Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer.

BACKGROUND: To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS: A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS: One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION: This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis.

PURPOSE: To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. METHODS: This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. RESULTS: kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). CONCLUSION: The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Construction of glutathione-responsive paclitaxel prodrug nanoparticles for image-guided targeted delivery and breast cancer therapy.

Paclitaxel (PTX) remains an essential drug in the treatment of breast cancer. To improve metabolic stability and real-time monitoring of drug location, we develop a visualized nano-prodrug. Novel hyaluronic acid (HA)-coated glutathione (GSH)-sensitive chitosan (CS)-based nano-prodrug (HA/TPE-CS-SS-PTX NPs) with aggregation-induced emission effects (AIE) were accomplished. The prodrug NPs (drug loading 29.32%, particle size 105 nm, regular sphericity) exhibit excellent fluorescence stability. The prodrug NPs could target tumor cells with high expression of CD44 and decompose in the presence of high concentrations of glutathione. In vitro evaluations revealed that the prodrug NPs have significant cytotoxicity on 4T1 cells, and due to their excellent AIE characteristics, their position in cells can be tracked. Moreover, the prodrug NPs also shown superior anti-tumor effects in vivo experimental. Overall, the HA/TPE-CS-SS-PTX NPs we constructed have excellent bio-imaging capabilities and can be served as a potential nanomedicine for PTX delivery against breast cancer.

Model-Free and Model-based Parameters Derived From CAIPIRINHA-Dixon-TWIST-VIBE DCE-MRI: Associations With Prognostic Factors and Molecular Subtypes of Invasive Ductal Breast Cancer.

BACKGROUND: CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) dynamic contrast-enhanced MRI (DCE-MRI) can be used to characterize breast cancer. However, the influence of the clinicopathologic factors and molecular subtypes of invasive breast carcinoma (IDC) on the model-free and model-based parameters has not been investigated. PURPOSE: To compare model-free and model-based parameters of CDTV DCE-MRI with both clinicopathologic factors and molecular subtypes of IDC. STUDY TYPE: Prospective. POPULATION: A total of 152 patients (mean age, 52 years) with IDC including 42 luminal A, 64 luminal B, 22 human epidermal growth factor receptor-2 (HER2) positive, and 24 triple-negative subtypes. FIELD STRENGTH/SEQUENCE: A 3 T; turbo-FLASH, Dixon VIBE, and CDTV. ASSESSMENT: Model-free parameters (initial enhancement rate [IER] and maximum slope [MS]) were estimated from the time-intensity curve. The mean, minimum, maximum, and range between the minimum and maximum values of inline model-based parameters (Ktrans , kep , and ve ) were measured to assess intratumoral heterogeneity of IDC lesions. STATISTICAL TESTS: Student's t tests, Mann-Whitney U tests, Kruskal-Wallis tests, post hoc Steel-Dwass tests, and receiver operating characteristic (ROC) curves. P < 0.05 was considered significant. RESULTS: No significant differences in IER and MS values were seen among the clinicopathologic factors and molecular subtypes (Bonferroni-corrected P = 0.011-0.862, P = 0.145-0.601, respectively). The minimum kep values in HER2-positive IDC were significantly lower than those in HER2-negative IDC. The mean and range kep values were independent predictors for distinguishing the high (grade 3) and low (grade 1 or 2) nuclear grade groups according to multivariable analyses. The post hoc test showed that the kep minimum and kep range values were significantly different between luminal A and HER2-positive tumor subtypes, yielding an area-under-the-curve of 0.820. DATA CONCLUSION: Compared with the model-free parameters, inline kep related model-based parameters on CDTV DCE-MRI can be applied as a feasible tool to differentiate luminal A from HER2-positive breast cancers. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.

The E3 ubiquitin ligase RING1 interacts with COP9 Signalosome Subunit 4 to positively regulate resistance to root-knot nematodes in Solanum lycopersicum L.

Globally, root-knot nematodes (RKNs) cause massive production losses in all major crops. E3 ubiquitin ligases are involved in plant growth, development and immune response. But their roles in plant defense against RKNs are largely unclear. Here, we show that tomato E3 ubiquitin ligase RING1 interacts with COP9 Signalosome Subunit 4 (CSN4) which is essential for jasmonic acid (JA)-dependent basal defense against RKNs. Tissue-specific expression analysis showed that RING1 expression was the highest in tomato roots and the expression was significantly increased with RKN (Meloidogyne incognita) infection. Compared with the wild-type plants, the number of egg masses in roots significantly increased in the ring1 mutants, while RING1 overexpression conferred resistance against RKNs. Furthermore, RKN infection increased the accumulation of CSN4 protein in the roots of wild-type plants, which was largely compromised in the ring1 mutants but was enhanced in the RING1 overexpressing plants. The RKN-induced transcripts of JA biosynthetic and signaling genes as well as the accumulation of JA and JA-isoleucine were compromised in ring1 mutants but were increased in RING1 overexpressing plants. These results suggest that RING1 positively regulates JA-dependent basal defense against RKNs by interacting with CSN4 proteins.

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm).

OBJECTIVES: To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm). METHODS: This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis. RESULTS: For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001). CONCLUSIONS: Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI. KEY POINTS: • For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient.

PURPOSE: The aim of this study was to investigate whether whole-lesion histogram and texture analysis using apparent diffusion coefficient can discriminate between idiopathic granulomatous mastitis (IGM) and invasive breast carcinoma (IBC), both of which appeared as non-mass enhancement lesions without rim-enhanced masses. METHOD: This retrospective study included 58 pathology-proven female patients at two independent study sites (27 IGM patients and 31 IBC patients). Diffusion-weighted imaging (3b values, 50, 400 or 500, and 800 s/mm2) was performed using 1.5 T or 3 T MR scanners from the same vendor. Whole-lesions were segmented and 11 features were extracted. Univariate analysis and multivariate logistic regression analysis were performed to identify significant variables for differentiating IGM from IBC. Receiver operating characteristic curve was assessed. The interobserver reliability between two observers for the histogram and texture measurement was also reported. RESULTS: The 5th percentile, difference entropy and entropy of apparent diffusion coefficient showed significant differences between the two groups. An area under the curve of 0.778 (95 % CI: 0.648, 0.908), accuracy of 79.3 %, and sensitivity of 87.1 % was achieved using these three significant features. No significant feature was found with the multivariate analysis. For the interobserver reliability, all apparent diffusion coefficient parameters except skewness and kurtosis indicated good or excellent agreement, while these two features showed moderate agreement. CONCLUSIONS: Whole-lesion histogram and texture analysis using apparent diffusion coefficient provide a non-invasive analytical approach to the differentiation between IGM and IBC, both presenting with non-mass enhancement without rim-enhanced masses.

Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer.

Objective: To investigate whether machine learning analysis of multiparametric MR radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer. Study design: One hundred and thirty-four consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively analyzed. A total of 2,498 features were extracted from the DCE and DWI images, together with the new calculated images, including DCE images changing over six time points (DCEsequential) and DWI images changing over three b-values (DWIsequential). We proposed a novel two-stage feature selection method combining traditional statistics and machine learning-based methods. The accuracies of the 4-IHC classification and triple negative (TN) vs. non-TN cancers were assessed. Results: For the 4-IHC classification task, the best accuracy of 72.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 20 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features, based on the DWIsequential with the LDA model, yielding an accuracy of 53.7%. The linear support vector machine (SVM) and medium k-nearest neighbor using eight features yielded the highest accuracy of 91.0% for comparing TN to non-TN cancers, and the maximum variance for DWIsequential alone, together with a linear SVM model, achieved an accuracy of 83.6%. Conclusions: Whole-tumor radiomics on MR multiparametric images, DCE images changing over time points, and DWI images changing over different b-values provide a non-invasive analytical approach for breast cancer subtype classification and TN cancer identification.

Resveratrol suppresses the invasion and migration of human gastric cancer cells via inhibition of MALAT1-mediated epithelial-to-mesenchymal transition.

Resveratrol, a natural polyphenolic phytoalexin, was reported to exert multiple anticancer effects as a traditional Chinese medicine. However, research regarding the anticancer mechanism of resveratrol for the treatment and prevention of gastric cancer has reported conflicting results. In the present study, it was determined that resveratrol inhibited cell viability in a dose-dependent manner in the human gastric cancer cell line BGC823. Cell migration and invasion were suppressed significantly following treatment with 200 µM resveratrol. Additionally, resveratrol inhibited metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) expression, which was overexpressed in gastric cancer cells. Further experiments revealed that MALAT1 knockdown suppressed cell viability, migration, invasion and epithelial-to-mesenchymal transition in BGC823 cells. The present study indicated that resveratrol inhibited migration and invasion in human gastric cancer cells via suppressing MALAT1-mediated epithelial-to-mesenchymal transition, providing novel evidence for understanding the anticancer mechanism of resveratrol.

Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging.

PURPOSE: To identify triple-negative (TN) breast cancer imaging biomarkers in comparison to other molecular subtypes using multiparametric MR imaging maps and whole-tumor histogram analysis. MATERIALS AND METHODS: This retrospective study included 134 patients with invasive ductal carcinoma. Whole-tumor histogram-based texture features were extracted from a quantitative ADC map and DCE semi-quantitative maps (washin and washout). Univariate analysis using the Student's t test or Mann-Whitney U test was performed to identify significant variables for differentiating TN cancer from other subtypes. The ROC curves were generated based on the significant variables identified from the univariate analysis. The AUC, sensitivity, and specificity for subtype differentiation were reported. RESULTS: The significant parameters on the univariate analysis achieved an AUC of 0.710 (95% confidence interval [CI] 0.562, 0.858) with a sensitivity of 63.6% and a specificity of 73.1% at the best cutoff point for differentiating TN cancers from Luminal A cancers. An AUC of 0.763 (95% CI 0.608, 0.917) with a sensitivity of 86.4% and a specificity of 72.2% was achieved for differentiating TN cancers from human epidermal growth factor receptor 2 (HER2) positive cancers. Also, an AUC of 0.683 (95% CI 0.556, 0.809) with a sensitivity of 54.5% and a specificity of 83.9% was achieved for differentiating TN cancers from non-TN cancers. There was no significant feature on the univariate analysis for TN cancers versus Luminal B cancers. CONCLUSIONS: Whole-tumor histogram-based imaging features derived from ADC, along with washin and washout maps, provide a non-invasive analytical approach for discriminating TN cancers from other subtypes. KEY POINTS: • Whole-tumor histogram-based features on MR multiparametric maps can help to assess biological characterization of breast cancer. • Histogram-based texture analysis may predict the molecular subtypes of breast cancer. • Combined DWI and DCE evaluation helps to identify triple-negative breast cancer.

Correlations of PD-L1 gene polymorphisms with susceptibility and prognosis in hepatocellular carcinoma in a Chinese Han population.

AIMS: This study was performed to investigate the effect of PD-L1 polymorphisms on the susceptibility and prognosis of hepatocellular carcinoma (HCC) in a Chinese Han population. METHODS: Four single nucleotide polymorphisms (SNPs) of the PD-L1 gene, including rs2297136 (C > T), rs4143815 (C > G), rs2890658 (A > C) and rs17718883 (C > G) were examined in 225 HCC patients and 200 healthy controls using polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) method. RESULTS: Data revealed that the rs2297136 (C > T) SNP TT (p = 0.03) and rs4143815 (C > G) SNP GG genotypes (p < 0.001) were associated with significantly increased risks of HCC. No association was found between rs2890658 (A > C) SNP and HCC risk and this risk was significantly decreased in individuals with the rs17718883 SNP CG + GG genotype (p < 0.001). The rs2297136 (C > T) SNP CC + CT genotypes, the rs4143815 (C > G) CC genotype and the rs2890658 (A > C) AA genotype were associated with increased overall survival compared to their counterpart allelic genotypes (p < 0.001). The rs2890658 (A > C) SNP had no impact on the risk and prognosis of HCC (p > 0.05). CONCLUSIONS: Our results indicated that three functional polymorphisms (rs2297136, rs4143815 and rs17718883) of the PD-L1 gene were associated with HCC risk and prognosis, suggesting that genetic variants of PD-L1 polymorphisms might be a possible prognostic marker for the prediction of HCC risk and development. Validation by a larger prospective study from a more diverse ethnic population is needed to confirm these findings.

Selection of personalized laparoscopic partial nephrectomy based on tumor characteristics: A preliminary study in a single center.

OBJECTIVES: To evaluate the feasibility and therapeutic outcome of a personalized laparoscopic partial nephrectomy (LPN) selection scheme designed according to tumor characteristics. METHODS: We included 187 patients in this study with clinical T1 renal tumors who were diagnosed between March 2012 and February 2014. 93 patients underwent the personalized LPN on the basis of tumor characteristics (Group A, n = 93), including no renal arterial clamping LPN, selective renal arterial clamping LPN and main renal arterial clamping LPN, and the other 94 patients underwent LPN with main renal arterial clamping (Group B, n = 94). Patient characteristics, tumor characteristics, perioperative data and renal function parameters were collected prospectively. RESULTS: 85 patients in Group A and 90 patients in Group B completed the designed surgery without conversion. Compared with Group B, the operation time was longer in Group A (110.2 vs. 122.3 min, p = 0.001), intraoperative blood loss was increased (127.8 vs. 151.1 ml, p = 0.017), and there was less reduction in GFR 12 months postoperative (7.6 vs. 5.1 ml/min, p < 0.001). The study was limited by the relatively small sample size and long-term postoperative renal function is still awaited. CONCLUSIONS: The result of our study showed that, although the intraoperative blood loss was greater, there was a less decrease in GFR 12 months postoperative in personalized LPN group.

Combined antegrade femoral artery and retrograde popliteal artery recanalization for chronic occlusions of the superficial femoral artery.

PURPOSE: To evaluate the efficacy and safety of a dual femoral-popliteal approach in the supine position after failed antegrade recanalization attempts in chronic total occlusion (CTO) of the superficial femoral artery (SFA). MATERIALS AND METHODS: From May 2011 to October 2012, 21 patients underwent dual femoral-popliteal recanalization for CTO of the SFA, with a mean lesion length of 87.4 mm ± 5.8. When contralateral antegrade recanalization of SFA occlusions via the common femoral artery could not be achieved, the occlusions were intrainterventionally accessed by retrograde approach via the popliteal artery, which was punctured anteriorly with gently flexed knee and crus extorsion. When the SFA had been recanalized, further angioplasty and stent placement procedures were completed via the femoral artery. RESULTS: A technical success rate of 100% (entailing puncture of the popliteal artery and SFA recanalization) was achieved, and no hemorrhage, hematoma, pseudoaneurysm, arteriovenous fistula, or other complications developed. During a mean follow-up of 9.8 months ± 1.5, claudication severity, rest pain, and toe ulcers improved significantly. The pulse of the distal arteries, as well as the filling of the veins, could be distinctly felt. Ankle-brachial index changed from 0.48 ± 0.17 to 0.84 ± 0.11 at 1 year after intervention (P < .001), and patency rates at 1, 6, and 12 months after interventions were 100%, 80%, and 42%, respectively. CONCLUSIONS: A dual femoral-popliteal approach in the supine position is an alternative backup option after failed attempts at the antegrade approach for patients with proximal barriers in CTO or lesions with major extending collateral vessels.

[Medical image retrieval by high level semantic features and low level content features of image].

Content-based image retrieval aims at searching the similar images using low level features,and medical image retrieval needs it for the retrieval of similar images. Medical images contain not only a lot of content data, but also a lot of semantic information. This paper presents an approach by combining digital imaging and communications in medicine (DICOM) features and low level features to perform retrieval on medical image databases. At the first step, the semantic information is extracted from DICOM header for the pre-filtering of the images, and then dual-tree complex wavelet transfrom(DT-CWT) features of pre-filtered images and example images are extracted to retrieve similar images. Experimental results show that by combining the high level semantics (DICOM features) and low level content features (texture) the retrieval time is reduced and the performance of medical image retrieval is increased.

[Association between clinical ischemic events and carotid calcification evaluated by 64 slices CT angiography].

OBJECTIVE: To explore the relationship between clinical ischemic events and carotid calcification detected by 64 slices CT angiography. METHODS: We retrospectively analyzed the carotid calcified plaques in 116 patients underwent the 64 slices CT angiography. The relationship between the carotid calcification and the clinical ischemic events was analyzed. RESULTS: A total of 377 plaques were detected and the incidence of carotid plaque calcification was 63.40%. Incidence of plaque calcification was significantly lower in patients with ages < 50 years than that in patients with ages 51 - 59 years, 60 - 69 years and higher than 70 years (P = 0.003, P = 0.002, P = 0.000, respectively). The incidence of plaque calcification in the common carotid artery was significantly lower than the carotid bifurcation (P = 0.000) and the internal carotid artery (P = 0.000). The incidences of calcification in the mild, moderate and severe degree of stenosis and occlusion were 46.54%, 33.33%, 26.67% and 0% respectively. The distribution of intraplaque calcium was similar between patients with ischemic event and non-ischemic event group. However, the incidence of calcification was significantly lower patients with ischemic event than that in patients without ischemic event (30.34% vs. 43.10%, P = 0.013). Calcified plaque was negatively associated with ischemic event (beta = -0.688, P = 0.006). CONCLUSION: 64 slices CT angiography can analyze the characterization of carotid plaque calcium.