Technology in Cancer Research & Treatment最新文献

IntroductionAccurate preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grading is crucial for developing individualized management and surgical strategies in lung adenocarcinomas (LUAD). Computed tomography (CT) radiomics serves as an important imaging biomarker for classification tasks in LUAD. However, the robustness and accuracy of radiomics models remain subjects of ongoing debate.MethodsIn this study, we conducted an analytical comparison of two critical steps in radiomics: dimensionality reduction and feature selection, aiming to differentiate between Grade 1 and Grade 2-3 tumors according to the preoperative IASLC grading system for LUAD. 1) For dimensionality reduction, we sequentially combined the T-test, Pearson correlation, and Least Absolute Shrinkage and Selection Operator (LASSO), while considering principal component analysis (PCA) for comparison. 2) For feature selection, we utilized various machine learning (ML) techniques including Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GBC), XGBoost, Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CatBoost). The diagnostic efficacy was evaluated using receiver operating characteristic curve (ROC) and the corresponding area under the curve (AUC).Result: The AUC for PCA combined with various ML feature selection methods ranged from 0.502 to 0.719 in this classification task. In contrast, the AUC for the combined T-test, Pearson, and LASSO dimensionality reduction methods, along with various ML feature selection methods, significantly increased from 0.818 to 0.869. Among these, the LGBM achieved the highest performance, reaching an AUC of 0.869, while LR displayed the lowest performance with an AUC of 0.818.ConclusionWe demonstrated that the T-test→Pearson→LASSO approach is more appropriate for radiomics feature dimensionality reduction compared to PCA. Additionally, we improved the commonly used LR feature selection method in medical research by employing the more advanced LGBM for distinguishing between Grade 1 and Grade 2-3 tumors in accordance with the preoperative IASLC grading system for LUAD.

IntroductionAxillary nodal burden reflects the biological aggressiveness and prognostic behavior of breast cancer. This study aimed to develop a subregional habitat radiomics model based on multiparametric magnetic resonance imaging (MRI) and to evaluate its performance in predicting high axillary nodal burden in patients with breast cancer.MethodsIn this retrospective study, a total of 221 patients who underwent axillary lymph node dissection were categorized as having limited (0-2 metastatic nodes) or high (≥3 metastatic nodes) nodal burden based on pathological findings. Morphological MRI features were visually evaluated by experienced radiologists. A clinical model was established using univariate and multivariate logistic regression analyses. Conventional radiomics (C-radiomics) and habitat radiomics features were extracted from the whole tumor and its subregions, respectively, based on multiparametric MRI. The clinical, C-radiomics, and habitat radiomics models were then integrated into a comprehensive nomogram for quantitative prediction of axillary nodal burden.ResultsIn predicting axillary nodal burden, the habitat radiomics model outperformed both the C-radiomics and clinical models, achieving areas under the curve (AUCs) of 0.791 (0.712-0.870) and 0.798 (0.686-0.911) in the training and validation cohorts, respectively. The C-radiomics model achieved AUCs of 0.733 (0.631-0.836) and 0.738 (0.612-0.865), while the clinical model achieved AUCs of 0.753 (0.663-0.843) and 0.733 (0.596-0.870). The combined nomogram demonstrated the highest diagnostic performance, with AUCs of 0.895 (0.839-0.951) and 0.885 (0.802-0.969) in the training and validation cohorts, respectively.ConclusionsThe integrated nomogram combining clinical, C-radiomics, and habitat radiomics models demonstrated strong predictive efficacy for preoperative assessment of axillary nodal burden in breast cancer. Future multicenter prospective studies are warranted to validate these results and refine the model's clinical applicability.

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 (p = .161). Groups A and B required an average of 9.04 ± 2.58 and 8.14 ± 1.72 intraoperative CT scans, respectively (p < .001). Mean procedural durations for Groups A and B were 12.77 ± 4.51 and 10.83 ± 2.51 min, respectively (p < .001). In Groups A and B, the immediate need for closed thoracic drainage was 4.6% (7/153) and 1.9% (3/154), respectively (p = .165), and incidence of haemoptysis was 10.5% (16/153) and 9.7% (15/154), respectively (p = .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.

IntroductionThis study aims to investigate the effectiveness and safety of combining the antibody-drug conjugate (ADC) disitamab vedotin (RC48) with the established PRaG regimen (PD-1 inhibitor, Radiotherapy, and GM-CSF) for treating HER2-expressing (IHC 3+, 2+, or 1+) advanced solid tumors. This novel PRaG3.0 regimen is hypothesized to leverage the tumor radiosensitizing and immunogenic cell death properties of ADCs to amplify the synergistic antitumor effects of radioimmunotherapy, offering a potential paradigm for pan-cancer therapy.MethodsThis study is a prospective, single-arm, open-label, multi-center clinical trial. Patients enrolled have confirmed HER2-expressing solid tumors (IHC 3+, 2+, or 1+) that have progressed after standard treatment or were intolerant to it. Patients received RC48 (2 mg/kg) via intravenous injection on day 1, followed by subcutaneous GM-CSF at 200 µg from days 3 to 7 and interleukin-2 (IL-2) at 2 million IU from days 8 to 12. Radiotherapy was initiated on day 3, targeting one lesion with hypofractionated radiotherapy (2-3 fractions of 5 or 8 Gy). PD-1/PD-L1 antibodies were administered within one week after completing radiotherapy. Treatment was repeated every three weeks, and if there were no target lesions, radiotherapy could be discontinued, with RC48 given for at least six cycles. After achieving a complete tumor response, maintenance therapy with PD-1/PD-L1 antibodies continued until disease progression or intolerable toxicity occurred. The primary endpoint was the objective response rate (ORR).ResultsThe study is currently in the recruitment phase (Registration No: NCT05115500). The primary endpoint is the objective response rate (ORR). Secondary endpoints include progression-free survival (PFS), overall survival (OS), disease control rate (DCR), safety, and QoL. Planned enrollment is 62 patients.ConclusionThe PRaG3.0 protocol represents an innovative approach combining ADC therapy with radioimmunotherapy to address HER2-expressing cancers, including those with HER2-low expression. If successful, this regimen could establish a highly effective combination strategy.

ObjectiveThis study aimed to assess renal cell carcinoma (RCC) complexity using the R.E.N.A.L. nephrometry score and evaluate the feasibility and safety of the non-clamping sutureless technique combined with a radiofrequency ultrasound scalpel (RFUS) for low-complexity RCC.MethodsThis retrospective, multi-center cohort study categorized patients into three groups: Group I (suture laparoscopic partial nephrectomy [LPN] with clamping renal artery), Group II (sutureless LPN without renal artery clamping, combined with monopolar electrocoagulation), and Group Ⅲ (sutureless laparoscopic nephron-sparing surgery [LNSS] without renal artery clamping combined with RFUS). Key outcomes included operative time, intraoperative blood loss, postoperative hospital stays, serum creatinine (Scr) levels, and the estimated glomerular filtration rate (eGFR) of the affected kidney. Post hoc power analysis was used to evaluate eGFR variation across groups, and a multiple linear regression model was employed to analyze factors influencing postoperative eGFR alterations.ResultsThe study included 60 patients with exophytic and low-complexity RCC. Blood loss in Group III was comparable to that in Group I but less than that in Group II (P = 0.035). Operative time and postoperative hospital stays were significantly shorter in Group III than in the other groups (P < 0.01). A statistically significant increase in Scr levels was observed during the early postoperative period, continuing through the 6-month follow-up assessment compared with preoperative levels (P < 0.01). The 6-month postoperative eGFR of the affected kidney in Group III was significantly higher than that in the other groups (P < 0.01). The decrease in preoperative and 6-month postoperative eGFR values was greatest in Group I (P < 0.01), followed closely by Group II (P < 0.01), while no significant reduction was observed in Group III. The post hoc power analysis revealed high detection power (0.912 or 1.0). Warm ischemia time emerged as the sole significant eGFR predictor (R² = 0.814, P < 0.05).ConclusionThe non-clamping sutureless technique combined with RFUS for RCC effectively alleviates renal ischemia-reperfusion injury, offering superior renal function protection while achieving comparable oncological outcomes.

IntroductionNiraparib and bevacizumab are two principal maintenance therapies for newly diagnosed advanced ovarian cancer (AOC) patients with BRCA wild-type (BRCAwt) status, regardless of homologous recombination deficiency (HRD). In China, however, a considerable proportion of BRCAwt patients have unknown or untested HRD status, complicating treatment selection.MethodsTo evaluate and compare the efficacy of niraparib and bevacizumab as maintenance therapy for BRCAwt AOC, we conducted a retrospective cohort study using real-world clinical data. Descriptive statistics were used to summarize clinical and demographic characteristics. Progression-free survival (PFS) was estimated using Kaplan-Meier analysis and compared using a stratified Cox proportional hazards model. A multivariable Cox regression was performed to adjust for potential confounding variables. Exploratory subgroup analyses were conducted, and propensity score matching (PSM) was applied as a sensitivity analysis.ResultsA total of 94 patients were included, with 51 receiving niraparib and 43 receiving bevacizumab. The median PFS was not reached in the niraparib group versus 13.77 months (95% CI, 4.12-23.41) in the bevacizumab group (HR = 0.240, 95% CI, 0.128-0.451; P < .001). After covariate adjustment, the median PFS was 19.55 months (95% CI, 9.40-NA) with niraparib and 8.64 months (95% CI, 4.53-NA) with bevacizumab, with an adjusted HR of 0.282 (95% CI, 0.136-0.587; P = .001). In the PSM sensitivity analysis, the median PFS was not reached (95% CI, 19.55-NR) in the niraparib group and was 18.33 months (95% CI, 8.90-25.26) in the bevacizumab group (HR = 0.360, 95% CI, 0.176-0.736; P = .005).ConclusionThis analysis suggests that niraparib may provide a progression-free survival advantage compared with bevacizumab in BRCAwt AOC patients, with both regimens appearing to be generally well tolerated in the real-world setting. These findings offer preliminary reference value for maintenance treatment selection in patients with newly diagnosed BRCAwt AOC.

The Day 14 bone marrow biopsy (D14 BMB) has historically been a key tool for early treatment response assessment in acute myeloid leukemia (AML), particularly following intensive chemotherapy. Traditionally, a blast count of <5% at D14 is associated with higher complete remission (CR) rates and improved overall survival (OS), while persistent disease often prompts re-induction therapy. However, emerging evidence suggests that while re-induction may increase CR rates, it does not consistently improve OS and is associated with significant morbidity. With the emergence of venetoclax-based and other targeted therapies, the traditional role of D14 BMB is being reconsidered, as these agents exhibit slower response kinetics, making later assessments and measurable residual disease (MRD) monitoring more reliable for treatment adaptation. This review critically examines the prognostic utility of D14 BMB in AML, its relevance across different treatment modalities, and its correlation with long-term outcomes. By synthesizing current evidence, we explore whether D14 BMB remains a valuable clinical tool or if a paradigm shift toward later assessments and MRD-guided decision-making is warranted in modern AML therapy.

IntroductionIntegrating deep learning within the Internet of Medical Things (IoMT) has revolutionized automated lesion detection in medical imaging. Yet, maintaining high diagnostic accuracy, interpretability and computational efficiency on resource-limited edge devices remains challenging. To address these gaps, we propose NeuroMorphFusion, a neuro-inspired hybrid framework that combines biologically plausible learning with mathematical modelling for interpretable and efficient lesion detection.MethodsNeuroMorphFusion integrates a lightweight ResNet18 backbone, a Spiking Neural Network (SNN) component to capture temporal dynamics, and a morphological attention mechanism that emphasizes structure-relevant regions in CT scans. The architecture employs a semi-supervised reinforcement learning strategy, where pseudo-label accuracy and the overlap between Grad-CAM visualizations and expert annotations define the reward, ensuring explainable updates under limited labelled data. Additionally, a genetic algorithm (GA) optimizes hyperparameters-learning rate, dropout rate, spiking time steps, and attention dimensionality - for domain generalization and reduced memory use. The optimization population is restricted to 20 individuals over 30 generations, converging within eight minutes on a Jetson Nano.ResultsA multi-objective optimization scheme balances lesion detection sensitivity, computational latency and explainability. Integrated SHAP and Grad-CAM visualizations enhance interpretability. Experimental evaluation on the IQ-OTHNCCD lung cancer CT dataset demonstrates that NeuroMorphFusion achieves 98.18% classification accuracy, outperforming VGG16, SqueezeNet, MobileNetV3, and ResNet18 in both transparency and efficiency.ConclusionNeuroMorphFusion effectively unites neuro-biological inspiration, mathematical interpretability, and edge-efficient computation for IoMT-based medical imaging. Its superior accuracy, explainability, and low-latency optimization highlight its potential for real-world clinical integration and scalable IoMT deployment.

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.