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Purpose@#Knowing the distinction between benign and borderline/malignant phyllodes tumors (PTs) can help in the surgical treatment course. Herein, we investigated the value of magnetic resonance imaging-based texture analysis (MRI-TA) in differentiating between benign and borderline/malignant PTs. @*Methods@#Forty-three women with 44 histologically proven PTs underwent breast MRI before surgery and were classified into benign (n = 26) and borderline/malignant groups (n = 18 [15 borderline, 3 malignant]). Clinical and routine MRI parameters (CRMP) and MRI-TA were used to distinguish benign from borderline/malignant PT. In total, 298 texture parameters were extracted from fat-suppression (FS) T2-weighted, FS unenhanced T1-weighted, and FS first-enhanced T1-weighted sequences. To evaluate the diagnostic performance, receiver operating characteristic curve analysis was performed for the K-nearest neighbor classifier trained with significantly different parameters of CRMP, MRI sequence-based TA, and the combination strategy. @*Results@#Compared with benign PTs, borderline/malignant ones presented a higher local recurrence (p = 0.045); larger size (p < 0.001); different time-intensity curve pattern (p = 0.010); and higher frequency of strong lobulation (p = 0.024), septation enhancement (p = 0.048), cystic component (p = 0.023), and irregular cystic wall (p = 0.045). TA of FS T2-weighted images (0.86) showed a significantly higher area under the curve (AUC) than that of FS unenhanced T1-weighted (0.65, p = 0.010) or first-enhanced phase (0.72, p = 0.049) images. The texture parameters of FS T2-weighted sequences tended to have a higher AUC than CRMP (0.79, p = 0.404). Additionally, the combination strategy exhibited a similar AUC (0.89, p = 0.622) in comparison with the texture parameters of FS T2-weighted sequences. @*Conclusion@#MRI-TA demonstrated good predictive performance for breast PT pathological grading and could provide surgical planning guidance. Clinical data and routine MRI features were also valuable for grading PTs.
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Objective To explore the relationship between the typing of MSCT and prognosis in infant with interstitial pneumonia (IP).Methods MSCT features of 44 infants with IP were analyzed retrospectively and classified according to the pathological pro-gress.The relationship between the MSCT typing and clinical prognosis was statistically analyzed.Results The result of the MSCT typing was as follows:the exudation in 22 cases,the proliferation in 18 cases and the ruin in 4 cases.There was significant difference for the clinical prognosis among the different MSCT groups (P＜0.01 ).And there was a significant correlation between the MSCT typing and clinical prognosis (r＝0.784,P＜0.01).The prognosis of the exudation type was better than the proliferation type,and both of them were better than the ruin type.Conclusion Based on the MSCT features,MSCT typing reveals the inflammatory patho-logical process of the infant IP,which plays an important role in treatment options decision and prognosis prediction.
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Objective To evaluate the value of MRI texture analysis based on gray level co-occurrence matrix to predict cervical lymph node metastasis in patients with tongue carcinoma. Methods A total of 70 patients with tongue carcinoma confirmed by pathology were analyzed retrospectively. The patients were divided into cervical lymph node (LN) metastasis group (unilateral LN+, n=18;bilateral LN+,n=22) and no cervical lymph node metastasis(LN-,n=30). T1W, T2W and contrast-enhanced T1W images of the largest section of tumor were selected. ROI of the lesion was manually drew and GLCM texture parameters (energy, contrast, correlation, inverse difference and entropy) were extracted. The tumor length, thickness and para-lingual distance between tumor and tongue midline were also measured.Differences of all parameters were compared between LN+ group and LN- group, unilateral and bilateral cervical lymph node metastasis group, the parameters with statistically significant difference in predicting the efficiency of cervical lymph node metastasis were analyzed. The diagnostic efficiency of lymph node metastasis was calculated. Results The correlation, inverse difference and entropy based on T2WI showed significant difference (Zcor elation=2.97, tinverse dif erence=5.14, tentropy=2.41; P<0.05), entropy showed the best diagnostic efficiency, the area under the ROC curve (AUC) was 0.90, the cut off value was 7.19, the sensitivity was 80.0％, specificity was 86.7％. Only entropy showed significant difference between unilateral LN+group and bilateral LN+group (P<0.05), the AUC was 0.82, the cut off value was 7.47, the sensitivity was 90.9％, specificity was 78.8％. The index of tumor length, thickness and para-lingual distance between tumor and tongue midline all showed significant difference between LN+group and LN-group (P<0.05), the thickness showed the best diagnostic efficiency, the AUC value was 0.81, the cut off value was 11.19, the sensitivity was 78.0％, specificity was 81.7％. The index of tumor length, thickness and para-lingual distance between tumor and tongue midline showed no significant difference between unilateral LN+ group and bilateral LN+ group (P>0.05). The diagnostic sensitivity of radiologists was 65.0％ (26/40), the specificity was 80.0％ (24/32) on cervical lymph node metastasis. Conclusion Texture analysis based on T2WI can predict cervical lymph node metastasis in patients with tongue carcinoma. Entropy has certain value in predicting bilateral cervical lymph node metastasis.