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This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.
RESUMO
We aimed to determine diagnostic accuracy of CT-guided bone lesion biopsy for the confirmation of bone metastases in patients with breast cancer and assessment of hormone receptor status in metastatic tissue. A total of 56 female patients with breast cancer that underwent CT-guided biopsy of suspected bone metastasis were enrolled in this retrospective study. Three different techniques were employed to obtain samples from various sites of skeleton. Collectively, 11 true negative and 3 false negative findings were revealed. The sensitivity of CT-guided biopsy for diagnosing bone metastases was 93.6%, specificity was 100% and accuracy was 94.8%. Discordance in progesterone receptor status and complete concordance in estrogen receptor status was observed. Based on our single-center experience, bone metastasis biopsy should be routinely performed in patients with breast cancer and suspicious bone lesions, due to the impact on further treatment.
RESUMO
OBJECTIVE: Diffusion tensor imaging (DTI) of the white matter is a biomarker for neurological disease burden in tuberous sclerosis complex (TSC). To clarify the basis of abnormal diffusion in TSC, we correlated ex vivo high-resolution diffusion imaging with histopathology in four tissue types: cortex, tuber, perituber, and white matter. METHODS: Surgical specimens of three children with TSC were scanned in a 3T or 7T MRI with a structural image isotropic resolution of 137-300 micron, and diffusion image isotropic resolution of 270-1,000 micron. We stained for myelin (luxol fast blue, LFB), gliosis (glial fibrillary acidic protein, GFAP), and neurons (NeuN) and registered the digitized histopathology slides (0.686 micron resolution) to MRI for visual comparison. We then performed colocalization analysis in four tissue types in each specimen. Finally, we applied a linear mixed model (LMM) for pooled analysis across the three specimens. RESULTS: In white matter and perituber regions, LFB optical density measures correlated with fractional anisotropy (FA) and inversely with mean diffusivity (MD). In white matter only, GFAP correlated with MD, and inversely with FA. In tubers and in the cortex, there was little variation in mean LFB and GFAP signal intensity, and no correlation with MRI metrics. Neuronal density correlated with MD. In the analysis of the combined specimens, the most robust correlation was between white matter MD and LFB metrics. INTERPRETATION: In TSC, diffusion imaging abnormalities in microscopic tissue types correspond to specific histopathological markers. Across all specimens, white matter diffusivity correlates with myelination.