Combined Diagnostic Accuracy of Diffusion and Perfusion MR Imaging to Differentiate Radiation-Induced Necrosis from Recurrence in Glioblastoma

We aimed to use quantitative values derived from perfusion and diffusion-weighted MR imaging (PWI and DWI) to differentiate radiation-induced necrosis (RIN) from tumor recurrence in Glioblastoma (GBM) and investigate the best parameters for improved diagnostic accuracy and clinical decision-making. Methods: A retrospective analysis of follow-up MRI with new enhancing observations was performed in histopathologically confirmed subjects of post-treated GBM, who underwent re-surgical exploration. Quantitative estimation of rCBV (relative cerebral blood volume) from PWI and three methods of apparent diffusion coefficient (ADC) estimation were performed, namely ADC R1 (whole cross-sectional area of tumor), ADC R2 (only solid enhancing lesion), and ADC R3 (central necrosis). ROC curve and logistic regression analysis was completed. A confusion matrix table created using Excel provided the best combination parameters to ameliorate false-positive and false-negative results. Results: Forty-four subjects with a mean age of 46 years (range, 19–70 years) underwent re-surgical exploration with RIN in 28 (67%) and recurrent tumor in 16 (33%) on histopathology. rCBV threshold of >3.4 had the best diagnostic accuracy (AUC = 0.93, 81% sensitivity and 89% specificity). A multiple logistic regression model showed significant contributions from rCBV (p p = 0.001). After analysis of confusion matrix ADC R3 > 2032 × 10−6 mm2 achieved 100% specificity with gain in sensitivity (94% vs. 56%). Conclusions: A combination of parameters had better diagnostic performance, and a stepwise combination of rCBV and ADC R3 obviated unnecessary biopsies in 10% (3/28), leading to improved clinical decision-making

Pathological Basis of Imaging in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer with major risk factors being chronic liver disease (CLD) including chronic liver inflammation, steatohepatitis and certain viral infections (Hepatitis B and C). Due to the poor prognosis, early detection is key for effective management. Imaging of HCC has developed over the years with specificity as high as 95%. The Liver Imaging Reporting and Data System (LI-RADS) provides a standardized reporting format that can be followed by radiologists and clinicians alike. This article focuses on the pathological basis of imaging observations described in the LI-RADS lexicon. A clear understanding of the pathological basis of imaging will help the radiologist to be more confident to resolve unequivocal observations apart from achieving a high degree of specificity in the diagnosis of HCC