Metastatic internal mammary lymph nodes or mimickers on parasternal ultrasonography: focusing on their distribution and depth.

Background Ultrasonography (US) is an important tool to evaluate the status of internal mammary lymph node (IMN). US features of metastatic IMNs could help determine when biopsy should be performed. Purpose To compare US features of metastatic IMNs to those of benign entities. Material and Methods In women with suspected IMN metastasis on US, their intercostal space (ICS) abnormalities were classified into metastatic IMN, benign IMN, and lymph node (LN) mimickers. US features, distribution, and depth of abnormalities in one ICS and involved ICS level were analyzed. Results Among 66 ICS abnormalities in 53 women, 46 were metastatic IMNs, ten were benign IMNs, and ten were LN mimickers. For metastatic IMNs, the second ICS was the most commonly involved ICS (n = 22), followed by the first (n = 13), the third (n = 8), and the fourth (n = 3). ICS level distribution of metastatic IMNs was not significantly ( P = 0.5407) different from that of non-metastatic lesions. Metastatic IMNs were predominantly seen in the posterior layer of ICS, significantly ( P < 0.0001) different from LN mimickers. Both metastatic IMNs and non-metastatic lesions were evenly distributed over the upper, middle, and lower part (in craniocaudal direction) of one ICS. Conclusion Metastatic IMNs are more likely to be found in the posterior layer of ICS. This can help distinguish them from LN mimickers. Any part (upper, middle, or lower) of one ICS in craniocaudal direction could be involved. The second ICS was the most commonly involved level.

Unenhanced CT and MRI Parameters That Can Be Used to Reliably Predict Fat-Invisible Angiomyolipoma.

OBJECTIVE: The objective of our study was to determine unenhanced CT and MRI parameters that can be used to reliably predict fat-invisible angiomyolipoma (AML). MATERIALS AND METHODS: A total of 152 patients with 155 histologically proven renal cell carcinomas (RCCs) and 16 patients with 18 histologically proven AMLs were included. No AML measured less than -20 HU on CT images. The tumor-to-cortex ratio was measured on unenhanced CT, T2-weighted MRI, and T1-weighted MRI to compare fat-invisible AML and RCC. On chemical-shift MRI, the signal intensity (SI) index and tumor-to-spleen ratio were calculated to compare these lesions. The unpaired t test and ROC with AUC were used for statistical analysis. The reference standard was pathologic examination. RESULTS: The mean tumor-to-cortex ratios on unenhanced CT of AML and RCC were 1.37 ± 0.66 (SD) and 0.83 ± 0.47, respectively (p < 0.001). However, the tumor-to-cortex ratio on unenhanced CT of AML was not different from that of chromophobe RCC (p = 0.093). The mean tumor-to-cortex ratios on T2-weighted MRI of AML and RCC were 0.75 ± 0.15 and 1.21 ± 0.41, respectively (p < 0.001). However, the tumor-to-cortex ratio on T2-weighted MRI of AML was not different from that of papillary RCC (p = 0.161). The tumor-to-spleen ratio on chemical-shift MRI, tumor-to-cortex ratio on T1-weighted MRI, and SI index on chemical-shift MRI of AML were not different from those of RCC (p = 0.104-0.670). The AUCs of the tumor-to-cortex ratio on T2-weighted MRI and the tumor-to-cortex ratio on unenhanced CT were 0.862 and 0.835, respectively. CONCLUSION: The tumor-to-cortex ratio on T2-weighted MRI and the tumor-to-cortex ratio on unenhanced CT are good parameters to differentiate fat-invisible AML from clear cell RCC. Nevertheless, percutaneous biopsy is necessary because these parameters are not reliable in differentiating fat-invisible AML from non-clear cell RCC.

Improvement in imaging diagnosis technique and modalities for solitary pulmonary nodules: from ground-glass opacity nodules to part-solid and solid nodules.

With advances in CT technology and the popularity of low-dose CT as a device for lung cancer screening, the detection rate of sub-solid pulmonary nodules as well as solid nodules has been increased. Distinguishing solid from sub-solid features is an essential step in the CT evaluation of solitary pulmonary nodules (SPNs) because strategies for nodule characterization and guidelines for management are different for each category. In addition to conventional CT parameters, numerous novel concepts and modalities have been developed. Although there is currently no single effective method for differentiating malignant from benign nodules, growth rate measurement using volumetry, evaluation of tumor vascularity on dynamic helical CT, dual-energy CT and MRI and physiologic evaluation with PET/CT can all be useful for nodule characterization. New techniques such as tomosynthesis can improve detection over radiography alone. The purpose of this article is to enhance our understanding of the evidence-based strategies involved in diagnosing SPNs.

Role of imaging biomarkers in predicting anaplastic lymphoma kinase-positive lung adenocarcinoma.

OBJECTIVES: For the ultimately efficient screening of anaplastic lymphoma kinase gene (ALK)-positive cases, combination of radiometabolic characteristics with clinicopathologic features may be more useful. Therefore, we determined radiologic characteristics and metabolic characteristics in patients with adenocarcinoma harboring ALK mutations. PATIENTS AND METHODS: We retrospectively reviewed clinicopathologic CT and PET/CT data from patients with advanced lung adenocarcinoma screened for identifying ALK rearrangement. Lesion size, solidity, location, margins, CT attenuation, and SUVmax were assessed. Pathologic features such as differentiation, the presence of signet ring cell, EGFR/KRAS mutation status, and TTF-1 expression were also reviewed. RESULTS: Of the screened 221 patients, 41 patients (19%) demonstrated ALK rearrangements, and these individuals were younger than the ALK-negative patients (P < 0.001) and showed significant association with no exposure to tobacco (P = 0.028). ALK-positive lung adenocarcinomas were significantly less differentiated than those without rearrangements (P = 0.005). ALK-positive tumors showed significantly higher SUVmax than ALK-negative tumors (P = 0.016). Multivariate logistic regression modeling for predicting the ALK-positive lung adenocarcinoma disclosed that lower CT attenuation and higher SUVmax were significant independent factors of ALK rearrangements (odds ratio, 0.87 and 1.06; P = 0.03 and 0.01, respectively). Receiver operating characteristic analysis showed that the area under the receiver operating characteristic curve for SUVmax/CT attenuation ratio was 0.788, and the optimal cutoff value of SUVmax/CT attenuation ratio for identifying ALK-positive lung adenocarcinoma was greater than or equal to 0.208 (sensitivity, 74.2%; specificity, 81.0%). CONCLUSIONS: This study shows that ALK-rearranged lung adenocarcinoma represents younger age, no history of smoking, the absence of spiculation on CT, and higher SUVmax/CT attenuation greater than or equal to 0.208 compared with those without ALK mutation.