To explore how the joint-teaching mode of tomographic-radiologic anatomy and topographic anatomy in a way of case-based learning (CBL) promotes the education of medical students.

Objective:To investigate the value of preoperative prediction of Ki-67 expression status in breast cancer based on multi-phase enhanced MRI combined with clinical imaging characteristics prediction model.Methods:This study was retrospective. A total of 213 breast cancer patients who underwent surgical treatment at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between June 2016 and May 2017 were enrolled. All patients were female, aged 24-78 (51±10) years, and underwent routine breast MRI within 2 weeks prior to surgery. According to the different Ki-67 expression of postoperative pathological results, patients were divided into high expression group (Ki-67≥20%, 153 cases) and low expression group (Ki-67<20%, 60 cases). The radiomic features of breast cancer lesions were extracted from phase 2 (CE-2) and phase 7 (CE-7) images of dynamic contrast enhanced (DCE)-MRI, and all cases were divided into training and test sets according to the ratio of 7∶3. The radiomic features were first selected using ANOVA and Wilcoxon signed-rank test, followed by the least absolute shrinkage and selection operator method regression model. The same method of parameters selection was applied to clinical information and conventional imaging features [including gland classification, degree of background parenchymal enhancement, multifocal/multicentric, lesion location, lesion morphology, lesion long diameter, lesion short diameter, T 2WI signal characteristics, diffusion-weighted imaging (DWI) signal characteristics, apparent diffusion coefficient (ADC) values, time-signal intensity curve type, and axillary lymph nodes larger than 1 cm in short axis]. Support vector machine (SVM) was then used to construct prediction models for Ki-67 high and low expression states. The predictive performance of the models were evaluated using receiver operating characteristic (ROC) curves and area under cueve(AUC). Results:Totally 1 029 radiomic features were extracted from CE-2 and CE-7 images, respectively, and 9 and 7 best features were obtained after selection, respectively. And combining the two sets of features for a total of 16 features constituted the CE-2+CE-7 image best features. Five valuable parameters including lesion location, lesion short diameter, DWI signal characteristics, ADC values, and axillary lymph nodes larger than 1 cm in short axis, were selected from all clinical image features. The SVM prediction models obtained from the radiomic features of CE-2 and CE-7 images had a high AUC in predicting Ki-67 expression status (>0.70) in both the training set and the test set. The models were constructed by combining the CE-2, CE-7, and CE-2+CE-7 radiomic features with clinical imaging features, respectively, and the corresponding model performance in predicting Ki-67 expression status was improved compared with the models obtained by using the CE-2, CE-7, and CE-2+CE-7 radiomic features alone. The SVM prediction model obtained from CE-2+CE-7 radiomic features combined with clinical imaging features had the best prediction performance, with AUC of 0.895, accuracy of 84.6%, sensitivity of 87.9%, and specificity of 76.2% for predicting Ki-67 expression status in the training set and AUC of 0.822, accuracy of 70.3%, sensitivity of 76.1%, and specificity of 55.6% in test sets.Conclusion:The SVM prediction model based on DCE-MRI radiomic features can effectively predict Ki-67 expression status, and the combination of radiomic features and clinical imaging features can further improve the model prediction performance.

Objective:To explore the value of MRI features in predicting the pathological grade of rectal neuroendocrine tumors and to develop a predicting model.Methods:A retrospective analysis was performed on 30 cases of rectal neuroendocrine tumors confirmed by surgery and pathology between 2013 and 2019. All of them underwent plain rectal MRI, DWI and dynamic contrast-enhanced MRI. The clinical features and MRI characteristics (ie. tumor location, maximum tumor diameter, boundary, growth pattern, enhancement of three-staged lesions, and the lymph node metastasis) were analyzed by statistical methods to evaluate the difference between different tumor pathologic grades (G1, G2 and G3). Characteristics with statistical significance were analyzed by collinearity diagnostics, and stepwise regression method was used to select independent predictors. Ordinal logistic regression analysis was then conducted to develop the predicting model.Results:Maximum tumor diameter, tumor boundary, growth pattern, mr-T, mr-N, EMVI, MRF, T2WI signal intensity, tumor enhancement degree in venous phase and distant metastasis were closely correlated with the pathological grade of rectal neuroendocrine tumors ( P<0.001, 0.001, 0.001, <0.001, 0.001, 0.004, 0.024, 0.015, 0.001, and <0.001, respectively). The mr-T and tumor enhancement degree in venous phase were identified as the independent predictors to construct the prediction model. The model got ideal performance in predicting the grades, with the areas under the receiver operating characteristic (ROC) curves (AUCs) of 0.945, 0.624 and 0.896, the sensitivities were 75.0%, 85.7%, and 90.9% and corresponding specificities were 88.9%, 52.6% and 93.3% for G1, G2 and G3 rectal neuroendocrine tumors, respectively. Conclusion:The model based on mr-T and tumor enhancement degree in venous phase can serve as a clinical tool for predicting the pathological grade of rectal neuroendocrine tumors.

Objective:To explore the value of MRI features in predicting the pathological grade of rectal neuroendocrine tumors and to develop a predicting model.Methods:A retrospective analysis was performed on 30 cases of rectal neuroendocrine tumors confirmed by surgery and pathology between 2013 and 2019. All of them underwent plain rectal MRI, DWI and dynamic contrast-enhanced MRI. The clinical features and MRI characteristics (ie. tumor location, maximum tumor diameter, boundary, growth pattern, enhancement of three-staged lesions, and the lymph node metastasis) were analyzed by statistical methods to evaluate the difference between different tumor pathologic grades (G1, G2 and G3). Characteristics with statistical significance were analyzed by collinearity diagnostics, and stepwise regression method was used to select independent predictors. Ordinal logistic regression analysis was then conducted to develop the predicting model.Results:Maximum tumor diameter, tumor boundary, growth pattern, mr-T, mr-N, EMVI, MRF, T2WI signal intensity, tumor enhancement degree in venous phase and distant metastasis were closely correlated with the pathological grade of rectal neuroendocrine tumors ( P<0.001, 0.001, 0.001, <0.001, 0.001, 0.004, 0.024, 0.015, 0.001, and <0.001, respectively). The mr-T and tumor enhancement degree in venous phase were identified as the independent predictors to construct the prediction model. The model got ideal performance in predicting the grades, with the areas under the receiver operating characteristic (ROC) curves (AUCs) of 0.945, 0.624 and 0.896, the sensitivities were 75.0%, 85.7%, and 90.9% and corresponding specificities were 88.9%, 52.6% and 93.3% for G1, G2 and G3 rectal neuroendocrine tumors, respectively. Conclusion:The model based on mr-T and tumor enhancement degree in venous phase can serve as a clinical tool for predicting the pathological grade of rectal neuroendocrine tumors.

Objective:To explore the diagnostic accuracy improved by magnetic resonance imaging (MRI) biomarkers for lymph node metastasis in T1-2 stage rectal cancer before treatment.Methods:Medical records of 327 patients with T1-2 rectal cancer who underwent pretreatment MRI and rectal tumor resection between January 2015 and November 2019 were retrospectively analyzed. Fifty-seven cases were divided into the lymph node metastasis group (N+ group) while other 270 cases in the non-lymph node metastasis group (N-group) according to the pathologic diagnosis. Two radiologist evaluated the tumor characteristics of MRI images. The relationship of the clinical and imaging characteristics of lymph node metastasis was assessed by using univariate analysis and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic abilities for the differentiation of N- from N+ tumors.Results:Among the 327 patients, MR-N evaluation was positive in 67 cases, which was statistically different from the pathological diagnosis ( P<0.001). The sensitivity, specificity and accuracy of MRI for lymph node metastasis were 45.6%, 84.8% and 78.0%, respectively. Multivariate regression analysis showed that tumor morphology ( P=0.002), including mucus or not ( P<0.001), and MR-N evaluation ( P<0.001) were independent influencing factors for stage T1-2 rectal cancer with lymph node metastasis. The area under the ROC curve of rectal cancer with lymph node metastasis analyzed by the logistic regression model was 0.786 (95% CI: 0.720~0.852). Conclusions:Tumor morphology, including mucus or not, and MR-N evaluation can serve as independent biomarkers for differentiation of N- and N+ tumors. The model combined with these biomarkers facilitates to improve the diagnostic accuracy of lymph node metastasis in T1-2 rectal cancers by using MRI.

Objective:To explore the diagnostic value of synthetic magnetic resonance imaging (syMRI) quantitative parameters for benign and malignant breast lesions.Methods:From September 2018 to March 2019, a total of 43 cases of breast lesions which were confirmed by surgery and pathology in Cancer Hospital, Chinese Academy of Medical Sciences were enrolled in this study. All patients underwent syMRI sequence scans before and after enhancement except for conventional T2WI, DWI, and enhancement scans. GE AW4.7 workstation was used to generate syMRI parameter maps (T1, T2, proton density mappings), and ITK-SNAP software was used to delineate the volume of interest. The T1, T2, PD values before and after dynamic contrast enhanced (DCE) were obtained, and the change values of each parameter were calculated. Meanwhile, the apparent diffusion coefficient (ADC) and time intensity curve (TIC) of the lesions were measured. The differences of each parameter value were compared between benign and malignant breast lesions, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of each parameter.Results:Among the 43 enrolled cases, 13 were benign and 30 were malignant. Among the syMRI parameters, the pre-enhancement parameters including T1pre (median 1 663.07 ms), T2pre (median 103.33 ms), post-enhancement parameters ΔT1 (median 1 022.68 ms) and ΔT2 (median 27.67 ms) of benign group, significantly higher than those of the malignant group (the medians were 1 141.74, 92.53, 664.95, and 16.19 ms, respectively, P<0.05). The ADC value of the benign group (median 1.66×10 -3mm 2/s) was significantly higher than that of the malignant group (median 1.00×10 -3mm 2/s, P<0.05). The benign group included 6 cases of TIC curve type Ⅰ, 5 cases of type Ⅱ, and 2 cases of type Ⅲ. The malignant group included 2 cases of TIC curve type Ⅰ, 17 cases of type Ⅱ, and 11 cases of type Ⅲ. The difference between the two groups was statistically significant ( P<0.05). The area under the ROC curve (AUC) of T1pre before DCE was 0.869, higher than 0.806 of ADC and 0.697 of TIC. When the best cut-off value of 1 282.94 ms was chosen, the sensitivity and specificity of diagnosis were 76.9% and 93.3%, respectively. The combination of T1pre and T2pre can further improve the diagnostic performance (AUC=0.908). Conclusions:Among the syMRI quantitative parameters, T1pre, T2pre, ΔT1 and ΔT2 have good value for the differential diagnosis of benign and malignant breast lesions. T1pre has the best diagnostic performance, and the combination of T1pre and T2pre can further improve the diagnostic performance.

Objective:To explore the diagnostic accuracy improved by magnetic resonance imaging (MRI) biomarkers for lymph node metastasis in T1-2 stage rectal cancer before treatment.Methods:Medical records of 327 patients with T1-2 rectal cancer who underwent pretreatment MRI and rectal tumor resection between January 2015 and November 2019 were retrospectively analyzed. Fifty-seven cases were divided into the lymph node metastasis group (N+ group) while other 270 cases in the non-lymph node metastasis group (N-group) according to the pathologic diagnosis. Two radiologist evaluated the tumor characteristics of MRI images. The relationship of the clinical and imaging characteristics of lymph node metastasis was assessed by using univariate analysis and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic abilities for the differentiation of N- from N+ tumors.Results:Among the 327 patients, MR-N evaluation was positive in 67 cases, which was statistically different from the pathological diagnosis ( P<0.001). The sensitivity, specificity and accuracy of MRI for lymph node metastasis were 45.6%, 84.8% and 78.0%, respectively. Multivariate regression analysis showed that tumor morphology ( P=0.002), including mucus or not ( P<0.001), and MR-N evaluation ( P<0.001) were independent influencing factors for stage T1-2 rectal cancer with lymph node metastasis. The area under the ROC curve of rectal cancer with lymph node metastasis analyzed by the logistic regression model was 0.786 (95% CI: 0.720~0.852). Conclusions:Tumor morphology, including mucus or not, and MR-N evaluation can serve as independent biomarkers for differentiation of N- and N+ tumors. The model combined with these biomarkers facilitates to improve the diagnostic accuracy of lymph node metastasis in T1-2 rectal cancers by using MRI.

Objective:To explore the diagnostic value of synthetic magnetic resonance imaging (syMRI) quantitative parameters for benign and malignant breast lesions.Methods:From September 2018 to March 2019, a total of 43 cases of breast lesions which were confirmed by surgery and pathology in Cancer Hospital, Chinese Academy of Medical Sciences were enrolled in this study. All patients underwent syMRI sequence scans before and after enhancement except for conventional T2WI, DWI, and enhancement scans. GE AW4.7 workstation was used to generate syMRI parameter maps (T1, T2, proton density mappings), and ITK-SNAP software was used to delineate the volume of interest. The T1, T2, PD values before and after dynamic contrast enhanced (DCE) were obtained, and the change values of each parameter were calculated. Meanwhile, the apparent diffusion coefficient (ADC) and time intensity curve (TIC) of the lesions were measured. The differences of each parameter value were compared between benign and malignant breast lesions, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of each parameter.Results:Among the 43 enrolled cases, 13 were benign and 30 were malignant. Among the syMRI parameters, the pre-enhancement parameters including T1pre (median 1 663.07 ms), T2pre (median 103.33 ms), post-enhancement parameters ΔT1 (median 1 022.68 ms) and ΔT2 (median 27.67 ms) of benign group, significantly higher than those of the malignant group (the medians were 1 141.74, 92.53, 664.95, and 16.19 ms, respectively, P<0.05). The ADC value of the benign group (median 1.66×10 -3mm 2/s) was significantly higher than that of the malignant group (median 1.00×10 -3mm 2/s, P<0.05). The benign group included 6 cases of TIC curve type Ⅰ, 5 cases of type Ⅱ, and 2 cases of type Ⅲ. The malignant group included 2 cases of TIC curve type Ⅰ, 17 cases of type Ⅱ, and 11 cases of type Ⅲ. The difference between the two groups was statistically significant ( P<0.05). The area under the ROC curve (AUC) of T1pre before DCE was 0.869, higher than 0.806 of ADC and 0.697 of TIC. When the best cut-off value of 1 282.94 ms was chosen, the sensitivity and specificity of diagnosis were 76.9% and 93.3%, respectively. The combination of T1pre and T2pre can further improve the diagnostic performance (AUC=0.908). Conclusions:Among the syMRI quantitative parameters, T1pre, T2pre, ΔT1 and ΔT2 have good value for the differential diagnosis of benign and malignant breast lesions. T1pre has the best diagnostic performance, and the combination of T1pre and T2pre can further improve the diagnostic performance.

Objective: To evaluate the value of T2WI signal intensity related parameters that can be obtained by magnetic resonance imaging (MRI) for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanved rectal cancer (LARC). Methods: Signal Intensity of Tumor (SIT) and Signal Intensity of Tumor/Muscle (SIT/M) of MR T2WI before and after neoadjuvant chemoradiotherapy of 101 patients with locally advanced rectal cancer were evaluated by two experienced readers independently. Signal Intensity of Tumor Reduction Rate (SITRR) and Signal Intensity of Tumor/Muscle Reduction Rate (SIT/MRR) were calculated. The difference of related parameters of T2WI tumor signal intensity between the pCR and the non-pCR group were analyzed. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic performance for predicting pCR. Results: Of the 101 patients, 18 were in pCR group and 83 were in non-pCR group. In all patients, the SITpre, SITpost, SITRR, SIT/Mpre, SIT/Mpost and SIT/MRR measured by reader 1 were 197.0 (133.0), 144.2 (69.7), 0.4% (0.5%), 2.6 (0.6), 3.0 (2.3) and 0.4 (0.2)% in pCR group, and 227.0 (99.0), 205 (95.4), 0.1% (0.6%), 2.6 (0.6), 2.6 (1) in non-pCR group, respectively. SITpre, SITpost, SITRR, SIT/Mpre, SIT/Mpost and SIT/MRR measured by reader 2 were 193.0 (135.0), 143.0 (69.8), 0.4% (0.2%), 2.6 (0.6), 1.5 (0.5) and 0.39% (0.2%) in pCR group, and 234.0(108.0), 203(96.5), 0.1% (0.3%), 2.6 (0.6%), 1.7 (0.7) and 0.25% (0.2%) in non-pCR group, respectively. Between the pCR and non-pCR group, there were significant differences in SITpost, SIT/Mpost and SIT/MRR measured by both readers (all P<0.01), but there was no significant differences in SITpre and SIT/Mpre (P>0.05). The difference of SITRR measured by reader 1 was not statistically significant (P=0.415), while the difference of SITRR measured by reader 2 was statistically significant (P=0.001). In patients with rectal non-mucinous adenocarcinoma, SITpost, SIT/Mpost, SITRR and SIT/MRR measured by two physicians were still statistically significant between the pCR and non-pCR group (all P<0.01), but SITpre and SIT/Mpre had no significant difference (P>0.05). ROC curve analysis showed that in all patients, the area under curve (AUC) of SITpost, SIT/Mpost and SIT/MRR for predicting pCR to neoadjuvant chemoradiotherapy in locally advanced rectal cancer was 0.694-0.762, the sensitivity was 68.2%-77.3%, and the specificity was 63.6%-77.3%. In rectal non-mucinous adenocarcinoma patients, the AUC, sensitivity and specificity was 0.704-0.764, 62.7%-78.9% and 66.2%-84.2%, respectively. Conclusions T2WI signal intensity related parameters are potential predictors for pCR in locally advanced rectal cancer after neoadjuvant chemoradiptherapy. The predictive value is higher in non-mucinous adenocarcinoma.

Objective To compare the value of diffusion kurtosis imaging (DKI) mode and mono-exponential mode in predicting the response to neoadjuvant chemotherapy (NAC) for locally advanced breast carcinoma using DWI.Methods From January 1,2013 to December 31,2016,eighty patients with locally advanced breast carcinoma were enrolled into this prospective clinical study.The diagnosis was confirmed on the basis of histopathological results.The clinical stage stayed at Ⅱ or Ⅲ.The patients would receive breast-conserving surgery after NAC.All the patients underwent DWI examination by using both mono-exponential mode and DKI mode before chemotherapy was initiated.The parameters included ADC,mean diffusivity (MD) and mean kurtosis (MK).Within 1 to 3 days before or after MRI examination,the patients underwent aspiration biopsy,received 4 to 8 cycles of NAC and followed by surgery.According to histologic grading before NAC,the patients were classified into well-differentiated and poor-differentiated group.According to the comparison between pathological results acquired from biopsy before NAC and specimen acquired after surgery,the patients were classified into pathologic complete response (pCR) and pathologic non-complete response (non-pCR) according to treatment effect.The imaging parameters were compared between the pCR and the non-pCR group using t test.The predicting ability of two imaging modes was compared and analyzed with ROC analysis.The relationships between multiple imaging parameters,pathologic,clinical characteristics of tumor and treatment effect were analyzed using logistic multi-variate regression analysis,and further analyzed using Wald test.Results There were 30 cases of pCR and 50 cases of non-pCR.The ADC and MD values were lower in the pCR group than in the non-pCR group (P＜0.05).MK value was higher in the pCR group than in the non-pCR group (P＜0.05).ROC analysis showed that the area under ROC curve of ADC,MD and MK in predicting treatment effect were 0.732,0.866 and 0.683 respectively.Logistic regression analysis showed that,according to predicting ability,MD,ADC and MK successively were the independent predictors for the early response to chemotherapy.Conclusion Compared with mono-exponential mode,DKI mode can reflect the real micro-environment and water diffusion restriction within the tumor area more reliably and accurately,and is more suitable to serve as an imaging technique for predicting the response to NAC for locally advanced breast carcinoma.