SM journal of clinical and medical imaging最新文献

{"title":"A rare case of conus medullaris and cauda equina infiltration in acute lymphoblastic leukemia: MRI imaging features","authors":"","doi":"10.36879/jcmi.18.000103","DOIUrl":"https://doi.org/10.36879/jcmi.18.000103","url":null,"abstract":"","PeriodicalId":91401,"journal":{"name":"SM journal of clinical and medical imaging","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76326387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use-fullness of dynamic contrast-enhanced MR imaging and diffusion weighted MR imaging for differentiation of benign and malignant parotid tumors","authors":"S. Zheng, Zf Xu, Xh Wu, A. Pan","doi":"10.36879/jcmi.18.000101","DOIUrl":"https://doi.org/10.36879/jcmi.18.000101","url":null,"abstract":"Objectives: To evaluate the usefullness of dynamic contrast-enhanced MR Imaging (DCE-MRI) and diffusion weighted imaging (DWI) for differentiating benign from malignant\u0000parotid tumors.\u0000Methods: Prospectively,DCE-MRI and DWI were performed in 112 patients, with 148 confirmed parotid masses. The differential optimal thresholds were determined.\u0000Results: WConsidering tumors with time-intensity curve (TIC) Type C as malignant, sensitivity, specificity, accuracy were 95%, 76%, 79%, respectively. Considering ADC threshold\u0000values 0.709×10-3mm2\u0000/s<ADC<0.948×10-3mm2\u0000/s as malignant, sensitivity, specificity, accuracy were 75%, 78%, 78%, respectively. Considering TIC Type C and ADC values\u00000.709×10-3mm2\u0000/s<ADC<0.948×10-3mm2\u0000/s as malignant, sensitivity, specificity, accuracy were 75%, 91%, 89%, respectively. With threshold Kep<1.118 min-1 and Ve\u0000>0.315 between\u0000Warthin and malignant tumors, threshold Kep>0.555 min-1 and Ve\u0000<0.605 between pleomorphic adenomas and malignant tumors, sensitivity, specificity, accuracy for malignancy\u0000were 70% vs 90%, 96% vs 74%, 92% vs 80%, respectively.\u0000Conclusion: DCE-MRI and DWI provide more information in differentiating benign from malignant parotid tumors.","PeriodicalId":91401,"journal":{"name":"SM journal of clinical and medical imaging","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76340532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression.","authors":"Edward Florez,&nbsp;Ali Fatemi,&nbsp;Pier Paolo Claudio,&nbsp;Candace M Howard","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Radiomics is an emerging area within clinical radiology research. It seeks to take full advantage of all the information contained in multiple medical imaging modalities. With a radiomics approach, medical images are not limited to providing only a qualitative assessment but can also provide quantitative data by parameterizing image features. These parameters can be used to identify regions and volumes of interest and discriminate normal healthy tissue from abnormal or diseased tissue. Radiomics is an interlinked sequence of processes of vital importance that begins with the acquisition and selection of medical images that involve standardization of acquisition protocols and inter-equipment normalization. This is followed by the identification and segmentation of regions or volumes of interest by expert radiologists through the use of computational tools that offer speed while reducing variability and bias. The segmentation process is the most critical stage in radiomics. This sometimes requires the incorporation of a pre-processing stage consisting of advanced techniques (reconstruction processes, filtering, etc.). Thereafter, representative characteristics of the region or volume of interest are extracted by approaches based on statistics, morphological features, and transform-based variables. Next, a statistical selection of the parameters that provide a high association and correlation with the clinical condition of interest is performed. Finally, processes such as data integration, standardization, classification, and mining processes can be applied as needed for particular applications. Ongoing research in radiomics aims to reduce the time and costs involved in interpreting medical images while simultaneously increasing the quality of diagnoses and monitoring of as well as the selection of treatment strategies. The results of many studies combining radiomics with standard medical techniques are highly encouraging, and these new approaches are increasingly used. This review article details the components of radiomics and discusses its applications, challenges, and future directions for this exciting new field of study.</p>","PeriodicalId":91401,"journal":{"name":"SM journal of clinical and medical imaging","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186380/pdf/nihms-1007682.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39078945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}