Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.

OBJECTIVES: As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs. METHODS: A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS). RESULTS: Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS. CONCLUSIONS: A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. CLINICAL RELEVANCE STATEMENT: Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. KEY POINTS: • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.

A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis.

INTRODUCTION: The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications. MATERIALS AND METHODS: A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA). RESULTS: A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort. CONCLUSION: The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.

The correlation between multimodal radiomics and pathology about thermal ablation lesion of rabbit lung VX2 tumor.

Objective: To explore the correlation of CT-MRI pathology with lung tumor ablation lesions by comparing CT, MRI, and pathological performance of rabbit lung VX2 tumor after thermal ablation. Methods: Thermal ablation including microwave ablation (MWA) and radiofrequency ablation (RFA) was carried out in 12 experimental rabbits with lung VX2 tumors under CT guidance. CT and MRI performance was observed immediately after ablation, and then the rabbits were killed and pathologically examined. The maximum diameter of tumors on CT before ablation, the central hypointense area on T2-weighted image (T2WI) after ablation, and the central hyperintense area on T1-weighted image (T1WI) after ablation and pathological necrosis were measured. Simultaneously, the maximum diameter of ground-glass opacity (GGO) around the lesion on CT after ablation, the surrounding hyperintense area on T2WI after ablation, the surrounding isointense area on T1WI after ablation, and the pathological ablation area were measured, and then the results were compared and analyzed. Results: Ablation zones showed GGO surrounding the original lesion on CT, with a central hypointense and peripheral hyperintense zone on T2WI as well as a central hyperintense and peripheral isointense zone on T1WI. There was statistical significance in the comparison of the maximum diameter of the tumor before ablation with a central hyperintense zone on T1WI after ablation and pathological necrosis. There was also statistical significance in the comparison of the maximum diameter of GGO around the lesion on CT with the surrounding hyperintense zone on T2WI and isointense on T1WI after ablation and pathological ablation zone. There was only one residual tumor abutting the vessel in the RFA group. Conclusions: MRI manifestations of thermal ablation of VX2 tumors in rabbit lungs have certain characteristics with a strong pathological association. CT combined with MRI multimodal radiomics is expected to provide an effective new method for clinical evaluation of the immediate efficacy of thermal ablation of lung tumors.