Predicting Prognosis of Phyllodes Tumors Using a Mammography- and Magnetic Resonance Imaging-Based Radiomics Model: A Preliminary Study.

PURPOSE: To investigate whether a radiomics model based on mammography (MG) and magnetic resonance imaging (MRI) can be used to predict disease-free survival (DFS) after phyllodes tumor (PT) surgery. METHOD: About 131 PT patients who underwent MG and MRI before surgery between January 2010 and December 2020 were retrospectively enrolled, including 15 patients with recurrence and metastasis and 116 without recurrence. 884 and 3138 radiomic features were extracted from MG and MR images, respectively. Then, multiple radiomics models were established to predict the recurrence risk of the patients by applying a support vector machine classifier. The area under the ROC curve (AUC) was calculated to evaluate model performance. After dividing the patients into high- and low-risk groups based on the predicted radiomics scores, survival analysis was conducted to compare differences between the groups. RESULTS: In total, 3 MG-related and 5 MRI-related radiomic models were established; the prediction performance of the T1WI feature fusion model was the best, with an AUC value of 0.93. After combining the features of MG and MRI, the AUC increased to 0.95. Furthermore, the MG, MRI and all-image radiomic models had statistically significant differences in survival between the high- and low-risk groups (P < .001). All-image radiomics model showed higher survival performance than the MG and MRI radiomics models alone. CONCLUSIONS: Radiomics features based on preoperative MG and MR images can predict DFS after PT surgery, and the prediction score of the image radiomics model can be used as a potential indicator of recurrence risk.

A cross-sectional study on the correlation of image quality, effective dose, and body composition with thyroid, chest, and abdominal computed tomography scans.

Background: The rapid increase in the use of radiodiagnostic examinations in China, especially computed tomography (CT) scans, has led to these examinations being the largest artificial source of per capita effective dose (ED). This study conducted a retrospective analysis of the correlation between image quality, ED, and body composition in 540 cases that underwent thyroid, chest, or abdominal CT scans. The aim of this analysis was to evaluate the correlation between the parameters of CT scans and body composition in common positions of CT examination (thyroid, chest, and abdomen) and ultimately inform potential measures for reducing radiation exposure. Methods: This study included 540 patients admitted to Fudan University Shanghai Cancer Center from January 2015 to December 2019 who underwent both thyroid or chest or abdominal CT scan and body composition examination. Average CT values and standard deviation (SD) values were collected for the homogeneous areas of the thyroid, chest, or abdomen, and the average CT values and SD values of adjacent subcutaneous fat tissue were measured in the same region of interest (ROI). All data were measured three times, and the average was taken to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for each area. The dose-length product (DLP) was recorded, and the ED was calculated with the following: formula ED = k × DLP. Dual-energy X-ray was used to determine body composition and obtain indicators such as percentage of spinal and thigh muscle. Pearson correlation coefficient was used to analyze the correlations between body composition indicators, height, weight, body mass index (BMI), and ED. Results: The correlation coefficients between the SNR of abdominal CT scan and weight, BMI, and body surface area (BSA) were -0.470 (P=0.001), -0.485 (P=0.001), and -0.437 (P=0.002), representing a moderate correlation strength with statistically significant differences. The correlation coefficients between the ED of chest CT scans and weight, BMI, spinal fat percentage, and BSA were 0.488 (P=0.001), 0.473 (P=0.002), 0.422 (P=0.001), and 0.461 (P=0.003), respectively, indicating a moderate correlation strength with statistical differences. There was a weak statistically significant correlation between the SNR, CNR, and ED of the other scans with each physical and body composition index (P=0.023). Conclusions: There were varying degrees of correlation between CT image quality and ED and physical and body composition indices, which may inform novel solutions for reducing radiation exposure.

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.

Local Tumor Progression Predictive Model Based on MRI for Colorectal Cancer Liver Metastases after Radiofrequency Ablation.

PURPOSE: To investigate the post-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) characteristics in patients with liver metastases from colorectal cancer and to build a predictive model for local tumor progression based on these imaging markers. MATERIALS AND METHODS: A cohort of 73 patients with 110 colorectal cancer liver metastases (CRCLM) who underwent RFA and MRI one month post-ablation was included in image signs analysis and predictive model training. Using a newly developed MRI appearance scoring criteria, MR Image Appearance Scoring at One Month after RFA (MRIAS 1MO), the semi-quantitative analysis of MRI findings within the ablation zone were conducted independently by two radiologists. The intraclass correlation coefficient (ICC) was calculated to evaluate measurement reliability. Differences in MRIAS 1MO scores were compared using Mann-Whitney U test, focusing on local tumor response outcomes. Using local tumor progression (LTP) as the primary end point, MRIAS 1MO scores and other lesion morphological and clinical characteristics were included to establish predictive model. Predication accuracy was subsequently evaluated using calibration curve, time-dependent concordance index (C index) curve, and LTP-free survival (LTPFS) curve. Another cohort comprising 60 patients with 76 CRCLMs provided additional MRIAS 1MO scores and clinical data associated with LTP. We evaluated the performance of the established predictive model using calibration curve, time-dependent C index curve, and LTPFS curve. RESULTS: The MRIAS 1MO criteria exhibited strong measurement reliability. The ICC values of T1S (scores from T1WI), T2S (scores form T2WI) and NCES (scores by adding T1S to T2S) MRIS (the overall scores) were 0.825, 0.779, 0.826 and 0.873, respectively. Lesions with LTP showed significantly higher median values for the overall MRIAS 1MO score (MRIS) compared to lesions without LTP (16 vs. 12, p < 0.001). MRIS and lesion diameter were independent prognostic factors of LTP and were included in predictive model (hazard ratio: MRIS over 13.5:4.275, lesion diameter larger than 30 mm: 2.056). The predictive model demonstrated an overall C index of 0.721 and risk stratification using the predictive model resulted in significantly different LPTFS times. In the validation cohort, the C index were 0.825, 0.794 and 0.764 at six, twelve and twenty-four months, respectively. Patients classified as high-risk in the validation cohort had a median LTPFS time of 10.0 months, while the median LTPFS time was not reached in the low-risk group. CONCLUSIONS: The semi-quantitative MRIAS 1MO criteria, used for post-RFA MRI appearance analysis, exhibited strong measurement reliability. Prediction models established based on overall MRIAS 1MO score (MRIS) and lesion diameter had good predictive performance for LTP in patients undergoing RFA for CRCLM treatment.

Coverage with the First Dose of Human Papillomavirus Vaccination among Females Aged 9-50 Years in Shenzhen, China: A Surveillance Based on Administrative Health Records in 2023.

China started to offer human papillomavirus (HPV) vaccines to females aged 9-45 years in 2016. However, there was a lack of reports about HPV vaccination coverage in a representative sample of females in China. Therefore, this study aimed to examine the current HPV coverage and associated factors among females aged 9-50 years in Shenzhen, China, based on administrative health records kept by community health centers. A multistage random sampling approach was used. The research team randomly selected 18 community health centers in Shenzhen, and 3118 health records of females aged 9-50 years were then randomly selected from these health centers. Among all participants, 18.7% received at least one dose of HPV vaccination. The highest coverage was observed among females aged 18-26 years (23.4%), followed by those aged 27-35 years (22.0%) and 36-45 years (20.2%). Such coverage was very low among females aged 9-17 years (4.6%) and those aged 46-50 years (3.2%). Among females aged 18 years or above, higher education level, having a family doctor, and permanent residency in Shenzhen were associated with higher HPV vaccination coverage, while older age and being married/divorced were negatively associated with coverage. The HPV vaccination coverage in Shenzhen was 18.7% and there is a strong need for improvement.

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model. METHODS: This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model. RESULTS: The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05). CONCLUSIONS: Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.

Objective.Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a computed tomography (CT) image-based hybrid deep radiomics model to predict EGFR mutation status in NSCLC and investigate the correlations between deep image and quantitative radiomics features.Approach.First, we retrospectively enrolled 818 patients from our centre and 131 patients from The Cancer Imaging Archive database to establish a training cohort (N= 654), an independent internal validation cohort (N= 164) and an external validation cohort (N= 131). Second, to predict EGFR mutation status, we developed three CT image-based models, namely, a multi-task deep neural network (DNN), a radiomics model and a feature fusion model. Third, we proposed a hybrid loss function to train the DNN model. Finally, to evaluate the model performance, we computed the areas under the receiver operating characteristic curves (AUCs) and decision curve analysis curves of the models.Main results.For the two validation cohorts, the feature fusion model achieved AUC values of 0.86 ± 0.03 and 0.80 ± 0.05, which were significantly higher than those of the single-task DNN and radiomics models (allP< 0.05). There was no significant difference between the feature fusion and the multi-task DNN models (P> 0.8). The binary prediction scores showed excellent prognostic value in predicting disease-free survival (P= 0.02) and overall survival (P< 0.005) for validation cohort 2.Significance.The results demonstrate that (1) the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-task DNN models, (2) the feature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSCLC prognosis, and (3) high correlations exist between some deep image and radiomics features.

Artificial intelligence in breast imaging: Current situation and clinical challenges.

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

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.

Concurrent Dual-Contrast Enhancement Using Fe3O4 Nanoparticles to Achieve a CEST Signal Controllability.

Traditional T2 magnetic resonance imaging (MRI) contrast agents have defects inherent to negative contrast agents, while chemical exchange saturation transfer (CEST) contrast agents can quantify substances at trace concentrations. After reaching a certain concentration, iron-based contrast agents can "shut down" CEST signals. The application range of T2 contrast agents can be widened through a combination of CEST and T2 contrast agents, which has promising application prospects. The purpose of this study is to develop a T2 MRI negative contrast agent with a controllable size and to explore the feasibility of dual contrast enhancement by combining T2 with CEST contrast agents. The study was carried out in vitro with HCT-116 human colon cancer cells. A GE SIGNA Pioneer 3.0 T medical MRI scanner was used to acquire CEST images with different saturation radio-frequency powers (1.25/2.5/3.75/5 µT) by 2D spin echo-echo planar imaging (SE-EPI). Magnetic resonance image compilation (MAGiC) was acquired by a multidynamic multiecho 2D fast spin-echo sequence. The feasibility of this dual-contrast enhancement method was assessed by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, ζ potential analysis, inductively coupled plasma, X-ray photoelectron spectroscopy, X-ray powder diffraction, vibrating-sample magnetometry, MRI, and a Cell Counting Kit-8 assay. The association between the transverse relaxation rate r2 and the pH of the iron-based contrast agents was analyzed by linear fitting, and the linear relationship between the CEST effect in different B1 fields and pH was analyzed by the ratio method. Fe3O4 nanoparticles (NPs) with a mean particle size of 82.6 ± 22.4 nm were prepared by a classical process, and their surface was successfully modified with -OH active functional groups. They exhibited self-aggregation in an acidic environment. The CEST effect was enhanced as the B1 field increased, and an in vitro pH map was successfully plotted using the ratio method. Fe3O4 NPs could stably serve as reference agents at different pH values. At a concentration of 30 µg/mL, Fe3O4 NPs "shut down" the CEST signals, but when the concentration of Fe3O4 NPs was less than 10 µg/mL, the two contrast agents coexisted. The prepared Fe3O4 NPs had almost no toxicity, and when their concentration rose to 200 µg/mL at pH 6.5 or 7.4, they did not reach the half-maximum inhibitory concentration (IC50). Fe3O4 magnetic NPs with a controllable size and no toxicity were successfully synthesized. By combining Fe3O4 NPs with a CEST contrast agent, the two contrast agents could be imaged simultaneously; at higher concentrations, the iron-based contrast agent "shut down" the CEST signal. An in vitro pH map was successfully plotted by the ratio method. CEST signal inhibition can be used to realize the pH mapping of solid tumors and the identification of tumor active components, thus providing a new imaging method for tumor efficacy evaluation.

Diabetes mellitus induces a novel inflammatory network involving cancer progression: Insights from bioinformatic analysis and in vitro validation.

Background: Patients with diabetes mellitus (DM) have a higher incidence of malignant tumors than people without diabetes, but the underlying molecular mechanisms are still unclear. Methods: To investigate the link between DM and cancer, we screened publicly available databases for diabetes and cancer-related genes (DCRGs) and constructed a diabetes-based cancer-associated inflammation network (DCIN). We integrated seven DCRGs into the DCIN and analyzed their role in different tumors from various perspectives. We also investigated drug sensitivity and single-cell sequencing data in colon adenocarcinoma as an example. In addition, we performed in vitro experiments to verify the expression of DCRGs and the arachidonic acid metabolic pathway. Results: Seven identified DCRGs, including PPARG, MMP9, CTNNB1, TNF, TGFB1, PTGS2, and HIF1A, were integrated to construct a DCIN. The bioinformatics analysis showed that the expression of the seven DCRGs in different tumors was significantly different, which had varied effects on diverse perspectives. Single-cell sequencing analyzed in colon cancer showed that the activity of the DCRGs was highest in Macrophage and the lowest in B cells among all cell types in adenoma and carcinoma tissue. In vitro experiments showed that the DCRGs verified by western bolt and PEG2 verified by ELISA were all highly expressed in COAD epithelial cells stimulated by high glucose. Conclusion: This study, for the first time, constructed a DCIN, which provides novel insights into the underlying mechanism of how DM increases tumor occurrence and development. Although further research is required, our results offer clues for new potential therapeutic strategies to prevent and treat malignant tumors.

Combinations of Digital Breast Tomosynthesis and Full-field Digital Mammography for Different Density Types of Breasts.

BACKGROUND: The combination of FFDM and DBT can significantly improve the diagnostic efficiency of breast cancer, but with the increase of breast radiation absorbed dose. OBJECTIVES: To compare and analyze the radiation dose and diagnostic performance of different mammography positions combinations of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) for different density types of breasts. METHODS: This retrospective study involved 1,195 patients who underwent simultaneous breast DBT and FFDM. The mammography combinations were Group A, FFDM(CC+MLO); Group B, FDM(CC)+DBT(MLO); Group C, FFDM(MLO)+DBT(CC); Group D, DBT(CC+MLO); and Group E, FFDM(CC+MLO)+DBT(CC+MLO). An intergroup comparative analysis of radiation dose and diagnostic performance of different combinations of mammography positions for different breast density types was performed using the pathologic and 24-month follow-up results as the diagnostic basis. RESULTS: Overall, 2,403 mammograms indicated 477 cases of non-dense breast tissues and 1,926 cases of dense breast tissues. Differences in the mean radiation dose for each non-dense and dense breast group were statistically significant. The areas under the diagnostic receiver operating characteristic (ROC) curves for the non-dense breast group were not statistically significant. In the dense breast group, the z-values were 1.623 (p = 0.105) and 1.724 (p = 0.085) for the area under the ROC curve in Group C compared with Groups D and E, respectively, and 0.724 (p = 0.469) when comparing Group D with Group E. The differences between the remaining groups were statistically significant. CONCLUSION: Group A had the lowest radiation dose and no significant difference in diagnostic performance compared with the other non-dense breast groups. Group C had high diagnostic performance in the dense breast group considering the low radiation dose.

Ultrasound-enhanced cascade chemodynamic tumor nanotherapy with lactic acid-enabled hydrogen peroxide self-production.

Chemodynamic therapy (CDT) is an effective therapeutic modality for cancer treatment with the action of a catalytic Fenton-like chemoreactive process. To furnish sufficient hydrogen peroxide (H2O2) for CDT, catalysts similar to superoxide dismutase are designed to be in cooperation with nanoplatforms. In this work, we rationally integrate lactate oxidase (LOD) with ultrasmall superparamagnetic iron oxide nanoparticles (USPION) to achieve high efficiency of the cascade Fenton reaction for efficient tumor therapy. During the sequential reaction, LOD converts lactic acid into H2O2 and pyruvate (PA) in situ, and then USPION with peroxidase-like activity generates large amounts of toxic hydroxyl radicals (˙OH) under the action of H2O2. Moreover, the reaction effectively utilizes the excess lactic acid of the tumor microenvironment (TME) as a new target of cancer treatment. To further achieve high-performance tumor treatment, ultrasound has been introduced for augmenting this specific chemoreactive tumor therapy, which can affect cancer cells mainly through sonoporation, cavitation, and thermal effect. With the effects of ultrasound irradiation, this work has constructed an efficient oncology treatment system for tumors. Moreover, the presence of USPION is highly desirable for contrast-enhanced T1-weighted MRI for monitoring the therapeutic process of cancer in real time.

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.

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.

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.

Untargeted metabolomics analysis of the hippocampus and cerebral cortex identified the neuroprotective mechanisms of Bushen Tiansui formula in an aß25-35-induced rat model of Alzheimer's disease.

Background: Bushen Tiansui Formula (BSTSF) is a traditional formulation of Chinese medicine that has been used to treat Alzheimer's disease (AD) for decades; however, the underlying mechanisms by which this formula achieves such therapeutic effects have yet to be elucidated. Prupose: To investigate the neuroprotective mechanisms of BSTSF against AD by analyzing metabolite profiles in the hippocampus and cortex of AD rats. Methods: The rat models of AD were established by the injection of Aß25-35. The Morris water maze (MWM) test was performed to evaluate the effect of BSTSF treatment on cognitive dysfunction. Hematoxylin and eosin (HE) staining was used to assess the effect of BSTSF on typical AD pathologies. Underlying mechanisms were investigated using LC-MS/MS-based untargeted metabolomics analysis of the cerebral cortex and hippocampus. Results: BSTSF significantly improved memory deficits and the typical histopathological changes of AD rats. Untargeted metabolomics analysis showed that 145 and 184 endogenous metabolites in the cerebral cortex and hippocampus, respectively, were significantly different in the BSTSF group when compared with the AD group. The differential metabolites in the cerebral cortex were primarily involved in cysteine and methionine metabolism, while those in the hippocampus were mainly involved in d-Glutamine and d-glutamate metabolism. Conclusion: In the present study, we confirmed the neuroprotective effects of BSTSF treatment against AD using a rat model. Our findings indicate that the BSTSF-mediated protective effects were associated with amelioration of metabolic disorders in the hippocampus and cerebral cortex.

A novel genomic instability-derived lncRNA signature to predict prognosis and immune characteristics of pancreatic ductal adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor of the digestive system. Its grim prognosis is mainly attributed to the lack of means for early diagnosis and poor response to treatments. Genomic instability is shown to be an important cancer feature and prognostic factor, and its pattern and extent may be associated with poor treatment outcomes in PDAC. Recently, it has been reported that long non-coding RNAs (lncRNAs) play a key role in maintaining genomic instability. However, the identification and clinical significance of genomic instability-related lncRNAs in PDAC have not been fully elucidated. Methods: Genomic instability-derived lncRNA signature (GILncSig) was constructed based on the results of multiple regression analysis combined with genomic instability-associated lncRNAs and its predictive power was verified by the Kaplan-Meier method. And real-time quantitative polymerase chain reaction (qRT-PCR) was used for simple validation in human cancers and their adjacent non-cancerous tissues. In addition, the correlation between GILncSig and tumor microenvironment (TME) and epithelial-mesenchymal transition (EMT) was investigated by Pearson correlation analysis. Results: The computational framework identified 206 lncRNAs associated with genomic instability in PDAC and was subsequently used to construct a genome instability-derived five lncRNA-based gene signature. Afterwards, we successfully validated its prognostic capacity in The Cancer Genome Atlas (TCGA) cohort. In addition, via careful examination of the transcriptome expression profile of PDAC patients, we discovered that GILncSig is associated with EMT and an adaptive immunity deficient immune profile within TME. Conclusions: Our study established a genomic instability-associated lncRNAs-derived model (GILncSig) for prognosis prediction in patients with PDAC, and revealed the potential functional regulatory role of GILncSig.