ABSTRACT
Imaging histology plays a key role in the diagnosis of tumors, prognostic assessment, and evaluation of tumor response to therapy. Multimodal magnetic resonance imaging (MRI) imaging histology can link the imaging histological presentation of a tumor to its molecular phenotype, offering greater advantages in the grading of gliomas and in the prediction and prognosis of treatment response. It utilizes conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions and can be used for the diagnosis of gliomas and the differentiation of gliomas from brain metastases. Semi-automated and automated tumor segmentation techniques have also been developed for the assessment of the recurrence of gliomas. In this review paper, the research progress in multimodal MRI imaging histology was reviewed, including the prediction of important molecular biological markers of glioma, graded diagnosis of glioma, differential diagnosis with brain metastases, and assessment of postoperative recurrence.
ABSTRACT
BACKGROUND: Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis of osteoporosis. As a new method of image analysis, radiomics has potential clinical significance in the diagnosis of osteoporosis. OBJECTIVE: To explore the diagnostic value of osteoporosis based on lumbar MRI imaging model. METHODS: Fifty female patients who underwent both lumbar-spine MRI and dual-energy X-ray absorptiometry in Outpatient Department of First Hospital of Hebei Medical University from February 2017 to October 2018 at the age of 40-84 years were enrolled in this study. Dual-energy X-ray absorptiometry showed 28 osteoporosis patients, and 22 normal bone mineral density. In the sagittal plane T1WI and T2WI images of lumbar MRI, five consecutive layers in the middle of L2-4 vertebral body were selected for image segmentation; radiomics features were extracted; diagnostic models were constructed. Combined with clinical risk factors, the clinical-radiomics model was constructed. The area under the receiver operating characteristic curve was used to assess the model performance. The experiment was approved by the Ethics Committee of First Hospital of Hebei Medical University. RESULTS AND CONCLUSION: (1) The 396 × 3 features were extracted from T1WI, T2WI and combined sequences respectively in 50 subjects. After feature dimensionality reduction, four features of T1WI sequence, six features of T2WI sequence and four features of T1WI and T2WI combined sequence were screened out respectively. After diagnostic model establishment, the areas under the receiver operating characteristic curve of T1WI and T2WI models were 0. 810 and 0. 820, respectively. The area under the receiver operating characteristic curve of combined T1WI+T2WI model was 0. 937, which was higher than that of a independent sequence. (2) Combining the T1WI + T 2WI radiomics features with clinical data, the diagnostic model suitable for female combined with clinical factors was constructed, and the area under the receiver operating characteristic curve of clinical-radiomics model was 0. 960. (3) The results showed that the radiomics features based on routine lumbar MRI can distinguish osteoporosis from normal bone mass, and the clinical-radiomics diagnosis model based on clinical and radiomics features can improve the diagnostic efficiency, which is valuable for diagnosis of osteoporosis.