BACKGROUND:The etiology of anterior cruciate ligament injury remains unclear yet, and some researchers have pointed that interior and exterior factors both contribute to anterior cruciate ligament injury;additionally, the genetic factor interior factors stand out. Collagen genes COL1A1, COL5A1, and COL12A1 are reported to be associated with anterior cruciate ligament injury in Caucasian populations. OBJECTIVE:To investigate the association of polymorphisms of COL1A1, COL5A1 and COL12A1 genes with anterior cruciate ligament injury in Chinese Han population . METHODS:105 patients with anterior cruciate ligament injury were enrolled and 110 patients without history of anterior cruciate ligament injury were as controlls. The first intron rs1800012 in COL1A1, rs127722 and rs13946 in the 3'-UTR region of COL5A1 gene, rs970547 and rs240736 in the 65 and 29 regions of COL12A1 extron were detected and classified by restriction fragment length polymorphism and genetic sequencing technology. RESULTS AND CONCLUSION:rs1800012, rs12722 and rs13946 genotypes, phenotypes and haplotypes in COL1A1 and COL5A1 genes showed no significant differences between groups. rs970547 and rs240736 genotypes as well as phenotypes and haplotypes in COL12A1 also showed no significant differences between groups. However, there was a significant difference in rs970547 gene frequence in male patients between groups. In conclusion, the Sp1 binding site of COL1A1 rs1800012 is not the susceptibility locus of anterior cruciate ligament injury in Chinese Han population. COL5A1 genes rs12722 and rs13946 in COL5A1 are not closely related to anterior cruciate ligament injury. COL12A1 rs970547 and rs240736 have a certain association with anterior cruciate ligament injury in Chinese men. Male individuals with COL12A1 rs970547 A allelicgene and AA genotype are likely to be susceptible to anterior cruciate ligament injury in Chinese Han population.

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.

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.