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OBJECT: Thyroid cancer represents the most frequent malignancy of the endocrine system with an increasing incidence worldwide. Novel imaging techniques are able to further characterize tumors and even predict histopathology features. Texture analysis is an emergent imaging technique to extract extensive data from an radiology images. The present study was therefore conducted to identify possible associations between texture analysis and histopathology parameters in thyroid cancer. METHODS: The radiological database was retrospectively reviewed for thyroid carcinoma. Overall, 13 patients (3 females, 23.1%) with a mean age of 61.6 years were identified. The MaZda program was used for texture analysis. The T1-precontrast and T2-weighted images were analyzed and overall 279 texture feature for each sequence was investigated. For every patient cell count, Ki67-index and p53 count were investigated. RESULTS: Several significant correlations between texture features and histopathology were identified. Regarding T1-weighted images, S(0;1)Sum Averg correlated the most with cell count (r = 0.82). An inverse correlations with S(5;0)AngScMom, S(5;0)DifVarnc S(5;0), DiffEntrp and GrNonZeros (r = −0.69, −0.66, −0.69 and −0.63, respectively) was also identified. For T2-weighted images, Variance with r = 0.63 was the highest coefficient, WavEnLL_S3 correlated inversely with cell count (r = −0.57). WavEnLL_S2 derived from T1-weighted images was the highest coefficient r = −0.80, S(0;5)SumVarnc was positively with r = 0.74. Regarding T2-weighted images WavEnHL_s-1 was inverse correlated with Ki67 index (r = −0.77). S(1;0)Correlat was with r = 0.75 the best correlation with Ki67 index. For T1-weighed images S(5;0)SumofSqs was the best with r = 0.65 with p53 count. For T2-weighted images S(1;−1)SumEntrp was the inverse correlation with r = −0.72, whereas S(0;4)AngScMom correlated positively with r = 0.63. CONCLUSIONS: MRI texture analysis derived from conventional sequences reflects histopathology features in thyroid cancer. This technique might be a novel noninvasive modality to further characterize thyroid cancer in clinical oncology.

BACKGROUND: Thyroid carcinomas represent the most frequent endocrine malignancies. Recent studies were able to distinguish malignant from benign nodules of the thyroid gland with diffusion-weighted imaging (DWI). Although this differentiation is undoubtedly helpful, presurgical discrimination between well-differentiated and undifferentiated carcinomas would be crucial to define the optimal treatment algorithm. Therefore, the aim of this study was to investigate if readout-segmented multishot echo planar DWI is able to differentiate between differentiated and undifferentiated subtypes of thyroid carcinomas. PATIENTS AND METHODS: Fourteen patients with different types of thyroid carcinomas who received preoperative DWI were included in our study. In all lesions, apparent diffusion coefficient (ADC)min, ADCmean, ADCmax, and D were estimated on the basis of region of interest measurements after coregistration with T1-weighted, postcontrast images. All tumors were resected and analyzed histopathologically. Ki-67 index, p53 synthesis, cellularity, and total and average nucleic areas were estimated using ImageJ version 1.48. RESULTS: Analysis of variance revealed a statistically significant difference in ADCmean values between differentiated and undifferentiated thyroid carcinomas (P = .022). Spearman Rho calculation identified significant correlations between ADCmax and cell count (r = 0.541, P = .046) as well as between ADCmax and total nuclei area (r = 0.605, P = .022). CONCLUSION: DWI can distinguish between differentiated and undifferentiated thyroid carcinomas.

(1) Background: About 15% of the patients undergoing neoadjuvant chemoradiation for locally advanced rectal cancer exhibit pathological complete response (pCR). The surgical approach is associated with major risks as well as a potential negative impact on quality of life and has been questioned in the past. Still, there is no evidence of a reliable clinical or radiological surrogate marker for pCR. This study aims to replicate previously reported response predictions on the basis of non-contrast CT scans on an independent patient cohort. (2) Methods: A total of 169 consecutive patients (126 males, 43 females) that underwent neoadjuvant chemoradiation and consecutive total mesorectal excision were included. The solid tumors were segmented on CT scans acquired on the same scanner for treatment planning. To quantify intratumoral 3D spatial heterogeneity, 1819 radiomics parameters were derived per case. Feature selection and algorithmic modeling were performed to classify pCR vs. non-pCR cases. A random forest model was trained on the dataset using 4-fold cross-validation. (3) Results: The model achieved an accuracy of 87%, higher than previously reported. Correction for the imbalanced distribution of pCR and non-PCR cases (13% and 87% respectively) was applied, yielding a balanced accuracy score of 0.5%. An additional experiment to classify a computer-generated random data sample using the same model led to comparable results. (4) Conclusions: There is no evidence of added value of a radiomics model based on on-contrast CT scans for prediction of pCR in rectal cancer. The imbalance of the target variable could be identified as a key issue, leading to a biased model and optimistic predictions.

Purpose Fluorodeoxyglucose-Positron-emission tomography (FDG-PET), quantified by standardized uptake values (SUV), is one of the most used functional imaging modality in clinical routine. It is widely acknowledged to be strongly associated with Glucose-transporter family (GLUT)-expression in tumors, which mediates the glucose uptake into cells. The present systematic review sought to elucidate the association between GLUT 1 and 3 expression with SUV values in various tumors. Methods MEDLINE library was screened for associations between FDG-PET parameters and GLUT correlation cancer up to October 2018. Results There were 53 studies comprising 2291 patients involving GLUT 1 expression and 11 studies comprising 405 patients of GLUT 3 expression. The pooled correlation coefficient for GLUT 1 was r = 0.46 (95% CI 0.40–0.52), for GLUT 3 was r = 0.35 (95%CI 0.24–0.46). Thereafter, subgroup analyses were performed. The highest correlation coefficient for GLUT 1 was found in pancreatic cancer r = 0.60 (95%CI 0.46–0.75), the lowest was identified in colorectal cancer with r = 0.21 (95% CI -0.57–0.09). Conclusion An overall only moderate association was found between GLUT 1 expression and SUV values derived from FDG-PET. The correlation coefficient with GLUT 3 was weaker. Presumably, the underlying mechanisms of glucose hypermetabolism in tumors are more complex and not solely depended on the GLUT expression.