The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.

BACKGROUND: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR). METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively. CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence.

BACKGROUND: To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy. METHODS: Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction. RESULTS: RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score. CONCLUSIONS: AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi-parametric magnetic resonance imaging (mp-MRI) radiomics in GC is less clear. PURPOSE: To compare and combine CT and mp-MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC. STUDY TYPE: Retrospective. POPULATION: Two hundred twenty-five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly. FIELD/SEQUENCE: T2-weighted fast spin echo (fat suppressed T2-weighted imaging [fs-T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T. ASSESSMENT: Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs-T2WI, ADC, DCE, and mp-MRI Radiomics score (Radscores) were compared between responders and non-responders. A multimodal nomogram combining CT and mp-MRI Radscores was developed. Patients were followed up for 3-65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated. STATISTICAL TESTS: A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan-Meier survival analysis and C-index. A P value <0.05 was considered statistically significant. RESULTS: CT Radscore, mp-MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834-0.937) and 0.871 (0.767-0.940) in training and validation datasets, respectively. The C-index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp-MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137). DATA CONCLUSIONS: mp-MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 2.

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.

Predicting Parametrial Invasion in Cervical Carcinoma (Stages IB1, IB2, and IIA): Diagnostic Accuracy of T2-Weighted Imaging Combined With DWI at 3 T.

OBJECTIVE: The objective of our study was to retrospectively evaluate the efficacy of combined analysis of T2-weighted imaging and DWI in the diagnosis of parametrial invasion (PMI) in cervical carcinoma. MATERIALS AND METHODS: The clinical records of 192 patients with cervical carcinoma who met the study requirements were reviewed for this retrospective study. The signal intensities of suspicious PMI tissue were assessed on T2-weighted images, DW images, and apparent diffusion coefficient maps independently by two experienced radiologists. The radiologist observers predicted the presence of PMI by scoring T2-weighted imaging alone and then by scoring T2-weighted imaging and DWI combined. The results were compared with histopathologic findings. RESULTS: Histopathologic findings revealed PMI in 24 of 192 study subjects. In positively predicting the presence of PMI, T2-weighted imaging and DWI combined scored significantly better than T2-weighted imaging alone, as proven by high sensitivity (T2-weighted imaging alone vs T2-weighted imaging and DWI combined: observer 1, 75.0% vs 83.3% [p = 0.477]; observer 2, 66.7% vs 91.7% [p < 0.05]), high specificity (T2-weighted imaging alone vs T2-weighted imaging and DWI combined: observer 1, 84.5% vs 98.8% [p < 0.001]; observer 2, 85.7% vs 98.8% [p < 0.001]), and high accuracy (T2-weighted imaging alone vs T2-weighted imaging and DWI combined: observer 1, 83.3% vs 96.9% [p < 0.001]; observer 2, 83.3% vs 97.9% [p < 0.001]). The area under the ROC curve was also significantly higher for T2-weighted imaging and DWI combined (observer 1, 0.911; observer 2, 0.952) than for T2-weighted imaging alone (observer 1, 0.798; observer 2, 0.762). Although the interobserver agreement was good for T2-weighted imaging (κ = 0.695) and excellent for T2-weighted imaging and DWI combined (κ = 0.753), the improvement failed to achieve statistical significance (p = 0.28). CONCLUSION: Combined analysis of T2-weighted imaging and DWI enhances the accuracy of diagnosing PMI in patients with cervical cancer compared with T2-weighted imaging alone.

Effect of intravenous gadolinium-DTPA on diffusion-weighted imaging of brain tumors: a short temporal interval assessment.

PURPOSE: To determine the effect of intravenous administration of gadolinium (Gd) contrast medium (Gd-DTPA) on diffusion-weighted imaging (DWI) for the evaluation of normal brain parenchyma vs. brain tumor following a short temporal interval. MATERIALS AND METHODS: Forty-four DWI studies using b values of 0 and 1000 s/mm(2) were performed before, immediately after, 1 min after, 3 min after, and 5 min after the administration of Gd-DTPA on 62 separate lesions including 15 meningioma, 17 glioma and 30 metastatic lesions. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) values of the brain tumor lesions and normal brain tissues were measured on pre- and postcontrast images. Statistical analysis using paired t-test between precontrast and postcontrast data were obtained on three brain tumors and normal brain tissue. RESULTS: The SNR and CNR of brain tumors and the SNR of normal brain tissue showed no statistical differences between pre- and postcontrast (P > 0.05). The ADC values on the three cases of brain tumors demonstrated significant initial increase on the immediate time point (P < 0.01) and decrease on following the 1 min time point (P < 0.01) after contrast. Significant decrease of ADC value was still found at 3min and 5min time point in the meningioma group (P < 0.01) with gradual normalization over time. The ADC values of normal brain tissues demonstrated significant initial elevation on the immediately postcontrast DWI sequence (P < 0.01). CONCLUSION: Contrast medium can cause a slight but statistically significant change on the ADC value within a short temporal interval after the contrast administration. The effect is both time and lesion-type dependent.

Development of a molecularly imprinted polymer for prometryne clean-up in the environment.

Molecularly imprinted polymers (MIPs) are prepared on the surface of modified silica gel using prometryne as a template, methacrylic acid as the functional monomer, ethylene glycol dimethacrylate as a crosslinker, and 2,2-azobisisobutyronitrile as an initiator. The structure of the MIPs was characterized using SEM and FTIR spectroscopy. The selectivity of the MIPs for the template molecule prometryne was proven by adsorption experiments. Highly selective SPE cartridges of MIP particles were developed and an optimized prometryne procedure was developed for the enrichment and clean-up of prometryne residues in water, soil, and wheat samples. The concentrations of prometryne in the samples were analyzed by HPLC. The average recoveries of prometryne spiked for water at 0.05∼0.8 mg/L were 101.47-106.65% and the RSD was 2.63-4.71%. The average recoveries of prometryne spiked for soil at 0.05∼0.8 mg/L were 87.34-94.91% with the RSD being 2.77-8.41%. The average recoveries of prometryne spiked for wheat plant at 0.2∼2.0 mg/kg were 91.04-97.76% with the RSD being 6.53-10.69%. The method developed here can be regenerated and repeatedly used more than two dozen times.

Application of intravoxel incoherent motion diffusion-weighted imaging for preoperative knowledge of lymphovascular invasion in gastric cancer: a prospective study.

PURPOSE: To investigate the potential of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer (GC). METHODS: This study prospectively enrolled 90 patients (62 males, 28 females, 60.79 ± 9.99 years old) who received radical gastrostomy. Abdominal MRI examinations including IVIM were performed within 1 week before surgery. Patients were divided into LVI-positive and -negative group according to pathological diagnosis after surgery. The apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f), were compared between the two groups. The relationship between MRI parameters and LVI was studied by Spearman's correlation analysis. Multivariable logistic regression analysis was used to screen independent predictors of LVI. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy. RESULTS: The ADC, D in LVI-positive group were lower, whereas tumor thickness and f parameter in LVI-positive group were higher than those in LVI-negative group, and they were statistically correlated with LVI (p < 0.05). D, f and tumor thickness were independent risk factors of LVI. The area under the curve of ADC, D, f, thickness, and the combined parameter (D + f + thickness) were 0.667, 0.754, 0.695, 0.792, and 0.876, respectively. The combined parameter demonstrated higher efficacy than any other parameters (p < 0.05). CONCLUSION: The ADC, D, and f can effectively distinguish LVI status of GC. The D, f and thickness were independent predictors. The combination of the three predictors further improved the efficacy.

CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.

Background: Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods: A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results: Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions: The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study.

Background: This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making. Methods: A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts. Results: The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts. Conclusions: The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.

Esophageal squamous cell carcinoma: assessing tumor angiogenesis using multi-slice CT perfusion imaging.

OBJECTIVES: The purpose of this study was to investigate the correlation between multi-slice computed tomographic perfusion imaging (CTPI) parameters and immunohistologic markers of angiogenesis in esophageal squamous cell carcinoma (ESCC). METHODS: Fifty patients with histologically proven esophageal squamous cell carcinoma were enrolled in this study. All subjects underwent multi-slice CT perfusion scan. The hemodynamic parameters of vascular tumor, including blood volume (BV), blood flow (BF), mean transit time (MTT) and permeability surface (PS) were generated. All the ESCC specimens were stained immunohistochemically to identify CD31 for quantification of microvessel density (MVD). CTPI parameters were correlated with MVD by using Pearson correlation analysis. RESULTS: The value of CT perfusion parameters of ESCC were as follows: BF 116.71 ± 47.59 ml/100 g/min, BV 6.74 ± 2.70 ml/100 g, MTT 6.42 ± 2.84 s, PS 13.82 ± 6.25 ml/100 g/min. The mean MVD of all 50 tumor specimens was 34.44 ± 19.75. The PS values were significantly higher in ESCC patients with involvement of lymph node than those without involvement of lymph node (p < 0.01). Blood volume and permeability surface were positively correlated with MVD (p < 0.01), whereas no significant correlation was observed between MVD and BF or between MVD and MTT. CONCLUSIONS: Blood volume and permeability surface were positively correlated with MVD. CTPI could reflect the angiogenesis in ESCC.

Preparation and utilization of molecularly imprinted polymer for chlorsulfuron extraction from water, soil, and wheat plant.

A molecularly imprinted polymer (MIP) was prepared using chlorsulfuron (CS), a herbicide as a template molecule, methacrylic acid as a functional monomer, ethylene glycol dimethacrylate (EDMA) as a cross-linker, methanol and toluene as a porogen, and 2,2-azobisisobutyronitrile as an initiator. The binding behaviors of the template chlorsulfuron and its analog on MIP were evaluated by equilibrium adsorption experiments, which showed that the MIP particles had specific affinity for the template CS. Solid-phase extraction (SPE) with the chlorsulfuron molecularly imprinted polymer as an adsorbent was investigated. The optimum loading, washing, and eluting conditions for chlorsulfuron molecularly imprinted polymer solid-phase extraction (CS-MISPE) were established. The optimized CS-MISPE procedure was developed to enrich and clean up the chlorsulfuron residue in water, soils, and wheat plants. Concentrations of chlorsulfuron in the samples were analyzed by HPLC-UVD. The average recoveries of CS spiked standard at 0.05~0.2 mg L(-1) in water were 90.2~93.3%, with the relative standard deviation (RSD) being 2.0~3.9% (n=3). The average recoveries of 1.0 mL CS spiked standard at 0.1~0.5 mg L(-1) in 10 g soil were 91.1~94.7%, with the RSD being 3.1~5.6% (n=3). The average recoveries of 1.0 mL CS spiked standard at 0.1~0.5 mg L(-1) in 5 g wheat plant were 82.3~94.3%, with the RSD being 2.9~6.8% (n=3). Overall, our study provides a sensitive and cost-effective method for accurate determination of CS residues in water, soils, and plants.

Comparison of computed tomography versus magnetic resonance imaging in assessing radiofrequency ablation margins after radiofrequency ablation in patients with hepatocellular carcinomas.

OBJECTIVE: To assess the diagnostic value of magnetic resonance imaging (MRI) in the follow-up of patients with hepatocellular carcinomas treated with radiofrequency ablation (RFA) and to compare it with that of computed tomography (CT). METHODS: From December 2009 to September 2011, 40 patients (47 hepatocellular carcinomas) were treated with RFA after transcatheter arterial chemoembolization and underwent MRI and CT for follow-up. RFA margins were assessed on a five-point scale with receiver operating characteristic curve analysis. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were evaluated. RESULTS: The interobserver agreement rate for MRI was significantly higher (Kappa=0.935) than for CT (Kappa=0.714; P < 0.05). The scores of 1 and 5 points for MRI, which confirms the presence or absence of residual tumor, accounted for 89.4% (84/94), while for CT accounting for only 31.9% (30/94). The area under the receiver operating characteristic curve of MRI was significantly higher than that of CT (P < 0.05), as were the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of detection rate (mean, 100%, 96.4%, 76.9%, 100%, and 96.8% for MRI, respectively, vs. 30.0%, 57.1%, 10.3%, 87.7%, and 63.8% for CT). CONCLUSION: MRI is superior to CT in assessing the RFA margins in terms of the diagnostic accuracy and detection rate .

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation.

Objectives: To develop and externally validate a spectral CT based nomogram for the preoperative prediction of LVI in patients with resectable GC. Methods: The two centered study contained a retrospective primary dataset of 224 pathologically confirmed gastric adenocarcinomas (161 males, 63 females; mean age: 60.57 ± 10.81 years, range: 20-86 years) and an external prospective validation dataset from the second hospital (77 males and 35 females; mean age, 61.05 ± 10.51 years, range, 31 to 86 years). Triple-phase enhanced CT scans with gemstone spectral imaging mode were performed within one week before surgery. The clinicopathological characteristics were collected, the iodine concentration (IC) of the primary tumours at arterial phase (AP), venous phase (VP), and delayed phase (DP) were measured and then normalized to aorta (nICs). Univariable analysis was used to compare the differences of clinicopathological and IC values between LVI positive and negative groups. Independent predictors for LVI were screened by multivariable logistic regression analysis in primary dataset and used to develop a nomogram, and its performance was evaluated by using ROC analysis and tested in validation dataset. Its clinical use was evaluated by decision curve analysis (DCA). Results: Tumor thickness, Borrmann classification, CT reported lymph node (LN) status and nICDP were independent predictors for LVI, and the nomogram based on these indicators was significantly associated with LVI (P<0.001). It yielded an AUC of 0.825 (95% confidence interval [95% CI], 0.769-0.872) and 0.802 (95% CI, 0.716-0.871) in primary and validation datasets (all P<0.05), with promising clinical utility by DCA. Conclusion: This study presented a dual energy CT quantification based nomogram, which enables preferable preoperative individualized prediction of LVI in patients with GC.

A clinical available decision support scheme for optimizing prostate biopsy based on mpMRI.

BACKGROUND: Combined MRI/Ultrasound fusion targeted biopsy (TBx) and systematic biopsy (SBx) results in better prostate cancer (PCa) detection relative to either TBx or SBx alone, while at the cost of higher number of biopsy cores and greater detection of clinically insignificant PCa. We therefore developed and evaluated a simple decision support scheme for optimizing prostate biopsy based on multiparametric (mp) MRI assessment. METHODS: Total 229 patients with suspicion of PCa underwent mpMRI before combined TBx/SBx were retrospectively included. Impacts of MRI characteristics such as Prostate Imaging-Reporting and Data System (PI-RADS) score, lesion size, zonal origination, and location on biopsy performance were evaluated. A clinically available decision support scheme relying on mpMRI assessment was subsequently developed as a triage test to optimize prostate biopsy process. Cost (downgrade, upgrade, and biopsy core)-to-Effectiveness (detection rate) was systemically compared. RESULTS: TBx achieved comparable detection rate to combined TBx/SBx in diagnosis of PCa and clinically significant PCa (csPCa) (PCa, 59% [135/229] vs 60.7% [139/229]; csPCa, 45.9% [105/229] vs 47.2% [108/229]; p-values > 0.05) and outperformed SBx (PCa, 42.4% [97/229]; csPCa, 28.4% [65/229]; p-values < 0.001). Specially, in personalized decision support scheme, TBx accurately detected all PCa (72.5% [74/102]) in PI-RADS 5 and larger (≥1 cm) PI-RADS 3-4 observations, adding SBx to TBx only resulted in 1.4% (1/74) upgrading csPCa. For smaller (<1 cm) PI-RADS 3-4 lesions, combined TBx/SBx resulted in relatively higher detection rate (51.2% [65/127] vs 48.0% [61/127]) and lower upgrading rate (30.6% [15/49] vs 36.7% [18/49]) than TBx. CONCLUSIONS: The benefit of SBx added to TBx was largely restricted to smaller PI-RADS score 3-4 lesions. Using our personalized strategy of solo TBx for PI-RADS 5 and larger (≥1 cm) PI-RADS score 3-4 lesions would avoid excess SBx in 44.5% (102/229) of all biopsy-naïve patients without compromising detection rate.

Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study.

BACKGROUND AND OBJECTIVE: To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE). METHODS: 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T2WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2. RESULTS: Among 4 MRI sequences, T2WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T2WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively. CONCLUSIONS: The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management.

Dynamic contrast-enhanced and diffusion-weighted MR imaging in early prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer.

PURPOSE: To investigate the efficacy of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the early prediction of the pathologic response to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC). METHODS: Fifty patients with LAGC who were treated with NAC followed by radical gastrectomy were enrolled. Uncontrasted and DCE-MRI were performed within 1 week before NAC. According to tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3). Apparent diffusion coefficients (ADC) and DCE-MRI kinetics (Ktrans, Ve, and Kep) were compared between the two groups. Logistic regression analysis was performed to screen independent factors to predict the NAC efficacy. The relationship between MRI parameters and TRG was studied by Spearman's correlation analysis. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy. RESULTS: ADC, Ktrans, and Kep values were higher in responders than in non-responders (p < 0.05) and correlated with TRG (p < 0.05). The ADC and Kep values were independent markers for predicting TRG. The area under the curve, sensitivities, specificities of ADC, Ktrans, Kep, and ADC + Kep were 0.813, 0.699, 0.709, 0.886;73.64%, 65.54%, 63.21%, 70.37%; 86.47%, 54.97%, 79.47%, 95.65%; respectively. ADC + Kep demonstrated a higher efficacy than Ktrans and Kep (p = 0.012, 0.011), but without improvement compared with ADC (p > 0.05). CONCLUSION: Both DWI and DCE-MRI can effectively predict the pathologic response to NAC in LAGC. A combination of ADC and Kep increased the efficacy, and ADC is the most valuable imaging parameter.

[Upper limbs motor maps in cortex and plasticity after the anatomical hemispherectomy].

OBJECTIVE: To locate motor functional area of patients who undergone modified anatomical hemispherectomy in order to analysis the plasticity of upper limbs motor. METHODS: The patients who undergone modified anatomical hemispherectomy were performed BOLD sequences, to locate functional cortical areas in their residual brain. RESULTS: 6 patients have performed examination of BOLD sequences by 3.0-T MRI.5 of them obtained contralateral upper limb motor areas in their residual brain, and 3 of them obtained ipsilateral and contralateral upper limb motor area map in cortex. The ipsilateral upper limb motor areas in the M1, SMA and posterior parietal cortex. CONCLUSIONS: The patients who undergone modified anatomical hemispherectomy is an excellent model to investigate mechanism of plasticity in the developing brain. Functional magnetic resonance (fMRI) provided fine spatial detail of brain responses, would describe the motor functional area of cortical maps. These patients exist ipsilateral motor areas in their residual mono hemisphere. The study indicated there maybe have somewhat extent of correlation between the surgical procedure and the outcome of neuroplasticity.