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.

Anti-Alzheimers molecular mechanism of icariin: insights from gut microbiota, metabolomics, and network pharmacology.

BACKGROUND: Icariin (ICA), an active ingredient extracted from Epimedium species, has shown promising results in the treatment of Alzheimer's disease (AD), although its potential therapeutic mechanism remains largely unknown. This study aimed to investigate the therapeutic effects and the underlying mechanisms of ICA on AD by an integrated analysis of gut microbiota, metabolomics, and network pharmacology (NP). METHODS: The cognitive impairment of mice was measured using the Morris Water Maze test and the pathological changes were assessed using hematoxylin and eosin staining. 16S rRNA sequencing and multi-metabolomics were performed to analyze the alterations in the gut microbiota and fecal/serum metabolism. Meanwhile, NP was used to determine the putative molecular regulation mechanism of ICA in AD treatment. RESULTS: Our results revealed that ICA intervention significantly improved cognitive dysfunction in APP/PS1 mice and typical AD pathologies in the hippocampus of the APP/PS1 mice. Moreover, the gut microbiota analysis showed that ICA administration reversed AD-induced gut microbiota dysbiosis in APP/PS1 mice by elevating the abundance of Akkermansia and reducing the abundance of Alistipe. Furthermore, the metabolomic analysis revealed that ICA reversed the AD-induced metabolic disorder via regulating the glycerophospholipid and sphingolipid metabolism, and correlation analysis revealed that glycerophospholipid and sphingolipid were closely related to Alistipe and Akkermansia. Moreover, NP indicated that ICA might regulate the sphingolipid signaling pathway via the PRKCA/TNF/TP53/AKT1/RELA/NFKB1 axis for the treatment of AD. CONCLUSION: These findings indicated that ICA may serve as a promising therapeutic approach for AD and that the ICA-mediated protective effects were associated with the amelioration of microbiota disturbance and metabolic disorder.

Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.

BACKGROUND: Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs). PURPOSE: To develop and validate a clinical-radiomics model based on multiparametric MRI and clinical information for the pretreatment differential diagnosis of PTs. STUDY TYPE: Retrospective. POPULATION: A total of 216 patients with PTs, 133 in the training cohort (55 benign PTs [BPTs] and 78 borderline/malignant PTs [BMPTs]) and 83 in the validation cohort (28 BPTs and 55 BMPTs). FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T; T2-weighted imaging (T2WI), precontrast T1-weighted imaging (T1WI) and dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI). ASSESSMENT: A total of 3138 radiomics features were computed to decode the imaging phenotypes of PTs. To build the classification models, the following workflow was followed: minimum-maximum scaling normalization method, recursive feature elimination based on ridge regression (Ridge-RFE), synthetic minority oversampling technique, and support vector machine classifier. We established several models based on the statistically significant features (Ridge-RFE selected) of each sequence to distinguish BPTs from BMPTs, including precontrast T1WI model, DCE-T1WI phase 1 model, T1WI feature fusion model, T2WI model, T1WI + T2WI model, clinical feature model, conventional MRI characteristics model, and combined clinical-radiomics model. STATISTICAL TESTS: Univariate analysis was utilized to compare variables between the BPT and BMPT groups. The receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of these models. RESULTS: In the training cohort, the clinical-radiomics model had excellent diagnostic efficiency, with an area under ROC (AUC) of 0.91 ± 0.02 (95% CI: 0.87-0.94). In the validation cohort, the AUCs were 0.79 ± 0.05 (95% CI: 0.70-0.87) for the combined model and 0.77 ± 0.05 (95% CI: 0.67-0.85) for the radiomics model. DATA CONCLUSION: Compared with conventional MRI characteristics, radiomics features extracted from multiparametric MRI are helpful for improving the accuracy of differentiating the pathological grades of PTs preoperatively. The model based on radiomics and clinical information is expected to become a potential noninvasive tool for the assessment of PTs grades. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

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.

Multiparametric MR Imaging Radiomics Signatures for Assessing the Recurrence Risk of ER+/HER2- Breast Cancer Quantified With 21-Gene Recurrence Score.

BACKGROUND: While the Oncotype DX 21-gene recurrence score (RS) has been recommended for guiding ER+/HER2- breast cancer treatment decisions, it is limited by cost and availability. PURPOSE: To develop a multiparametric MRI-based radiomics model for assessing ER+/HER2- breast cancer patients' 21-gene RS. STUDY TYPE: Retrospective. SUBJECTS: A total of 151 patients with pathologically confirmed ER+/HER2- breast cancers, who underwent preoperative breast MR examinations and 21-gene expression assays, divided into training (n = 106) and validation (n = 45) cohorts. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhancement (DCE) sequence at 1.5 T or 3 T. ASSESSMENT: A total of 1046 radiomics features were extracted from each MRI sequence with a manual lesion segmentation method. After feature dimension reduction by the recursive feature elimination method and dataset balance by the synthetic minority oversampling technique, linear support vector machine classifier models were built to distinguish high RS (RS ≥ 26) from low RS (RS < 26) from T2WI, DWI apparent diffusion coefficient (ADC) maps, DCE and their combination (multiparametric). A model based on clinical characteristics and a fusion model combining clinical characteristics and multiparametric MRI were also built. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve analysis and De Long's test with Bonferroni correction were used. A P value <0.01 was considered statistically significant. RESULTS: The area under the ROC curve (AUC) value of multiparametric radiomics model was 0.92, significantly higher than DCE (0.83), T2WI (0.78), and ADC (0.77) models in the training cohort. The radiomics model also achieved good performance in the validation cohort (AUC = 0.77). The fusion model had significantly higher performance than the clinical model in both the training (AUC = 0.92 and 0.64, respectively) and validation cohorts (AUC = 0.78 and 0.62, respectively). DATA CONCLUSION: The proposed multiparametric MRI-based radiomics models may have potential to help distinguish ER+/HER2- breast cancer patients' recurrence risk. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Exploring the mechanisms of action of Zengye decoction (ZYD) against Sjogren's syndrome (SS) using network pharmacology and animal experiment.

CONTEXT: Zengye decoction (ZYD) has been considered to have a curative effect on Sjogren's syndrome (SS). However, its therapeutic mechanisms remain obscure. OBJECTIVES: This research explores the mechanisms of ZYD against SS. MATERIALS AND METHODS: The active compounds and targets of ZYD were searched in the TCMSP and BATMAN-TCM databases. SS-related targets were obtained from the GeneCards database. The GO and KEGG enrichment analyses elucidated the molecular mechanisms. Animal experiments were performed using 8 C57BL/6 mice that served as the control group (physiological saline treatment) and 16 NOD mice randomly divided into the model group (physiological saline treatment) and the ZYD group (ZYD treatment) for 8 weeks to verify the therapeutic effects of ZYD on SS. RESULTS: Twenty-nine active compounds with 313 targets of ZYD and 1038 SS-related targets were screened. Thirty-two common targets were identified. ß-Sitosterol and stigmasterol might be important components. GO analysis suggested that the action of ZYD against SS mainly involved oxidative stress, apoptotic processes, and tumor necrosis factor receptor superfamily binding, etc. KEGG analysis indicated the most significant signaling pathway was apoptosis-multiple species. Animal experiments showed that ZYD improved lymphocytic infiltration of the submandibular glands (SMGs), reduced the serum levels of TNF-α, IL-1ß, IL-6, and IL-17, upregulated the expression of Bcl-2, and downregulated the expression of Bax and Caspase-3 in the model mice. DISCUSSION AND CONCLUSION: ZYD has anti-inflammatory and anti-apoptotic effects on SS, which provides a theoretical basis for the treatment of SS with ZYD.

Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor-Positive Breast Cancer: A Multicohort Study.

Background Radiogenomics explores the association between imaging features and genomic assays to uncover relevant prognostic features; however, the prognostic implications of the derived signatures remain unclear. Purpose To identify preoperative radiogenomic signatures of estrogen receptor-positive breast cancer associated with the Oncotype DX recurrence score (RS) and to evaluate whether they are biomarkers for survival and responses to neoadjuvant chemotherapy (NACT). Materials and Methods In this retrospective multicohort study, three data sets were analyzed. The radiogenomic development data set, with preoperative dynamic contrast-enhanced MRI and RS data obtained between January 2016 and October 2019 was used to identify radiogenomic signatures. Prognostic implications of the imaging signatures were assessed by measuring overall survival and recurrence-free survival in the prognostic assessment data set using a multivariable Cox proportional hazards model. The therapeutic implication of the radiogenomic signatures was evaluated by determining their ability to predict the response to NACT using the treatment assessment data set obtained between August 2015 and March 2019. Prediction performance was estimated by using the area under the receiver operating characteristic curve (AUC). Results The final cohorts included a radiogenomic development data set with 130 women (mean age, 52 years ± 10 [standard deviation]), a prognostic assessment data set with 116 women (mean age, 48 years ± 9), and a treatment assessment data set with 135 women (mean age, 50 years ± 11). Radiogenomic signatures (n = 11) of texture and morphologic and statistical features were identified to generate the predicted RS (R2 = 0.33, P < .001). A predicted RS greater than 29.9 was associated with poor overall and recurrence-free survival (P = .001 and P = .007, respectively); predicted RS was greater in women with a good NACT response (30.51 ± 6.92 vs 27.35 ± 4.04 [responders vs nonresponders], P = .001). By combining the predicted RS and complementary features, the model achieved improved performance in prediction of the NACT response (AUC, 0.85; P < .001). Conclusion Radiogenomic signatures associated with genomic assays provide markers of prognosis and treatment in estrogen receptor-positive breast cancer. © RSNA, 2021 Online supplemental material is available for this article.

A Multiomics Signature Highlights Alterations Underlying Homologous Recombination Deficiency in Triple-Negative Breast Cancer.

BACKGROUND: Homologous recombination (HR) is a key pathway in DNA double-strand damage repair. HR deficiency (HRD) occurs more commonly in triple-negative breast cancers (TNBCs) than in other breast cancer subtypes. Several clinical trials have demonstrated the value of HRD in stratifying breast cancer patients into distinct groups based on their responses to poly(ADP ribose) polymerase inhibitors and chemotherapy. METHODS: We retrospectively collected TNBC samples to establish a multiomics cohort (n = 343) and explored the biological and phenotypic mechanisms underlying the better prognosis of patients with high HRD scores. Gene set enrichment analysis was conducted to elucidate the underlying pathways in patients with low HRD scores, and a radiomics model was established to predict the HRD score via a noninvasive method. RESULTS: Multivariable Cox analysis revealed the independent prognostic value of a low HRD score (hazard ratio 2.20, 95% confidence interval 1.05-4.59; p = 0.04). Furthermore, amino acid and lipid metabolism pathways were highly enriched in tumors from patients with low HRD scores, which was also demonstrated by differential abundant metabolite analysis. A noninvasive radiomics method was developed to predict the HRD status and it performed well in the independent validation cohort (support vector machine model: area under the curve [AUC] 0.739, sensitivity 0.571, and specificity 0.824; logistic regression model: AUC 0.695, sensitivity 0.571, and specificity 0.882). CONCLUSIONS: We revealed the prognostic value of the HRD score, predicted the HRD status with noninvasive radiomics features, and preliminarily explored druggable targets for TNBC patients with low HRD scores.

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.

High relaxivity Gd3+-based organic nanoparticles for efficient magnetic resonance angiography.

Contrast-enhanced MR angiography (MRA) is a critical technique for vascular imaging. Nevertheless, the efficacy of MRA is often limited by the low rate of relaxation, short blood-circulation time, and metal ion-released potential long-term toxicity of clinical available Gd-based contrast agents. In this work, we report a facile and efficient strategy to achieve Gd-chelated organic nanoparticles with high relaxivity for T1-weighted MRA imaging. The Gd-chelated PEG-TCPP nanoparticles (GPT NPs) have been engineered composite structured consisting of Gd-chelated TCPP and PEG. The spherical structure of TCPP offers more chemical sites for Gd3+ coordination to improve the relaxivity and avoid leakage of the Gd3+ ions. The synthesized GPT NPs exhibit a high relaxation rate of 35.76 mM- 1 s- 1 at 3.0 T, which is higher than the rates for most reported MR contrast agents. Therefore, GPT NPs can be used for MRA with much stronger vascular signals, longer circulation time, and high-resolution arterial vascular visualization than those using clinical MR contrast agents at the same dose. This work may make the T1 MRI contrast agents for high-resolution angiography possible and offer a new candidate for preclinical and clinical applications of MR vascular imaging and vascular disease diagnosis.

TBX5-AS1, an enhancer RNA, is a potential novel prognostic biomarker for lung adenocarcinoma.

BACKGROUND: Enhancer RNAs (eRNAs) are demonstrated to be closely associated with tumourigenesis and cancer progression. However, the role of eRNAs in lung adenocarcinoma (LUAD) remains largely unclear. Thus, a comprehensive analysis was constructed to identify the key eRNAs, and to explore the clinical utility of the identified eRNAs in LUAD. METHODS: First, LUAD expression profile data from the Cancer Genome Atlas (TCGA) dataset and eRNA-relevant information were integrated for Kaplan-Meier survival analysis and Spearman's correlation analysis to filtered the key candidate eRNAs that was associated with survival rate and their target genes in LUAD. Then, the key eRNA was selected for subsequent clinical correlation analysis. KEGG pathway enrichment analyses were undertaken to explore the potential signaling pathways of the key eRNA. Data from the human protein atlas (HPA) database were used to validate the outcomes and the quantitative real time-polymerase chain reaction (qRT-PCR) analysis was conducted to measure eRNA expression levels in tumor tissues and paired normal adjacent tissues from LUAD patients. Finally, the eRNAs were validated in pan-cancer. RESULTS: As a result, TBX5-AS1 was identified as the key eRNA, which has T-box transcription factor 5 (TBX5) as its regulatory target. KEGG analysis indicated that TBX5-AS1 may exert a vital role via the PI3K/AKT pathway, Ras signaling pathway, etc. Additionally, the qRT-PCR results and the HPA database indicated that TBX5-AS1 and TBX5 were significantly downregulated in tumour samples compared to matched-adjacent pairs. The pan-cancer validation results showed that TBX5-AS1 was associated with survival in four tumors, namely, adrenocortical carcinoma (ACC), LUAD, lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). Correlations were found between TBX5-AS1 and its target gene, TBX5, in 26 tumor types. CONCLUSION: Collectively, our results indicated that TBX5-AS1 may be a potential prognostic biomarker for LUAD patients and promote the targeted therapy of LUAD.

A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI.

OBJECTIVE: This study aims to develop a machine learning model for prediction of malignancy in T2 hyperintense mesenchymal uterine tumors based on T2-weighted image (T2WI) features and clinical information. METHODS: This retrospective study included 134 patients with T2 hyperintense uterine mesenchymal tumors (104 patients in training cohort and 30 in testing cohort). A total of 960 radiomics features were initially computed and extracted from each 3D segmented tumor depicting on T2WI. The support vector machine (SVM) classifier was applied to build computer-aided diagnosis (CAD) models by using selected clinical and radiomics features, respectively. Finally, an observer study was conducted by comparing with two radiologists to evaluate the diagnostic performance. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model. RESULTS: Comparing with the T2WI-based radiomics model (AUC: 0.76 ± 0.09) and the clinical model (AUC: 0.79 ± 0.09), the combined model significantly improved the AUC value to 0.91 ± 0.05 (p < 0.05). The clinical-radiomics combined model yielded equivalent or higher performance than two radiologists (AUC: 0.78 vs. 0.91, p = 0.03; 0.90 vs.0.91, p = 0.13). There was a significant difference between the AUC values of two radiologists (p < 0.05). CONCLUSIONS: It is feasible to predict malignancy risk of T2 hyperintense uterine mesenchymal tumors by combining clinical variables and T2WI-based radiomics features. Machine learning-based classification model may be useful to assist radiologists in decision-making. KEY POINTS: • Radiomics approach has the potential to distinguish between benign and malignant mesenchymal uterine tumors. • T2WI-based radiomics analysis combined with clinical variables performed well in predicting malignancy risk of T2 hyperintense uterine mesenchymal tumors. • Machine learning-based classification model may be useful to assist radiologists in characterization of a T2 hyperintense uterine mesenchymal tumor.

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.

OBJECTIVES: To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). METHODS: In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. RESULTS: In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). CONCLUSIONS: The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS: • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

Identification and Validation of Six Autophagy-related Long Non-coding RNAs as Prognostic Signature in Colorectal Cancer.

Colorectal cancer (CRC) is a commonly occurring tumour with poor prognosis. Autophagy-related long non-coding RNAs (lncRNAs) have received much attention as biomarkers for cancer prognosis and diagnosis. However, few studies have focused on their prognostic predictive value specifically in CRC. This research aimed to construct a robust autophagy-related lncRNA prognostic signature for CRC. Autophagy-related lncRNAs from The Cancer Genome Atlas database were screened using univariate Cox, LASSO, and multivariate Cox regression analyses, and the resulting key lncRNAs were used to establish a prognostic risk score model. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was performed to detect the expression of several lncRNAs in cancer tissues from CRC patients and in normal tissues adjacent to the cancer tissues. A prognostic signature comprising lncRNAs AC125603.2, LINC00909, AC016876.1, MIR210HG, AC009237.14, and LINC01063 was identified in patients with CRC. A graphical nomogram based on the autophagy-related lncRNA signature was developed to predict CRC patients' 1-, 3-, and 5-year survival. Overall survival in patients with low risk scores was significantly better than in those with high risk scores (P < 0.0001); a similar result was obtained in an internal validation sample. The nomogram was shown to be suitable for clinical use and gave correct predictions. The 1- and 3-year values of the area under the receiver operating characteristic curve were 0.797 and 0.771 in the model sample, and 0.656 and 0.642 in the internal validation sample, respectively. The C-index values for the verification samples and training samples were 0.756 (95% CI = 0.668-0.762) and 0.715 (95% CI = 0.683-0.829), respectively. Gene set enrichment analysis showed that the six autophagy-related lncRNAs were greatly enriched in CRC-related signalling pathways, including p53 and VEGF signalling. The qRT-PCR results showed that the expression of lncRNAs in CRC was higher than that in adjacent tissues, consistent with the expression trends of lncRNAs in the CRC data set. In summary, we established a signature of six autophagy-related lncRNAs that could effectively guide clinical prediction of prognosis in patients with CRC. This lncRNA signature has significant clinical implications for improving the prediction of outcomes and, with further prospective validation, could be used to guide tailored therapy for CRC patients.

Evaluation of Background Parenchymal Enhancement and Histogram-Based Diffusion-Weighted Image in Determining the Molecular Subtype of Breast Cancer.

RATIONALE AND OBJECTIVES: This study aimed to evaluate the value of background parenchymal enhancement (BPE) and diffusion-weighted image (DWI) histogram features in differentiating among different molecular subtypes of breast cancers and investigate the relationship between BPE and DWI features. MATERIALS AND METHODS: We prospectively enrolled 142 patients with breast cancer between January and November 2018. All patients underwent breast magnetic resonance imaging before core needle biopsy. The quantitative BPE from dynamic enhanced images and the first-order histogram features extracted from DWI were analyzed. Univariate analysis of variance was used to compare differences in DWI histogram features and BPE characteristics among different molecular subtypes. Spearman test was used to compare the correlation between these imaging indexes. RESULTS: A total of 142 patients had 142 lesions, including 17 cases of triple-negative breast cancer, 12 cases of luminal A type breast cancer, 39 cases of luminal B type breast cancer, and 74 cases of human epidermal growth factor receptor 2-positive breast cancer. The apparent diffusion coefficient (ADC) 95th percentile, ADC kurtosis, and BPE were significantly different among 4 subtype groups (P < 0.05), especially between the triple-negative subtype and any other subtype (P < 0.05 in pairwise comparisons). There was a weak but significant correlation between BPE and kurtosis of ADC (r = -0.176, P = 0.036). CONCLUSIONS: Diffusion-weighted image histogram features (95th percentile ADC value and kurtosis value of ADC) and BPE features were different in the 4 molecular subtypes of breast cancer, especially in the triple-negative breast cancer subtype. Background parenchymal enhancement was negatively correlated with the kurtosis value of ADC.

Identification of the protective effect of Polygonatum sibiricum polysaccharide on d-galactose-induced brain ageing in mice by the systematic characterization of a circular RNA-associated ceRNA network.

CONTEXT: Polygonatum sibiricum polysaccharide (PSP), derived from Polygonatum sibiricum Delar. ex Redoute (Liliaceae), is known to be able to delay the ageing process. However, the specific mechanisms underlying these effects are not clear. OBJECTIVE: To investigate the mechanisms underlying the effects of PSP treatment on brain ageing by the application of transcriptomic analysis. MATERIALS AND METHODS: Forty Kunming mice were randomly divided into four groups (control, d-galactose, low-dose PSP, high-dose PSP). Mice were administered d-galactose (50 mg/kg, hypodermic injection) and PSP (200 or 400 mg/kg, intragastric administration) daily for 60 days. Behavioural responses were evaluated with the Morris water maze and the profiles of circRNA, miRNA, and mRNA, in the brains of experimental mice were investigated during the ageing process with and without PSP treatment. RESULTS: PSP improved cognitive function during brain ageing, as evidenced by a reduced escape latency time (p < 0.05) and an increase in the number of times mice crossed the platform (p < 0.05). A total of 37, 13, and 679, circRNAs, miRNAs, and mRNAs, respectively, were significantly altered by PSP treatment (as evidenced by a fold change ≥2 and p < 0.05). These dysregulated RNAs were closely associated with synaptic activity. PSP regulated regulate nine mRNAs (Slc6a5, Bean1, Ace, Samd4, Olfr679, Olfr372, Dhrs9, Tsc1, Slc12a6), three miRNAs (mmu-miR-5110, mmu-miR-449a-5p, mmu-miR-1981-5p), and two circRNAs (2:29227578|29248878 and 5:106632925|106666845) in the competing endogenous RNA (ceRNA) network. DISCUSSION AND CONCLUSIONS: Our analyses showed that multiple circRNAs, miRNAs, and mRNAs responded to PSP treatment in mice experiencing brain ageing.

Integrated transcriptomic and metabolomic analyses to characterize the anti-cancer effects of (-)-epigallocatechin-3-gallate in human colon cancer cells.

(-)-Epigallocatechin-3-gallate (EGCG) is the main bioactive component in tea (Camellia sinensis) catechins, and exhibits potential antitumor activity against colorectal cancer (CRC). However, the underlying mechanisms are largely unclear. We investigated the effects of EGCG on activities of CRC cells and the exact molecular mechanism. We used human colon cancer cells (HT-29) and exposed them to EGCG at various concentrations. The MTT assay, flow cytometry, and TUNEL staining were used to study the underlying mechanisms of EGCG (proliferation, apoptosis, autophagy). Western blotting was used to measure expression of marker proteins of the cell cycle, apoptosis, and autophagy. Using a combined microarray-based transcriptomic and ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight tandem mass spectrometry (UHPLC-QTOF/MS)-based metabolomic approach, we investigated the perturbed pathways induced by EGCG treatment at transcript and metabolite levels. Transcriptomic analyses showed that 486 genes were differentially expressed between untreated and EGCG-treated cells. Also, 88 differentially expressed metabolites were identified between untreated and EGCG-treated cells. The altered metabolites were involved in the metabolism of glutathione, glycerophospholipids, starch, sucrose, amino sugars, and nucleotide sugars. There was substantial agreement between the results of transcriptomics and metabolomics analyses. Our data indicate that the anticancer activity of EGCG against HT-29 cells is mediated by induction of cell-cycle arrest, apoptosis, and autophagy. EGCG modulates cancer-cell metabolic pathways. These results provide a platform for future molecular mechanistic studies of EGCG.

Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis.

OBJECTIVES: To explore whether clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal cell carcinoma (cRCC) can be distinguished using radiomics features extracted from magnetic resonance (MR) images. METHODS: Seventy-seven patients (ccRCC = 32, pRCC = 23, cRCC = 22) underwent MRI before surgery between May 2013 and August 2018 in this retrospective study. Thirty-nine radiomics features were extracted from tumor volumes on three sequences (T2WI, EN-T1WI CMP, and EN-T1WI NP). The Kruskal-Wallis test with Bonferonni correction and variance threshold were used for feature selection among the three RCC subtypes. ROC curves for the three subtypes were generated based on radiomics features. AUC, accuracy, sensitivity, and specificity for subtype differentiation are reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics features. RESULTS: Significant radiomics features among the three subtypes were identified, and ROC curves achieved excellent AUCs for T2WI, EN-T1WI CMP, EN-T1WI NP, and combined three MR sequences (0.631, 0.790, 0.959, and 0.959 between ccRCC and cRCC; 0.688, 0.854, 0.909, and 0.955 between pRCC and cRCC; 0.747, 0.810, 0.814, and 0.890 between ccRCC and pRCC). In addition, LDA demonstrated the three RCC subtypes were correctly classified by radiomics analysis (66.2% for EN-T1WI CMP, 71.4% for EN-T1WI NP, 55.8% for T2WI, and 71.4% for the combined three MR sequences). CONCLUSIONS: Radiomics analysis can be used to differentiate among ccRCC, pRCC, and cRCC based on radiomics features extracted from multiple-sequence MRI and may help diagnose and treat RCC patients in the future, while further study is still needed. KEY POINTS: • Radiomics features on multiple-sequence MRI can help differentiate the three subtypes of renal cell carcinoma (clear cell, papillary renal cell, and chromophobe renal cell carcinoma). • Radiomics features based on MRI indicate greater textural heterogeneity on ccRCCs than pRCCs and cRCCs (the highest AUCs on EN-T1WI NP are 0.814 for ccRCCs vs pRCCs and 0.959 for ccRCCs vs cRCCs, respectively). • There is a significant difference in the textural heterogeneity of radiomics features between pRCCs and cRCCs (the AUC is 0.909, 0.854, and 0.688 on EN-T1WI NP, EN-T1WI CMP, and T2WI, respectively).

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists. METHODS: First, we retrospectively collected 828 histopathologically confirmed GGNs of 644 patients from two centers. Among them, 209 GGNs are confirmed IA and 619 are non-IA, including 409 adenocarcinomas in situ and 210 minimally invasive adenocarcinomas. Second, we applied a series of pre-preprocessing techniques, such as image resampling, rescaling and cropping, and data augmentation, to process original CT images and generate new training and testing images. Third, we built an AI scheme based on a deep convolutional neural network by using a residual learning architecture and batch normalization technique. Finally, we conducted an observer study and compared the prediction performance of the AI scheme with that of two radiologists using an independent dataset with 102 GGNs. RESULTS: The new AI scheme yielded an area under the receiver operating characteristic curve (AUC) of 0.92 ± 0.03 in classifying between IA and non-IA GGNs, which is equivalent to the senior radiologist's performance (AUC 0.92 ± 0.03) and higher than the score of the junior radiologist (AUC 0.90 ± 0.03). The Kappa value of two sets of subjective prediction scores generated by two radiologists is 0.6. CONCLUSIONS: The study result demonstrates using an AI scheme to improve the performance in predicting IA, which can help improve the development of a more effective personalized cancer treatment paradigm. KEY POINTS: • The feasibility of using a deep learning method to predict the likelihood of the ground-glass nodule being invasive adenocarcinoma. • Residual learning-based CNN model improves the performance in classifying between IA and non-IA nodules. • Artificial intelligence (AI) scheme yields higher performance than radiologists in predicting invasive adenocarcinoma.

Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network.

Digital breast tomosynthesis (DBT) is a newly developed three-dimensional tomographic imaging modality in the field of breast cancer screening designed to alleviate the limitations of conventional digital mammography-based breast screening methods. A computer-aided detection (CAD) system was designed for masses in DBT using a faster region-based convolutional neural network (faster-RCNN). To this end, a data set was collected, including 89 patients with 105 masses. An efficient detection architecture of convolution neural network with a region proposal network (RPN) was used for each slice to generate region proposals (i.e., bounding boxes) with a mass likelihood score. In each DBT volume, a slice fusion procedure was used to merge the detection results on consecutive 2D slices into one 3D DBT volume. The performance of the CAD system was evaluated using free-response receiver operating characteristic (FROC) curves. Our RCNN-based CAD system was compared with a deep convolutional neural network (DCNN)-based CAD system. The RCNN-based CAD generated a performance with an area under the ROC (AUC) of 0.96, whereas the DCNN-based CAD achieved a performance with AUC of 0.92. For lesion-based mass detection, the sensitivity of RCNN-based CAD was 90% at 1.54 false positive (FP) per volume, whereas the sensitivity of DCNN-based CAD was 90% at 2.81 FPs/volume. For breast-based mass detection, RCNN-based CAD generated a sensitivity of 90% at 0.76 FP/breast, which is significantly increased compared with the DCNN-based CAD with a sensitivity of 90% at 2.25 FPs/breast. The results suggest that the faster R-CNN has the potential to augment the prescreening and FP reduction in the CAD system for masses.