Apparent diffusion coefficient-based histogram analysis differentiates histological subtypes of periampullary adenocarcinoma

BACKGROUND For periampullary adenocarcinoma,the histological subtype is a better prognostic predictor than the site of tumor origin.Intestinal-type periampullary adenocarcinoma(IPAC)is reported to have a better prognosis than the pancreatobiliary-type periampullary adenocarcinoma(PPAC).However,the classification of histological subtypes is difficult to determine before surgery.Apparent diffusion coefficient(ADC)histogram analysis is a noninvasive,nonenhanced method with high reproducibility that could help differentiate the two subtypes.AIM To investigate whether volumetric ADC histogram analysis is helpful for distinguishing IPAC from PPAC.METHODS Between January 2015 and October 2018,476 consecutive patients who were suspected of having a periampullary tumor and underwent magnetic resonance imaging(MRI)were reviewed in this retrospective study.Only patients who underwent MRI at 3.0 T with different diffusion-weighted images(b-values=800 and 1000 s/mm^2)and who were confirmed with a periampullary adenocarcinoma were further analyzed.Then,the mean,5th,10th,25th,50th,75th,90th,and 95th percentiles of ADC values and ADCmin,ADCmax,kurtosis,skewness,and entropy were obtained from the volumetric histogram analysis.Comparisons were made by an independent Student's t-test or Mann-Whitney U test.Multiple-class receiver operating characteristic curve analysis was performed to determine and compare the diagnostic value of each significant parameter.RESULTS In total,40 patients with histopathologically confirmed IPAC(n=17)or PPAC(n=23)were enrolled.The mean,5th,25th,50th,75th,90th,and 95th percentiles and ADCmax derived from ADC1000 were significantly lower in the PPAC group than in the IPAC group(P<0.05).However,values derived from ADC800 showed no significant difference between the two groups.The 75th percentile of ADC1000 values achieved the highest area under the curve(AUC)for differentiating IPAC from PPAC(AUC=0.781;sensitivity,91%;specificity,59%;cut-off value,1.50×10^-3 mm^2/s).CONCLUSION Volumetric ADC histogram analysis at a b-value of 1000 s/mm2 might be helpful for differentiating the histological subtypes of periampullary adenocarcinoma before surgery.

Assessing the Early Response of Advanced Cervical Cancer to Neoadjuvant Chemotherapy Using Intravoxel Incoherent Motion Diffusion-weighted Magnetic Resonance Imaging： A Pilot Study

Background： Diffusion-weighted imaging （DWI） with the intravoxel incoherent motion （IVIM） model has shown promising results for providing both diffusion and perfusion intbrmation in cervical cancer; however, its use to predict and monitor the efficacy ofneoadjuvant chemotherapy （NACT） in cervical cancer is relatively rare. The study aimed to evaluate the use of DWl with 1VIM and monoexponential models to predict and monitor the efficacy of NACT in cervical cancer. Methods： Forty-two patients with primary cervical cancer underwent magnetic resonance exams at 3 time points （pre-NACT, 3 weeks after the first NACT cycle, and 3 weeks after the second NACT cycle）. The response to treatment was determined according to the response evaluation criteria in solid tumors 3 weeks after the second NACT treatment, and the subjects were classified as two groups： responders and nonresponders groups. The apparent diffusion coefficient （ADC）, true diffusion coefficient （D）, perfusion-related pseudo-diffusion coefficient （D＊）, and perfusion fraction （f） values were determined. The differences in IVlM-derived variables and ADC between the different groups at the different time points were calculated using an independent samples t-test. Results： The D and ADC values were all significantly higher for the responders than tbr the nonresponders at all 3 time points, but no significant differences were observed in the D＊ and fvalues. An analysis of the receiver operating characteristic （ROC） curves indicated that a D value threshold 〈0.93 × 10 3 mm2/s and an ADC threshold 〈1.11× 10 3 mm2/s could differentiate responders from nonresponders at pre-NACT time point, yielding area under the curve （AUC） of which were 0.771 and 0.806, respectively. The ROC indicated that the AUCs of D and ADC at the 3 weeks after the first NACT cycle and 3 weeks after the second NACT cycle were 0.823, 0.763, and 0.787, 0.794, respectively. The AUC values of D and ADC at these 3 time points were not significantly different （P = 0.641, 0.512, and 0.547, respectively）. Conclusions： D and ADC values may be useful for predicting and monitoring the efficacy of NACT in cervical cancer. An IVIM model may be equal to monoexponential model in predicting and monitoring the efficacy of NACT in cervical cancer.

Emerging theories and technologies on computational imaging

Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging.

Testing Serial Correlation in Semiparametric Varying-Coefficient Partially Linear EV Models

This paper studies estimation and serial correlation test of a semiparametric varying-coefficient partially linear EV model of the form Y = X^Tβ ＋Z^Tα（T） ＋ε,ξ = X ＋ η with the identifying condition E[（ε,η^T）^T] =0, Cov[（ε,η^T）^T] = σ^2Ip＋1. The estimators of interested regression parameters /3 , and the model error variance σ2, as well as the nonparametric components α（T）, are constructed. Under some regular conditions, we show that the estimators of the unknown vector β and the unknown parameter σ2 are strongly consistent and asymptotically normal and that the estimator of α（T） achieves the optimal strong convergence rate of the usual nonparametric regression. Based on these estimators and asymptotic properties, we propose the VN,p test statistic and empirical log-likelihood ratio statistic for testing serial correlation in the model. The proposed statistics are shown to have asymptotic normal or chi-square distributions under the null hypothesis of no serial correlation. Some simulation studies are conducted to illustrate the finite sample performance of the proposed tests.