This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC). In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined. The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as evidenced by higher AUC values in the training set (0.820 versus 0.810 and 0.700, p = 0.049 and p = 0.031, respectively) and in the test set (0.805 versus 0.771 and 0.732, p = 0.041 and p = 0.025, respectively). Finally, the hybrid model consistently demonstrated robust predictive capabilities in identifying presence of MP/S components, achieving AUC of 0.830 in the training set and 0.849 in the test set (all p < 0.05). The ITH derived from sub-region within the tumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC.

Critical-sized bone defects represent a significant clinical challenge due to their inability to undergo spontaneous regeneration, necessitating graft interventions for effective treatment. The development of tissue-engineered scaffolds and regenerative medicine has made bone tissue engineering a highly viable treatment for bone defects. The physical and biological properties of nanocomposite biomaterials, which have optimized structures and the ability to simulate the regenerative microenvironment of bone, are promising for application in the field of tissue engineering. These biomaterials offer distinct advantages over traditional materials by facilitating cellular adhesion and proliferation, maintaining excellent osteoconductivity and biocompatibility, enabling precise control of degradation rates, and enhancing mechanical properties. Importantly, they can simulate the natural structure of bone tissue, including the specific microenvironment, which is crucial for promoting the repair and regeneration of bone defects. This manuscript provides a comprehensive review of the recent research developments and applications of structure-optimized and microenvironment-inspired nanocomposite biomaterials in bone tissue engineering. This review focuses on the properties and advantages these materials offer for bone repair and tissue regeneration, summarizing the latest progress in the application of nanocomposite biomaterials for bone tissue engineering and highlighting the challenges and future perspectives in the field. Through this analysis, the paper aims to underscore the promising potential of nanocomposite biomaterials in bone tissue engineering, contributing to the informed design and strategic planning of next-generation biomaterials for regenerative medicine.

The novel grading system developed by the International Association for the Study of Lung Cancer (IASLC) for clinical stage IA lung adenocarcinomas has demonstrated remarkable prognostic capabilities. Notably, tumors classified as grade 3 have been associated with poor prognostic outcomes, thereby playing a crucial role in the formulation of personalized surgical strategies. The objective of this study is to develop a radiomics nomogram that utilizes the optimal volume of interest (VOI) derived from high-resolution CT (HRCT) scans to accurately predict the presence of grade 3 tumors in patients with clinical IA lung adenocarcinomas. In this multi-center, large-population study, clinical, pathological, and HRCT imaging data from 1418 patients who were pathologically diagnosed with lung adenocarcinomas were retrospectively collected. The data was obtained from four hospital databases between January 2018 and May 2022. From this patient cohort, 1206 individuals were screened from three databases and randomly divided into training and internal validation datasets in a 7:3 ratio. An additional dataset consisting of 212 individuals was used for external validation dataset. Radiomics features were extracted from HRCT images at various scales, including VOI^−2mm, VOI ^entire, VOI ^+2mm, and VOI ^+4mm. To reduce dimensionality, select relevant features, and build radiomics signatures, the maximal redundancy minimal relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were utilized. Univariate and multivariate logistic regression analyses were conducted to identify independent clinic-radiological (Clin-Rad) predictors. Receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) were used to evaluate the diagnostic efficiency. A nomogram predicting the risk of grade 3 in clinical stage IA lung adenocarcinoma was constructed based on multivariate logistic regression, combining independent predictors and the optimal radiomics signatures. Multivariate logistic regression revealed that males exhibited a higher prevalence of grade 3 tumors, and solid nodules were frequently observed through radiological assessments. The utilization of radiomics features extracted from the VOI ^entire resulted in significant improvements in predictive performance, as evidenced by AUC values of 0.900 (0.880-0.942), 0.885 (0.824-0.946), and 0.888 (0.782-0.993) for the training, internal validation, and external validation datasets, respectively. Furthermore, the nomogram that combined VOI ^entire -based radiomics signatures and Clin-Rad characteristics, exhibited remarkable predictive performance. This was indicated by AUC values of 0.910(0.873-0.942), 0.891 (0.845-0.937), and 0.905 (0.846-0.964) for the training, internal validation, and external validation datasets, respectively. The extraction of radiomics features from both the indented and peri-tumoral regions does not offer any additional benefits in predicting grade 3 tumors according to the IASLC system. However, when combining the VOI ^entire-based radiomics model with Clin-Rad characteristics, the resulting integrated nomogram exhibited remarkable predictive performance.

Background: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. Purpose: To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). Material and Methods: In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland–Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. Results: The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 ( P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group ( P < 0.001). Conclusion: Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

Background: Research on the sequencing of brain radiotherapy and targeted chemotherapy after brain metastasis (BM) in HER2-positive breast cancer patients is limited and inconclusive. This study investigated the efficacy of sequential delivery of radiotherapy and targeted therapy in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer with BM. Methods: Fifty-seven patients were categorized into two groups: the targeted-radiotherapy group (receiving 2– 8 cycles of anti-HER2-targeted therapy followed by radiotherapy after BM) and the radiotherapy-targeted group (undergoing radiotherapy first, followed by regular anti-HER2-targeted therapy). The study endpoints were intracranial progression-free survival (iPFS) and overall survival. Factors associated with intracranial progression and mortality were assessed by univariate and multivariate Cox proportional hazards analysis. Results: Patients in the radiotherapy-targeted group had better iPFS (P < 0.001), while there was no significant difference in overall survival between the two groups (P = 0.145). Multivariate Cox analysis showed that different sequential treatment groups were independent prognostic factors for iPFS. In patients with a modified breast graded prognostic assessment score of 3.5– 4.0, the median survival time was 26 months in the radiotherapy-targeted group and 22 months in the targeted-radiotherapy group (P = 0.019). Conclusion: Overall, radiotherapy followed by targeted therapy may improve survival in HER2-positive breast cancer patients with BM, particularly in those with a modified breast graded prognostic assessment score of 3.5– 4.0.

Background: Claustrophobia is a psychological disease. It is estimated to occur in 2.1–14.3% of all magnetic resonance imaging (MRI) examinations. Mindfulness decompression is an effective means to treat and reduce fear and anxiety. There is a rare report on the application of mindfulness-based stress reduction therapy in the magnetic resonance examinations of patients with claustrophobia to date.Purpose: The purpose of this study is to explore the intervention effect of mindfulness respiratory decompression therapy on the MRI inspection of patients with claustrophobia.Methods: A total of 86 patients with claustrophobia requiring MRI in our hospital from January 2018 to December 2020 were divided into two groups. The control group was given routine psychological nursing, and the observation group was given a mindfulness breathing technique on the basis of the control group. Before and after the intervention, we compared the intervention effect, satisfaction with nurses’ psychological intervention technique, severe autonomic nervous symptoms during the examination, self-rating anxiety scale (SAS) scores, and profile of mood states revised (POMS-R) scores.Results: The total effective rate of intervention in the observation group was 90.90%, which was significantly higher (χ² = 6.857, p = 0.00004) than that in the control group (26.19%). Severe autonomic nervous symptoms in the observation group were significantly lower than those in the control group (p < 0.05). After the intervention, SAS scores and POMS-R scores in the observation group decreased with statistical significance (p < 0.05).Conclusion: Mindfulness respiratory decompression therapy can effectively help claustrophobic patients complete an MRI examination, which may be worthy of wide promotion and application in the clinic.