A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial.

BACKGROUND: This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer. METHODS: We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC. RESULTS: This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05). CONCLUSIONS: The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists. TRIAL REGISTRATION NUMBER: The clinical trial numbers were NCT03154749 and NCT04858529.

Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. SUMMARY BACKGROUND DATA: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. METHODS: This study enrolled 1048 patients with breast cancer from four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U- test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed followed by model integration. The AI system was validated in three external validation cohorts. (EVCs, n=713). RESULTS: Among the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. CONCLUSIONS: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.

Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer.

OBJECTIVES: To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction. METHODS: In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3+, CD4+, and CD8+ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55). RESULTS: iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001). CONCLUSIONS: iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection. KEY POINTS: • Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.

BACKGROUND: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images. MATERIALS AND METHODS: Open-source data sets and multicenter data sets have been used in this study. A three-dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results. RESULTS: The sensitivity and specificity of this well-trained model were found to be 84.4% (95% confidence interval [CI], 80.5%-88.3%) and 83.0% (95% CI, 79.5%-86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10-30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three-dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment. CONCLUSION: Under the companion diagnostics, the three-dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices. IMPLICATIONS FOR PRACTICE: The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.

Image quality evaluation of iterative model reconstruction on low tube voltage (80 kVp) coronary CT angiography in an animal study.

BACKGROUND: Iterative model reconstruction (IMR) is a newer generation iterative reconstruction method, but its value on coronary computed tomography (CT) angiography requires investigation. PURPOSE: To evaluate coronary image quality using IMR at a low-tube voltage of 80 kVp on coronary CT angiography in miniature pigs. MATERIAL AND METHODS: Five healthy miniature pigs underwent prospective electrocardiogram-gated coronary 256-slice CT angiography at 120 kVp and 80 kVp tube voltages, respectively. Filtered back projection (FBP) was used to reconstruct the 120 kVp standard-dose (SD) image sets (SD-FBP group), while iDose(4) and IMR were used to reconstruct the 80 kVp low-dose (LD) image sets (LD-iDose(4) and LD-IMR groups). Objective and subjective image qualities were compared among three groups. RESULTS: There were no significant differences in mean CT values of the ascending aorta, left main coronary artery, and right coronary artery between the LD-IMR and LD-iDose(4) groups (P > 0.05), but the values were significantly lower in the SD-FBP group than in those two groups (P < 0.05). The image noise in the LD-IMR group (21.5 ± 3.9 HU) was significantly lower than in the LD-iDose(4) (63.7 ± 9.8 HU) and SD-FBP (50.6 ± 4.6 HU) groups (P < 0.05). The signal-to-noise and contrast-to-noise ratios were significantly higher in the LD-IMR group than in the LD-iDose(4) and SD-FBP groups, respectively (P < 0.05). Subjective scoring of image noise, streak artifacts, and overall image quality indicated that the LD-IMR group was consistently superior to the LD-iDose(4) and SD-FBP groups (P < 0.05). CONCLUSION: IMR can significantly improve image quality at a low-tube voltage (80 kVp) with a 66% radiation dose reduction on coronary 256-slice CT angiography in miniature pigs.

Detection of Active Sacroiliitis with Ankylosing Spondylitis through Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging.

OBJECTIVE: To confirm feasibility and assess intravoxel incoherent motion (IVIM) to differentiate active sacroiliitis and ankylosing spondylitis.. METHODS: Forty-one patients were divided into two groups, an active group (n = 20) and a chronic group (n = 21), according to the Bath Ankylosing Spondylitis (AS) Disease Activity Index (BASDAI) and laboratory parameters. In addition, 21 healthy volunteers were chosen as the control group. Tissue diffusivity (Dslow), perfusion fraction (f), and pseudo-diffusion coefficient (Dfast) values were obtained for all three groups. One-way analysis of variance and receiver operating characteristic analysis were performed for all parameters. RESULTS: There was good interobserver agreement on the measurements between the two observers. The optimal cut-off values (with respective AUC, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio) between active and chronic groups were Dslow = 0.53 × 10(-3) mm(2)/s (0.976, 90%, 95.2%, 18.9, 0.10) and f = 0.09 (0.545, 20%, 95.5%, 4.2, 0.84), and between chronic and control groups were Dslow = 0.22 × 10(-3) mm(2)/s (0.517, 9.52%, 100%, no number, 0.9) and f = 0.09 (0.935, 95.24%, 80.95%, 5, 0.059). CONCLUSION: Dslow and f of IVIM diffusion-weighted (DW)-MRI in AS show a significant difference in the values of diffusion of water molecules and fractional perfusion-related volume among the three groups. KEY POINTS: • D slow can be used to differentiate the activity of AS. • With perfusion fraction, the sensitivity of differentiating the AS activity is improved. • IVIM DWI plays an important role in detecting the activity in patients with AS.

Initial experience of correlating parameters of intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging at 3.0 T in nasopharyngeal carcinoma.

PURPOSE: To determine the correlation between intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters. METHODS: Thirty-eight newly diagnosed NPC patients were prospectively enrolled. Diffusion-weighted images (DWI) at 13 b-values were acquired using a 3.0-T MRI system. IVIM parameters including the pure molecular diffusion (D), perfusion-related diffusion (D*), perfusion fraction (f), DCE-MRI parameters including maximum slope of increase (MSI), enhancement amplitude (EA) and enhancement ratio (ER) were calculated by two investigators independently. Intra- and interobserver agreement were evaluated using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. Relationships between IVIM and DCE-MRI parameters were evaluated by calculation of Spearman's correlation coefficient. RESULTS: Intra- and interobserver reproducibility were excellent to relatively good (ICC = 0.887-0.997; narrow width of 95 % limits of agreement). The highest correlation was observed between f and EA (r = 0.633, P < 0.001), with a strong correlation between f and MSI (r = 0.598, P = 0.001). No correlation was observed between f and ER (r = -0.162; P = 0.421) or D* and DCE parameters (r = 0.125-0.307; P > 0.119). CONCLUSION: This study suggests IVIM perfusion imaging using 3.0-T MRI is feasible in NPC, and f correlates significantly with EA and MSI. KEY POINTS: Assessment of tumour perfusion is important in nasopharyngeal carcinoma. DCE-MRI provided perfusion information with the use of intravenous contrast media. Perfusion information could be provided by non-invasive IVIM MRI. IVIM parameter f correlated with DCE-MRI parameters.

Decoding tumor stage by peritumoral and intratumoral radiomics in resectable esophageal squamous cell carcinoma.

PURPOSE: To evaluate the potential application of radiomics in predicting Tumor-Node-Metastasis (TNM) stage in patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS: This retrospective study included 122 consecutive patients (mean age, 57 years; 27 women). Corresponding tumor of interest was identified on axial arterial-phase CT images with manual annotation. Radiomics features were extracted from intra- and peritumoral regions. Features were pruned to train LASSO regression model with 93 patients to construct a radiomics signature, whose performance was validated in a test set of 29 patients. Prognostic value of radiomics-predicted TNM stage was estimated by survival analysis in the entire cohort. RESULTS: The radiomics signature incorporating one intratumoral and four peritumoral features was significantly associated with TNM stage. This signature discriminated tumor stage with an area under curve (AUC) of 0.823 in the training set, with similar performance in the test set (AUC 0.813). Recurrence-free survival (RFS) was significantly different between different radiomics-predicted TNM stage groups (Low-risk vs high-risk, log-rank P = 0.004). Univariate and multivariate Cox regression analyses revealed that radiomics-predicted TNM stage was an independent preoperative factor for RFS. CONCLUSIONS: The proposed radiomics signature combing intratumoral and peritumoral features was predictive of TNM stage and associated with prognostication in ESCC.

Induced bone marrow mesenchymal stem cells improve cardiac performance of infarcted rat hearts.

We investigated whether transplantation of bone marrow mesenchymal stem cells (BMSC) with induced BMSC (iBMSC) or uninduced BMSC (uBMSC) into the myocardium could improve the performance of post-infarcted rat hearts. BMSCs were specified by flowcytometry. IBMSCs were cocultured with rat cardiomyocyte before transplantation. Cells were injected into borders of cardiac scar tissue 1 week after experimental infarction. Cardiac performance was evaluated by echocardiography at 1, 2, and 4 weeks after cellular or PBS injection. Langendorff working-heart and histological studies were performed 4 weeks after treatment. Myogenesis was detected by quantitative PCR and immunofluorescence. Echocardiography showed a nearly normal ejection fraction (EF) in iBMSC-treated rats and all sham control rats but a lower EF in all PBS-treated animals. The iBMSC-treated heart, assessed by echocardiography, improved fractional shortening compared with PBS-treated hearts. The coronary flow (CF) was decreased obviously in PBS and uBMSC-treated groups, but recovered in iBMSC-treated heart at 4 weeks (P < 0.01). Immunofluorescent microscopy revealed co-localization of Superparamagnetic iron oxide (SPIO)-labeled transplanted cells with cardiac markers for cardiomyocytes, indicating regeneration of damaged myocardium. These data provide strong evidence that iBMSC implantation is of more potential to improve infarcted cardiac performance than uBMSC treatment. It will open new promising therapeutic opportunities for patients with post-infarction heart failure.

Radiologic and anatomic study of the extraperitoneal space associated with the rectum.

OBJECTIVE: The objective of our study was to clarify the anatomic and radiologic features of the extraperitoneal fasciae and fascial spaces associated with the rectum. MATERIALS AND METHODS: Fourteen embalmed cadavers were studied: two for gross anatomy; six for sectional anatomy, of which two underwent histologic study; and six for space perfusion study. These examinations were followed by CT and cross dissection to observe the pelvic extraperitoneal fasciae and the role of the fasciae in the anatomic subdivision and communication of the fascial spaces. Eighty healthy subjects underwent CT or MRI to identify the imaging characteristics of the pelvic fasciae. RESULTS: Cadaver dissection and histologic study revealed a distinct sheath consisting of dense connective tissue encasing the rectum and surrounding adipose tissue like a sleeve that divided the rectal extraperitoneal space into the perirectal space and pararectal space. Perfusion studies showed communication between the pararectal spaces and the vesical extraperitoneal space anteriorly and the anterior pararenal space superiorly, but not with the perirectal space. In healthy subjects, both CT (95.0%) and MRI (97.5%) showed a circular or linear structure representing the rectal fascia outside the rectum encasing the rectum and its surrounding adipose tissue. CONCLUSION: The extraperitoneal segment of the rectum and its surrounding adipose tissue are encased by a fascia, like a sleeve, that can be seen on CT and MRI in healthy populations. The fascia divides the rectal extraperitoneal space into the perirectal space and pararectal space, and it may prevent lesions of the rectum from spreading to other pelvic extraperitoneal spaces.

[Clinical study of transcatheter arterial chemoembolization plus radiofrequency ablation in hepatocellular carcinoma by magnetic resonance imaging and functional diffusion-weighted imaging].

OBJECTIVE: To evaluate the apparent diffusion coefficient (ADC) value features of the lesions after transcatheter arterial chemoembolization (TACE) plus radiofrequency ablation in hepatocellular carcinoma (HCC) with 3.0T magnetic resonance imaging (MRI) and diffusion-weight imaging (DWI) and analyze the value of 3.0T DWI in detecting the pathological lesion features of post-TACE plus radiofrequency ablation in HCC. METHODS: Twenty-eight HCC patients were enrolled to receive TACE firstly. Then all viable tumors around the lesions underwent radiofrequency ablation. At 1-4 months after radiofrequency ablation, 3.0T MRI and DWI (b = 600 sec/mm(2)) were performed to measure the ADC values of different lesions of post-TACE plus radiofrequency ablation. The features of MRI and ADC values of different lesions, the difference of contrast enhancement sequence and DWI in evaluating the lesions of post-TACE plus radiofrequency ablation were analyzed. RESULTS: Viable tumors occurred in 14 of 28 HCC patients after TACE plus radiofrequency ablation. The ADC values of necrotic tissues with lipiodol, necrotic tissues without lipiodol, viable tumors and normal liver tissues were 1.905 ± 0.487, 0.726 ± 0.116, 1.449 ± 0.054 and 1.777 ± 0.094 (10(-3) mm(2)/sec) respectively. There was no significant difference of ADC values between necrotic tissues with lipiodol and normal tissues (P = 0.115). But there were significant differences of ADC values among necrotic tissues with lipiodol, necrotic tissues without lipiodol and viable tumors (P < 0.05). The viable tumor tissues after TACE plus radiofrequency ablation appeared as nodular lesions with slightly heightened signal intensities around the necrotic tissues, the lesions with heterogeneous enhancement during arterial phase, portal vein phase and parenchymal phase. Necrotic tissues without lipiodol occurred outside necrotic tissues without lipiodol, around normal liver tissues, with low signal intensities on T2WI, without enhancement during arterial phase, portal vein phase and parenchymal phase. There were no significant difference between contrast enhancement and DWI sequence in detecting viable tumors after TACE plus radiofrequency ablation (P > 0.05). CONCLUSION: The ADC values of 3.0T MR DWI may be used to distinguish the viable residue or recurrent tumor tissues, necrotic tissues in HCC after TACE plus radiofrequency ablation.

Influence of tube voltage, tube current and newer iterative reconstruction algorithms in CT perfusion imaging in rabbit liver VX2 tumors.

PURPOSE: We aimed to explore the influence of tube voltage, current and iterative reconstruction (IR) in computed tomography perfusion imaging (CTPI) and to compare CTPI parameters with microvessel density (MVD). METHODS: Hepatic CTPI with three CTPI protocols (protocol A, tube voltage/current 80 kV/40 mAs; protocol B, tube voltage/current 80 kV/80 mAs; protocol C: tube voltage/current 100 kV/80 mAs) were performed in 25 rabbit liver VX2 tumor models, and filtered back projection (FBP) and IR were used for reconstruction of raw data. Hepatic arterial perfusion (HAP), hepatic portal perfusion (HPP), total perfusion (TP), hepatic arterial perfusion index (HPI), blood flow (BF) and blood volume (BV) of VX2 tumor and normal hepatic parenchyma were measured. Image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified and radiation dose was recorded. MVD was counted using CD34 stain and compared with CTPI parameters. RESULTS: The highest radiation dose was found in protocol C, followed by protocols B and A. IR lowered image noise and improved SNR and CNR in all three protocols. There was no statistical difference between HAP, HPP, TP, HPI, BF and BV of VX2 tumor and normal hepatic parenchyma among the three protocols (P > 0.05) with FBP or IR reconstruction, and no statistical difference between IR and FBP reconstruction (P > 0.05) in either protocol. MVD had a positive linear correlation with HAP, TP, BF, with best correlation observed with HAP; MVD of VX2 tumor showed no or poor correlation with HPI and BV. CONCLUSION: CTPI parameters are not affected by tube voltage, current or reconstruction algorithm; HAP can best reflect MVD, but no correlation exists between BV and MVD.

In vitro labeling of mesenchymal stem cells with superparamagnetic iron oxide by means of microbubble-enhanced US exposure: initial experience.

PURPOSE: To investigate the feasibility of magnetically labeling stem cells with superparamagnetic iron oxide (SPIO) by means of microbubble-enhanced ultrasonographic (US) exposure (MUE) and to study the effects of this approach--without secondary transfection agents--on the viability, proliferation activity, and differentiation capability of MUE-labeled stem cells. MATERIALS AND METHODS: Institutional review board approval was obtained for this study. Human mesenchymal stem cells (MSCs) ([1 to 2] x 10(6)/mL) were studied in four experiment groups: sham exposure to US with microbubbles and SPIO (group A), exposure to US with SPIO but without microbubbles (group B), exposure to US with microbubbles and SPIO (group C), and sham exposure to US without SPIO or microbubbles (group D). Intracellular iron uptake was analyzed qualitatively at light and electron microscopy. The viability and proliferation activity of MSCs were evaluated. The adipogenic and osteogenic differentiation capability of the labeled MSCs was also evaluated. Ninety-five percent confidence intervals were derived for assessment of differences in cell viability and proliferation activity between groups C and D. RESULTS: Light and electron microscopy revealed intracytoplasmic iron uptake and nearly 100% cell labeling efficiency. The MUE-labeled MSCs had unaltered viability and uncompromised proliferation activity compared with the nonlabeled MSCs. Similar to the nonlabeled MSCs, the MUE-labeled MSCs differentiated into adipogenic and osteogenic lineages. CONCLUSION: Initial study results show that stem cells can be effectively labeled with SPIO by using MUE without secondary transfection agents and thus that MUE labeling is an appealing alternative cell-labeling approach that warrants investigation for intracellular magnetic labeling of stem cells. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.2531081974/-/DC1.

[In vivo magnetic resonance imaging tracking of transplanted adipose-derived stem cells labeled with superparamagnetic iron oxide in rat hearts].

OBJECTIVE: To investigate the feasibility of in vivo magnetic resonance imaging (MRI) tracking of transplanted adipose-derived stem cells (ADSCs) labeled with superparamagnetic iron oxide (SPIO) in rat heart. METHODS: ADSCs were labeled with poly-L-lysine (PLL)-SPIO complexes. Intracellular iron uptake was identified by Prussian blue stain and transmission electromicroscopy. Trypan blue staining was used to test the viability of the labeled cells. In vitro MRI of labeled cells was performed. SPIO-labeled ADSCs were transplanted into normal rat hearts and were in vivo imaged with MRI. Image findings on MRI were correlated with histological findings of the rat hearts. RESULTS: The labeling efficacy of ADSCs with PLL-SPIO was nearly 100%. Light microscopy revealed the SPIO particles were located in the cytoplasm of the ADSCs by Prussian blue staining. Transmission electromicroscopy revealed that the SPIO particles were located in the endosomes in the cytoplasm. There was no significantly deference in viability between labeled and unlabeled groups demonstrated by Trypan blue test (P > 0.05). MRI showed signal loss in gel mixed with labeled cells as compared with the unlabeled cells group and blank group. Signal void on rat hearts were demonstrated on MRI and were well correlated with histological findings where Prussian-blue-stain positive cells presented. CONCLUSION: MRI can be used to in vivo track the transplanted ADSCs labeled with SPIO into rat hearts and facilitate to understand the conditions of the labeled cells in the transplanted areas.

[Efficacy comparison between transplanting microenvironmental induced and non-induced bone marrow mesenchymal stem cells in ischemic rat hearts].

OBJECTIVE: To compare the efficacy of transplanting bone marrow mesenchymal stem cell (BMSC) or microenvironmental induced BMSC (iBMSC) into the ischemic myocardium of rats with myocardial infarction. METHODS: iBMSC was defined as BMSC co-cultured with myocardial cells for 2 weeks. The stem cells or equal volume PBS were injected into ischemic border zone 1 wk after experimental infarction. Cardiac performance was evaluated at 1, 2, and 4 wk after cell transplantation by echocardiography and analyzed histologically at 4 wk after cell transplantations. RESULTS: Compared with PBS group, both BMSC and iBMSC transplantations reduced infarct size. iBMSC enhanced the beneficial effects of BMSC on improving cardiac function (FS: 28.5% +/- 4.3% in PBS, 29.0% +/- 2.0% in BMSC and 45.1% +/- 3.1% in iBMSC group at 4 weeks post transplantation, iBMSC group vs. PBS group P < 0.05, iBMSC group vs. BMSC group P < 0.05). Immunofluorescence microscopy results revealed co-localization of SPIO-labeled transplanted cells with cardiac markers for cardiomyocytes, indicating regeneration of damaged myocardium. CONCLUSION: Our data suggest that iBMSC implantation is more effective on improving cardiac function than BMSC implantation in this model. iBMSC might serve as a new promising therapeutic cell source for regenerating ischemic myocardium in patients with post-infarction heart failure.

Nomogram for predicting disease-free survival among a multicenter cohort of Chinese patients with locally advanced rectal cancer.

Purpose: This study aimed to develop and validate a nomogram for predicting 3-year disease-free survival (DFS) among a multicenter cohort of Chinese patients with locally advanced rectal cancer (LARC) who underwent preoperative therapy followed by surgery. This nomogram might help identify patients who would benefit from postoperative adjuvant chemotherapy and close follow-up. Materials and methods: All data from 228 patients in two independent Chinese cohorts (118 patients and 110 patients) were pooled and subjected to survival analysis. One cohort's data were used to develop multivariate nomograms based on Cox regression, and the second cohort was used for external validation. The variables were sex, age, clinical tumor stage, tumor location, preoperative therapy protocol, adjuvant chemotherapy, surgical procedure, surgical approach, pTNM stage, tumor deposit, tumor regression grade, lymphovascular invasion, perineural invasion, pretreatment serum carcinoembryonic antigen (CEA) level, preoperative CEA level, and postoperative CEA level. The model's performance was evaluated based on its discrimination, calibration, and clinical usefulness. Results: The nomogram was based on ypT stage and ypN stage, and the C-index values for 3-year DFS were 0.70 in the training cohort (95% confidence interval: 0.62-0.78) and 0.78 in the validation cohort (95% confidence interval: 0.68-0.89). The Hosmer-Lemeshow calibration test revealed good calibration for predicting 3-year DFS in the training and validation cohorts, and decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion: This nomogram including the ypT stage and ypN stage could predict DFS at 3 years after surgery, which may help better identify Chinese patients who would benefit from additional postoperative adjuvant systemic treatment.

Comparison of microvascular perfusion evaluation among IVIM-DWI, CT perfusion imaging and histological microvessel density in rabbit liver VX2 tumors.

OBJECT: To explore microcirculation features with intravoxel incoherent motion (IVIM) and to compare IVIM with CT perfusion imaging (CTPI) and microvessel density (MVD). MATERIALS AND METHODS: Hepatic CTPI and IVIM were performed in 16 rabbit liver VX2 tumor models. Hepatic arterial perfusion (HAP), hepatic arterial perfusion index (HPI), Blood flow (BF), and blood volume (BV) from CTPI were measured. Apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*) from IVIM were measured. MVD was counted with CD34 stain. The microcirculation features with IVIM were compared with CTPI parameters and MVD. RESULTS: Strong linear correlations were found between D value (0.89±0.21×10-3mm2/s) and HAP (15.83±6.97ml/min/100mg) (r=0.755, P=0.001) and between f value (12.64±6.66%) and BV (9.74±5.04ml/100mg) (r=0.693, P=0.004). Moderate linear correlations were observed between ADC (1.07±0.32×10-3mm2/s) and HAP (r=0.538, P=0.039), respectively; and between D value and MVD (9.31±2.57 vessels at 400×magnification) (r=0.509, P=0.044). No correlations were found between D* (119.90±37.67×10-3mm2/s) and HAP, HPI (68.34±12.91%), BF (4.95±2.16ml/min/100mg), BV. CONCLUSION: IVIM parameters can characterize microcirculation to certain extent and separate it from pure water molecular diffusion. There is fair correlation between D or ADC value and CTPI parameters or MVD, but no correlation between D* or f value and CTPI parameters or MVD except f value and BV, which is still unclear and need further clinical studies to validate.

[Value of mamography, CT and DCE-MRI in detecting axillary lymph node metastasis of breast cancer].

OBJECTIVE: To evaluate the diagnostic value of mammography, computed tomography (CT), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for axillary lymph node staging in breast cancer patients. METHODS: From February, 2014 to October, 2015, 109 women with breast cancer received examinations with preoperative mamography, CT, and DCE-MRI. The diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the 3 modalities were evaluated using histopathologic assessments as the gold standard. RESULTS: In total, 39.4% (43/109) of the patients had axillary lymph node metastasis. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of mamography for determining lymph node metastasis were 14.0%, 84.8%, 56.9%, 37.5% and 60.0%, respectively; those of CT were 93.0%, 57.6%, 71.6%,58.8% and 92.7%, and those of DCE-MRI were 95.3%, 65.2%, 77.1%, 64.1% and 95.6%, respectively. Compared with the histopathologic result, the Kappa coefficients of mamography, CT, and DCE-MRI were -0.13, 0.459 and 0.558, respectively. The specificity of mamography was significantly higher (P<0.05), but its sensitivity, accuracy, positive predictive value, and negative predictive value were significantly lower than those of CT and DCE-MRI (P<0.05). Compared with CT, DCE-MRI had significantly higher sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for diagnosis of lymph node metastasis (P<0.05). CONCLUSION: DCE-MRI has a greater diagnostic power than CT and mammography, and CT has a greater diagnostic power than mammography for axillary lymph node metastasis in breast cancer patients. Mamography alone should be used cautiously for the diagnosis of lymph node metastasis.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

PURPOSE: To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). PATIENTS AND METHODS: The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. RESULTS: The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

[CT and MRI features of Castleman's disease of the abdomen and pelvis].

OBJECTIVE: To analyze the CT/MRI features of Castleman's disease of the abdomen and pelvis. METHODS: CT/MRI images of 6 cases of pathologically confirmed Castleman's disease of the abdomen and pelvis were retrospectively reviewed. All the patients received plain CT scan and dynamic enhanced scan, and one had an additional MR scan. RESULTS: One case was identified as the disseminated type with multicentric enlarged lymph nodes and hepatosplenomegaly, and 5 cases were found to have localized type, of which 3 had retroperitoneal, 1 had mesentery and 1 had pelvic lesions. On CT scan, all the 5 cases with localized lesions showed single, round or ellipse soft tissue masses, with intra-tumoral calcification in 2 cases, fascial thickening around the mass in 3 cases, and satellite nodules in 4 cases. Enhanced scanning revealed obvious enhancement in the arterial phase and continuous enhancement in the portal vein and delayed phase in all the lesions, with an attenuation pattern similar to that of large vessels; enlarged blood vessels within or around the mass were displayed in each case. In 4 cases, the intra-tumoral radial or fissured non-enhanced areas in early stage of enhancement were gradually filled up as the scan time was delayed. The patient receiving MRI showed hypo-intensity on T(1)WI and hyper-intensity on T(2)WI, presenting with an enhancement feature similar to that of CT. CONCLUSION: Castleman's disease in the abdomen and pelvis is rare and liable to misdiagnosis, but its characteristic imaging features can help in the diagnosis and differential diagnosis.