruskal-Walli's test.ResultsThe homogeneity index (HI), conformity index (CI), and V95% target are increased in both IMRT and VMAT, whereas the SU and DVS are reduced.ConclusionHigher statistical uncertainty and grid size significantly reduced dose calculation time, independent of technique or device. surface dose at depth (1-5mm) decreased with increasing grid size and increased with lower statistical uncertainty. IMRT consistently showed higher skin doses than VMAT across all devices, with vac-lock immobilization yielding the highest surface dose in both techniques. These findings show that the surface dose is influenced by beam selection, parameter settings, and the optimization time allocated during treatment planning.

Rising global cancer rates are projected to significantly increase by 2050, highlighting the urgent need for improved scalable prevention, early detection, and personalized therapy tools. Artificial intelligence (AI) has demonstrated significant capabilities in diverse oncology tasks, leveraging high-dimensional data from medical imaging, molecular profiles, and electronic health records for applications in radiology, digital pathology, genomics, prognostication, and treatment selection. Nevertheless, the clinical adoption of most AI systems is still limited by the black box issue, that is, prediction without clear explanation, which, in turn, limits the confidence and accountability of clinicians as well as their ability to communicate with patients. In this review, we searched sources over the years (2015-2025) from PubMed, Scopus, and Web of Science for evidence on explainable AI (XAI) methodologies that may provide greater interpretability and trust in oncologic practice. Local interpretable model-agnostic explanation and Shapley additive explanations (LIME and SHAP) are model-agnostic methods that offer local and global feature attribution and help clinicians to understand the main influential factors behind model predictions. The complementary approaches, such as Gradient-weighted Class Activation Mapping (Grad-CAM), Integrated Gradients and DeepLift, also bring the explainability to image- and genomics-based processes, whereas more recent strategies (eg, Anchors, Prototypical Part Network (ProtoPNet), and contrastive or counterfactual explanations) also focus on enhancing stability and clinical utility. Irrespective of such developments, several issues continue to be experienced, including computational load, inconsistency in explanations, domain transfer, deployment into clinical processes, bias, privacy issues, and changing regulatory requirements. In general, XAI can transform oncology AI to become clinically interpretable, transparent prediction of outcomes, which will make its application safer by adhering to strict validation procedures, human control, and patient-centered communication. By providing a comprehensive and clinically grounded overview, this review aims to support researchers, clinicians, and stakeholders in advancing trustworthy and transparent AI deployment in oncology.

IntroductionThis retrospective study aimed to investigate the correlation between TP53 identified via next-generation sequencing (NGS) and p53 expression in colorectal adenocarcinoma (CRC), as assessed by immunohistochemistry (IHC). Additionally, we characterized p53 IHC staining patterns and sought to determine the optimal threshold for p53 expression as a surrogate for TP53 mutation status.MethodsIn this retrospective cohort analysis, we included 294 archived surgically resected CRC specimens from patients who did not receive preoperative chemotherapy were analyzed. All data were collected from pathology database and electronic medical records. TP53 mutations were identified using NGS, and p53 expression was evaluated by IHC. The correlation between mutation status and IHC staining patterns was assessed, and sensitivity and specificity were calculated.ResultsThe TP53 mutation rate was 78.2%, comprising missense (68.4%), nonsense (12.4%), frameshift (11.0%), and splice-site (8.3%) mutations. Missense mutations were associated with nuclear p53 staining, while frameshift mutations mostly showed loss of expression. Nonsense and splice-site mutations exhibited diverse patterns, including loss of expression, nuclear staining with/without cytoplasmic staining, or cytoplasmic staining alone. Among cases with loss of p53 expression, the TP53 mutation rate was 88.9%. When the proportion of strong p53-positive cells exceeded 55%, the missense mutation-positivity rate increased significantly (P < 0.05). The sensitivity and specificity of p53 IHC in predicting TP53 mutations were 92.3% and 94.8%, respectively.ConclusionsCRC predominantly exhibited missense TP53 mutations. p53 IHC revealed diverse expression patterns, including overexpression, complete loss, cytoplasmic staining, and normal-type patterns. Strong p53 expression (>55%) correlated closely with TP53 missense mutations, supporting IHC as a reliable surrogate. However, cases showing loss of p53 expression should undergo gene sequencing to confirm mutation status.

IntroductionDespite the advent of anti-PD-1 immunotherapy as a promising treatment for HCC, there remains a significant gap in the comprehensive analysis of peripheral blood immunological markers that could predict treatment response. This study aims to identify peripheral blood immunological markers predictive of anti-PD-1 therapy response in HCC patients to improve clinical outcomes.MethodsWe retrospectively analyzed 69 HCC patients treated with anti-PD-1 therapy, divided into a training cohort (n = 30) and a validation cohort (n = 39). Clinical characteristics, hematological indices, cytokine levels, and serum PD-1 were assessed. Logistic regression and ROC curve analyses were performed to evaluate prognostic value, with bootstrap validation to assess model robustness. In addition, tumor samples from 6 patients underwent WES, and bioinformatic analyses were conducted to explore mutational profiles and their associations with immune infiltration as supportive mechanistic validation.ResultsThe IL-2/IL-10 ratio was significantly associated with tumor progression after adjustment for covariates (OR 2.918, 95% CI 1.191-7.150, p = 0.019) and achieved superior predictive performance (AUC 0.884, 95% CI 0.766-1.000) compared with conventional inflammation-based scores. Bootstrap validation confirmed model stability (corrected AUC ≈ 0.88), and external validation supported predictive value. Whole-exome sequencing revealed that mutations in genes such as FLT3, TET2, and IDH2 were commonly present in HCC. Immune infiltration analyses indicated that these mutations were associated with increased Treg and decreased Th1 infiltration, consistent with the clinical trend. Additional analyses of public transcriptomic datasets further supported these observations.ConclusionOur study reveals that a low IL-2/IL-10 ratio is significantly associated with adverse prognosis in HCC patients and may serve as a practical and biologically relevant biomarker for predicting the efficacy of anti-PD-1 therapy. Moreover, systematic evaluation of immune status could provide important guidance for predicting immunotherapy efficacy and supporting future clinical decision-making in HCC management.

IntroductionCT-guided biopsy has good diagnostic accuracy, but adverse events such as pneumothorax are common. There are few reports on the safety and efficacy of CT-guided biopsy in the elderly.MethodsThis was a retrospective single-centre cohort study. Patients who underwent CT-guided lung biopsy between February 2017 and August 2024 were included. Patient background, disease background, examination status, and adverse events were ascertained. Elderly were defined as those aged 75 years and older. The primary outcome was the incidence of all adverse events, and the secondary outcomes were the incidence of pneumothorax and diagnostic accuracy. Categorical variables were compared by Chi-square test, and continuous variables by t-test. Multivariable analysis was performed by logistic regression analysis adjusted for age, sex, lung comorbidities, and radiological findings of target lesion.ResultsThere were significant differences between the two groups in ECOG-PS and the distance from the surface to pleura and target. In the primary outcome, any adverse events occurred in 207 patients (56.2%), with no significant difference between elderly (97/180, 53.9%) and non-elderly (110/188, 58.5%) patients (p = 0.401). Pneumothorax was the most common adverse event, occurring in 151 (41.0%) patients, with no significant difference between elderly (68/180, 37.8%) and non-elderly (83/188, 44.1%) (p = 0.244). On multivariate analysis, elderly (75years or older) was not clearly associated with the occurrence of all or severe adverse events, pneumothorax, and confirmed diagnosis. Location in the lower lung field and distant from the pleura were significantly associated with the incidence of all adverse event. In the secondary outcomes, emphysema or interstitial pneumonia, location in the lower lung field, and distant from the pleura were significantly associated with pneumothorax. There was no significant difference in the diagnostic accuracy disease between the elderly and non-elderly patients.ConclusionsThe incidence of adverse events and diagnostic accuracy of CT-guided biopsy are similar in elderly and non-elderly patients, and this method is useful even in elderly patients.Key pointThe safety and efficacy of CT-guided lung biopsy in elderly patients are equivalent to those in non-elderly patients.

IntroductionRBF neural networks are widely used in gastric carcinoma prognostic models, but they face challenges including difficulty in determining the Gaussian radial basis function parameters of the hidden layer and the diversity/ambiguity of factors affecting gastric carcinoma prognosis. The cloud model, a key tool in uncertainty theory, is adept at handling fuzziness and randomness of complex medical data by quantifying uncertainty. This study integrates the cloud model with RBF neural networks to address the aforementioned limitations.MethodsThe study included 11,474 gastric carcinoma patients from the SEER database and 769 from the Linzhou Centre for Disease Control and Prevention database. A new model combining a cloud model with RBF neural networks was used, where high-dimensional cloud transformations identified RBF hidden layer neurons to optimize the network structure.ResultsComparison with conventional methods showed that the new model predicted overall survival (OS) with a C-index of 0.715. This value is not only significantly higher than that of clinical standard TNM staging (0.591) but also outperforms machine learning methods including random forest (0.614) and traditional RBF neural networks (0.632). It achieves excellent prognostic accuracy meeting the clinical criterion of good discriminative ability, even relying solely on simple clinical factors, which enhances its clinical applicability.ConclusionThe model is a new and effective prognostic model that provides better and more accurate prognostic assessment for gastric carcinoma patients.

IntroductionEpithelial-mesenchymal transition (EMT) is a key driver of tumor invasion and metastasis, which is closely associated with poor prognosis in patients with surgically resected lung cancer. Hypercapnic acidosis (HCA) is a common comorbidity in various lung diseases; however, its specific role in regulating EMT in lung cancer remains unclear. Acid-sensing ion channel (ASIC) genes have been implicated in tumor progression, but their expression patterns and prognostic value in lung cancer, as well as their involvement in HCA-mediated EMT regulation, require further investigation.MethodsThe expression levels of ASIC genes and their prognostic significance were analyzed in lung adenocarcinoma and lung squamous cell carcinoma using the Gene Expression Profiling Interactive Analysis (GEPIA) database. A549 lung cancer cells were exposed to HCA conditions (10% CO₂, pH 6.69 ± 0.02) for five days to induce EMT phenotypes. Cell proliferation, migration, and invasion capacities were evaluated using corresponding functional assays. The expression levels of EMT-related markers (E-cadherin and vimentin) and ASIC3 were quantified by immunohistochemical staining, western blot analysis, and reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR). Additionally, amiloride was used to inhibit ASIC3 expression to verify its regulatory role in HCA-induced EMT.ResultsBioinformatics analysis showed that overexpression of ASIC3 mRNA was significantly correlated with reduced overall survival in lung cancer patients (P < .05). In vitro experiments demonstrated that HCA exposure significantly upregulated ASIC3 expression (P < .01) and promoted EMT in A549 cells, as evidenced by downregulated E-cadherin expression and upregulated vimentin expression. Moreover, HCA significantly enhanced the migration and invasion abilities of A549 cells (P < .01). Importantly, inhibition of ASIC3 expression by amiloride reversed all these HCA-induced effects, including the alterations in EMT markers and the enhancement of cell migratory/invasive capacities.ConclusionThe HCA microenvironment induces EMT in A549 lung cancer cells through the activation of ASIC3. These findings suggest that ASIC3 may serve as a potential therapeutic target for the treatment of lung cancer, which could help improve clinical outcomes by inhibiting tumor invasion and metastasis mediated by EMT.

IntroductionRecent advances in deep learning have significantly improved the ability to solve ill-posed problems, making 4D cone-beam CT (CBCT) reconstruction from projections of 3D CBCT imaging achievable. However, extracting respiratory signal from CBCT projections for 4D CBCT phase sorting remains a challenge. This study aims to evaluate conventional and deep learning methods for extracting respiratory signal from projections of clinical 3D CBCT imaging.MethodsThis study analyzed 70 sets of projections from clinical 3D CBCT imaging, involving thoracic and abdominal cancer patients with regular and irregular respiratory motion patterns. Using the labeled apex of the diaphragm as a reference, respiratory signals extracted using conventional methods-including intensity analysis (IA), Fourier transform (FT), Amsterdam Shroud (AS), and local principal component analysis (LPCA)-as well as a deep learning-based method (U-Net) were compared through correlation analysis and phase-sorting capability.ResultsThe U-Net significantly outperformed the conventional methods across varying conditions, achieving a correlation coefficient of 0.93 ± 0.07. Among the conventional methods, LPCA and AS outperformed IA and FT, with LPCA is considered superior because the AS method is influenced by the cutoff frequencies of the bandpass filter.ConclusionThe U-Net demonstrates superiority in extracting respiratory signals from clinical 3D CBCT projections, highlighting its potential to enhance respiratory phase sorting and 4D CBCT reconstruction.

IntroductionOsteosarcoma (OS) is a highly aggressive primary bone malignancy with poor prognosis. Histone modifications play crucial roles in tumor progression, but their systematic investigation in OS remains unexplored.MethodsThis study integrated single-cell RNA sequencing data and large-scale clinical information to systematically analyze the spatial heterogeneity of histone modifications in OS and their clinical significance. We employed Seurat for single-cell data analysis, CellChat for cell-cell communication network analysis, and LASSO Cox regression to construct a prognostic model. Additionally, we conducted functional enrichment analysis, immune characteristics analysis, and drug sensitivity prediction.ResultsWe identified five major cell types in the OS microenvironment and discovered significant differences in histone modification levels among different cell types, with osteosarcoma cells and endothelial cells exhibiting higher modification levels. Cell-cell communication network analysis revealed the importance of signaling pathways such as SPP1, CypA, MIF, IGFBP, and VEGF in OS. Based on nine histone modification-related genes, we constructed an efficient prognostic model (AUC values of 0.713, 0.845, and 0.888 for 1-, 3-, and 5-year predictions, respectively), which was validated in an external cohort (AUC = 0.808). Immune microenvironment analysis showed significantly higher proportions of CD8+ T cells and Treg cells in the low-risk group. Drug sensitivity analysis revealed that the low-risk group was more sensitive to Imatinib, Rapamycin, and Sunitinib, while the high-risk group was more sensitive to MAPK pathway inhibitors.ConclusionThis study systematically revealed the spatial heterogeneity of histone modifications in OS and their clinical significance for the first time, proposing an "epigenetic-immune" regulatory network hypothesis and developing a histone modification-based prognostic model. Our proposed "epigenetic-guided personalized medication strategy" provides new insights for precision treatment of OS, potentially significantly improving patient prognosis.

Colorectal cancer (CRC) remains a formidable global health challenge, with the majority of patients exhibiting microsatellite stable (MSS) and proficient mismatch repair (pMMR) tumors that are largely unresponsive to immune checkpoint inhibitors (ICIs). The management of MSS/pMMR CRC remains a clinical challenge due to the intrinsic resistance to ICIs. The innovative strategy of combining cetuximab, an EGFR-targeting monoclonal antibody with immunomodulatory properties, offers a promising strategy to enhance the immunotherapeutic response in MSS/pMMR CRC. This combination therapy is rooted in the complementary therapeutic mechanisms of cetuximab and ICIs, which may synergistically improve overall response rates and durability of response. Although some preclinical and clinical data have suggested additional promising results, there are still some challenges and questions that need to be addressed. Further large-scale, randomized, phase III clinical trials are required to confirm the efficacy and safety of this combination therapy. The ongoing clinical trials evaluating the safety and efficacy of cetuximab-ICI combinations are eagerly anticipated to pave the way for a new era in personalized immunotherapy for MSS/pMMR CRC.