Changhong Liang

Lymph node metastasis in head and neck squamous carcinoma: Efficacy of intravoxel incoherent motion magnetic resonance imaging for the differential diagnosis

PURPOSE To evaluate the value of pure molecular diffusion(D), perfusion-related diffusion (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) based on intravoxel incoherent motion (IVIM) theory for differential diagnosis of metastatic lymph nodes (LNs) in head and neck squamous cell carcinoma(HNSCC). MATERIALS AND METHODS 29 patients with HNSCC and 20 patients with lymph node hyperplasia (LNH) were enrolled in this retrospective study, underwent magnetic resonance (MR) examination. IVIM Diffusion-weighted imaging (IVIM-DWI) was performed with 13 b values. D, D*, f and ADC values were compared between two groups. The diagnostic value of ADC, D, D* and D·D* value were evaluated by Receiver operating characteristic (ROC) curve. Two radiologists measured D, D*, f and ADC values independently. RESULTS 33 malignant LNs in HNSCC group and 22 benign LNs in LNH group (minimum diameter, ≥5mm) were successfully examined, ADC(P<0.05), D (P<0.01) and f (P<0.01) were significantly lower in malignant LNs than that in benign LNs, whereas D* was significantly higher (P<0.01). The area under the ROC curve (AUC) for D·D* was 0.983 and was larger than that for D* (0.952), D (0.78) and ADC (0.67). CONCLUSION Our results indicate that IVIM DWI is feasible in the diagnosis of LN metastasis. D was significantly decreased in malignant LNs reflected increased nuclear-to-cytoplasmic ratio tissue, and D* was significantly increased reflected increased blood vessel generation and parenchymal perfusion in malignant LNs.

Texture analysis of baseline multiphasic hepatic computed tomography images for the prognosis of single hepatocellular carcinoma after hepatectomy: A retrospective pilot study

OBJECTIVE To assess the prognostic value of texture analysis for single hepatocellular carcinomas (HCCs) after hepatectomy. MATERIALS AND METHODS A total of 61 HCC patients were enrolled in this retrospective study. Textural characteristics of the computed tomography (CT) images were quantified. The differences between the hepatic arterial phase and the portal venous phase were obtained (the Dif.). The receiver operating characteristic (ROC) curves were used for data screening. Cox regression analyses were performed to determine independent factors adjusted with the derived clinical and radiological variables. Model identifications were based on Akaike information criteria. Kaplan-Meier and log-rank tests were performed for overall survival (OS) and disease-free survival (DFS). RESULTS ROC and Cox regression analyses identified five parameters. Filter 1.0 achieved the best performance, in which the Dif.Scale 1.2 was a superior indicative independent marker for OS (p=0.05). Kaplan-Meier analyses further demonstrated that the Dif.Scale2.2 at filter 0 (p=0.001), Dif.Scale1.2 (p=0.006), Dif.Scale3.2 (p=0.005) at filter 1.0, Dif.Wavelet 8 at filter 1.5 (p<0.001), and corona (p=0.032) were associated with OS. Moreover, Dif.Scale 2.2 at filter 0 (p=0.039), Dif.Scale1.2 at filter 1.0 (p=0.001), and Dif.Wavelet 8 at filter 1.5 (p=0.007) were associated with DFS, while the Barcelona-Clínic Liver Cancer (BCLC) parameters showed no statistical correlation with OS (p=0.057). CONCLUSIONS For patients with a single HCC treated by hepatectomy, the textural features for Gabor and Wavelet, especially the varying Dif., potentially provided prognostic information beyond traditional indicators such as those of the BCLC.

Optimization of hybrid iterative reconstruction level and evaluation of image quality and radiation dose for pediatric cardiac computed tomography angiography.

BackgroundHybrid iterative reconstruction can reduce image noise and produce better image quality compared with filtered back-projection (FBP), but few reports describe optimization of the iteration level.ObjectiveWe optimized the iteration level of iDose4 and evaluated image quality for pediatric cardiac CT angiography.Materials and methodsChildren (n = 160) with congenital heart disease were enrolled and divided into full-dose (n = 84) and half-dose (n = 76) groups. Four series were reconstructed using FBP, and iDose4 levels 2, 4 and 6; we evaluated subjective quality of the series using a 5-grade scale and compared the series using a Kruskal-Wallis H test. For FBP and iDose4-optimal images, we compared contrast-to-noise ratios (CNR) and size-specific dose estimates (SSDE) using a Student’s t-test. We also compared diagnostic-accuracy of each group using a Kruskal-Wallis H test.ResultsMean scores for iDose4 level 4 were the best in both dose groups (all P < 0.05). CNR was improved in both groups with iDose4 level 4 as compared with FBP. Mean decrease in SSDE was 53% in the half-dose group. Diagnostic accuracy for the four datasets were in the range 92.6–96.2% (no statistical difference).ConclusioniDose4 level 4 was optimal for both the full- and half-dose groups. Protocols with iDose4 level 4 allowed 53% reduction in SSDE without significantly affecting image quality and diagnostic accuracy.

Image quality of ct angiography using model-based iterative reconstruction in infants with congenital heart disease: Comparison with filtered back projection and hybrid iterative reconstruction

PURPOSE To compare the image quality, rate of coronary artery visualization and diagnostic accuracy of 256-slice multi-detector computed tomography angiography (CTA) with prospective electrocardiographic (ECG) triggering at a tube voltage of 80kVp between 3 reconstruction algorithms (filtered back projection (FBP), hybrid iterative reconstruction (iDose4) and iterative model reconstruction (IMR)) in infants with congenital heart disease (CHD). METHODS Fifty-one infants with CHD who underwent cardiac CTA in our institution between December 2014 and March 2015 were included. The effective radiation doses were calculated. Imaging data were reconstructed using the FBP, iDose4 and IMR algorithms. Parameters of objective image quality (noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR)); subjective image quality (overall image quality, image noise and margin sharpness); coronary artery visibility; and diagnostic accuracy for the three algorithms were measured and compared. RESULTS The mean effective radiation dose was 0.61±0.32 mSv. Compared to FBP and iDose4, IMR yielded significantly lower noise (P<0.01), higher SNR and CNR values (P<0.01), and a greater subjective image quality score (P<0.01). The total number of coronary segments visualized was significantly higher for both iDose4 and IMR than for FBP (P=0.002 and P=0.025, respectively), but there was no significant difference in this parameter between iDose4 and IMR (P=0.397). There was no significant difference in the diagnostic accuracy between the FBP, iDose4 and IMR algorithms (χ2=0.343, P=0.842). CONCLUSIONS For infants with CHD undergoing cardiac CTA, the IMR reconstruction algorithm provided significantly increased objective and subjective image quality compared with the FBP and iDose4 algorithms. However, IMR did not improve the diagnostic accuracy or coronary artery visualization compared with iDose4.

Validation of Breast Cancer Models for Predicting the Nonsentinel Lymph Node Metastasis After a Positive Sentinel Lymph Node Biopsy in a Chinese Population

Objectives: Over the years, completion axillary lymph node dissection is recommended for the patients with breast cancer if sentinel lymph node metastasis is found. However, not all of these patients had nonsentinel lymph node metastasis on final histology. Some predicting models have been developed for calculating the risk of nonsentinel lymph node metastasis. The aim of our study was to validate some of the predicting models in a Chinese population. Method: Two hundred thirty-six patients with positive sentinel lymph node and complete axillary lymph node dissection were included. Patients were applied to 6 models for evaluation of the risk of nonsentinel lymph node involvement. The receiver–operating characteristic curves were shown in our study. The calculation of area under the curves and false negative rate was done for each model to assess the discriminative power of the models. Results: There are 105 (44.5%) patients who had metastatic nonsentinel lymph node(s) in our population. Primary tumor size, the number of metastatic sentinel lymph node, and the proportion of metastatic sentinel lymph nodes/total sentinel lymph nodes were identified as the independent predictors of nonsentinel lymph node metastasis. The Seoul National University Hospital and Louisville scoring system outperformed the others, with area under the curves of 0.706 and 0.702, respectively. The area under the curve values were 0.677, 0.673, 0.432, and 0.674 for the Memorial Sloan-Kettering Cancer Center, Tenon, Stanford, and Shanghai Cancer Hospital models, respectively. With adjusted cutoff points, the Louisville scoring system outperformed the others by classifying 26.51% of patients with breast cancer to the low-risk group. Conclusion: The Louisville and Seoul National University Hospital scoring system were found to be more predictive among the 6 models when applied to the Chinese patients with breast cancer in our database. Models developed at other institutions should be used cautiously for decision-making regarding complete axillary lymph node dissection after a positive biopsy in sentinel lymph node.

Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma

ObjectivesTo determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients.MethodsData of 230 consecutive patients were retrospectively analyzed (154 in the training set and 76 in the test set). A total of 1576 radiomics features were extracted from arterial-phase CT images of the whole primary tumor. LASSO logistic regression was performed to choose the key features and construct a radiomics signature. A radiomics nomogram incorporating this signature was developed on the basis of multivariable analysis in the training set. Nomogram performance was determined and validated with respect to its discrimination, calibration and reclassification. Clinical usefulness was estimated by decision curve analysis.ResultsThe radiomics signature including five features was significantly associated with LN metastasis. The radiomics nomogram, which incorporated the signature and CT-reported LN status (i.e. size criteria), distinguished LN metastasis with an area under curve (AUC) of 0.758 in the training set, and performance was similar in the test set (AUC 0.773). Discrimination of the radiomics nomogram exceeded that of size criteria alone in both the training set (p <0.001) and the test set (p=0.005). Integrated discrimination improvement (IDI) and categorical net reclassification improvement (NRI) showed significant improvement in prognostic value when the radiomics signature was added to size criteria in the test set (IDI 17.3%; p<0.001; categorical NRI 52.3%; p<0.001). Decision curve analysis supported that the radiomics nomogram is superior to size criteria.ConclusionsThe radiomics nomogram provides individualized risk estimation of LN metastasis in ESCC patients and outperforms size criteria.Key Points• A radiomics nomogram was built and validated to predict LN metastasis in resectable ESCC.• The radiomics nomogram outperformed size criteria.• Radiomics helps to unravel intratumor heterogeneity and can serve as a novel biomarker for determination of LN status in resectable ESCC.

Radiomics approach for preoperative identification of stages I−II and III−IV of esophageal cancer

Objective To predict preoperative staging using a radiomics approach based on computed tomography (CT) images of patients with esophageal squamous cell carcinoma (ESCC). Methods This retrospective study included 154 patients (primary cohort: n=114; validation cohort: n=40) with pathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. High throughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomics signature was constructed using the least absolute shrinkage and selection operator (Lasso). Associations between radiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomics approach and tumor volume for discriminating between stages I−II and III−IV was evaluated and compared using the receiver operating characteristics (ROC) curves and net reclassification improvement (NRI). Results A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomics signature after feature dimension reduction. The radiomics signature was significantly associated with ESCC staging (P<0.001), and yielded a better performance for discrimination of early and advanced stage ESCC compared to tumor volume in both the primary [area under the receiver operating characteristic curve (AUC): 0.795vs. 0.694, P=0.003; NRI=0.424)] and validation cohorts (AUC: 0.762 vs. 0.624, P=0.035; NRI=0.834). Conclusions The quantitative approach has the potential to identify stage I−II and III−IV ESCC before treatment.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

Objective To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). Methods After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. Results The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrells concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Conclusions Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

CT-based Radiomics Signature to Discriminate High-grade From Low-grade Colorectal Adenocarcinoma

RATIONALE AND OBJECTIVES To develop and validate a computed tomography-based radiomics signature for preoperatively discriminating high-grade from low-grade colorectal adenocarcinoma (CRAC). MATERIALS AND METHODS This retrospective study was approved by our institutional review board, and the informed consent requirement was waived. This study enrolled 366 patients with CRAC (training dataset: n = 222, validation dataset: n = 144) from January 2008 to August 2015. A radiomics signature was developed with the least absolute shrinkage and selection operator method in training dataset. Mann-Whitney U test was applied to explore the correlation between radiomics signature and histologic grade. The discriminative power of radiomics signature was investigated with the receiver operating characteristics curve. An independent validation dataset was used to confirm the predictive performance. We further performed a stratified analysis to validate the predictive performance of radiomics signature in colon adenocarcinoma and rectal adenocarcinoma. RESULTS The radiomics signature demonstrated discriminative performance for high-grade and low-grade CRAC, with an area under the curve of 0.812 (95% confidence interval [CI]: 0.749-0.874) in training dataset and 0.735 (95%CI: 0.644-0.826) in validation dataset. Stratified analysis demonstrated that radiomics signature also showed distinguishing ability for histologic grade in both colon adenocarcinoma and rectal adenocarcinoma, with area under the curve of 0.725 (95%CI: 0.653-0.797) and 0.895 (95%CI: 0.838-0.952), respectively. CONCLUSIONS We developed and validated a radiomics signature as a complementary tool to differentiate high-grade from low-grade CRAC preoperatively, which may make contribution to personalized treatment.