Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer.

OBJECTIVE: To investigate the diagnostic value of diffusion kurtosis magnetic resonance imaging (DKI) and conventional diffusion-weighted imaging (DWI) for evaluating the response to first-line chemotherapy in unresectable pancreatic cancer. MATERIALS AND METHODS: We retrospectively analyzed 21 patients with clinically and pathologically confirmed unresected pancreatic cancer who received palliative chemotherapy. Three-tesla MRI examinations containing DWI sequences with b values of 0, 100, 700, 1400, and 2100 s/mm2 were performed before and after chemotherapy. Parameters included the apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), and mean diffusional kurtosis (MK). The performances of the DWI and DKI parameters in distinguishing the response to chemotherapy were evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Overall survival (OS) was calculated from the date of first treatment to the date of death or the latest follow-up date. RESULTS: The ADCchange and MDchange were significantly higher in the responding group (PR group) than in the nonresponding group (non-PR group) (ADCchange: 0.21 ± 0.05 vs. 0.11 ± 0.09, P = 0.02; MDchange: 0.37 ± 0.24 vs. 0.10 ± 0.12, P = 0.002). No statistical significance was shown when comparing ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost between the PR and non-PR groups. The ROC curve analysis indicated that MDchange (AUC = 0.898, cutoff value = 0.7143) performed better than ADCchange (AUC = 0.806, cutoff value = 0.1369) in predicting the response to chemotherapy. CONCLUSION: The ADCchange and MDchange demonstrated strong potential for evaluating the response to chemotherapy in unresectable pancreatic cancer. The MDchange showed higher specificity in the classification of PR and non-PR than the ADCchange. Other parameters, including ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost, are not suitable for response evaluation. The combined model SUMchange demonstrated superior performance compared to the individual DWI and DKI models. Further experiments are needed to evaluate the potential of DWI and DKI parameters in predicting the prognosis of patients with unresectable pancreatic cancer.

PTPN13 rs989902 and CHEK2 rs738722 are associated with esophageal cancer.

PURPOSE: Individual genetic background can play an essential role in determining the development of esophageal squamous cell carcinoma (ESCC). PTPN13 and CHEK2 play important roles in the pathogenesis of ESCC. This case-control study aimed to analyze the association between gene polymorphisms and ESCC susceptibility. METHODS: DNA was extracted from the peripheral blood of patients. The Agena MassARRAY platform was used for the genotyping. Statistical analysis was conducted using the chi-squared test or Fisher's exact test, logistic regression analysis, and stratification analysis. RESULTS: The 'G' allele of rs989902 (PTPN13) and the 'T' allele of rs738722 (CHEK2) were both associated with an increased risk of ESCC (rs989902: OR = 1.23, 95% CI = 1.02-1.47, p = 0.028; rs738722: OR = 1.28, 95% CI = 1.06-1.55, p = 0.011). Stratification analysis showed that SNPs (rs989902 and rs738722) were notably correlated with an increased risk of ESCC after stratification for age, sex, smoking, and drinking status. In addition, rs738722 might be associated with lower stage, while rs989902 had a lower risk of metastasis. CONCLUSION: Our findings display that PTPN13 rs989902 and CHEK2 rs738722 are associated with an increased risk of ESCC in the Chinese Han population.

Association of PTGER4 and PRKAA1 genetic polymorphisms with gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common malignancies, affected by several genetic loci in the clinical phenotype. This study aimed to determine the association between PTGER4 and PRKAA1 gene polymorphisms and the risk of GC. METHODS: A total of 509 GC patients and 507 age and sex-matched healthy controls were recruited to explore the association between PTGER4 and PRKAA1 genetic polymorphisms and GC susceptibility. Logistic regression analysis was used to study the correlation between these SNPs and GC, with odd ratio (OR) and 95% confidence interval (CI) as indicators. Multifactor dimensionality reduction was utilized to analyze the genetic relationships among SNPs. was conducted to predict gene expression, the impact of SNPs on gene expression, and the signaling pathways involved in PTGER4 and PRKAA1. RESULTS: Overall, rs10036575 in PTGER4 (OR = 0.82, p = 0.029), rs10074991 (OR = 0.82, p = 0.024) and rs13361707 (OR = 0.82, p = 0.030) in PRKAA1 were associated with susceptibility to GC. Stratification analysis revealed that the effects of these SNPs in PTGER4 and PRKAA1 on GC susceptibility were dependent on smoking and were associated with a reduced risk of adenocarcinoma (p < 0.05). Bioinformatics analysis showed an association between SNPs and corresponding gene expression (p < 0.05), and PRKAA1 may affect GC by mediating RhoA. CONCLUSION: This study suggests that PTGER4 and PRKAA1 SNPs might affect the susceptibility of GC, providing a new biological perspective for GC risk assessment, pathogenesis exploration, and personalized treatment.

Concurrent Dual-Contrast Enhancement Using Fe3O4 Nanoparticles to Achieve a CEST Signal Controllability.

Traditional T2 magnetic resonance imaging (MRI) contrast agents have defects inherent to negative contrast agents, while chemical exchange saturation transfer (CEST) contrast agents can quantify substances at trace concentrations. After reaching a certain concentration, iron-based contrast agents can "shut down" CEST signals. The application range of T2 contrast agents can be widened through a combination of CEST and T2 contrast agents, which has promising application prospects. The purpose of this study is to develop a T2 MRI negative contrast agent with a controllable size and to explore the feasibility of dual contrast enhancement by combining T2 with CEST contrast agents. The study was carried out in vitro with HCT-116 human colon cancer cells. A GE SIGNA Pioneer 3.0 T medical MRI scanner was used to acquire CEST images with different saturation radio-frequency powers (1.25/2.5/3.75/5 µT) by 2D spin echo-echo planar imaging (SE-EPI). Magnetic resonance image compilation (MAGiC) was acquired by a multidynamic multiecho 2D fast spin-echo sequence. The feasibility of this dual-contrast enhancement method was assessed by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, ζ potential analysis, inductively coupled plasma, X-ray photoelectron spectroscopy, X-ray powder diffraction, vibrating-sample magnetometry, MRI, and a Cell Counting Kit-8 assay. The association between the transverse relaxation rate r2 and the pH of the iron-based contrast agents was analyzed by linear fitting, and the linear relationship between the CEST effect in different B1 fields and pH was analyzed by the ratio method. Fe3O4 nanoparticles (NPs) with a mean particle size of 82.6 ± 22.4 nm were prepared by a classical process, and their surface was successfully modified with -OH active functional groups. They exhibited self-aggregation in an acidic environment. The CEST effect was enhanced as the B1 field increased, and an in vitro pH map was successfully plotted using the ratio method. Fe3O4 NPs could stably serve as reference agents at different pH values. At a concentration of 30 µg/mL, Fe3O4 NPs "shut down" the CEST signals, but when the concentration of Fe3O4 NPs was less than 10 µg/mL, the two contrast agents coexisted. The prepared Fe3O4 NPs had almost no toxicity, and when their concentration rose to 200 µg/mL at pH 6.5 or 7.4, they did not reach the half-maximum inhibitory concentration (IC50). Fe3O4 magnetic NPs with a controllable size and no toxicity were successfully synthesized. By combining Fe3O4 NPs with a CEST contrast agent, the two contrast agents could be imaged simultaneously; at higher concentrations, the iron-based contrast agent "shut down" the CEST signal. An in vitro pH map was successfully plotted by the ratio method. CEST signal inhibition can be used to realize the pH mapping of solid tumors and the identification of tumor active components, thus providing a new imaging method for tumor efficacy evaluation.

Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.

BACKGROUND: Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE: To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE: Retrospective. POPULATION: A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT: A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS: Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS: In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION: Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Contribution of ZBTB20 Polymorphisms to Esophageal Cancer Risk Among the Chinese Han Population.

Background: ZBTB20 was overexpressed in esophageal cancer (EC). The study aimed to identify genotypes of ZBTB20 polymorphisms and their correlation with EC occurrence in a Chinese Han population. Methods: Four single nucleotide polymorphisms (SNPs) in ZBTB20 were randomly selected for genotyping through Agena MassARRAY system among 525 EC patients and 522 healthy controls. Multiple genetic models were applied to assess the association of ZBTB20 polymorphisms with EC susceptibility by calculating odds ratios (ORs) with 95% confidence intervals (CIs). Results: Rs10934270 was associated with lower EC susceptibility (OR = 0.64, p = 0.004) with statistical power >90% in overall analysis. Specifically, the correlation of rs10934270 with EC susceptibility was found in subgroups including patients with esophageal squamous cell carcinoma (ESCC), males, subjects aged ≤65 years, subjects with BMI ≤ 24 kg/m2, and smokers. Rs9841504 might be a risk-increasing factor for ESCC. Moreover, rs9288999 in subjects aged ≤65 years and rs73230612 in females were related to lower EC risk. Conclusion: Our research is the first to report that ZBTB20 rs10934270 is associated with reduced EC susceptibility in the Chinese Han population. These data provide a scientific basis for understanding the influence of the ZBTB20 gene on EC occurrence.

The associations between serum high-density lipoprotein cholesterol levels and malignant behavior in pancreatic neuroendocrine neoplasms.

BACKGROUND: The role of serum high-density lipoprotein cholesterol (HDL-c) in tumorigenesis are observed in several endocrine-related cancers. However, its role in pancreatic neuroendocrine neoplasms (PNENs) has not been understood. In the current study, the relationship between HDL-c levels and malignant behavior in PNENs was explored. METHODS: One hundred ninety-seven patients with histopathology confirmed PNENs were included. PNENs were divided into three grades (G1, G2 and G3) as 2017 WHO classification based on ki67 index and mitosis count. The demographic data, clinical information, tumor morphological and pathological features (organs invasion, lymph node metastasis, vascular invasion and perineural invasion), and serum tumor biomarkers were collected. The relationships between HDL-c levels and malignant behaviors in PNENs were analyzed using logistic regression analysis. Models were also developed for the identification of high grade PNENs. RESULTS: The levels of serum HDL-c in G2/G3 tumor were significantly lower than that in G1 tumor (P = 0.031). However, no such difference was found between G3 and G1/G2. The proportions of low HDL-c (≤ 0.9 mmol/L) were higher in high-grade PNENs (G2/G3 or G3) than those in low-grade (G1 or G1/G2) (29.0 vs 15.2%, P = 0.032; 37.0 vs 20.5%, P = 0.023). The risk of G2/G3 tumors in patients with high serum HDL-c levels was decreased (odds ratio (OR) = 0.35, 95% confidence interval (CI): 0.12-0.99). Similarly, the risk of G3 PNENs increased in patients with low HDL-c levels (OR = 2.51, 95%CI:1.12-5.60). HDL-c level was also associated with a high ki67 index (> 55%) (OR = 0.10, 95%CI: 0.02-0.51) and neuroendocrine carcinoma G3 (OR = 0.21, 95%CI: 0.06-0.80). The area under the curve (AUC) of HDL-c + tumor size + age was 0.85 (95% CI: 0.79-0.91) in identifying G2/G3 PNENs, and HDL-c (> 0.9 mmol/L) + tumor size + age had an AUC of 0.77 (95% CI: 0.70-0.84) in identifying G3 PNENs. HDL-c level was associated with lymph node metastasis (OR = 0.24, 95%CI:0.08-0.99). CONCLUSION: Serum HDL-c levels were significantly associated with malignant behaviors in PNENs, in particular to tumor grade and lymph node metastasis.

Cystic Neoplasms of the Pancreas: Differential Diagnosis and Radiology Correlation.

Although the probability of pancreatic cystic neoplasms (PCNs) being detected is raising year by year, their differential diagnosis and individualized treatment are still a challenge in clinical work. PCNs are tumors containing cystic components with different biological behaviors, and their clinical manifestations, epidemiology, imaging features, and malignant risks are different. Some are benign [e.g., serous cystic neoplasms (SCNs)], with a barely possible that turning into malignant, while others display a low or higher malignant risk [e.g., solid pseudopapillary neoplasms (SPNs), intraductal papillary mucinous neoplasms (IPMNs), and mucinous cystic neoplasms (MCNs)]. PCN management should concentrate on preventing the progression of malignant tumors while preventing complications caused by unnecessary surgical intervention. Clinically, various advanced imaging equipment are usually combined to obtain a more reliable preoperative diagnosis. The challenge for clinicians and radiologists is how to accurately diagnose PCNs before surgery so that corresponding surgical methods and follow-up strategies can be developed or not, as appropriate. The objective of this review is to sum up the clinical features, imaging findings and management of the most common PCNs according to the classic literature and latest guidelines.

Predicting the pathological grade of breast phyllodes tumors: a nomogram based on clinical and magnetic resonance imaging features.

OBJECTIVE: To explore the potential factors related to the pathological grade of breast phyllodes tumors (PTs) and to establish a nomogram to improve their differentiation ability. METHODS: Patients with PTs diagnosed by post-operative pathology who underwent pretreatment magnetic resonance imaging (MRI) from January 2015 to June 2020 were retrospectively reviewed. Traditional clinical features and MRI features evaluated according to the fifth BI-RADS were analyzed by statistical methods and introduced to a stepwise multivariate logistic regression analysis to develop a prediction model. Then, a nomogram was developed to graphically predict the probability of non-benign (borderline/malignant) PTs. RESULTS: Finally, 61 benign, 73 borderline and 48 malignant PTs were identified in 182 patients. Family history of tumor, diameter, lobulation, cystic component, signal on fat saturated T2 weighted imaging (FS T2WI), BI-RADS category and time-signal intensity curve (TIC) patterns were found to be significantly different between benign and non-benign PTs. The nomogram was finally developed based on five risk factors: family history of tumor, lobulation, cystic component, signal on FS T2WI and internal enhancement. The AUC of the nomogram was 0.795 (95% CI: 0.639, 0.835). CONCLUSION: Family history of tumor, lobulation, cystic components, signals on FS T2WI and internal enhancement are independent predictors of non-benign PTs. The prediction nomogram developed based on these features can be used as a supplemental tool to pre-operatively differentiate PTs grades. ADVANCES IN KNOWLEDGE: More sample size and characteristics were used to explore the factors related to the pathological grade of PTs and establish a predictive nomogram for the first time.

MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases.

PURPOSE: The purpose of the study was to determine whether the pre-treated MR texture features of colorectal liver metastases (CRLMs) are predictive of therapeutic response after chemotherapy. METHODS: The study included twenty-six consecutive patients (a total of 193 liver metastasis) with unrespectable CRLMs at our institution from August 2014 to February 2016. Lesions were categorized into either responding group or non-responding group according to changes in size. Texture analysis was quantified on T2-weighted images by two radiologists with consensus on regions of interest which were manually drawn on the largest cross-sectional area of the lesions. Five histogram features (mean, variance, skewness, kurtosis, and entropy1) and five gray level co-occurrence matrix features (GLCM; angular second moment (ASM), entropy2, contrast, correlation, and inverse difference moment (IDM)) were extracted. The texture parameters were statistically analyzed to identify the differences between the two groups, and the potential predictive parameters to differentiate the responding group from the non-responding group were subsequently tested using multivariable logistic regression analysis. RESULTS: A total of 107 responding and 86 non-responding lesions were evaluated. A higher variance, entropy1, contrast, entropy2 and a lower ASM, correlation, IDM were independently (P < 0.05) associated with a good response to chemotherapy with the areas under the ROC curves (AUCs) of 0.602-0.784. Variance (P < 0.001) and ASM (P = 0.001) remained potential predictive values to discriminate responding lesions from non-responding lesions when tested using multivariable logistic regression analysis. The highest AUC of the predictors from the association of variance and ASM was 0.814. CONCLUSION: MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.

Three-Dimensional CT Texture Analysis to Differentiate Colorectal Signet-Ring Cell Carcinoma and Adenocarcinoma.

PURPOSE: The objective of this research was to validate the diagnostic value of three-dimensional texture parameters and clinical characteristics in the differentiation of colorectal signet-ring cell carcinoma (SRCC) and adenocarcinoma (AC). METHODS: We retrospectively analyzed data from 102 patients with SRCC or AC confirmed by pathology, including 51 SRCC (from January 2015 to July 2019) and 51 AC patients (from January 2019 to July 2019). CT findings and clinical data, including age, gender, clinical symptoms, serological biomarkers, tumor size, and tumor location, were compared between SRCC and AC. CT texture features were quantified on portal phase images using three-dimensional analysis. A list of texture parameters was generated with MaZda software for the classification of tumors. The texture features, clinical data and CT findings were statistically analyzed for the discrimination ability of SRCC and AC, and the potential predictive parameters that may be used to differentiate the two groups were subsequently tested using the least absolute shrinkage and selection operator (LASSO) and logistic regression analyses. The receiver operating characteristic curve (ROC) provided a range of values for establishing the cutoff value, as well as the sensitivity and specificity of prediction for each significant variable. RESULTS: SRCC occurred more often in men than AC did (80.39% vs 49.02%, P < 0.01). The patients were younger in the SRCC group than in the AC group, without a statistically significant difference (55.84 vs 59.20 years, P = 0.216). There were no significant differences in the clinical symptoms, tumor size, or tumor location between the two groups (P=0.505, P=0.19, P=0.843, respectively). The elevation of serological biomarker CA724 was more common in SRCC than in AC (P< 0.001). Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg were lower in the SRCC group than in the AC group during the portal phase, with the areas under curve (AUCs) of 0.892-0.929, sensitivity of 76.5-84.3% and specificity of 88.2-96.1%. In the differentiation between SRCC and AC, the 1-NN minimal classification error (MCR) was 29.41%. CONCLUSION: Three-dimensional texture parameters, including Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg, exhibited a favorable discriminatory ability to distinguish SRCC from AC.

Diffusion kurtosis imaging in the characterisation of rectal cancer: utilizing the most repeatable region-of-interest strategy for diffusion parameters on a 3T scanner.

OBJECTIVES: Our goal was to investigate the correlation between histopathology and diffusion parameters by utilising the most repeatable region-of-interest (ROI) strategy for diffusion parameters in rectal cancer on a 3T scanner. METHODS: 113 patients underwent DKI-MR and 66 of these patients received surgery without neoadjuvant chemoradiotherapy. Two readers independently measured the parameters using three slice protocols including single slice, three slices and whole-tumour slice (WTS), combined with one of two ROIs, including outline and round ROI. ANOVA, Kruskal-Wallis, a paired sample t-test, interclass correlation coefficient (ICC), Bland-Altman, Student's t-tests, receiver operating characteristic curves and z statistic were used for statistical analysis. RESULTS: There were no significant differences among the three slice protocols in ADC values (p = 0.822, 0.987), K values (p = 0.842, 0.859) and D values (p = 0.917, 0.988) using round and outline ROI, respectively. The ADC and D values derived from outline ROIs were higher than those from round ROIs (all p < 0.001 for ADC, all p < 0.001 for D), while K values derived from outline ROIs were lower than those from round ROIs (p < 0.001, p = 0.001, p < 0.001) using three slice protocols, respectively. The WTS-outline ROI resulted in the best intra- and inter-observer ICC. Utilising the WTS-outline ROI method, the AUC for assessment of well-differentiated tumours was 0.871 by K and 0.809 by ADC; and the AUC for T2 was 0.768 by K. CONCLUSIONS: The most repeatable strategy was the WTS-outline ROI method. In addition to DWI, DKI also have diagnostic value for rectal cancer histopathological characteristics utilising the WTS-outline ROI on a 3T scanner. KEY POINTS: • DKI using a 3T scanner is feasible for assessing rectal cancer. • ROI and slice protocol show considerable influence on DKI parameters. • DKI parameters exhibit excellent repeatability using whole-tumour slice-outline ROI on 3T scanner. • DKI has considerable diagnostic value for the estimation of rectal cancer characteristics.

The value of diffusion kurtosis imaging in assessing pathological complete response to neoadjuvant chemoradiation therapy in rectal cancer: a comparison with conventional diffusion-weighted imaging.

OBJECTIVES: The aim of this study is to comprehensively evaluate the advantage of diffusion kurtosis imaging (DKI) in distinguishing pathological complete response (pCR) from non-pCR patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiation therapy (CRT) in comparison to conventional diffusion-weighted imaging (DWI). MATERIAL AND METHODS: Fifty-six consecutive patients diagnosed with LARC were prospectively enrolled and underwent pre- and post-CRT MRI on a 3.0 T MRI scanner. Apparent diffusion coefficient (ADC), mean diffusion (MD) and mean kurtosis (MK) values of the tumor were measured in pre- and post-CRT phases and then compared to histopathologic findings after total mesorectal excision (TME). Both Mann-Whitney U-test and Kruskal-Wallis test were used as statistical methods. Diagnostic performance was determined using receiver operating characteristic (ROC) curve analysis. RESULTS: For a total of 56 rectal lesions (pCR, n = 14; non-pCR, n = 42), the MKpre and MKpost values were much lower for the pCR patients (mean±SD, 0.72±0.09 and 0.56±0.06, respectively) than those for the non-pCR patients (0.89±0.11 and 0.68±0.08, respectively) (p < 0.001). The ADCpost and the change ratio of apparent diffusion coefficient (ADCratio) values was significantly higher for the pCR patients (mean±SD, 1.31±0.13 and 0.64±0.34, respectively) than for the non-pCR patients (1.12±0.16 and 0.33±0.27, respectively) (p < 0.001 and p = 0.001, respectively). In addition, the MDpost and the change ratio of mean diffusion (MDratio) (2.45±0.33 vs. 1.95±0.30, p < 0.001; 0.80±0.43 vs. 0.35±0.32, p < 0.001, respectively) also increased, whereas the ADCpre, MDpre and the change ratio of mean kurtosis (MKratio) of the pCR (0.82±0.11, 1.40±0.21, and 0.23±0.010, respectively) exhibited a neglectable difference with that of the non-pCR (p = 0.332, 0.269, and 0.678, respectively). The MKpost showed relatively high sensitivity (92.9%) and high specificity (83.3%) in comparison to other image indices. The area under the receiver operating characteristic curve (AUROC) that is available for the assessment of pCR using MKpost (0.908, cutoff value = 0.6196) were larger than other parameters and the overall accuracy of MKpost (85.7%) was the highest. CONCLUSIONS: Both DKI and conventional DWI hold great potential in predicting treatment response to neoadjuvant chemoradiation therapy in rectal cancer. The DKI parameters, especially MKpost, showed a higher specificity than conventional DWI in assessing pCR and non-pCR in patients with LARC, but the pre-CRT ADC and MD are unreliable.

Digital breast tomosynthesis plus mammography, magnetic resonance imaging plus mammography and mammography alone: A comparison of diagnostic performance in symptomatic women.

PURPOSE: To compare the diagnostic efficiency of digital breast tomosynthesis (DBT) plus digital mammography (DM) and magnetic resonance imaging (MRI) plus DM in symptomatic women. MATERIALS AND METHODS: The protocol used in our study was accepted by the ethics committee at our hospital, and informed consent was obtained from all patients. Between June and December 2014, 197 patients with 238 histologically proven lesions all underwent DM, DBT and MRI. Two radiologists were responsible for interpreting all images according to the Breast Imaging Reporting and Data System (BI-RADS). The diagnostic performance of each method was assessed by receiver-operating characteristic (ROC) curve. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were compared using McNemar's test and Fisher's exact test. A Kappa test was used to assess the interobserver agreement. RESULTS: The area under the ROC curve (AUC) was lower in the group that underwent DM alone (Radiologist1 [R1], 0.849; Radiologist2 [R2], 0.850) than in the group that underwent DBT plus DM (R1, 0.907, P = 0.0204; R2, 0.900, P = 0.0239) and MRI plus DM (R1, 0.939, P = 0.0006; R2, 0.935, P = 0.0009). However, the difference between the group that received DBT plus DM and the group that received MRI plus DM was not significant (R1, P = 0.1262; R2, P = 0.0843). The accuracy (R1, 71.8%; R2, 71.4%) and sensitivity (R1, 71.9%; R2, 71.2%) of DM were lower than those of DBT ((accuracy: R1, 85.3%, P = 0.001; R2, 83.6%, P < 0.001; sensitivity: R1,92.1%, P < 0.001; R2, 90.8%, P < 0.001) and MRI combined with DM (accuracy: R1, 90.3%, P = 0.001; R2, 90.7%, P < 0.001; sensitivity: R1, 94.7%, P < 0.001; R2, 95.4%, P < 0.001). In contrast, no significant difference was observed between DBT and MRI combined with DM (accuracy: R1, P = 0.644; R2, P = 0.360; sensitivity: R1, P = 0.502; R2, P = 0.359). The interobserver agreement of each method was excellent (k = 0.894 0.919 and 0.882 for DM, DBT and MRI combined with DM, respectively). CONCLUSION: The diagnostic performance of DBT and MRI combined with DM is superior to that of DM alone in symptomatic women; MRI plus DM is slightly better than that of DBT plus DM, but this difference was not statistically significant.