Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features.

OBJECTIVE: We aim to develop a predictive model for lymphovascular invasion (LVI) in patients with invasive breast cancer (IBC), using magnetic resonance imaging (MRI)-based radiomics features. METHODS: A total of 204 patients with IBC admitted to our hospital were included in this retrospective study. The data was split into training and validation sets at a 7:3 ratio. Feature normalization was conducted, followed by feature selection using ANOVA, correlation analysis, and LASSO in the training set. The final step involved building a logistic regression model. The LVI prediction models were established by single sequence image and combined different sequence images as follows: A: prediction model based on the optimal sequence in the 7-phase enhanced MRI scans; B: prediction model based on the optimal sequences in the sequences T1WI, T2WI, and DWI; and C: the combined model based on the optimal sequences selected from A and B. Subjects' work characteristic curves (ROC) and decision curves (DCA) were plotted to determine the extent to which they predicted LVI performance in the training and validation sets. Simultaneously, nomogram models were constructed by integrating radiomics features and independent risk factors. In addition, an additional 16 patients from the center between January and August 2024 were collected as the Nomogram external validation set. The ROC and DCA were used to evaluate the performance of the model. RESULTS: In the enhanced images, Model A built based on the enhanced 2-phase achieved the best average AUC, with a validation set of 0.764. Model B built based on the T2WI had better results, with a validation set of 0.693. Model C built by combining enhanced 2-phase and T2WI sequences had a mean AUC of 0.705 in the validation set. In addition, the tumor size, whether the tumor boundary was clear or not, and whether there was a coelom in the tumor tissue had a statistically significant effect on the LVI of IBC, and a clinical-radiomics nomogram was established. DCAs as well as Nomogram also indicate that Model A has good clinical utility. The AUC of the nomogram in the training set, internal validation set, and external validation set were 0.703, 0.615, and 0.609, respectively. The DCA also showed that the radiomics nomogram combined with clinical factors had good predictive ability for LVI. CONCLUSION: In IBC, MRI radiomics can serve as a noninvasive predictor of LVI. The clinical-MRI radiomics model, as an efficient visual prognostic tool, shows promise in forecasting LVI. This highlights the significant potential of pre-radiomics prediction in enhancing treatment strategies.

Magnetic separation-assisted cluster-amplified versatile fluorescent aptasensors for the sensitive detection of target biomolecules.

A sensitive and versatile platform for detecting diverse target biomolecules was developed by combining a magnetic separation module and a fluorescence amplification module in a plug-and-play manner. The magnetic separation module was constructed using magnetic beads (MBs), whose surfaces were modified with aptamer-blocked captor DNAs. The fluorescence amplification module was constructed by loading the fluorescent dye rhodamine 6G (Rh6G) into the pores of mesoporous silica nanoparticles (MSNs). The MSN surfaces were modified with prey DNAs, of which the MSN-near ends hybridized with complementary DNAs (sealing DNAs) to form duplexes to seal the pores, and the free ends were designed to be single-stranded that were complementary to the captor DNAs. Upon binding of targets to their aptamers, the captor DNAs were unblocked and thus were able to hybridize with the prey DNAs, to capture Rh6G-laden MSNs, forming MB-MSN clusters. The clusters were isolated by magnetic separation and heated to dissociate the DNA duplexes, to unseal the MSN pores and release the inner Rh6G; thus a target was converted into a cluster of Rh6G dyes. By simply changing the target aptamers and related DNA connectors, this strategy detected ATP, thrombin, and platelet-derived growth factor BB with detection limits of 2.1 nM, 4.1 pM, and 2.4 pM, respectively. A wide range of targets, high amplification efficiency and universal functional modules endow the aptasensors with good potential as versatile platforms for detecting target molecules in vitro and in medical research.

Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.

PURPOSE: To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. METHODS: A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC). RESULTS: The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896. CONCLUSIONS: A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.

The value of a dual-energy CT Iodine map radiomics model for the prediction of collagen fiber content in the ccRCC tumor microenvironment.

BACKGROUND AND PURPOSE: Renal cell carcinoma (RCC) is a heterogeneous group of cancers. The collagen fiber content in the tumor microenvironment of renal cancer has an important role in tumor progression and prognosis. A radiomics model was developed from dual-energy CT iodine maps to assess collagen fiber content in the tumor microenvironment of ccRCC. METHODS: A total of 87 patients with ccRCC admitted to our hospital were included in this retrospective study. Among them, 59 cases contained large amounts of collagen fibers and 28 cases contained a small amount of collagen fibers. We established a radiomics model using preoperative dual-energy CT scan Iodine map (IV) imaging to distinguish patients with multiple collagen fibers from those with few collagen fibers in the tumor microenvironment of ccRCC. We extracted features from dual-energy CT Iodine map images to evaluate the effects of six classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT). The effects of the models built based on the dynamic and venous phases are also compared. Model performance was evaluated using quintuple cross-validation and area under the receiver operating characteristic curve (AUC). In addition, a clinical model was developed to assess the clinical factors affecting collagen fiber content. RESULTS: Compared to KNN, SVM, and LR classifiers, RF, DT, and XGBoost classifiers trained with higher AUC values, with training sets of 0.997, 1.0, and 1.0, respectively. In the validation set, the highest AUC was found in the SVM classifier with a size of 0.722. In the comparative test of the active and intravenous phase models, the SVM classifier had the best effect with its validation set AUC of 0.698 and 0.741. In addition, there was a statistically significant effect of patient age and maximum tumor diameter on the collagen fiber content in the tumor microenvironment of kidney cancer. CONCLUSION: Radionics features based on preoperative dual-energy CT IV can be used to predict the amount of collagen fibers in the tumor microenvironment of renal cancer. This study better informs clinical prognosis and patient management. Iodograms may add additional value to dual-energy CTs.

Relative Computed Tomography (CT) Enhancement Value for the Assessment of Microvascular Architecture in Renal Cell Carcinoma.

BACKGROUND To investigate the correlation between the relative computed tomography (CT) enhancement value and the microvascular architecture in different pathologic subtypes of renal cell carcinoma (RCC). MATERIAL AND METHODS This retrospective study included 55 patients with pathologically confirmed RCC. Immunohistochemistry for CD34 was performed for all surgical specimens. Microvascular architecture parameters (density, area, diameter, and perimeter) for the microvessels and the microvessels with lumen were determined. The CT scan was performed during arterial phase or venous phase. The correlation of parameters on CT and tumor angiogenesis was investigated. RESULTS Density of microvessels showed a positive correlation with CT values of tumors, ratios of tumor to cortex, and differences of tumor and medulla, but no correlation with CT value ratio of tumor to aorta or tumor to medulla. CT parameters were positively correlated with microvascular parameters. However, no CT parameter differences between hypo-vascular clear cell RCC and papillary RCC was observed. Strikingly, the density and area of the microvessels were significantly higher in hypo-vascular clear cell RCC than that in papillary RCC, while the density of the microvessels with lumen in the cyst-present RCC was significantly higher than that in the cyst-absent RCC. The values (especially those of microvessels with lumen) of area density, diameter, and perimeter were higher in the capsule-absent RCC than in the capsule-present RCC. CONCLUSIONS The relative CT enhancement value of RCC was associated with vascular architecture parameters including density, area, and perimeter. Quantitative and semi-quantitative parameters on enhanced CT may shed some light on tumor vasculature and function as indicators of the biological behavior of RCC.

Aggressive angiomyxoma of the pelvis: a series of four cases and literature review.

The purpose of this study was to evaluate the value of CT and MRI in aggressive angiomyxoma (AAM) of the pelvis. A series of four cases from three institutions are reviewed. Among the four cases, three were initially misdiagnosed, and local recurrence necessitated reoperation or angiographic embolization. The fourth case, with accurate preoperative diagnosis, was followed with no recurrence. CT and MR imaging demonstrated a well-defined mass, which displaced adjacent structures. Attenuation of the mass was less than that of muscle on unenhanced CT, and a swirling or layering internal architecture was found using both enhanced CT and TI-weighted MR imaging. In one patient, a layering internal architecture was seen on unenhanced CT images. MRI demonstrated the relation of the tumor to the pelvic floor better than CT. The authors concluded that both CT and MRI show the characteristic imaging pattern and trans-diaphragmatic extent of these tumors, and the diagnosis should be considered in any young woman presenting with a well-defined mass arising from the pelvis or perineum.

Study of radiomics based on dual-energy CT for nuclear grading and T-staging in renal clear cell carcinoma.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is the most lethal subtype of renal cell carcinoma with a high invasive potential. Radiomics has attracted much attention in predicting the preoperative T-staging and nuclear grade of ccRCC. OBJECTIVE: The objective was to evaluate the efficacy of dual-energy computed tomography (DECT) radiomics in predicting ccRCC grade and T-stage while optimizing the models. METHODS: 200 ccRCC patients underwent preoperative DECT scanning and were randomized into training and validation cohorts. Radiomics models based on 70 KeV, 100 KeV, 150 KeV, iodine-based material decomposition images (IMDI), virtual noncontrasted images (VNC), mixed energy images (MEI) and MEI + IMDI were established for grading and T-staging. Receiver operating characteristic analysis and decision curve analysis (DCA) were performed. The area under the curve (AUC) values were compared using Delong test. RESULTS: For grading, the AUC values of these models ranged from 0.64 to 0.97 during training and from 0.54 to 0.72 during validation. In the validation cohort, the performance of MEI + IMDI model was optimal, with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.70. The AUC value for the 70 KeV model was higher than those for the 100 KeV, 150 KeV, and MEI models. For T-staging, these models achieved AUC values of 0.83 to 1.00 in training and 0.59 to 0.82 in validation. The validation cohort demonstrated AUCs of 0.82 and 0.70, sensitivities of 0.71 and 0.71, and specificities of 0.80 and 0.60 for the MEI + IMDI and IMDI models, respectively. In terms of grading and T-staging, the MEI + IMDI model had the highest AUC in validation, with IMDI coming in second. There were statistically significant differences between the MEI + IMDI model and the 70 KeV, 100 KeV, 150 KeV, MEI, and VNC models in terms of grading (P < .05) and staging (P ≤ .001). DCA showed that both MEI + IDMI and IDMI models outperformed other models in predicting grade and stage of ccRCC. CONCLUSIONS: DECT radiomics models were helpful in grading and T-staging of ccRCC. The combined model of MEI + IMDI achieved favorable results.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.

OBJECTIVE: We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). METHODS: A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. RESULTS: The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. CONCLUSION: DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

The potential value of dual-energy CT radiomics in evaluating CD8+, CD163+ and αSMA+ cells in the tumor microenvironment of clear cell renal cell carcinoma.

PURPOSE: This study aims to develop radiomics models and a nomogram based on machine learning techniques, preoperative dual-energy computed tomography (DECT) images, clinical and pathological characteristics, to explore the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC). METHODS: We retrospectively recruited of 87 patients diagnosed with ccRCC through pathological confirmation from Center I (training set, n = 69; validation set, n = 18), and collected their DECT images and clinical information. Feature selection was conducted using variance threshold, SelectKBest, and the least absolute shrinkage and selection operator (LASSO). Radiomics models were then established using 14 classifiers to predict TME cells. Subsequently, we selected the most predictive radiomics features to calculate the radiomics score (Radscore). A combined model was constructed through multivariate logistic regression analysis combining the Radscore and relevant clinical characteristics, and presented in the form of a nomogram. Additionally, 17 patients were recruited from Center II as an external validation cohort for the nomogram. The performance of the models was assessed using methods such as the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: The validation set AUC values for the radiomics models assessing CD8+, CD163+, and αSMA+ cells were 0.875, 0.889, and 0.864, respectively. Additionally, the external validation cohort AUC value for the nomogram reaches 0.849 and shows good calibration. CONCLUSION: Radiomics models could allow for non-invasive assessment of TME cells from DECT images in ccRCC patients, promising to enhance our understanding and management of the tumor.

Radiomic features of the hippocampal based on magnetic resonance imaging in the menopausal mouse model linked to neuronal damage and cognitive deficits.

Estrogen deficiency in the early postmenopausal phase is associated with an increased long-term risk of cognitive decline or dementia. Non-invasive characterization of the pathological features of the pathological hallmarks in the brain associated with postmenopausal women (PMW) could enhance patient management and the development of therapeutic strategies. Radiomics is a means to quantify the radiographic phenotype of a diseased tissue via the high-throughput extraction and mining of quantitative features from images acquired from modalities such as CT and magnetic resonance imaging (MRI). This study set out to explore the correlation between radiomics features based on MRI and pathological features of the hippocampus and cognitive function in the PMW mouse model. Ovariectomized (OVX) mice were used as PWM models. MRI scans were performed two months after surgery. The brain's hippocampal region was manually annotated, and the radiomic features were extracted with PyRadiomics. Chemiluminescence was used to evaluate the peripheral blood estrogen level of mice, and the Morris water maze test was used to evaluate the cognitive ability of mice. Nissl staining and immunofluorescence were used to quantify neuronal damage and COX1 expression in brain sections of mice. The OVX mice exhibited marked cognitive decline, brain neuronal damage, and increased expression of mitochondrial complex IV subunit COX1, which are pathological phenomena commonly observed in the brains of AD patients, and these phenotypes were significantly correlated with radiomics features (p < 0.05, |r|>0.5), including Original_firstorder_Interquartile Range, Original_glcm_Difference Average, Original_glcm_Difference Average and Wavelet-LHH_glszm_Small Area Emphasis. Meanwhile, the above radiomics features were significantly different between the sham-operated and OVX groups (p < 0.01) and were associated with decreased serum estrogen levels (p < 0.05, |r|>0.5). This initial study indicates that the above radiomics features may have a role in the assessment of the pathology of brain damage caused by estrogen deficiency using routinely acquired structural MR images.

Primary breast carcinoma: association of mammographic calcifications with osteopontin expression.

PURPOSE: To evaluate the osteopontin (OPN) protein expression levels in breast carcinomas to determine if they correlate with mammographic appearances such as calcifications. MATERIALS AND METHODS: This retrospective study was institutional review board approved. Informed consent was obtained from patients. Clinical history, histopathologic findings, mammographic features, and OPN expression as determined with immunohistochemistry results were evaluated in 141 women with breast cancer. The median age of patients was 53 years (range, 29-82 years). Mammographic features and clinicopathologic characteristics were correlated with tumor OPN expression. chi(2) And Fisher exact tests were used to evaluate the association of OPN expression with mammographic and clinicopathologic features. RESULTS: Calcifications on mammograms (P = .012), spiculated margins of mass on mammograms (P = .02), "triple-negative" (ie, cancer that is estrogen receptor, progesterone receptor, and human epidermal growth factor receptor negative) phenotype (P = .02), and lymph node metastasis (P < .0001) were significantly associated with OPN status. In contrast to OPN-negative tumors, OPN-positive tumors were more likely to have spiculated margins (57.6% vs 9.2%), to be associated with calcifications (54.3% vs 30.6%), to be a triple-negative phenotype (26% vs 8.1%), and to have axillary lymph node metastasis (81.5% vs 38.8%). Most calcifications were of pleomorphic morphology (60.4% vs 11.8%, P = .046). CONCLUSION: OPN could play a role in the formation of calcifications that often are associated with breast cancer.

TGF-ß1 mediates lncRNA GAPLINC expression to promote the migration and invasion of non-small cell lung cancer.

Purpose: The present study aims to investigate the involvement of lncRNA GAPLINC in non-small lung cancer (NSCLC). Patients and methods: The study included 70 patients with NSCLC (39 males and 31 females, 33 to 68 years, 49.3 ± 6.4 years). RT-qPCR, transient cell transfections, measurement of in vitro cell migration and invasion abilities and western blot were carrying out during the research. Results: We showed that GAPLINC was up-regulated in NSCLC tissues and positively correlated with TGF-ß1. In vitro cell experiment showed that over-expression of TGF-ß1 significantly up-regulated the expression of GAPLINC, while over-expression of GAPLINC failed to affect TGF-ß1. Follow-up study showed that high GAPLINC level in NSCLC tissue was closely correlated with poor survival rate of NSCLC patients. Over-expressions of TGF-ß1 and GAPLINC resulted to accelerated migration and invasion of NSCLC cells. In addition, the silencing of GAPLINC siRNA attenuated the effect of TGF-ß1 treatment. Conclusion: TGF-ß1 may mediate lncRNA GAPLINC expression to promote NSCLC cell invasion and migration.

FORCE dual-energy CT in pathological grading of clear cell renal cell carcinoma.

The aim of the present study was to examine the value of FORCE dual-energy CT in grading the clear cell renal cell carcinoma (ccRCC). A total of 35 cases of ccRCC were included. Hematoxylin and eosin staining was performed, and the cases were divided into low- (Fuhrman I-II) and high-grade (Fuhrman III-IV) groups. FORCE dual-energy CT parameters, including virtual network computing CT value (VNCV), iodine overlay value (IOV), mixed energy CT value (MEV), iodine concentration (IC), normalized iodine concentration (NIC), NIC based on aorta (NICA), NIC based on cortex (NICC) and NIC based on medulla (NICM), were analyzed and compared. Receiver operating characteristic analysis was also performed. There were significant differences in the arterial phase IOV, MEV and IC, and the venous phase IOV and IC between the low- and high-grade groups. No significant differences were observed in VNCV and MEV between the low -and high-grade groups in the venous phase. Significant differences were observed in the NICA and NICC between these two groups, however no difference was observed in NICM. There were significant differences in the tumor CT values for the arterial phase at the 40, 60, 80 and 100 kiloelectron volt (keV) between the low- and high-grade groups, while no significant differences were observed at the 120-140 keV levels. The k-slope for the low-grade group was significantly higher than the high-grade group. In addition, the area under curve for the arterial phase IOV, arterial phase MEV, arterial phase IC, aortic NIC, cortical NIC, venous phase IOV, venous phase IC and curve slope K of mono-energy CT value suggested high value in diagnosis of low- and high-grade ccRCC cases.

Imaging manifestations of intravascular leiomyoma involving the right atrium: a case report and literature review.

The aim of this study is to investigate the imaging features of intravascular leiomyoma (IVL) involving the heart and the imaging techniques in the diagnosis of this disease. The imaging features of contrast-enhanced computed tomography (CT), the clinical data and the pathological data of a case of IVL involving the right atrium were retrospectively analyzed and the literatures were reviewed. A 42-year-old woman was admitted to Jinan Central Hospital with a 7-day history of lower extremity weakness, chest tightness and short breath. Contrast-enhanced CT scanning revealed that there was a mass in the inferior vena cava and right atrium, which was heterogeneously enhanced. There was a gap between the vessel wall and the mass. Spiral CT scanning with multiplanner reformation (MPR) reconstruction revealed the morphology, scope and extension pathways of the tumor clearly. Ultrasonography (US) and magnetic resonance imaging (MRI) also played important roles in the diagnosis and differential diagnosis of IVL. To the patients with a history of uterine fibroids, spiral CT scanning, US and MRI could be used to improve the correct diagnosis rates of IVL before surgery.