Dual-Modality Imaging-Guided Manganese-Based Nanotransformer for Enhanced Gas-Photothermal Therapy Combined Immunotherapeutic Strategy Against Triple-Negative Breast Cancer.

Activating the stimulator of the interferon gene (STING) is a promising immunotherapeutic strategy for converting "cold" tumor microenvironment into "hot" one to achieve better immunotherapy for malignant tumors. Herein, a manganese-based nanotransformer is presented, consisting of manganese carbonyl and cyanine dye, for MRI/NIR-II dual-modality imaging-guided multifunctional carbon monoxide (CO) gas treatment and photothermal therapy, along with triggering cGAS-STING immune pathway against triple-negative breast cancer. This nanosystem is able to transfer its amorphous morphology into a crystallographic-like formation in response to the tumor microenvironment, achieved by breaking metal-carbon bonds and forming coordination bonds, which enhances the sensitivity of magnetic resonance imaging. Moreover, the generated CO and photothermal effect under irradiation of this nanotransformer induce immunogenic death of tumor cells and release damage-associated molecular patterns. Simultaneously, the Mn acts as an immunoactivator, potentially stimulating the cGAS-STING pathway to augment adaptive immunity, resulting in promoting the secretion of type I interferon, the proliferation of cytotoxic T lymphocytes and M2-macrophages repolarization. This nanosystem-based gas-photothermal treatment and immunoactivating therapy synergistic effect exhibit excellent antitumor efficacy both in vitro and in vivo, reducing the risk of triple-negative breast cancer recurrence and metastasis; thus, this strategy presents great potential as multifunctional immunotherapeutic agents for cancer treatment.

Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.

OBJECTIVE: To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT. METHODS: This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed. RESULTS: Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI. CONCLUSION: Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading. KEY POINTS: • Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.

Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis.

OBJECTIVE: (1) To evaluate the diagnostic performance of radiomics in differentiating high-grade glioma from brain metastasis and how to improve the model. (2) To assess the methodological quality of radiomics studies and explore ways of embracing the clinical application of radiomics. METHODS: Studies using radiomics to differentiate high-grade glioma from brain metastasis published by 26 July 2021 were systematically reviewed. Methodological quality and risk of bias were assessed using the Radiomics Quality Score (RQS) system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. Pooled sensitivity and specificity of the radiomics model were also calculated. RESULTS: Seventeen studies combining 1,717 patients were included in the systematic review, of which 10 studies without data leakage suspicion were employed for the quantitative statistical analysis. The average RQS was 5.13 (14.25% of total), with substantial or almost perfect inter-rater agreements. The inclusion of clinical features in the radiomics model was only reported in one study, as was the case for publicly available algorithm code. The pooled sensitivity and specificity were 84% (95% CI, 80-88%) and 84% (95% CI, 81-87%), respectively. The performances of feature extraction from the volume of interest (VOI) or (semi) automatic segmentation in the radiomics models were superior to those of protocols employing region of interest (ROI) or manual segmentation. CONCLUSION: Radiomics can accurately differentiate high-grade glioma from brain metastasis. The adoption of standardized workflow to avoid potential data leakage as well as the integration of clinical features and radiomics are advised to consider in future studies. KEY POINTS: • The pooled sensitivity and specificity of radiomics for differentiating high-grade gliomas from brain metastasis were 84% and 84%, respectively. • Avoiding potential data leakage by adopting an intensive and standardized workflow is essential to improve the quality and generalizability of the radiomics model. • The application of radiomics in combination with clinical features in differentiating high-grade gliomas from brain metastasis needs further validation.

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.

OBJECTIVE: To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC). METHODS: This study retrospectively collected 163 patients with pathologically proven small renal mass, including 118 RCC and 45 AMLwvf patients. Target region of interest (ROI) delineation, followed by texture feature extraction, was performed on a representative slice with the largest lesion area on each phase of the four-phase CT images. Fifteen concatenations of the four-phasic features were fed into 224 classification models (built with 8 classifiers and 28 feature selection methods), classification performances of the 3360 resultant discriminative models were compared, and the top-ranked features were analyzed. RESULTS: Image features extracted from the unenhanced phase (UP) CT image demonstrated dominant classification performances over features from other three phases. The two discriminative models "SVM + t_score" and "SVM + relief" achieved the highest classification AUC of 0.90. The 10 top-ranked features from UP included 1 shape feature, 5 first-order statistics features, and 4 texture features, where the shape feature and the first-order statistics features showed superior discriminative capabilities in differentiating RCC vs. AMLwvf through the t-SNE visualization. CONCLUSION: Image features extracted from UP are sufficient to generate accurate differentiation between AMLwvf and RCC using machine learning-based classification model. KEY POINTS: • Radiomics extracted from unenhanced CT are sufficient to accurately differentiate angiomyolipoma without visible fat and renal cell carcinoma using machine learning-based classification model. • The highest discriminative models achieved an AUC of 0.90 and were based on the analysis of unenhanced CT, alone or in association with images obtained at the nephrographic phase. • Features related to shape and to histogram analysis (first-order statistics) showed superior discrimination compared with gray-level distribution of the image (second-order statistics, commonly called texture features).

Clear cell renal cell carcinoma: the value of sex-specific abdominal visceral fat measured on CT for prediction of Fuhrman nuclear grade.

OBJECTIVE: To explore whether sex-specific abdominal visceral fat composition on CT can predict the Fuhrman nuclear grade of clear cell renal cell carcinoma (ccRCC). METHODS: One hundred seventy-one patients (123 males and 48 females) from four hospitals (multicentre group) and 159 patients (109 males and 50 females) from the cancer imaging archive (TCIA-KIRC group) with pathologically proven ccRCC (multicentre: 124 low grade and 47 high grade; TCIA-KIRC: 79 low grade and 80 high grade) were retrospectively included. Abdominal fat was segmented into subcutaneous fat area (SFA) and visceral fat area (VFA) on CT using ImageJ. The total fat area (TFA) and relative VFA (rVFA) were then calculated. Clinical characteristics (age, sex, waist circumference and maximum tumour diameter) were also assessed. Univariate and multivariate logistic regression analyses were performed to identify the association between general or sex-specific visceral fat composition and Fuhrman grade. RESULTS: Females with high-grade ccRCC from the multicentre group had a higher rVFA (42.4 vs 31.3, p = 0.001) than those with low-grade ccRCC after adjusting for age. There was no significant difference in males. The rVFA remained a stable and independent predictor for females high-grade ccRCC in both the univariate (multicentre: OR 1.205, 95% CI 1.074-1.352, p = 0.001; TCIA-KIRC: OR 1.171, 95% CI 1.016-1.349, p = 0.029) and multivariate (multicentre: OR 1.095, 95% CI 1.024-1.170, p = 0.003; TCIA-KIRC: OR 1.103, 95% CI 1.024-1.187, p = 0.010) models. CONCLUSIONS: Sex-specific visceral fat composition has different values for predicting high-grade ccRCC and could be used as an independent predictor for females with high-grade ccRCC. KEY POINTS: • Visceral fat measurement (rVFA) as an independent predictor for high-grade ccRCC had good predictive power in females, but not in males. • Sex-specific visceral fat composition was significantly associated with high-grade ccRCC in females only. • The rVFA could be considered one of the risk factors for high-grade ccRCC for females.

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI.

PURPOSE: To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. METHODS: 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. RESULTS: The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). CONCLUSION: The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.

Quantitative Measurement on Contrast-Enhanced CT Distinguishes Small Clear Cell Renal Cell Carcinoma From Benign Renal Tumors: A Multicenter Study.

RATIONALE AND OBJECTIVES: To evaluate the potential of quantitative measurements on contrast-enhanced CT (CECT) in differentiating small (≤4 cm) clear cell renal cell carcinoma (ccRCC) from benign renal tumors, including fat-poor angiomyolipoma (fpAML) and renal oncocytoma (RO). MATERIALS AND METHODS: 244 patients with pathologically confirmed ccRCC (n = 184) and benign renal tumors (fpAML, n = 50; RO, n = 10) were randomly assigned into training cohort (n = 193) and test cohort 1 (n = 51), while external test cohort 2 (n = 50) was from another hospital. Quantitative parameters were obtained from CECT (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP; excretory phase, EP) by measuring attenuation of renal mass and cortex and subsequently calculated. Univariable and multivariable logistic regression analyses were performed to evaluate the association between these parameters and ccRCC. Finally, the constructed models were compared with radiologists' diagnoses. RESULTS: In univariable analysis, UP-related parameters, particularly UPC-T (cortex minus tumor attenuation on UP), demonstrated AUC of 0.766 in training cohort, 0.901 in test cohort 1, 0.805 in test cohort 2. The heterogeneity-related parameter SD (standard deviation) showed AUC of 0.781, 0.834, and 0.875 respectively. In multivariable analysis, model 1 incorporating UPC-T, NPC-T (cortex minus tumor attenuation on NP), CMPT-UPT (tumor attenuation on CMP minus UP), and SD yielded AUC of 0.866, 0.923, and 0.949 respectively. When compared with radiologists, multivariate models demonstrated higher accuracy (0.800-0.860) and sensitivity (0.794-0.971) than radiologists' assessments (accuracy: 0.700-0.720, sensitivity: 0.588-0.706). CONCLUSION: Quantitative measurements on CECT, particularly UP- and heterogeneity-related parameters, have potential to discriminate ccRCC and benign renal tumors (fpAML, RO).

iRGD-mediated liposomal nanoplatforms for improving hepatocellular carcinoma targeted combination immunotherapy and monitoring tumor response via IVIM-MRI.

The combination therapy of targeted treatments and immune checkpoint blockade (ICB) holds great promise for hepatocellular carcinoma (HCC) treatment. However, challenges such as immunogenicity, off-target toxicity of ICB antibodies, low drug co-delivery efficiency, and lack of effective biomarkers to monitor treatment response limit the efficacy of existing targeted immunotherapies. Herein, we synthesized iRGD-modified pH-sensitive liposomal nanoparticles co-encapsulating lenvatinib (Len) and the small molecule PD-1/PD-L1 inhibitor BMS-202 (iRGD-lip@Len/BMS-202) to address issues related to inadequate tumor enrichment and distinct pharmacokinetics of these drugs. Furthermore, intravoxel incoherent motion-magnetic resonance imaging (IVIM-MRI), which is calculated using a biexponential model, can simultaneously reflect both the diffusion of water molecules within the tissue and the microcirculatory perfusion of capillaries. Consequently, we further assessed the feasibility of using IVIM-MRI to monitor the cancer treatment response in nanodrug therapy. These results demonstrated that the iRGD-targeted liposomal nanodrug effectively accumulated in tumors and released in acidic microenvironments. The sustained release of Len facilitated tumor vascular normalization, decreased the presence of Tregs and MDSCs and activated the IFN-γ signaling pathway. This led to increased PD-L1 expression in tumor cells, enhancing the sensitivity of BMS-202. Consequently, there was a synergistic amplification of antitumor immune therapy, resulting in the shrinkage of subcutaneous and orthotopic HCC and inhibition of lung metastasis. Furthermore, IVIM-MRI technology facilitated the non-invasive monitoring of the tumor microenvironment (TME), revealing critical therapeutic response indicators such as the normalization of tumor blood vessels and the degree of hypoxia. Collectively, the combination of Food and Drug Administration (FDA)-approved drugs with iRGD-modified liposomes presents a promising strategy for HCC treatment. Simultaneously, IVIM-MRI provides a non-invasive method to accurately predict the response to this nanodrug.

Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation learning method.

BACKGROUND: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive subtype of HCC and is associated with poor survival. PURPOSE: To investigate the performance of a representation learning-based feature fusion strategy that employs a multiphase contrast-enhanced CT (mpCECT)-based latent feature fusion (MCLFF) model for MTM-HCC identification. METHODS: A total of 206 patients (54 MTM HCC, 152 non-MTM HCC) who underwent preoperative mpCECT with surgically confirmed HCC between July 2017 and December 2022 were retrospectively included from two medical centers. Multiphasic radiomics features were extracted from manually delineated volume of interest (VOI) of all lesions on each mpCECT phase. Representation learning based MCLFF model was built to fuse multiphasic features for MTM HCC prediction, and compared with competing models using other fusion methods. Conventional imaging features and clinical factors were also evaluated and analyzed. Prediction performance was validated by ROC analysis and statistical comparisons on an internal validation and an external testing dataset. RESULTS: Fusion of radiomics features from the arterial phase (AP) and portal venous phase (PAP) using MCLFF demonstrated superior performance in MTM HCC prediction, with a higher AUC of 0.857 compared with all competing models in the internal validation set. Integration of multiple radiological or clinical features further improved the overall performance, with the highest AUCs of 0.857 and 0.836 respectively achieved in the internal validation and external testing set. CONCLUSIONS: Multiphasic radiomics features of AP and PVP fused by the MCLFF have demonstrated substantial potential in the accurate prediction of MTM HCC. Clinical factors and Radiological features in mpCECT contribute incremental values to the developed MCLFF strategy.

Nanoparticle-mediated blockade of CXCL12/CXCR4 signaling enhances glioblastoma immunotherapy: Monitoring early responses with MRI radiomics.

The limited therapeutic efficacy of checkpoint blockade immunotherapy against glioblastoma is closely related to the blood-brain barrier (BBB) and tumor immunosuppressive microenvironment, where the latter is driven primarily by tumor-associated myeloid cells (TAMCs). Targeting the C-X-C motif chemokine ligand-12/C-X-C motif chemokine receptor-4 (CXCL12/CXCR4) signaling orchestrates the recruitment of TAMCs and has emerged as a promising approach for alleviating immunosuppression. Herein, we developed an iRGD ligand-modified polymeric nanoplatform for the co-delivery of CXCR4 antagonist AMD3100 and the small-molecule immune checkpoint inhibitor BMS-1. The iRGD peptide facilitated superior BBB crossing and tumor-targeting abilities both in vitro and in vivo. In mice bearing orthotopic GL261-Luc tumor, co-administration of AMD3100 and BMS-1 significantly inhibited tumor proliferation without adverse effects. A reprogramming of immunosuppression upon CXCL12/CXCR4 signaling blockade was observed, characterized by the reduction of TAMCs and regulatory T cells, and an increased proportion of CD8+T lymphocytes. The elevation of interferon-γ secreted from activated immune cells upregulated PD-L1 expression in tumor cells, highlighting the synergistic effect of BMS-1 in counteracting the PD-1/PD-L1 pathway. Finally, our research unveiled the ability of MRI radiomics to reveal early changes in the tumor immune microenvironment following immunotherapy, offering a powerful tool for monitoring treatment responses. STATEMENT OF SIGNIFICANCE: The insufficient BBB penetration and immunosuppressive tumor microenvironment greatly diminish the efficacy of immunotherapy for glioblastoma (GBM). In this study, we prepared iRGD-modified polymeric nanoparticles, loaded with a CXCR4 antagonist (AMD3100) and a small-molecule checkpoint inhibitor of PD-L1 (BMS-1) to overcome physical barriers and reprogram the immunosuppressive microenvironment in orthotopic GBM models. In this nanoplatform, AMD3100 converted the "cold" immune microenvironment into a "hot" one, while BMS-1 synergistically counteracted PD-L1 inhibition, enhancing GBM immunotherapy. Our findings underscore the potential of dual-blockade of CXCL12/CXCR4 and PD-1/PD-L1 pathways as a complementary approach to maximize therapeutic efficacy for GBM. Moreover, our study revealed that MRI radiomics provided a clinically translatable means to assess immunotherapeutic efficacy.

Integrated Diffusion Tensor Imaging and Renal Parenchymal Volume for Early Detection and Grading of Split Renal Functional Impairment in Lupus Nephritis.

RATIONALE AND OBJECTIVES: To investigate the effectiveness of combining split diffusion tensor imaging (DTI) measurements with split renal parenchymal volume (RPV) for assessing split renal functional impairment in patients with lupus nephritis (LN). MATERIALS AND METHODS: Seventy-four participants [48 LN patients and 26 healthy volunteers (HV)] were included in the study. All participant underwent conventional MR and DTI (b = 0, 400, and 600 s/mm2) examinations using a 3.0 T MRI scanner to determine the split renal DTI measurements and split RPV. In LN patients, renography glomerular filtration rate (rGFR) was measured using 99mTc-DTPA scintigraphy based on Gates' method, serving as the reference standard to categorize all split kidneys of LN patients into LN with mild impairment (LNm, n = 65 kidneys) and LN with moderate to severe (LNms, n = 31 kidneys) groups according to the threshold of 30 ml/min in spilt rGFR. All statistical analyses were performed using SPSS 25.0 and MedCalc 20.0 software packages. RESULTS: Only split medullary fractional anisotropy (FA) and the product of split medullary FA and RPV could distinguish pairwise subgroups among the HV and each LN subgroup (all p < 0.05). ROC curve analysis demonstrated that split medullary FA (AUC = 0.866) significantly outperformed other parameters in differentiating HV from LNm groups, while the product of split medullary FA and split RPV was superior in distinguishing LNm and LNms groups (AUC = 0.793) than other parameters. The combination of split medullary FA and split RPV showed best correlation with split rGFR (r = 0.534, p < 0.001). CONCLUSION: Split medullary FA, and its combination with split RPV, are valuable biomarkers for detecting early functional changes in renal alterations and predicting disease progression in patients with LN.

Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC.

Objective.To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model.Approach.466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFFmodel (fused features from all MRI sequences), RADCmodel (ADC radiomics feature), StratifiedADCmodel (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADCmodel were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n= 337) and test set (n= 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy.Main results.Both the RFFand StratifiedADCmodels demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADCmodel revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p <0.05). The integrated RFF-StratifiedADCmodel demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p <0.05).Significance.The RFF-StratifiedADCmodel through integrating various tumor habitats' information from whole-tumor ADC maps-based StratifiedADCmodel and radiomics information from mpMRI-based RFFmodel, exhibits tremendous promise for identifying TNBC.

Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists' diagnosis.

Objective: To investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively. Methods: 460 patients with 466 pathology-confirmed BCs who underwent breast mpMRI at 1.5T in our center were retrospectively included hormone receptor (HR) positive (HR+) (n=336) and HR negative (HR-) (n=130). The HR- patients were further categorized into human epidermal growth factor receptor 2 (HER-2) enriched BC (HEBC) (n=76) and triple negative BC (TNBC) (n=54). All lesions were divided into a training/validation cohort (n=337) and a test cohort (n=129). Volumes of interest (VOIs) delineation, followed by radiomics feature extraction, was performed on T2WI, DWI600 (b=600 s/mm2), DWI800 (b=800 s/mm2), ADC map, and DCE1-6 (six continuous DCE-MRI) images of each lesion. Simulating a radiologist's work pattern, 150 classification base models were constructed and analyzed to determine the top four optimum sequences for classifying HR+ vs. HR-, TNBC vs. HEBC, TNBC vs. non-TNBC in a random selected training cohort (n=337). Building upon these findings, the optimal single sequence models (Rss) and combined sequences models (RFF) were developed. The AUC, sensitivity, accuracy and specificity of each model for subtype differentiation were evaluated. The paired samples Wilcoxon signed rank test was used for performance comparison. Results: During the three classification tasks, the optimal single sequence for classifying HR+ vs. HR- was DWI600, while the ADC map, derived from DWI800 performed the best in distinguishing TNBC vs. HEBC, as well as identifying TNBC vs. non-TNBC, with corresponding training AUC values of 0.787, 0.788, and 0.809, respectively. Furthermore, the integration of the top four sequences in RFF models yielded improved performance, achieving AUC values of 0.809, 0.805 and 0.847, respectively. Consistent results was observed in both the training/validation and testing cohorts, with AUC values of 0.778, 0.787, 0.818 and 0.726, 0.773, 0.773, respectively (all p < 0.05 except HR+ vs. HR-). Conclusion: The RFF model, integrating mpMRI radiomics features, demonstrated promising ability to mimic radiologists' diagnosis for preoperative identification of molecular receptors of BC.

Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma.

OBJECTIVE: To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features. MATERIALS AND METHODS: A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed. RESULTS: Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model "Bagging + CMIM" achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features. CONCLUSION: Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning-based classification modeling.

Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography.

OBJECTIVE: To investigate microvascular invasion (MVI) of HCC through a noninvasive multi-disciplinary team (MDT)-like radiomics fusion model on dynamic contrast enhanced (DCE) computed tomography (CT). METHODS: This retrospective study included 111 patients with pathologically proven hepatocellular carcinoma, which comprised 57 MVI-positive and 54 MVI-negative patients. Target volume of interest (VOI) was delineated on four DCE CT phases. The volume of tumor core (V tc ) and seven peripheral tumor regions (V pt , with varying distances of 2, 4, 6, 8, 10, 12, and 14 mm to tumor margin) were obtained. Radiomics features extracted from different combinations of phase(s) and VOI(s) were cross-validated by 150 classification models. The best phase and VOI (or combinations) were determined. The top predictive models were ranked and screened by cross-validation on the training/validation set. The model fusion, a procedure analogous to multidisciplinary consultation, was performed on the top-3 models to generate a final model, which was validated on an independent testing set. RESULTS: Image features extracted from V tc +V pt(12mm) in the portal venous phase (PVP) showed dominant predictive performances. The top ranked features from V tc +V pt(12mm) in PVP included one gray level size zone matrix (GLSZM)-based feature and four first-order based features. Model fusion outperformed a single model in MVI prediction. The weighted fusion method achieved the best predictive performance with an AUC of 0.81, accuracy of 78.3%, sensitivity of 81.8%, and specificity of 75% on the independent testing set. CONCLUSION: Image features extracted from the PVP with V tc +V pt(12mm) are the most reliable features indicative of MVI. The MDT-like radiomics fusion model is a promising tool to generate accurate and reproducible results in MVI status prediction in HCC.

Diffusion basis spectrum imaging measures anti-inflammatory and neuroprotective effects of fingolimod on murine optic neuritis.

OBJECTIVE: To prospectively determine whether diffusion basis spectrum imaging (DBSI) detects, differentiates and quantitates coexisting inflammation, demyelination, axonal injury and axon loss in mice with optic neuritis (ON) due to experimental autoimmune encephalomyelitis (EAE), and to determine if DBSI accurately measures effects of fingolimod on underlying pathology. METHODS: EAE was induced in 7-week-old C57BL/6 female mice. Visual acuity (VA) was assessed daily to detect onset of ON after which daily oral-treatment with either fingolimod (1 mg/kg) or saline was given for ten weeks. In vivo DBSI scans of optic nerves were performed at baseline, 2-, 6- and 10-weeks post treatment. DBSI-derived metrics including restricted isotropic diffusion tensor fraction (putatively reflecting cellularity), non-restricted isotropic diffusion tensor fraction (putatively reflecting vasogenic edema), DBSI-derived axonal volume, axial diffusivity, λ∥ (putatively reflecting axonal integrity), and increased radial diffusivity, λ⊥ (putatively reflecting demyelination). Mice were killed immediately after the last DBSI scan for immunohistochemical assessment. RESULTS: Optic nerves of fingolimod-treated mice exhibited significantly better (p < 0.05) VA than saline-treated group at each time point. During ten-week of treatment, DBSI-derived non-restricted and restricted-isotropic-diffusion-tensor fractions, and axonal volumes were not significantly different (p > 0.05) from the baseline values in fingolimod-treated mice. Transient DBSI-λ∥ decrease and DBSI-λ⊥ increase were detected during Fingolimod treatment. DBSI-derived metrics assessed in vivo significantly correlated (p < 0.05) with the corresponding histological markers. CONCLUSION: DBSI was used to assess changes of the underlying optic nerve pathologies in EAE mice with ON, exhibiting great potential as a noninvasive outcome measure for monitoring disease progression and therapeutic efficacy for MS.

A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis.

This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).

Investigating the Role of the N-Terminal Loop of PD-1 in Binding Process Between PD-1 and Nivolumab via Molecular Dynamics Simulation.

The blockade of immune checkpoints, such as programmed death receptor 1 (PD-1) and programmed death ligand 1 protein (PD-L1), is a promising therapeutic approach in cancer immunotherapy. Nivolumab, a humanized IgG4 antibody targeting PD-1, was approved by the US Food and Drug Administration for several cancers in 2014. Crystal structures of the nivolumab/PD-1 complex show that the epitope of PD-1 locates at the IgV domain (including the FG and BC loops) and the N-terminal loop. Although the N-terminal loop of PD-1 has been shown to play a dominant role in the complex interface of the static structure, its role in the dynamic binding process has not been illustrated clearly. Here, eight molecular systems were established for nivolumab/PD-1 complex, and long-time molecular dynamics simulations were performed for each. Results showed that the N-terminal loop of PD-1 prefers to bind with nivolumab to stabilize the interface between IgV and nivolumab. Furthermore, the binding of the N-terminal loop with nivolumab induces the rebinding between the IgV domain and nivolumab. Thus, we proposed a two-step binding model for the nivolumab/PD-1 binding, where the interface switches to a high-affinity state with the help of the N-terminal loop. This finding suggests that the N-terminal loop of PD-1 might be a potential target for anti-PD-1 antibody design, which could serve as an important gatekeeper for the anti-PD-1 antibody binding.

Heroin body packing: clearly discerning drug packets using CT.

BACKGROUND: To determine if heroin body packing has occurred using computed tomography (CT), and to evaluate the role of CT in screening such cases. METHODS: We collected 158 cases of suspected drug packers' imaging materials (all underwent CT, 42 cases were imaged using plain x-ray film) from September 5, 2005 to April 23, 2008. Abdominal-pelvic CT appearances (shape, size, number, location and density) and abdominal plain x-ray film manifestations were retrospectively observed for those who were finally confirmed as heroin body packers through the passing of evacuated drug packets. RESULTS: Among 158 cases of suspected drug packers in our study, 124 cases were finally diagnosed as heroin body packers. This was consistent with the CT results. However, there were 2 false-negative cases of abdominal imaging taken with plain x-ray film. All of the evacuated heroin body packets were produced mechanically. CT and plain film characteristic findings included the presence of uniform shape, varied density, and well-defined round or ovoid intra-luminal foreign-body shadows arranged closely along the gastrointestinal (GI) tract and/or vagina. We also found that the "air-ring sign" and "onion sign" were valuable characteristics that were seen on the CT scan, which helped to positively confirm the detection of heroin packets. CONCLUSION: Heroin body packing has clearly defined diagnostic features that can be seen with CT. Furthermore, conventional abdominal-pelvic CT is the imaging modality of choice in the evaluation of suspected body packers.

Noninvasive high-resolution electromyometrial imaging of uterine contractions in a translational sheep model.

In current clinical practice, uterine contractions are monitored via a tocodynamometer or an intrauterine pressure catheter, both of which provide crude information about contractions. Although electrohysterography/electromyography can measure uterine electrical activity, this method lacks spatial specificity and thus cannot accurately measure the exact location of electrical initiation and location-specific propagation patterns of uterine contractions. To comprehensively evaluate three-dimensional uterine electrical activation patterns, we describe here the development of electromyometrial imaging (EMMI) to display the three-dimensional uterine contractions at high spatial and temporal resolution. EMMI combines detailed body surface electrical recording with body-uterus geometry derived from magnetic resonance images. We used a sheep model to show that EMMI can reconstruct uterine electrical activation patterns from electrodes placed on the abdomen. These patterns closely match those measured with electrodes placed directly on the uterine surface. In addition, modeling experiments showed that EMMI reconstructions are minimally affected by noise and geometrical deformation. Last, we show that EMMI can be used to noninvasively measure uterine contractions in sheep in the same setup as would be used in humans. Our results indicate that EMMI can noninvasively, safely, accurately, robustly, and feasibly image three-dimensional uterine electrical activation during contractions in sheep and suggest that similar results might be obtained in clinical setting.