Identification of Novel Hb Guiyang [HBA2: c.151C > A α2 50 (CE8) His- Asn] and Phenotype- Genotype Correlation of Abnormal Hemoglobins in Guizhou, Southwest China.

Purpose: To analyze the composition of abnormal hemoglobin and the relationship between genotype and phenotype by screening abnormal hemoglobin in a subpopulation of Guizhou, China. Patients and Methods: Routine blood evaluation, capillary electrophoresis of hemoglobin, and mutation of α - and ß - thalassemia genes were evaluated in 19,976 individuals for thalassemia screening in Guizhou. Sanger sequencing of HBA1, HBA2 and HBB genes was performed in samples with abnormal bands or unexplained increases of normal bands. The types of abnormal hemoglobin were obtained by sequence analysis. Results: Abnormal hemoglobin was detected in 84 individuals (detection rate, 0.42%). Ten types each of α and ß globin chain variants were detected, including most commonly Hb E, Hb New York and Hb Port Phillip. In this study, the abnormal Hb Mizuho was identified for the first time in a Chinese population, and a novel abnormal hemoglobin Hb Guiyang (HBA2: c.151C > A) was detected for the first time. Except for Hb Mizuho, other abnormal hemoglobin heterozygotes without thalassemia or iron deficiency had no significant hematological changes. Conclusion: This study enriched the molecular epidemiological data of abnormal hemoglobin in Guizhou, China and provided reference data for genetic counseling and prenatal diagnosis of abnormal hemoglobin.

A Rare Case of Abnormal Hemoglobin Variant Hb Mizuho: [HBB: c.206T > C ß 68(E12) Leu-Pro]: A First Report in the Chinese Population.

A 6-month-old female infant presented with unexplained hemolytic anemia, showing no abnormalities by capillary electrophoresis and genetic testing for α- and ß-thalassemia mutations that are commonly seen in the Chinese population. A rare Hb Mizuho: [HBB: c.206T > C ß 68(E12) Leu- Pro] variant was identified by next-generation sequencing (NGS) and verified by Sanger sequencing. Hb Mizuho: [HBB: c.206T > C ß 68(E12) Leu- Pro] is not easily detectable because it is extremely unstable, and the correct diagnosis is usually made via DNA sequencing. This is the first report of this variant in the Chinese population.

Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma.

The purpose of the present study was to investigate the value of contrast-enhanced magnetic resonance imaging (CE-MRI) texture analysis for preoperatively predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Accordingly, a retrospective study of 142 patients with pathologically confirmed HCC was performed. The patients were divided into two cohorts: The training cohort (n=99) and the validation cohort (n=43), including the MVI-positive group (n=53) and MVI-negative group (n=89). On the basis of three-dimensional texture analysis, 58 features were extracted from the preoperative CE-MR images of arterial-phase (AP) and portal-venous-phase (PP). The t-test or Kruskal-Wallis test, univariate logistic regression analysis and Pearson correlation were applied for feature reduction. Clinical-radiological features were also analyzed. Multivariate logistic regression analysis was used to build the texture model and combined model with clinical-radiological features. The MVI-predictive performance of the models was evaluated using receiver operating characteristic (ROC) analysis and presented using nomogram. Among the clinical features, a significant difference was found in maximum tumor diameter (P=0.002), tumor differentiation (P=0.026) and α-fetoprotein level (P=0.025) between the two groups in the training cohort. Four MR texture features in AP and five in PP images were identified through feature reduction. On ROC analysis, the AP texture model showed better diagnostic performance than did the PP model in the validation cohort, with an area under the curve (AUC) of 0.773 vs. 0.623, sensitivity of 0.750 vs. 0.500, and specificity of 0.815 vs. 0.926. Together with the clinical features, the combined model of AP improved the AUC, sensitivity and specificity to 0.810, 0.811 and 0.790, respectively, which was demonstrated in nomogram. To conclude, model-based texture analysis of CE-MRI could predict MVI in HCC preoperatively and noninvasively, and the AP image shows better predictive efficiency than PP image. The combined model of AP with clinical-radiological features could improve MVI prediction ability.

Histogram analysis combined with morphological characteristics to discriminate adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma appearing as pure ground-glass nodule.

OBJECTIVE: To construct a predictive model to discriminate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs) using computed tomography (CT) histogram analysis combined with morphological characteristics and to evaluate its diagnostic performance. MATERIALS AND METHODS: Two hundred eighty-nine patients with surgically resected solitary pGGN and pathologically diagnosed with AIS, MIA, or IAC in our institution from January 2014 to May 2018 were enrolled in our study. Two hundred twenty-six pGGNs (79 AIS, 84 MIA, and 63 IAC) were randomly selected and assigned to a model-development cohort, and the remaining 63 pGGNs (11 AIS, 29 MIA and 23 IAC) were assigned to a validation cohort. The morphological characteristics were established as model A and histogram parameters as model B. The diagnostic performances of model A, model B, and model A + B were evaluated and compared via receiver operating curve (ROC) analysis and logistic regression analysis. RESULTS: Entropy (odd ratio [OR] = 23.25, 95%CI: 6.83-79.15, p < 0.001), microvascular sign (OR = 8.62, 95%CI: 3.72-19.98, p < 0.001) and the maximum diameter (OR = 4.37, 95%CI: 2.44-7.84, p < 0.001) were identified as independent predictors in the IAC group. The area under the ROC (Az value), accuracy, sensitivity and specificity of model A + B were 0.896, 88.1%, 79.4% and 91.4%, respectively, exhibiting a significantly higher Az value than either model A or model B alone (0.785 vs 0.896, p < 0.001; 0.849 vs 0.896, p = 0.029). Model A + B also conveyed a good diagnostic performance in the validation cohort, with an Az value of 0.851. CONCLUSION: Histogram analysis combined with morphological characteristics exhibit a superior diagnostic performance in discriminating AIS-MIA from IAC appearing as pGGNs.

Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Histogram Analysis of Apparent Diffusion Coefficient Maps With Standard and Ultrahigh b-value Diffusion-weighted MR Imaging.

OBJECTIVE: To compare the diagnostic performance of standard and ultrahigh b-value Diffusion-weighted Imaging (DWI) using volumetric histogram analysis in differentiating transition zone (TZ) cancer from benign prostatic hyperplasia (BPH). METHODS: 57 TZ cancer and 61 BPH patients received standard (1000 s/mm) and ultrahigh b-value (2000 s/mm) DWI. The diagnostic ability of ADC histogram parameters derived from two DWI for differentiating TZ cancer from BPH was determined by receiver operating characteristic curve. RESULTS: Median, minimum, the 10th, 25th percentile ADC in both ADC1000 and ADC2000 and skewness in ADC2000 had significant differences between TZ cancer and BPH (for all, P < 0.05).The 10th percentile ADC showed highest area under the ROC curve (AUC) in both ADC1000 and ADC2000.The 10th percentile ADC of ADC2000 showed significantly higher AUC than did ADC1000 (P = 0.0385). CONCLUSIONS: The 10th percentile ADC obtained from ultrahigh b-value DWI performed better for differentiating TZ cancer from BPH.

Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI.

BACKGROUND: Tumor heterogeneity can be assessed by texture analysis (TA). TA has been applied using diffusion-weighted imaging and apparent diffusion coefficient maps to predict pathological responses to preoperative chemoradiation therapy (CRT) in patients with locally advanced rectal cancer (LARC). PURPOSE: To evaluate the texture parameters obtained from K trans maps derived from dynamic contrast-enhanced (DCE)-MRI for predicting pathological responses to preoperative CRT for LARCs. STUDY TYPE: Retrospective. POPULATION: Altogether, 83 patients (26 women, 57 men) with rectal cancer met the inclusion criteria. FIELD STRENGTH/SEQUENCE: 3.0T/T1 -weighted DCE-MRI sequence. ASSESSMENT: After CRT, each tumor was assessed by a pathologist who assigned a tumor regression grade (TRG), thereby identifying pathologically complete responders (pCR; TRG 1) and good responders (GR; TRG1 + TRG2). TA was then applied to the DCE-MRI K trans maps. The K trans value, several TA parameters, and tumor volumes were calculated. STATISTICAL TESTS: The Shapiro-Wilk test was used to verify that the data had normal distribution. Results of parameters measured before and after CRT were compared using paired-sample t-tests. Value changes of each parameter in the combined pCR/GR group were compared using independent sample t-tests. Receiver operating characteristic curves and areas under the curve (AUC) were calculated to assess the diagnostic performance of each parameter related to CRT effectiveness. RESULTS: There were 15 pCR (16.9%) and 21 GR (25.3%) patients. Tumor volume, mean K trans , entropy, and correlation decreased and energy values increased significantly in these groups compared with those of the non-PCR and non-GR groups. ΔCorrelation (Δcorrelation = postcorrelation - precorrelation) was found to be a valuable parameter for identifying pCR/GR patients (AUC 0.895, sensitivity 86.7%, specificity 81.8%). DATA CONCLUSION: TA parameters from the DCE-MRI K trans map can predict the efficacy of CRT for treating LARCs. Also, Δcorrelation may be useful for identifying patients who will be responsive to CRT. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:885-893.

Dynamic contrast-enhanced MRI in orbital lymphoproliferative disorders: Effects of region of interest selection methods on time efficiency, measurement reproducibility, and diagnostic ability.

BACKGROUND: Previous studies indicated that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could serve as a useful biomarker for differentiating malignant from benign orbital lymphoproliferative disorders (OLPDs). PURPOSE: To investigate the influence of different region of interest (ROI) selection methods on the measurements of DCE-MRI parameters, and their diagnostic ability in discriminating malignant from benign OLPDs. STUDY TYPE: Retrospective study. POPULATION: In all, 46 patients with OLPDs (22 benign and 24 malignant). FIELD STRENGTH/SEQUENCE: 3.T DCE-MRI using a 2D turbo fast low angle shot sequence postcontrast. ASSESSMENT: DCE-MRI data were analyzed using three different ROI selection methods, including whole-tumor ROI (ROIWT ), single-slice ROI (ROISS ) and hot-spot ROI (ROIHS ). Quantitative parameters (Ktrans , Kep , Ve ) were calculated based on a modified Tofts model. STATISTICAL TESTING: Analysis of variance test, intraclass correlation coefficient (ICC), Bland-Altman plots, independent t-test, and receiver operating characteristic curve analyses were used for statistical analyses. RESULTS: The time required for outlining ROIWT was significantly longer than ROISS and ROIHS (P < 0.001). The measurements of DCE-MRI-derived parameters based on ROIHS demonstrated lowest ICC, followed by ROISS and ROIWT . Malignant OLPDs showed significantly higher Kep than benign mimics (P < 0.001), while no significant differences were found on Ktrans (ROIWT , P = 0.535; ROISS , P = 0.557; ROIHS , P = 0.400) and Ve (ROIWT , P = 0.071; ROISS , P = 0.079; ROIHS , P = 0.057). Kep -ROIWT showed the highest area under curve for differentiating malignant from benign OLPDs, followed by Kep -ROISS , and Kep-ROIHS ; however, the differences were not significant (ROIWT vs. ROISS , P = 0.407; ROIWT vs. ROIHS , P = 0.363; ROISS vs. ROIHS , P = 0.887). DATA CONCLUSION: ROI selection methods could have an influence on the measurements of DCE-MRI parameters. Taking measurement time, reproducibility, and diagnostic ability into account, we suggest single-slice ROI to be used for differentiating malignant from benign OLPDs in clinical practice. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1298-1305.

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival.

OBJECTIVES: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. METHODS: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis. RESULTS: The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively. CONCLUSIONS: The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival. KEY POINTS: • Radiomic biomarker on CT was designed to identify phenotypes of lung adenocarcinoma • Built up radiomic signature for lung adenocarcinoma manifested as subsolid nodules • Retrospective study showed radiomic signature had greater diagnostic accuracy than volumetric analysis • Radiomics help to evaluate intratumour heterogeneity within lung adenocarcinoma • Medical decision can be given with more confidence.

Volumetric analysis of intravoxel incoherent motion imaging for assessment of solitary pulmonary lesions.

Background Differentiating between malignant and benign solitary pulmonary lesions (SPLs) is challenging. Purpose To determine diagnostic performance of intravoxel incoherent motion-based diffusion-weighted imaging (DW-IVIM) in distinguishing malignant from benign SPLs, using histogram analysis derived whole-tumor and single-section region of interest (ROI). Material and Methods This retrospective study received institutional review board approval. A total of 129 patients with diagnosed SPLs underwent DW-IVIM and apparent diffusion coefficient (ADC). ADC, slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were calculated separately by outlining whole-tumor and single-section ROI. Inter-observer reliability was assessed by inter-class correlation coefficient (ICC). ADC and DW-IVIM parameters were analyzed using independent-sample T-test. Receiver operating characteristic (ROC) analysis was constructed to determine diagnostic performance. Multiple logistic regression was performed to identify independent factors associated with malignant SPLs. Results There were 48 benign SPLs found in 35 patients and 94 patients with lung cancer (LC). ICC for whole-tumor ROI (range, 0.89-0.95) was higher than that for single-section ROI (range, 0.61-0.71). Mean ADC and D were significantly lower in the malignant group. ADC and D 10th showed significantly higher AUC values than did mean ADC and D. D showed significantly higher diagnostic accuracy in mean, 10th, and 25th percentiles than ADC values (all Ps < 0.05). D 10th was found to be an independent factor in discriminating LCs with an odds ratio of -1.217. Conclusion Volumetric analysis had higher reproducibility and diagnostic accuracy than did single-section. Further, compared to ADC, D value differentiated benign SPLs from LCs more accurately.

Lung adenocarcinoma: Assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image.

PURPOSE: To evaluate the diagnostic performance of extended models of diffusion-weighted (DW) imaging to help differentiate the epidermal growth factor receptor (EGFR) mutation status in stage IIIA-IV lung adenocarcinoma. MATERIALS AND METHODS: This retrospective study had institutional research board approval and was HIPAA compliant. Preoperative extended DW imaging including intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) 3 Tesla MRI were retrospectively evaluated in 53 patients with pathologically confirmed non-early stage (IIIA-IV) lung adenocarcinoma. EGFR mutationsat exons 18-21 were determined by using polymerase chain reaction-based ARMS. Quantitative parameters (mean, kurtosis, skewness, 10th and 90th percentiles) of IVIM (true-diffusion coefficient D, pseudo-diffusion coefficient D*, and perfusion fraction f) and DKI (kurtosis value Kapp, kurtosis corrected diffusion coefficient Dapp) were calculated by outlining entire-volume histogram analysis. Receiver operating characteristic analysis was constructed to determine the diagnostic performance of each parameter. Multivariate logistic regression was used to differentiate the probability of EGFR mutation status. RESULTS: Twenty-four of 53 patients with lung adenocarcinoma were EGFR mutations, which occurred most often in acinar (10 of 13 [76.9%]) and papillary predominant tumors (9 of 13 [69.2%]). Patients with EGFR mutation showed significant higher 10th percentile of D, lower D* value in terms of kurtosis, and lower Kapp value in terms of mean, skewness, 10th and 90th percentiles (all P values < 0.05). The 90th Kapp showed significantly higher sensitivity (97%; P < 0.05) and Az (0.817; P < 0.05) value. Multivariate logistic regression showed 90th Kapp was a independent factor for determining EGFR mutation with odds ratio -1.657. CONCLUSION: Multiple IVIM and DKI parameters, especially the histogram 90th Kapp value, helped differentiate EGFR mutation status in stage IIIA-IV lung adenocarcinoma. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:281-289.

Benign and malignant orbital lymphoproliferative disorders: Differentiating using multiparametric MRI at 3.0T.

PURPOSE: To determine the optimal combination of parameters derived from 3T multiparametric (conventional magnetic resonance imaging [MRI], diffusion-weighted [DW] and dynamic contrast-enhanced [DCE]) MRI for differentiating malignant from benign orbital lymphoproliferative disorders (OLPDs). MATERIALS AND METHODS: Forty patients with OLPDs (18 benign and 22 malignant) underwent conventional 3.0T MR, DW, and DCE-MRI examination for presurgery evaluation. Conventional MRI features (including tumor laterality, shape, number of involved quadrants, signal intensity on T1 -weighted imaging (WI) and T2 WI, flow void sign on T2 WI, and findings suggestive of sinusitis) were reviewed, and multivariate logistic regression analysis was used to identify the most significant conventional MRI features. Apparent diffusion coefficient (ADC) and DCE-MRI derived parameters (area under curve [AUC], time to peak [TTP], maximum rise slope [Slopemax ]) were measured and compared between two groups. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic ability of each combination that was established based on identified qualitative and quantitative parameters. RESULTS: Multivariate logistic regression analysis showed that the presence of flow void sign on T2 WI significantly associated with benign OLPDs (P = 0.034). Malignant OLPDs demonstrated significantly lower ADC (P = 0.001) and AUC (P = 0.002) than benign mimics. ROC analyses indicted that, ADC alone showed the optimal sensitivity (threshold value, 0.886 × 10-3 mm2 /s; sensitivity, 90.9%), while a combination of no presence of flow void sign on T2 WI + ADC ≤ 0.886 × 10-3 mm2 /s + AUC ≤ 7.366 showed optimal specificity (88.9%) in differentiating benign from malignant OLPDs. CONCLUSION: Multiparametric MRI can help to differentiate malignant from benign OLPDs. DWI offers optimal sensitivity, while the combination of conventional MRI, DWI, and DCE-MRI offers optimal specificity. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:167-176.

Comparison of intravoxel incoherent motion diffusion-weighted MR imaging with dynamic contrast-enhanced MRI for differentiating lung cancer from benign solitary pulmonary lesions.

BACKGROUND: To compare intravoxel incoherent motion (IVIM) and pharmacokinetic analysis dynamic contrast-enhanced MR imaging (DCE-MRI) in distinguishing lung cancer (LC) from benign solitary pulmonary lesions (SPL). METHODS: This prospective study was approved by the institutional review board, and written informed consent was obtained. Eighty-one consecutive patients considered for SPL underwent DW-IVIM and DCE-3T MRI. ADC, D, D*, and f were calculated with mono- and bi-exponential models. K(trans) , kep , ve , and vp were calculated with the modified Tofts model. Receiver operating characteristic (ROC) analysis was constructed to determine the diagnostic performance of IVIM and DCE-MRI in discriminating LC from benignity. RESULTS: There were 29 patients with a total of 48 benign SPL and 52 LCs: 4 small cell carcinomas (SCLC), 19 squamous cell carcinomas (SCC), and 29 adenocarcinomas (Adeno-Ca). Both Adeno-Ca (ADC: 1.19 ± 0.23 × 10(-3) mm(2) /s; D:1.12 ± 0.35 × 10(-3) mm(2) /s; ve :0.27 ± 0.13; K(trans) :0.24 ± 0.09 min(-1) ; kep :0.90 ± 0.45 min(-1) ) and SCC (1.13± 0.28 × 10(-3) mm(2) /s; 1.02 ± 0.32 10(-3) mm(2) /s; 0.32 ± 0.14; 0.26 ± 0.08 min(-1) ; 0.90 ± 0.48 min(-1) ) had significantly lower ADC, D, ve and larger K(trans) , kep than benignity (1.37 ± 0.38 × 10(-3) mm(2) /s; 1.34 ± 0.45 × 10(-3) mm(2) /s; 0.42 ± 0.19; 0.19 ± 0.08 min(-1) ; 0.53 ± 0.26 min(-1) ). D (72.2%) had significantly higher accuracy (72.2%) and higher sensitivity (91.3%) than other imaging indices (accuracy: 55.5-68.0%; sensitivity: 41.3-78.3%; all P < 0.01) except for accuracy in kep (70.8%; P > 0.05) in discriminating LC from benignity. K(trans) exhibited significantly higher specificity (84.6%) than the other indices (38.5-73.1%; P < 0.01). These results can be improved by combined D and K(trans) , leading to a sensitivity, specificity and accuracy of 94.2%, 92%, and 93.5%, respectively. CONCLUSION: IVIM-derived D and DCE-derived K(trans) are two promising parameters for differentiating LC from benignity.