Objective To evaluate the value of differential diagnosis between benign and malignant thyroid lesions by susceptibility weighted imaging(SWI).Methods 53 patients with 20 malignant thyroid lesions and 71 benign thyroid lesions confirmed by surgery and pathology were analyzed retrospectively.All cases received conventional MRI and SWI preoperatively.Location,volume,SWI parameters including signal to noise ratio(SNR),contrast noise ratio(CNR)and intratumor susceptibility hypointensity (ITSHIA)datas in benign and malignant lesions were compared and analyzed.Results There was no statistical significance between benign and malignant thyroid lesions in the location,volume,SNR and CNR(χ2 or t =0.014,0.286,0.927,1.169;P =0.907,0.778,0.368,0.259 respectively).The maximum diameter (1.90 mm±0.32 mm),degree of maximum diameter(1.33±0.47),frequence(1.40±0.20)and area ratio(1.09±0.28)for benign thyroid lesions were less than those for malignant lesions(3.39 mm±0.79 mm,2.25±0.44,1.40±0.68,1.70±0.47)respectively (t or Z =12.629,5.788,3.41 5,5.795;P =0.000,0.000,0.001,0.000).Conclusion SWI semiquantitative assessment of pathlogical vascularity is useful in differential diagnosis of benign and malignant thyroid lesions.

Objective To investigate the value of diffusion weighted imaging (DWI)in identification of different molecular sub-types for breast cancer classifications.Methods All patients with breast cancer were divided into four subtypes groups by immuno-histochemistry results including Luminal A subtype,Luminal B subtype,HER2-over expressing (HER2-OE)subtype,and triple negative breast cancer (TNBC),respectively.The means of maximum,average,and minimum ADC of the lesions in all patients were recorded.The analysis of ANOVA and least significant difference test (LSD-t )were used for the statistical evaluation.Results There were significant differences in maximum ADC,average ADC,and minimum ADC among Luminal A subtype (n=21),Lu-minal B subtype (n=22),HER2-OE subtype (n=1 7)and TNBC subtype (n=12)groups (P =0.025,0.039 and 0.041,respec-tively).However,paired comparison in mean of maximum ADC,average ADC and minimum ADC by LSD-t multiple comparisons among Luminal A,Luminal B,HER2-OE and TNBC respectively were not significantly different.Conclusion DWI may be difficult to discriminate the molecular subtypes of breast cancer classification before surgery or biopsy.

Objective To investigate the value of ADC histogram analysis in the assessment of response to neoadjuvant chemotherapy (NACT) in patients with in locally advanced breast cancer (LABC). Methods Thirty?five female patients with invasive ductal carcinoma proved by pathology before NACT and treated with operation after NACT were retrospectively analyzed. All patients were received MR examination (including non?enhanced MRI, enhanced?MRI, and DWI) breast before NACT. After neoadjuvant chemotherapy, 19 of 35 patients were categorized as responders and 16 were categorized as non?responders according response evaluation criteria in solid tumors criteria. Per?patient weighted ADC histograms were generated. Mean ADC, mode ADC, maximum ADC, minimum ADC, median ADC, skewness, and kurtosis were analyzed by using t test between responders and non responders groups. ROC curves were constructed to determine the optimum threshold for each histogram parameter to differentiate non?responders and responders in breast cancers. The optimal threshold values, determined by maximal Youden index were selected when significant differences existed in two groups. Results Mean, minimum, skewness, and kurtosis of ADC between responders and non?responders group were(0.955 ± 0.135)× 10?3mm2/s,(0.535 ± 0.115)×10?3mm2/s,0.85±0.61, 2.93±0.17,and(1.103±0.233)×10?3 mm2/s,(0.650±0.104)×10?3mm2/s,-0.42± 0.17, 3.11 ± 0.25,respectively. Significant differences were found mean ADC, minimum ADC, skewness, and kurtosis (t=2.345, 3.096, 8.051 and 2.524,P<0.05), and there was no differences in mode, median, maximum between responders and non?responders(P>0.05).We set the optimal threshold criteria of mean ADC (0.956×10?3mm2/s), minimum ADC (0.580×10?3mm2/s), skewness (0.890), sensitivity, specificity of three parameters for predicting responders in LABC were 73.7%,62.5%, 78.9%,68.8%, and 63.2%,75.0%, respectively, and the areas under ROC curve of mean ADC, minimum ADC, skewness was 0.678, 0.770, and 0.890, respectively. Kurtosis of responders and non?responders did not get cutoff value for much more overlap. Conclusion ADC histogram analysis is valuable in predicting LABC in patients with NACT effect, the minimum and skewness of ADC is highest sensitivity, specificity, respectively.