Objective To evaluate the accuracy of presurgically resting-state fMRI(rs-fMRI) with subject order-independent group independent component analysis(ICA), compared to electric cortical stimulations. Methods Twenty-three patients with the lesion in motor area, which were recorded by our hospital from Jan, 2014 to Dec, 2015, were collected as the study sample. The data of 9 patients were excluded because of excessive head motion. As a result, 14 patients were included in this study. Rs-fMRI data before the surgery and the results of electric cortical stimulations were collected. Results All of this 14 patients were preoperatively located by rs-fMRI with SOI-GICA, including all the SMA and the ipsilesional primary motor area. On the side with lesion, the number of functional location of motor area was decreased compared with healthy side. Evaluate the accuracy of ICA by comparing the coincidence rate of these two techniques, based on the standardized electrical cortical stimulation in operation. The completely concordance between rs-fMRI with the SOI-GICA and electrical cortical stimulation in operation was 11 (11/14). Meanwhile the basically concordance of corresponded case was 3(3/14). Conclusions Rs-fMRI with the ICA has a relatively high accuracy rate in localizing motor area. Rs-fMRI has a remarkably referential contribution to the presurgically function assessment and surgical planning in implementation.

Objective: To compare and quantify the differences in size-specific dose estimates (SSDE) obtained by effective diameter and water-equivalent diameter from the central slice of the scan range in head CT examination. Methods: A total of 111 consecutive adult patients who underwent head CT examination were enrolled in this study. All of CTDI_vol values in the dose report were documented. The dataset was assigned into group A and group B, based on the individual size-dependent conversion factors (f) of effective diameter (d_eff) and water-equivalent diameter (d_w) at the central slice multiplied by normalized volume computed tomography dose index (CTDI_vol ) respectively. Body size, f and SSDE were calculated. With SSDE_gross served as the reference level, the performance of SSDE_deff and SSDE_dw was evaluated. Results: Statistically significant differences were found in body size (t=47.587, P<0.05) and f(z=-9.242, P<0.05) between group A and group B. Statistically significant difference also existed in SSDE (t=-46.687, P<0.05), (56.20±2.66) and (53.49±2.48) mGy for group A and group B respectively. Strongly positive correlation was shown in body size (r=0.873, R² =0.761) and SSDE (r=0.974, R² =0.949) between group A and group B(all P<0.05). Positive correlation was also found between SSDE_deff and SSDE_gross(r=0.900, R² =0.809), SSDE_dw and SSDE_gross (r=0.904, R² =0.817, all P<0.05). Mean absolute difference was 2.34 and 0.78 mGy, for SSDE_deff vs. SSDE_gross and SSDE_dw vs. SSDE_gross respectively; mean absolute relative difference was 4.38%, 1.40%; root mean square difference was 1.17 mGy (2.17%), 1.06 mGy (1.91%). Interquartile range and full range of SSDE_deff and SSDE_dw were 3.22 vs. 2.39 mGy, 13.65 vs. 12.48mGy, respectively. A less degree of variation was observed in SSDE_dw than that in SSDE_deff. Conclusions SSDE_dw values based on the water-equivalent diameter at the central slice of the scan range got better agreement with those derived from all slices, which could serve as a simpler and more valid indicator to represent the average value of size-specific dose estimates of the whole scan range in head CT examination.

Objective To explore the validity of the size-specific dose estimate (SSDE) derived from the water-equivalent diameter (Dw)value of the slice located in the middle of the scan range in the head CT examination. Methods A total of 197 patients underwent head CT nonenhanced scan were enrolled in this retrospective study. The Dw, size-dependent conversion factor (f), normalized volume CT dose index (CTDIvol) and SSDE values of all slices were calculated. Two sets of SSDE, SSDEgroand SSDEcenbased on the Dwvalues slice by slice (Dw-gro) and the Dwvalues of the slices in the middle of the scan range (Dw-cen), were obtained across all patients. Pearson correlation analysis and linear regression analysis were performed for Dw-grovs Dw-cen, Spearman correlation analysis and linear regression analysis for SSDEgrovs SSDEcen, SSDE vs Dw, CTDIvolvs Dw. With the reference of SSDEgrovalue, mean absolute relative difference (MARD) of SSDEcen values were calculated to assess its accuracy and the correlated factors of MARD was analyzed with multivariate linear stepwise regression analysis. Results The minimal Dwvalue close to the roof of the skull corresponded to the maximal value of f and SSDE, which was the minimal value of CTDIvol. The significant positive correlation was showed between Dw-grovs Dw-cen, SSDEgrovs SSDEcen, SSDE vs Dw, CTDIvolvs Dw(r=0.947, 0.931, 0.416, 0.626;P＜0.05). The values of Dw,groand Dw-cenwere (16.94±0.69) and (18.50±0.62) cm respectively. The values of SSDEgroand SSDEcenwere [54.10 (52.29, 56.39)] mGy and [53.77 (51.85, 55.25)] mGy respectively. An approximation of SSDEcenvalues with an average of 1.62% of the gross MARD was found to match the reference value. Multivariate linear stepwise regression analysis indicated that MARD had negative correlation with Dw(β=–1.319,P＜0.05), positive correlation with CTDIvol(β=0.202,P＜0.05), and f was not included in the multivariate regression equation. Conclusion SSDEcenbased on the Dwvalue of the slice located at the center of the scan range yields small MARD value and can represent a reliable SSDE estimation in the head CT examination.

Objective:To explore the feasibility for taking weight and body mass index (BMI) to calculate the size-specific dose estimate (SSDE) in abdomen-pelvis CT examination.Methods:512 adult patients undergoing abdomen-pelvis CT examination were retrospectively analyzed. The in-house software based on MATLAB platform were used to calculate automatically water equivalent diameter ( dw), size-dependent conversion factor ( f), SSDE, together with their respective averaged values. The correlations between age, height, weight and BMI with dw were calculated by using Spearman correlation analysis. Two regression equations were established to calculate the SSDE (SSDE weight, SSDE BMI), one for the correlation of weight with dw based on first half of these cases and the other for that between BMI with dw based on another half as the cases to be verified. With reference of the SSDE derived from the in-house software, the averaged relative differences and root-mean-square errors in SSDE weightand SSDE BMI were calculated, respectively. Results:No statistically significant correlation between age and d w ( P>0.05) was shown, but weak correlation between height and dw( r=0.260, P<0.05), strong correlation between either weight or BMI with dw( r=0.879, 0.851, P<0.05). Two regression equations were described as dw=13.808+ 0.184×weight, dw=11.142+ 0.618×BMI. The mean SSDE, SSDE weight and SSDE BMI for the verified patients were (13.55±1.66) mGy, (13.84±2.03) mGy and (13.83±2.02) mGy, respectively. As compared to actual SSDE, the averaged relative differences in SSDE weight and SSDE BMI were 1.97% and 1.87%; 0.38% and 2.75% for male patients; 4.58% and 0.43% for female patients; 0.11% and 3.32% for patients with BMI<18.5 kg/m 2;1.92% and 2.06% for those with 18.5 kg/m 2≤BMI<24.0 kg/m 2;2.57% and 1.57% for those with 24 kg/m 2≤BMI<28.0 kg/m 2;3.28% and -1.36% for those with BMI≥28.0 kg/m 2. The averaged root-mean-square errors in SSDE weight and SSDE BMI were both 0.80 mGy; 0.65 and 0.67 mGy for male patients; 0.98 and 0.59 mGy for female patients; 0.73 and 1.03 mGy for underweight, 0.74 and 0.66 mGy for normal weight, 0.85 and 0.79 mGy for overweight, and 1.10 and 1.32 mGy for obesity. Conclusions:Weight and BMI can be used as the surrogate dw to compute SSDE in adult abdomen-pelvis CT examination. However, Weight rather than BMI is more applied to male patients, and BMI is more suitable for female patients.

Objective To explore the clinical feasibility of predicting synchronous liver metastases based on MRI radiomics nomogram based on T2WI in rectal cancer. Methods The imaging and clinical data of 261 patients with primary rectal cancer admitted to Zhejiang People′s Hospital from April 2012 to May 2018 were retrospectively analyzed. 101 patients were accompanied by synchronous liver metastasis All cases were divided into training group (n=182) and verification group (n=79). T2WI image of each patient was selected to extract texture features by AK analysis software of GE company. A radiomics signature was constructed after reduction of dimension in training group by the least absolute shrinkage and selection operator (LASSO). Univariate logistic regression was used to select for independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models and nomogram. ROC was used to assess the accuracy of the nomogram in the training group and to verify them by the validation group. Finally, the clinical efficacy of each patient′s synchronized liver metastasis risk factor was calculated based on the nomogram. Results A total of 328 texture features were extracted from the T2WI. Seven most valuable features were selected after reducing the dimension by LASSO algorithm, including 3 co-occurrence matrices (GLCM) and 4 run-length matrices(RLM). Tumor staging and radiomic signatures were included in the Multifactor logistic regression to build the prediction model and nomogram. The accuracy of predicting SRLM was 0.862 and 0.844 in the training and the verification group, respectively. To evaluate the accuracy of the nomogram, radiomics signature and the tumor staging in all cases were 0.857, 0.832 and 0.663, respectively. There was no significant difference in the number of SRLM cases between the high risk group and the low risk group based on nomogram (P>0.05). Conclusion The radiomics nomogram based on T2WI can be used as a quantitative tool to predict synchronous liver metastases of rectal cancer.

Objective To investigate the difference of size-specific dose estimates ( SSDEs ) based on effective diameter ( def ) and water equivalent diameter ( dw ) in coronary computed tomography angiography ( CCTA) and explore the causes. Methods A total of 99 patients undergoing CCTA were enrolled in this retrospective study. SSDEs ( SSDEd ef and SSDEdw ) were calculated in two approaches using def and dw , respectively. Mean absolute relative difference ( MARD) was computed as an index to quantify the consistency of SSDEd ef and SSDEdw . Multivariate stepwise regression analysis was performed to study the factors influencing MARD. Results The values def and dw were positively correlated with body mass index (BMI) (r=0. 869, 0. 823, P<0. 05). The median (interquartile range) of SSDEdef and SSDEdw were 12. 34 ( 11. 75, 12. 98) mGy, 13. 78 ( 13. 02, 15. 04) mGy, respectively. SSDEdef was lower by 10. 45％ than SSDEdw( Z=-8. 186, P<0. 05) . Both SSDEdef and SSDEdw were negatively correlated with BMI and dw(r=-0. 765, -0. 680, -0. 701, -0. 840, P<0. 05). MARD of SSDEdef and SSDEdw was generally at 11. 39％. No statistical significance was found in the correlation of MARD with BMI ( r=0. 031, P>0. 05) , however, positive correlation was shown between MARD and def ( r=0. 251, P<0. 05) , but negative correlation for MARD and dw(r=-0. 379, P<0. 05). With respect to the factors influencing MARD, four variables were included into the regression equation. MARD was positively correlated with the area of both air-filled lungs ( Arealow ) and soft tissues ( Areasoft ) (β=0. 634, 0. 102, P<0. 05) , and negatively correlated with the area of bone, enhanced cardiac chambers and aorta ( Areahigh ) and the CT value of air-filled lungs ( SIlow ) (β=-0. 234,-0. 343, P<0. 05) . Conclusions SSDEdef was approximately 10. 45％ lower than SSDEdw , which was predominantly influenced by the area of air-filled lungs due to the characteristics of low X-ray attenuation in CCTA.

OBJECTIVETo explore the application value of texture analysis of magnetic resonance images (MRI) in predicting the efficacy of neoadjuvant chemoradiotherapy(nCRT) for rectal cancer.

METHODSA total of 34 rectal cancer patients who were hospitalized at Zhejiang Provincial People's Hospital from February 2015 to April 2017 were prospectively enrolled and received 3.0T MRI examination at pre-nCRT (1 day before nCRT), early stage (at 10-day after nCRT) and middle stage (at 20-day after nCRT).

INCLUSION CRITERIAdistance from tumor lower margin to anal edge was less than 12 cm under rectoscope; rectal cancer was confirmed by preoperative pathology; clinical stage was T3 or above; lymph node metastasis existed but without distant metastasis; functions of liver, kidney and heart present no contraindications of operation.

EXCLUSION CRITERIAunfinished nCRT, surgery and three examinations of MRI; image motion artifacts; lack of postoperative pathological results. All the patients underwent rectal cancer long-term three-dimensional radiotherapy and chemotherapy combined with nCRT (oxaliplatin plus capecitabine). The tumor regression grading (TRG) was divided into TRG 0 to 4 grade after nCRT, and TRG 4 was classified as pathological complete remission (pCR); TRG 2 to 3 was classified as partial remission (PR); the rest was no remission (NR). Extraction and analysis of texture features in T2-weighted MR-defined tumor region were performed using Omni Kinetics texture software. The texture values of each time point were statistically analyzed, and the differences of texture values and change differences between pCR and PR+NR, and NR and pCR+PR were compared respectively. Statistically significant texture values were screened and were used in receiver operating characteristic (ROC) curve to assess the prediction of the efficacy of nCRT.

RESULTSOf 34 patients, 21 were males and 13 were females with median age of 49.3 years. Nineteen (55.9%) patients were low rectal adenocarcinoma and 15 (44.1%) patients were middle rectal adenocarcinoma. Nine (26.5%) cases belonged to pCR, 13 (38.2%) belonged to PR, and 12 (35.3%) belonged to NR. Before nCRT, the entropy of tumor area in pCR patients was significantly higher than that in PR+NR patients (7.164±0.272 vs. 6.823±0.309, t=2.925, P=0.006). At the middle stage of nCRT, as compared with PR+NR patients for the texture features of tumor region, the variance (1566±281 vs. 2883±867, t=-4.435, P=0.000) and entropy(5.436±0.934 vs. 6.803±0.577, t=-4.118,P=0.002) of pCR patients were significantly lower; kurtosis(4.800±1.288 vs. 3.206±1.211, t=3.333, P=0.002) and energy (0.016±0.005 vs. 0.010±0.004, t=3.240, P=0.003) of pCR patients were significantly higher. As compared to pCR+PR patients, the kurtosis(2.461±0.931 vs. 4.264±1.205, t=-4.493, P=0.000) and energy (0.011±0.004 vs. 0.014±0.004, t=-3.453, P=0.000) of the NR patients were significantly lower. As for texture change values between early stage and middle stage, the entropy difference was significant between pCR and PR+NR, NR and pCR+PR (1.344±0.819 vs. 0.489±0.319, t=3.047, P=0.014; 0.446±0.213 vs. 0.917±0.677, t=-3.638, P=0.001, respectively). As for texture change values between pre-nCRT and middle stage, variance and entropy differences between pCR and PR+NR (1759±1226 vs. 977±842, t=2.113, P=0.042; 1.728±0.918 vs. 0.524±0.355, t=3.832, P=0.004), and the change values of entropy between NR and pCR+PR (0.475±0.349 vs. 1.044±0.860, t=-2.722, P=0.011) were statistically significant. The above indicators were included in the ROC curve. The results revealed that at the middle stage, entropy value >5.983 indicated the best efficacy for the diagnosis of pCR, with the area under the ROC curve (AUC) of 0.885, the sensitivity of 100%, and the specificity of 66.7%; the energy <0.010 indicated the best AUC for diagnosis of NR was 0.902, with the sensitivity of 91.7% and specificity of 81.8%.

CONCLUSIONSTexture analysis based on T2 weighted images can predict the efficacy of nCRT for rectal cancer. The middle stage of nCRT is the best time of prediction. The entropy and energy of this period are texture parameters having higher predictive ability.

Chemoradiotherapy ; Female ; Humans ; Magnetic Resonance Spectroscopy ; Male ; Middle Aged ; Neoadjuvant Therapy ; Neoplasm Staging ; Predictive Value of Tests ; Prognosis ; Rectal Neoplasms ; diagnostic imaging ; therapy ; Treatment Outcome

Objective: To explore the risk factors of predicting white matter hyperintensities progression based on radiomics of MRI of whole-brain white matter. Methods: The imaging and clinical data of 152 patients with white matter hyperintensities admitted to Zhejiang People′s Hospital from March 2014 to October 2018 were retrospectively analyzed. The whole brain white matter on baseline T₁WI images of each patient were segmented by SPM12 software package, and images of white matter were imported into AK software for texture feature extraction and dimensionality reduction. At last, least absolute shrinkage and selection operator(LASSO) was used to calculate the score of radiomics signature of each patient. According to the improved Fazekas scale, patients with WMH progression were divided into three groups: any white matter hyperintensities (AWMH), periventricular white matter hyperintensities (PWMH) and deep white matter hyperintensities (DWMH). Statistical differences of clinical factors and radiomics signature between WMH progression subgroups and non-progression subgroups were compared with independent sample t test or Mann-Whitney U test, and Univariate logistic regression was used to select independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models, which was evaluated by ROC curve. And the correlation between the clinical indicators of independent risk factors and the texture feature of radiomics signature was analyzed. Results: The efficacy of the model for the detection for AWMH progression, PWMH progression and DWMH progression was 0.818,0.778 and 0.824, respectively. Age is an independent risk factor for AWMH progression and DWMH progression[OR(95%CI), 4.776(2.152-10.601) vs. 3.851(1.101-8.245); P<0.05]. BMI is an independent risk factor for DWMH[OR(95%CI), 3.004(1.204-7.370); P<0.05], and hyperlipidemia is an independent risk factor for AWMH and PWMH[OR(95%CI), 4.062(1.834-8.998) vs. 3.549(1.666-7.563); P<0.05]. In AWMH subgroup, Surface Area was negatively correlated with age and low density lipoprotein(LDL) (r=-0.401, -0.312), and Inverse Difference Moment_ALLDirection_offset 1_SD was negatively correlated with age(r=-0.412). In PWMH subgroup, Compactness 1 was negatively correlated with LDL(r=-0.198), and Inverse Difference Moment_angle 0_offset 7 was positively correlated with LDL(r=0.252). In DWMH subgroup, LongRunEmphasis_ALLDirection_offset 7 was negatively correlated with age(r=-0.322), and GLCMEntropy_angle0_offset 4 was negatively correlated with age(r=-0.278). GLCMEntropy_AllDirection_offset4 was negatively correlated with body mass index(r=-0.514). Conclusion Radiomics based on whole-brain white matter MR imaging can predict WMH progression and identify the risk factors in high-risk populations, thus providing early additional information to conventional magnetic resonance imaging to predict WMH progression.