Diagnostic value of dual-layer spectral detector CT in differentiating lung adenocarcinoma from squamous cell carcinoma.

Background and objective: The pathological type of non-small cell lung cancer is considered to be an important factor affecting the treatment and prognosis. The purpose of this study was to investigate the diagnostic value of spectral parameters of dual-layer spectral detector computed tomography (DLCT) in determining efficacy to distinguish adenocarcinoma (AC) and squamous cell carcinoma (SC), and their combined diagnostic efficacy was also analyzed. Methods: This is a single-center prospective study, and we collected 70 patients with lung SC and 127 patients with lung AC confirmed by histopathological examination. Morphological parameters, plain scan CT value, biphasic enhanced CT value, and spectral parameters were calculated. The diagnostic efficiency of morphological parameters, spectral parameters, and spectral parameters combined with morphological parameters was obtained by statistical analysis. Results: In univariate analysis, seven morphological CT features differed significantly between SC and AC: tumor location (distribution), lobulation, spicule, air bronchogram, vacuole sign, lung atelectasis and/or obstructive pneumonia, and vascular involvement (all p < 0.05). In the arterial phase and the venous phase, the spectral parameters of AC were higher than those of SC (AP-Zeff: 8.07 ± 0.23 vs. 7.85 ± 0.16; AP-ID: 1.41 ± 0.47 vs. 0.94 ± 0.28; AP-NID: 0.13 ± 0.04 vs. 0.09 ± 0.03; AP-λ: 3.42 ± 1.10 vs. 2.33 ± 0.96; VP-Zeff: 8.26 ± 0.23 vs. 7.96 ± 0.16; VP-ID: 1.18 ± 0.51 vs. 1.16 ± 0.30; VP-NID: 0.39 ± 0.13 vs. 0.29 ± 0.08; VP-λ: 4.42 ± 1.28 vs. 2.85 ± 0.72; p < 0.001). When conducting multivariate analysis combining CT features and DLCT parameters with the best diagnostic efficacy, the independent predictors of AC were distribution on peripheral (OR, 4.370; 95% CI, 1.485-12.859; p = 0.007), presence of air bronchogram (OR, 5.339; 95% CI, 1.729-16.484; p = 0.004), and presence of vacuole sign ( OR, 7.330; 95% CI, 1.030-52.184; p = 0.047). Receiver operating characteristic curves of the SC and AC showed that VP-λ had the best diagnostic performance, with an area under the curve (AUC) of 0.864 and sensitivity and specificity rates of 85.8% and 74.3%, respectively; the AUC was increased to 0.946 when morphological parameters were combined, and sensitivity and specificity rates were 89.8% and 87.1%, respectively. Conclusion: The quantitative parameters of the DLCT spectrum are of great value in the diagnosis of SC and AC, and the combination of morphological parameters and spectral parameters is helpful to distinguish SC from AC.

Dual-layer spectral detector computed tomography parameters can improve diagnostic efficiency of lung adenocarcinoma grading.

Background: It is difficult to distinguish the pathological grade of lung adenocarcinoma (LUAD) with traditional computed tomography (CT). The aim of this study was to assess tumor differentiation by dual-layer spectral detector CT combined with morphological parameters. Methods: In this prospective study, a total of 67 patients with pathologically diagnosed LUAD were enrolled: 39 patients in the well- and moderately-differentiated group (14 and 25 patients, respectively) and 28 patients in the poorly-differentiated group. Morphological parameters, non-enhanced CT number, double-enhanced CT number, effective atomic number, monoenergetic CT images (40 and 70 keV), iodine density, and thoracic aorta iodine density of tumors were measured. The slope of the spectral curve and normalized iodine density were calculated. The diagnostic efficiency of the spectral parameters alone, and the combined spectral and morphological parameters were obtained by statistical analysis. Results: The morphological signs of LUAD (the vessel convergence sign, bronchus encapsulated air sign, and liquefactive necrosis) were higher in the poorly-differentiated group than in the well-moderately-differentiated group (57.1% vs. 30.8%, 40.0%; 60.7% vs. 28.2%, 32.0%; 64.3% vs. 28.2%, 24.0%; all P<0.05). There were significant differences in normalized iodine density (arterial phase: 0.10±0.04 vs. 0.12±0.05, 0.13±0.04; venous phase: 0.31±0.07 vs. 0.39±0.17, 0.40±0.17) among the poorly-differentiated group and moderately-differentiated group as well as the well-differentiated group (all P<0.05). Receiver operating characteristic (ROC) curves of the poorly-differentiated group and well-moderately-differentiated group showed that the normalized iodine density had the best diagnostic efficiency in the arterial phase, with an area under the curve (AUC) of 0.817, sensitivity of 92.9%, and specificity of 82.1% (P<0.001). The AUC increased to 0.916 when the morphological parameters were included, and sensitivity and specificity were 96.4% and 82.1% (P<0.001), respectively. Conclusions: The parameters of dual-layer spectral detector CT can help discriminate the pathological grade of LUAD. Among the spectral parameters, the normalized iodine density in the arterial phase has the best diagnostic efficiency, and the combination of spectral and morphological parameters improves the pathological grading of LUAD.

Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate.

BACKGROUND: There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI. PURPOSE: To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa. STUDY TYPE: Prospective. POPULATION: A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92). FIELD STRENGTH/SEQUENCE: A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE. ASSESSMENT: Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively). STATISTICAL TESTS: Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05. RESULTS: APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P > 0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10-3  mm2 /sec vs. stromal BPH 0.91 ± 0.14 × 10-3  mm2 /sec vs. glandular BPH 1.08 ± 0.18 × 10-3  mm2 /sec) among three lesions. APTw (OR = 12.18, 11.80, respectively) and 1/ADC (OR = 703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH. DATA CONCLUSION: APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Parameters of Dual-layer Spectral Detector CT Could be Used to Differentiate Non-Small Cell Lung Cancer from Small Cell Lung Cancer.

BACKGROUND AND OBJECTIVE: Differentiating non-small cell lung cancer (NSCLC) from small cell lung cancer (SCLC) remains a substantial challenge. This study aimed at evaluating the performance of dual-layer spectral detector CT (DLCT) in differentiating NSCLC from SCLC. METHODS: Spectral images of 247 cancer patients confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP), including 197 cases of NSCLC and 50 cases of SCLC. Effective atomic number (Z-eff), Spectral CT-Mono Energetic (MonoE [40keV~90keV]), iodine density (ID) and thoracic aorta iodine density (IDaorta) in contrast-enhanced images were measured and compared between the SCLC and NSCLC subgroups of tumors. The slope of the spectral curve (λ, interval of 10 keV) and normalized iodine density (NID) were also calculated between the SCLC and NSCLC. Through the statistical analysis, the diagnostic efficiency of each spectral parameter was calculated, and the difference in their efficiency was analyzed. RESULTS: Both in NSCLS and SCLC, all parameters in VP were significantly higher than those in AP (p<0.001), except for λ90. There were significant differences in all spectral parameters between NSCLS and SCLC, both in AP and VP (p < 0.001). Except for VP-λ90, there was no significant difference in ROC curves of all spectral parameters. VP-NID exhibited the best diagnostic performance with an AUC value of 0.917 (95%[CI]: 0.870~0.965), sensitivity and specificity of 92.9% and 80%, and a diagnostic threshold of 0.217. CONCLUSION: All parameters of DLCT have high diagnostic efficiency in differentiating NSCLC from SCLC except for VP-λ90, and VP-NID has the highest diagnostic efficiency.

Prevalence and Consequences of Cerebral Small Vessel Diseases: A Cross-Sectional Study Based on Community People Plotted Against 5-Year Age Strata.

Purpose: To study the variation tendency of cerebral small vessel disease (CSVD) imaging markers and total burden with aging and to research the relationship between aging, CSVD markers and cognitive function. Methods: Participants in local urban communities were recruited for neuropsychological and magnetic resonance imaging assessments. Montreal Cognitive Assessment (MoCA), Mini-mental State Examination (MMSE), Number Connection Test A (NCT-A) and Digital Symbol Test (DST) were adopted as neuropsychological scale. Age was stratified at 5-year intervals, and the variation tendency of imaging markers and variables of neuropsychological scales in different age groups was studied. We further studied the relationship between aging, image markers and neuropsychological scales by multi-linear regression. Results: Finally, a total of 401 stroke-free participants (age, 54.83±7.74y; 45.9% were male) were included in the present analysis. With the increase of age, the incidence of imaging markers of CSVD were increased with aging except cerebral microbleeds. The performance results of NCT-A and DST were significant difference in 6 age groups (P < 0.001). In addition, linear decline of the neuropsychological function reflected by NCT-A and DST variables was observed. Linear regression found that age was an independent factor affecting the neuropsychological function reflected by NCT-A and DST variables, and the standard correction coefficients among different age groups increased gradually with age. In addition, brain atrophy is an independent factor affecting neuropsychological variables (odds ratio: -2.929, 95% CI: [-5.094 to -0.765]). There was no correlation between the number of neuroimaging markers and neuropsychological variables after full adjustment. Conclusion: There are many CVSD markers even in younger people, the incidence rate and CVSD marker numbers increase with age. Aging and CSVD may eventually affect cognitive function through brain atrophy.

Amide Proton Transfer MRI Could Be Used to Evaluate the Pathophysiological Status of White Matter Hyperintensities.

BACKGROUND: The pathophysiology of white matter hyperintensities (WMH) remains unclear, investigations of amide proton transfer (APT) signals in WMH disease may provide relevant pathophysiological information. PURPOSE: To evaluate the APT signals differences and heterogeneity of WMH and adjacent normal-appearing white matter (NAWM) at different Fazekas grades and different locations. STUDY TYPE: Prospective. POPULATION: In all, 180 WMH patients (age, 40-76; male/female, 77/103) and 59 healthy controls (age, 42-70; male/female, 23/36). FIELD STRENGTH/SEQUENCE: A 3 T; 3D fluid-attenuated inversion recovery (FLAIR), 3D APT-weighted (APTw). ASSESSMENT: The mean APTw values (APTwmean ) and the APTw signals heterogeneity (APTwmax-min ) among different grades WMH and NAWM and the APTwmean of the same grade deep WMH (DWMH) and paraventricular WMH (PWMH) were calculated and compared. Regions of interests were delineated on WMH lesions, NAWM and healthy white matter. STATISTICAL TESTS: One-way analysis of variance (ANOVA); independent sample t test; Chi-square test. Significance level: P < 0.05. RESULTS: APTwmean among different grade WMH (from grade 0 to 3, 0.58 ± 0.14% vs. 0.29 ± 0.23% vs. 0.37 ± 0.24% vs. 0.61 ± 0.22%, respectively) were significantly different except between grade 1 and 2 (P = 0.27) and between grade 0 and 3 (P = 0.97). The differences in APTwmean between WMH and NAWM were significant (WMH vs. NAWM from grade 1 to 3, 0.29% ± 0.23% vs. 0.55% ± 0.27%; 0.37% ± 0.24% vs. 0.59% ± 0.22%; 0.61% ± 0.22% vs. 0.42% ± 0.24%, respectively). Lower APTwmean values were found only in grade 3 NAWM than other grades NAWM and controls. The APTwmax-min values of grade 1-3 WMH (0.38% ± 0.27% vs. 0.51% ± 0.31% vs. 0.67% ± 0.34%, respectively) were significantly different. Higher APTmean values were found only in grade 2 PWMH (0.47% ± 0.22% vs. 0.32% ± 0.24%). DATA CONCLUSION: Significant differences of APT signals were found in WMH of different Fazekas grades and different locations. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Amide Proton Transfer-Weighted MRI Might Help Distinguish Amnestic Mild Cognitive Impairment From a Normal Elderly Population.

Objectives: To evaluate whether 3D amide proton transfer weighted (APTw) imaging based on magnetization transfer analysis can be used as a novel imaging marker to distinguish amnestic mild cognitive impairment (aMCI) patients from the normal elderly population by measuring changes in APTw signal intensity in the hippocampus and amygdala. Materials and Methods: Seventy patients with aMCI and 74 age- and sex-matched healthy volunteers were recruited for routine MRI and APT imaging examinations. Magnetic transfer ratio asymmetry (MTRasym) of the amide protons (at 3.5 ppm), or APTw values, were measured in the bilateral hippocampus and amygdala on three consecutive cross-sectional APT images and were compared between the aMCI and control groups. The independent sample t-test was used to evaluate the difference in APTw values of the bilateral hippocampus and amygdala between the aMCI and control groups. Receiver operator characteristic analysis was used to assess the diagnostic performance of the APTw. The paired t-test was used to assess the difference in APTw values between the left and right hippocampus and amygdala, in both the aMCI and control groups. Results: The APTw values of the bilateral hippocampus and amygdala in the aMCI group were significantly higher than those in the control group (left hippocampus 1.01 vs. 0.77% p < 0.001; right hippocampus 1.02 vs. 0.74%, p < 0.001; left amygdala 0.98 vs. 0.70% p < 0.001; right amygdala 0.94 vs. 0.71%, p < 0.001). The APTw values of the left amygdala had the largest AUC (0.875) at diagnosis of aMCI. There was no significant difference in APTw values between the left and right hippocampus and amygdala, in either group. (aMCI group left hippocampus 1.01 vs. right hippocampus 1.02%, p = 0.652; healthy control group left hippocampus 0.77 vs. right hippocampus 0.74%, p = 0.314; aMCI group left amygdala 0.98 vs. right amygdala 0.94%, p = 0.171; healthy control group left amygdala 0.70 vs. right amygdala 0.71%, p = 0.726). Conclusion: APTw can be used as a new imaging marker to distinguish aMCI patients from the normal elderly population by indirectly reflecting the changes in protein content in the hippocampus and amygdala.