Qinghe Han

With use of MRI reports of orbital or head-and-neck tumors, Generative Pretrained Transformer-5-Thinking showed diagnostic accuracy similar to that of subspecialists, and its use was associated with higher generalist diagnostic accuracy.

Background: Non-invasive preoperative prediction of lymphovascular space invasion (LVSI) in cervical cancer (CC) is clinically important for guiding surgical planning and adjuvant therapy, while avoiding the risks associated with invasive procedures. However, current studies using amide proton transfer-weighted (APTw) MRI for LVSI prediction typically analyze only the mean values from a limited number of intratumoral regions of interest (ROIs), which fails to fully capture tumor heterogeneity. This study investigates the added value of whole-tumor APTw habitat radiomics in predicting LVSI and its advantages over conventional analysis methods.Methods: This prospective study included consecutive adult patients with suspected CC who underwent APTw MRI between December 2022 and December 2024; a portion of the cohort has been reported previously. APTw values were extracted using two methods: (1) the conventional approach, calculating the mean signal from three ROIs on a representative slice; and (2) habitat radiomics, involving whole-tumor segmentation, k-means clustering to identify functional subregions, and radiomic feature extraction. Pathological assessment of LVSI from hysterectomy specimens served as the reference standard. Multivariable logistic regression identified variables associated with LVSI and developed diagnostic models. Model robustness was evaluated by 5-fold cross-validation, with AUC and DeLong’s test used for performance assessment.Results: Among 124 patients (74 LVSI−, 50 LVSI+), the APTw_h3 model achieved a higher AUC (0.796 [95% CI: 0.709–0.882]) for predicting LVSI positivity than the clinical-radiological model (AUC = 0.733, 95% CI: 0.638–0.817). The combined model integrating clinical, radiological, and APTw_h3 features achieved the highest AUC (0.903, 95% CI: 0.841–0.952), which was significantly higher than those of both the clinical-radiological and APTw_h3 models (both P < 0.001). Moreover, the addition of APTw_h3 to the clinical-radiological model improved sensitivity (88% vs. 82%) and specificity (83.8% vs. 64.9%) for determining LVSI positivity.Conclusion: Whole-tumor APTw habitat radiomics demonstrates superior performance over conventional mean-value APTw analysis for preoperative prediction of LVSI in CC. Notably, integrating habitat radiomic features with clinical and radiological parameters further improves predictive accuracy, demonstrating potential for enhanced individualized patient management.

To develop and validate a diagnostic framework integrating intralesional (ILN) and perilesional (PLN) radiomics derived from multiparametric MRI (mpMRI) for distinguishing IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma. This multicenter retrospective study analyzed 214 histopathologically confirmed cases (68 IgG4-ROD, 146 MALT lymphoma) from two institutions (2019–2024). A LASSO-SVM classifier was optimized through comparative evaluation of seven machine learning models, incorporating fused radiomic features (1,197 features) from ILN/PLN regions. Diagnostic performance was benchmarked against two subspecialty radiologists (10–20 years’ experience) using receiver operating characteristics - area under the curve (AUC), precision-recall AUC (PR-AUC), and decision curve analysis (DCA), adhering to CLEAR/METRICS guidelines. The fusion model (FR_RAD) achieved state-of-the-art performance, with an AUC of 0.927 (95% CI 0.902–0.958) and a PR-AUC of 0.901 (95% CI 0.862–0.940) in the training set, and an AUC of 0.907 (95% CI 0.857–0.965) and a PR-AUC of 0.872 (95% CI 0.820–0.924) on external testing. In contrast, subspecialty radiologists achieved lower AUCs of 0.671–0.740 (95% CI 0.630–0.780) and PR-AUCs of 0.553–0.632 (95% CI 0.521–0.664) (all p < 0.001). FR_RAD also outperformed radiologists in accuracy (88.6% vs. 66.2% and 71.3%; p < 0.01). DCA demonstrated a net benefit of 0.18 at a high-risk threshold of 30%, equivalent to avoiding 18 unnecessary biopsies per 100 cases. The fusion model integrating multi-regional radiomics from mpMRI achieves precise differentiation between IgG4-ROD and orbital MALT lymphoma, outperforming subspecialty radiologists. This approach highlights the transformative potential of spatial radiomics analysis in resolving diagnostic uncertainties and reducing reliance on invasive procedures for orbital lesion characterization.

The role of amino acids (AAs) with bone health is still controversial. We examined the association between AAs and osteoporosis in a cross-sectional study of 135 participants aged 45 years or older from the Second Hospital of Jilin University. Plasma AAs were measured with targeted quantitative methodology. We measured bone mineral density (BMD) with dual energy x-ray absorptiometry, and osteoporosis was defined as a T-score ≤ -2.5. We estimated odds ratios (OR) and corresponding 95% confidence intervals (CIs) for the associations between AAs (per 1 standard deviation increase) with osteoporosis. Approximately 18.5% of participants (n = 25) had osteoporosis. Total (adjusted β = 0.052; P = 0.002) and non-essential AA (adjusted β = 0.064; P = 0.002) levels were associated with femoral neck BMD T-scores. Greater levels of total (adjusted OR: 0.734; 95% CI: 0.655–0.821), essential (adjusted OR: 0.763; 95% CI: 0.623–0.934) and non-essential AAs (adjusted OR: 0.721; 95% CI: 0.629–0.826) were associated with lower odds of osteoporosis. Higher tryptophan (adjusted OR: 0.498; 95% CI: 0.281–0.882), cysteine (adjusted OR: 0.561; 95% CI: 0.321–0.983), glycine (adjusted OR: 0.513; 95% CI: 0.285–0.922), and ornithine levels (adjusted OR: 0.581; 95% CI: 0.345–0.978) were associated with reduced osteoporosis risk. Higher AA levels were associated with higher femoral neck BMD, and lower odds of osteoporosis.

Background: Parametrial infiltration (PMI) is an important indicator for staging and treatment of cervical cancer (CC). The potential of amide proton transfer-weighted (APTw) parameters of peritumor tissue in predicting PMI is still uncertain. This study aims to explore whether the APTw parameters of peritumor tissue can improve diagnostic value of diffusion-weighted imaging (DWI) in magnetic resonance imaging (MRI). Methods: Eighty-one patients with pathologic analysis-confirmed CC were enrolled in this retrospective study. All patients underwent APTw MRI and DWI. The APTw values of tumor (APTw-t), APTw values in peritumor tissues (APTw-p) and apparent diffusion coefficient (ADC) values were independently reviewed by two radiologists to map the regions of interest and measure the corresponding values. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of these quantitative parameters. Results: The study patients were divided into the PMI group (n=22) and non-PMI group (n=59). The APTw-t and APTw-p values (%) of PMI group were higher than those of the non-PMI group [3.71 (interquartile range, IQR, 3.60–3.98) and 2.75 (IQR, 2.68–2.77) vs. 3.33 (IQR, 3.24–3.60) and 1.98 (IQR, 1.82–2.36); P−3vs. 0.95 (IQR, 0.88–1.04)×10⁻³ mm²/sec; PConclusions: APTw in peritumor tissues, combined with ADC value can be used to efficiently distinguish PMI of CC.

Liver cancer has high incidence and mortality rates and its treatment generally requires the use of a combination treatment strategy. Therefore, the early detection and diagnosis of liver cancer is crucial to achieving the best treatment effect. In addition, it is imperative to explore multimodal combination therapy for liver cancer treatment and the synergistic effect of two liver cancer treatment drugs while preventing drug resistance and drug side effects to maximize the achievable therapeutic effect. Gold nanoparticles are used widely in applications related to optical imaging, CT imaging, MRI imaging, biomarkers, targeted drug therapy, etc., and serve as an advanced platform for integrated application in the nano-diagnosis and treatment of diseases. Dual-drug-delivery nano-diagnostic and therapeutic agents have drawn great interest in current times. Therefore, the present report aims to review the effectiveness of dual-drug-delivery nano-diagnostic and therapeutic agents in the field of anti-tumor therapy from the particular perspective of liver cancer diagnosis and treatment.

Background and purpose: Inverted papilloma (IP) and nasal polyp (NP), as two benign lesions, are difficult to distinguish on MRI imaging and clinically, especially in predicting whether the olfactory nerve is damaged, which is an important aspect of treatment and prognosis. We plan to establish a new biomarker to distinguish IP and NP that may invade the olfactory nerve, and to analyze its diagnostic efficacy.Materials and methods: A total of 74 cases of IP and 55 cases of NP were collected. A total of 80% of 129 patients were used as the training set (59 IP and 44 NP); the remaining were used as the testing set. As a multimodal study (two MRI sequences and clinical indicators), preoperative MR images including T2-weighted magnetic resonance imaging (T2-WI) and contrast-enhanced T1-weighted magnetic resonance imaging (CE-T1WI) were collected. Radiomic features were extracted from MR images. Then, the least absolute shrinkage and selection operator (LASSO) regression method was used to decrease the high degree of redundancy and irrelevance. Subsequently, the radiomics model is constructed by the rad scoring formula. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model have been calculated. Finally, the decision curve analysis (DCA) is used to evaluate the clinical practicability of the model.Results: There were significant differences in age, nasal bleeding, and hyposmia between the two lesions (p < 0.05). In total, 1,906 radiomic features were extracted from T2-WI and CE-T1WI images. After feature selection, using 12 key features to bulid model. AUC, sensitivity, specificity, and accuracy on the testing cohort of the optimal model were, respectively, 0.9121, 0.828, 0.9091, and 0.899. AUC on the testing cohort of the optimal model was 0.9121; in addition, sensitivity, specificity, and accuracy were, respectively, 0.828, 0.9091, and 0.899.Conclusion: A new biomarker combining multimodal MRI radiomics and clinical indicators can effectively distinguish between IP and NP that may invade the olfactory nerve, which can provide a valuable decision basis for individualized treatment.

Among orbital lymphoproliferative disorders, about 55% of diagnosed cancerous tumors are orbital lymphomas, and nearly 50% of benign cases are immunoglobulin G4‐related ophthalmic disease (IgG4‐ROD). However, due to nonspecific characteristics, the differentiation of the two diseases is challenging. In this study, conventional magnetic resonance imaging‐based radiomics approaches were explored for clinical recognition of orbital lymphomas and IgG4‐ROD. We investigated the value of radiomics features of axial T1‐ (T1WI‐) and T2‐weighted (T2WI), contrast‐enhanced T1WI in axial (CE‐T1WI) and coronal (CE‐T1WI‐cor) planes, and 78 patients (orbital lymphoma, 36; IgG4‐ROD, 42) were retrospectively reviewed. The mass lesions were manually annotated and represented with 99 features. The performance of elastic net‐based radiomics models using single or multiple modalities with or without feature selection was compared. The demographic features showed orbital lymphoma patients were significantly older than IgG4‐ROD patients (p < 0.01), and most of the patients were male (72% in the orbital lymphoma group vs. 23% in the IgG4‐ROD group; p = 0.03). The MR imaging findings revealed orbital lymphomas were mostly unilateral (81%, p = 0.02) and wrapped eyeballs or optic nerves frequently (78%, p = 0.02). In addition, orbital lymphomas showed isointense in T1WI (100%, p < 0.01), and IgG4‐ROD was isointense (60%, p < 0.01) or hyperintense (40%, p < 0.01) in T1WI with well‐defined shape (64%, p < 0.01). The experimental comparison indicated that using CE‐T1WI radiomics features achieved superior results, and the features in combination with CE‐T1WI‐cor features and the feature preselection method could further improve the classification performance. In conclusion, this study comparatively analyzed orbital lymphoma and IgG4‐ROD from demographic features, MR imaging findings, and radiomics features. It might deepen our understanding and benefit disease management.