Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer.

OBJECTIVE: The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features. METHODS: This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy. Patients with NPM means patients have neither PM nor other distant metastases, indicating there is no evidence of distant metastases in patients with AGC. Patients' clinical characteristics and CT features such as tumor marker, Borrmann IV, enhancement patterns, and pelvic ascites were observed by 2 experienced radiologists. Computed tomography features and clinical characteristics were combined to construct an indicator for identifying the presence of OPM in patients with AGC based on a logistic regression model. Receiver operating characteristic curves and the area under the receiver operating characteristic curve (AUC) were generated to assess the diagnostic performance of the combined indicator. RESULTS: Four independent predictors (Borrmann IV, pelvic ascites, carbohydrate antigen 125, and normalized arterial CT value) differed significantly between OPM and NPM and performed outstandingly in distinguishing patients with OPM from those without PM (AUC = 0.643-0.696). The combined indicator showed a higher AUC value than the independent risk factors (0.820 vs 0.643-0.696). CONCLUSIONS: The combined indicator based on abdominopelvic CT features and carbohydrate antigen 125 may assist clinicians in identifying the presence of CT OPMs in patients with AGC.

Comparison of Computed Tomography Features of Gastric and Small Bowel Gastrointestinal Stromal Tumors With Different Risk Grades.

OBJECTIVE: This study aimed to compare the computed tomography (CT) features of gastric and small bowel gastrointestinal stromal tumors (GISTs) and further identify the predictors for risk stratification of them, respectively. METHODS: According to the modified National Institutes of Health criteria, patients were classified into low-malignant potential group and high-malignant potential group. Two experienced radiologists reviewed the CT features including the difference of CT values between arterial phase and portal venous phase (PVPMAP) by consensus. The CT features of gastric and small bowel GISTs were compared, and the association of CT features with risk grades was analyzed, respectively. Determinant CT features were used to construct corresponding models. RESULTS: Univariate analysis showed that small bowel GISTs tended to present with irregular contour, mixed growth pattern, ill-defined margin, severe necrosis, ulceration, tumor vessels, heterogeneous enhancement, larger size, and marked enhancement compared with gastric GISTs. According to multivariate analysis, tumor size (P < 0.001; odds ratio [OR], 3.279), necrosis (P = 0.008; OR, 2.104) and PVPMAP (P = 0.045; OR, 0.958) were the independent influencing factors for risk stratification of gastric GISTs. In terms of small bowel GISTs, the independent predictors were tumor size (P < 0.001; OR, 3.797) and ulceration (P = 0.031; OR, 4.027). Receiver operating characteristic curve indicated that the CT models for risk stratification of gastric and small bowel GISTs both achieved the best predictive performance. CONCLUSIONS: Computed tomography features of gastric and small bowel GISTs are different. Furthermore, the qualitative and quantitative CT features of GISTs may be favorable for preoperative risk stratification.

RNA-binding protein Musashi2 stabilizing androgen receptor drives prostate cancer progression.

The androgen receptor (AR) pathway is critical for prostate cancer carcinogenesis and development; however, after 18-24 months of AR blocking therapy, patients invariably progress to castration-resistant prostate cancer (CRPC), which remains an urgent problem to be solved. Therefore, finding key molecules that interact with AR as novel strategies to treat prostate cancer and even CRPC is desperately needed. In the current study, we focused on the regulation of RNA-binding proteins (RBPs) associated with AR and determined that the mRNA and protein levels of AR were highly correlated with Musashi2 (MSI2) levels. MSI2 was upregulated in prostate cancer specimens and significantly correlated with advanced tumor grades. Downregulation of MSI2 in both androgen sensitive and insensitive prostate cancer cells inhibited tumor formation in vivo and decreased cell growth in vitro, which could be reversed by AR overexpression. Mechanistically, MSI2 directly bound to the 3'-untranslated region (UTR) of AR mRNA to increase its stability and, thus, enhanced its transcriptional activity. Our findings illustrate a previously unknown regulatory mechanism in prostate cancer cell proliferation regulated by the MSI2-AR axis and provide novel evidence towards a strategy against prostate cancer.

Direct Comparison of Four Presurgical Stratifying Schemes for Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced MRI.

BACKGROUND: Microvascular invasion (MVI) is implicated in the poor prognosis of hepatocellular carcinoma (HCC). Presurgical stratifying schemes have been proposed for HCC-MVI but lack external validation. PURPOSE: To perform external validation and comparison of four presurgical stratifying schemes for the prediction of MVI using gadoxetic acid-based MRI in a cohort of HCC patients. STUDY TYPE: Retrospective. SUBJECTS: Included were 183 surgically resected HCCs from patients who underwent pretreatment MRI. FIELD STRENGTH/SEQUENCE: This includes 1.5-3.0 T with T2 , T1 , diffusion-weighted imaging (DWI), and dynamic gadoxetic acid contrast-enhancement imaging sequences. ASSESSMENT: A two-trait predictor of venous invasion (TTPVI), Lei model, Lee model, and Xu model were compared. We relied on preoperative characteristics and imaging findings via four independent radiologists who were blinded to histologic results, as required by the tested tools. STATISTICAL TEST: Tests of accuracy between predicted and observed HCC-MVI rates using receiver operating characteristic (ROC) curve and decision curve analysis. The intraclass correlation coefficient (ICC) and Cronbach's alpha statistics were used to evaluate reproducibility. RESULTS: HCC-MVI was identified in 52 patients (28.4%). The average ROC curves (AUCs) for HCC-MVI predictions were 0.709-0.880, 0.714-0.828, and 0.588-0.750 for the Xu model, Lei model, and Lee model, respectively. The rates of accuracy were 60.7-81.4%, 69.9-75.9%, and 65.6-73.8%, respectively. Decision curve analyses indicated a higher benefit for the Xu and Lei models compared to the Lee model. The ICC and Cronbach's alpha index were highest in the Lei model (0.896/0.943), followed by the Xu model (0.882/0.804), and the Lee model (0.769/0.715). The TTPVI resulted in a Cronbach's alpha index of 0.606 with a sensitivity of 34.6-61.5% and a specificity of 76.3-91.6%. DATA CONCLUSION: Stratifying schemes relying on gadoxetic acid-enhanced MRI provide an additional insight into the presence of preoperative MVI. The Xu model outperformed the other models in terms of accuracy when performed by an experienced radiologist. Conversely, the Lei model outperformed the other models in terms of reproducibility. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:433-447.

Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma.

AIMS: The aim of the study was to predict and assess treatment response by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to patients with locally advanced esophageal squamous cell carcinoma receiving chemoradiotherapy (CRT). MATERIALS AND METHODS: Seventy-two patients with locally advanced esophageal squamous cell carcinoma who underwent DCE-MRI before and after chemoradiotherapy were enrolled and divided into the complete response (CR) group and the non-CR group based on RECIST. The histogram parameters (10th percentile, 90th percentile, median, mean, standard deviation, skewness, and kurtosis) of pre-CRT and post-CRT were compared using a paired Student's t test in the CR and non-CR groups, respectively. The histogram parameter differences between the CR and the non-CR groups were compared using an unpaired Student's t test. A receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance. RESULTS: The histogram parameters of Ktrans values were observed to have significantly decreased after chemoradiotherapy in the CR group. The CR responders showed significantly higher median, mean, and 10th and 90th percentile of pre-Ktrans values than those of the non-CR group. The histogram analysis indicated the decreased heterogeneity in the CR group after CRT. Esophageal cancer with higher pre-Ktrans and lower post-Ktrans values indicated a good treatment response to CRT. Pre-Ktrans-10th showed the best diagnostic performance in predicting the chemoradiotherapy response. CONCLUSIONS: The histogram parameters of Ktrans are useful in the assessment and prediction of the chemoradiotherapy response in patients with advanced esophageal squamous cell carcinoma. DCE-MRI could serve as an adjunctive imaging technique for treatment planning.

Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma.

BACKGROUND & AIMS: Microvascular invasion (MVI) impairs surgical outcomes in patients with hepatocellular carcinoma (HCC). As there is no single highly reliable factor to preoperatively predict MVI, we developed a computational approach integrating large-scale clinical and imaging modalities, especially radiomic features from contrast-enhanced CT, to predict MVI and clinical outcomes in patients with HCC. METHODS: In total, 495 surgically resected patients were retrospectively included. MVI-related radiomic scores (R-scores) were built from 7,260 radiomic features in 6 target volumes. Six R-scores, 15 clinical factors, and 12 radiographic scores were integrated into a predictive model, the radiographic-radiomic (RR) model, with multivariate logistic regression. RESULTS: Radiomics related to tumor size and intratumoral heterogeneity were the top-ranked MVI predicting features. The related R-scores showed significant differences according to MVI status (p <0.001). Regression analysis identified 8 MVI risk factors, including 5 radiographic features and an R-score. The R-score (odds ratio [OR] 2.34) was less important than tumor capsule (OR 5.12), tumor margin (OR4.20), and peritumoral enhancement (OR 3.03). The RR model using these predictors achieved an area under the curve (AUC) of 0.909 in training/validation and 0.889 in the test set. Progression-free survival (PFS) and overall survival (OS) were significantly different between the RR-predicted MVI-absent and MVI-present groups (median PFS: 49.5 vs. 12.9 months; median OS: 76.3 vs. 47.3 months). RR-computed MVI probability, histologic MVI, tumor size, and Edmondson-Steiner grade were independently associated with disease-specific recurrence and mortality. CONCLUSIONS: The computational approach, integrating large-scale clinico-radiologic and radiomic features, demonstrates good performance for predicting MVI and clinical outcomes. However, radiomics with current CT imaging analysis protocols do not provide statistically significant added value to radiographic scores. LAY SUMMARY: The most effective treatment for hepatocellular carcinoma (HCC) is surgical removal of the tumor but often recurrence occurs, partly due to the presence of microvascular invasion (MVI). Lacking a single highly reliable factor able to preoperatively predict MVI, we developed a computational approach to predict MVI and the long-term clinical outcome of patients with HCC. In particular, the added value of radiomics, a newly emerging form of radiography, was comprehensively investigated. This computational method can enhance the communication with the patient about the likely success of the treatment and guide clinical management, with the aim of finding drugs that reduce the risk of recurrence.

A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma.

OBJECTIVES: This study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value. METHODS: For this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients. RESULTS: The radiomics signature comprised eight LN-status-related features and showed significant association with LN metastasis in both cohorts (p < 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both p < 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival. CONCLUSIONS: Our radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making. KEY POINTS: • The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup. • Prognosis of high-risk patients remains dismal after curative-intent resection. • The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.

Acute kidney damage induced by low- and iso-osmolar contrast media in rats: Comparison study with physiologic MRI and histologic-gene examination.

PURPOSE: To investigate the physiopathological effects of low- and iso-osmolar contrast media (CM) on renal function with physiologic MRI and histologic-gene examination. MATERIALS AND METHODS: Forty-eight rats underwent time-course DWI and DCE-MRI at 3.0 Tesla (T) before and 5-15 min after exposure of CM or saline (Iop.370: 370 mgI/mL iopromide; Iod.320: 320 mgI/mL iodixanol; Iod.270: 270 mgI/mL iodixanol; 4 gI/kg body weight). Intrarenal viscosity was reflected by apparent diffusion coefficient (ADC). Renal physiologies were evaluated by DCE-derived glomerular filtration rate (GFR), renal blood flow (RBF), and renal blood volume (RBV). Potential acute kidney injury (AKI) was determined by histology and the expression of kidney injury molecule 1 (Kim-1). RESULTS: Iop.370 mainly increased ADC in inner-medulla (△ADCIM : 12.3 ± 11.1%; P < 0.001). Iod.320 and Iod.270 mainly decreased ADC in outer-medulla (△ADCIM ; Iod.320: 16.8 ± 7.5%; Iod.270: 18.1 ± 9.5%; P < 0.001) and inner-medulla (△ADCIM ; Iod.320: 28.4 ± 9.3%; Iod.270: 30.3 ± 6.3%; P < 0.001). GFR, RBF and RBV were significantly decreased by Iod.320 (△GFR: 45.5 ± 24.1%; △RBF: 44.6 ± 19.0%; △RBV: 35.2 ± 10.1%; P < 0.001) and Iod.270 (33.2 ± 19.0%; 38.1 ± 15.6%; 30.1 ± 10.1%; P < 0.001), while rarely changed by Iop.370 and saline. Formation of vacuoles and increase in Kim-1 expression was prominently detected in group of Iod.320, while rarely in Iod.270 and Iop.370. CONCLUSION: Iso-osmolar iodixanol, given at high-dose, produced prominent AKI in nonhydrated rats. This renal dysfunction could be assessed noninvasively by physiologic MRI. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:291-302.

MR-based prognostic nomogram for prostate cancer after radical prostatectomy.

PURPOSE: To assess a magnetic resonance imaging (MRI)-based nomogram in the prediction of prostate cancer (PCa) biochemical recurrence (BCR) within 3 years after prostatectomy. MATERIALS AND METHODS: Between 2009 and 2013, 205 patients with biopsy-confirmed PCa had MRI before prostatectomy. BCR was defined as a PSA failure (>0.2 ng/ml) after prostatectomy. MR features (cancer location, diameter, apparent diffusion coefficients [ADCs], PI-RADS v2 score, dynamic contrast-enhanced [DCE] type, and MR T-stage) were retrospectively evaluated for predicting 3-year BCR based on partial least square regression analysis. Second, imaging features were added to a popularized D'Amico and CAPRA scheme to determine imaging contribution to published nomograms. Lastly, a multivariable Cox regression analysis was employed to determine the independent risk factors of time to BCR. RESULTS: Three-year BCR rate (median follow-up of 44.9 mo) was 25.4% (52/205). The area under receiver operating characteristic (ROC) curve (Az) for MR nomogram (0.909, 95% confidence interval [CI]: 0.861-0.944) was higher than popularized D'Amico (0.793, 95% CI: 0.731-0.846, P = 0.001) and CAPRA (0.809, 95% CI: 0.748-0.860, P = 0.001). The performance of D'Amico (Az: 0.901, 95% CI: 0.852-0.938, P < 0.001) and CAPRA (Az: 0.894, 95% CI: 0.843-0.932, P = 0.004) was significantly improved by adding MR findings. Tumor ADCs (hazard ratio [HR] = 1.747; P = 0.011), PI-RADS score (HR = 4.123; P = 0.039), pathological Gleason score (HR = 3.701; P = 0.004), and surgical-T3b (HR = 6.341; P < 0.001) were independently associated with time to BCR. CONCLUSION: Multiparametric MRI, when converted into a prognostic nomogram, can predict the clinical outcome in patients with PCa after prostatectomy. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017;45:586-596.

Diffusion Kurtosis Imaging Helps to Predict Upgrading in Biopsy-Proven Prostate Cancer With a Gleason Score of 6.

OBJECTIVE: The purpose of this study was to investigate whether diffusion kurtosis imaging (DKI) is useful for predicting upgrades in Gleason score (GS) in biopsy-proven prostate cancer with a GS of 6. MATERIALS AND METHODS: A total of 46 patients with biopsy-proven GS 6 prostate cancer, 3-T DWI results, and surgical pathologic results were retrospectively included in the study. DWI data were postprocessed with monoexponential and DK models to quantify the apparent diffusion coefficient (ADC), apparent diffusion for gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). The volume of the lesions, prostate-specific antigen (PSA) level, and diffusion variables (ADCmin, Dappmin, Kappmax, ADCmean, Dappmean, and Kappmean) were evaluated. PSA and DKI were combined as a parameter in a logistic regression model. The utility of these parameters in predicting an upgrade in GS was analyzed with ROC regression. RESULTS: The rate of GS upgrade was 50.0% (23/46). The GS upgrade group had significantly lower ADCmin (p = 0.007), ADC mean (p = 0.003), D appmin (p < 0.001), and Dappmean (p = 0.001) values and significantly higher Kappmax (p = 0.003), Kappmean (p = 0.005), and PSA (p = 0.004) values than the group that did not have an upgrade. Among single parameters, Kappmax had the highest ROC AUC value (0.819, p < 0.05), and among all the parameters and models, PSA-Kappmax had the highest AUC (0.868, p < 0.05) and Youden index (0.6522). CONCLUSION: The results showed that DKI may help in prediction of GS upgrade in biopsy-proven GS 6 prostate cancer. The comprehensive consideration of DKI and PSA may be a promising approach to predicting GS upgrade.

Classifying CT/MR findings in patients with suspicion of hepatocellular carcinoma: Comparison of liver imaging reporting and data system and criteria-free Likert scale reporting models.

PURPOSE: To compare the Liver Imaging Reporting and Data System (LI-RADS) and a criteria-free Likert scale (LS) reporting models for classifying computed tomography/magnetic resonance imaging (CT/MR) findings of suspicious hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Imaging data of 281 hepatic nodules in 203 patients were retrospectively included. Imaging characteristics including diameter, arterial hyperenhancement, washout, and capsule were reviewed independently by two groups of readers using LI-RADS and LS (range, score 1-5). LS is primarily based on the overall impression of image findings without using fixed criteria. Interreader agreement (IRA), intraclass agreement (ICA), and diagnostic performance were determined by Fleiss, Cohen's kappa (κ), and logistic regression, respectively. RESULTS: There were 167 contrast-enhanced CT (CECT) versus 114 MR data. Overall, IRA was moderate (κ = 0.47, 0.52); IRA was moderate-to-good for arterial hyperenhancement, washout, and capsule (κ = 0.56-0.69); excellent for diameter and tumor embolus (κ = 0.99). Overall, ICA between LI-RADS and LS was moderate (κ = 0.44-0.50); ICA was good for scores 1-2 (κ = 0.71-0.90), moderate for scores 3 and 5 (κ = 0.41-0.52), but very poor for score 4 (κ = 0.11-0.19). LI-RADS produced significantly lower accuracy (78.6% vs. 87.2%) and sensitivity (72.1% vs. 92.8%), higher specificity (97.3% vs. 71.2%) and positive likelihood ratio (+LR: 26.32 vs. 3.23) in diagnosis of HCC. CECT produced relatively low IRA, ICA, and diagnostic ability against MR. CONCLUSION: There were substantial variations in liver observations between LI-RADS and LS. Further study is needed to investigate ICA between CECT and MR.

New RESOLVE-Based Diffusional Kurtosis Imaging in MRI-Visible Prostate Cancer: Effect of Reduced b Value on Image Quality and Diagnostic Effectiveness.

OBJECTIVE: The purpose of this article was to investigate whether a new readout segmentation of long variable echo-trains (RESOLVE)-based diffusional kurtosis imaging (DKI) with reduced b value technique can affect image quality and diagnostic effectiveness in MRI-visible prostate cancer (PCA). SUBJECTS AND METHODS: Prostatic RESOLVE DKI (0-1400 s/mm2) was prospectively performed for 12 volunteers. The optimal protocol was then performed in 108 MRI-visible PCAs to determine whether it can compete against a preferred b-value set (0-2000 s/mm(2)) regarding image quality and diagnostic effectiveness. Images were interpreted by two independent radiologists using the prostate imaging reporting and data system (PI-RADS). Readers' concordance and diagnostic effectiveness were tested with the Fleiss kappa and area under the ROC curve (Az) analyses. RESULTS: A b value of 1400 s/mm(2) generated a larger apparent diffusion coefficient of gaussian distribution (Dapp) (1.35 ± 0.31 vs 1.30 ± 0.30 mm(2)/s; p < 0.001) and apparent kurtosis coefficient (Kapp) (1.11 ± 0.26 vs 1.00 ± 0.21; p < 0.001) in PCA than did a b value of 2000 s/mm(2). Interreader agreement using PI-RADS was relatively low when Dapp and Kapp maps were excluded from image interpretations (κ = 0.39-0.41 vs κ = 0.66-0.68 with Dapp and Kapp maps). Interreader agreement in staging PCA was relatively high (κ > 0.80) and was not influenced by reducing the b value. The power of Dapp and Kapp to differentiate PCA from normal tissue (Az = 0.97-0.98), tissue with a Gleason score less than or equal to 3 + 4 from tissue with a Gleason score greater than 3 + 4 (Az = 0.77-0.82), and PCA stage lower than pT3 from stage pT3 and higher PCA (Az = 0.70-0.75) was not significantly degraded by reducing the b value. CONCLUSION: We found that b values significantly influenced image quality, PI-RADS score, and DKI outputs but did not degrade the diagnostic effectiveness of DKI parameters to detect and classify PCA.

The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer.

OBJECTIVE: To evaluate histogram analysis of intravoxel incoherent motion (IVIM) for discriminating the Gleason grade of prostate cancer (PCa). METHODS: A total of 48 patients pathologically confirmed as having clinically significant PCa (size > 0.5 cm) underwent preoperative DW-MRI (b of 0-900 s/mm(2)). Data was post-processed by monoexponential and IVIM model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analysis was performed by outlining entire-tumour regions of interest (ROIs) from histological-radiological correlation. The ability of imaging indices to differentiate low-grade (LG, Gleason score (GS) ≤6) from intermediate/high-grade (HG, GS > 6) PCa was analysed by ROC regression. RESULTS: Eleven patients had LG tumours (18 foci) and 37 patients had HG tumours (42 foci) on pathology examination. HG tumours had significantly lower ADCs and D in terms of mean, median, 10th and 75th percentiles, combined with higher histogram kurtosis and skewness for ADCs, D and f, than LG PCa (p < 0.05). Histogram D showed relatively higher correlations (ñ = 0.641-0.668 vs. ADCs: 0.544-0.574) with ordinal GS of PCa; and its mean, median and 10th percentile performed better than ADCs did in distinguishing LG from HG PCa. CONCLUSION: It is feasible to stratify the pathological grade of PCa by IVIM with histogram metrics. D performed better in distinguishing LG from HG tumour than conventional ADCs. KEY POINTS: • GS had relatively higher correlation with tumour D than ADCs. • Difference of histogram D among two-grade tumours was statistically significant. • D yielded better individual features in demonstrating tumour grade than ADC. • D* and f failed to determine tumour grade of PCa.

Comparison of Utility of Histogram Apparent Diffusion Coefficient and R2* for Differentiation of Low-Grade From High-Grade Clear Cell Renal Cell Carcinoma.

OBJECTIVE: The purpose of this study was to compare histogram analysis of apparent diffusion coefficient (ADC) and R2* for differentiating low-grade from high-grade clear cell renal cell carcinoma (RCC). MATERIALS AND METHODS: Forty-six patients with pathologically confirmed clear cell RCC underwent preoperative BOLD and DWI MRI of the kidneys. ADCs based on the entire tumor volume were calculated with b value combinations of 0 and 800 s/mm(2). ROI-based R2* was calculated with eight TE combinations of 6.7-22.8 milliseconds. Histogram analysis of tumor ADCs and R2* values was performed to obtain mean; median; width; and fifth, 10th, 90th, and 95th percentiles and histogram inhomogeneity, kurtosis, and skewness for all lesions. RESULTS: Thirty-three low-grade and 13 high-grade clear cell RCCs were found at pathologic examination. The TNM classification and tumor volume of clear cell RCC significantly correlated with histogram ADC and R2* (ρ = -0.317 to 0.506; p < 0.05). High-grade clear cell RCC had significantly lower mean, median, and 10th percentile ADCs but higher inhomogeneity and median R2* than low-grade clear cell RCC (all p < 0.05). Compared with other histogram ADC and R2* indexes, 10th percentile ADC had the highest accuracy (91.3%) in discriminating low- from high-grade clear cell RCC. R2* in discriminating hemorrhage was achieved with a threshold of 68.95 Hz. At this threshold, high-grade clear cell RCC had a significantly higher prevalence of intratumor hemorrhage (high-grade, 76.9%; low-grade, 45.4%; p < 0.05) and larger hemorrhagic area than low-grade clear cell RCC (high-grade, 34.9% ± 31.6%; low-grade, 8.9 ± 16.8%; p < 0.05). CONCLUSION: A close relation was found between MRI indexes and pathologic findings. Histogram analysis of ADC and R2* allows differentiation of low- from high-grade clear cell RCC with high accuracy.

DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer.

PURPOSE: To investigate diagnostic efficiency of DWI using entire-tumor histogram analysis in differentiating the low-grade (LG) prostate cancer (PCa) from intermediate-high-grade (HG) PCa in comparison with conventional ROI-based measurement. METHODS: DW images (b of 0-1400 s/mm(2)) from 126 pathology-confirmed PCa (diameter >0.5 cm) in 110 patients were retrospectively collected and processed by mono-exponential model. The measurement of tumor apparent diffusion coefficients (ADCs) was performed with using histogram-based and ROI-based approach, respectively. The diagnostic ability of ADCs from two methods for differentiating LG-PCa (Gleason score, GS ≤ 6) from HG-PCa (GS > 6) was determined by ROC regression, and compared by McNemar's test. RESULTS: There were 49 LG-tumor and 77 HG-tumor at pathologic findings. Histogram-based ADCs (mean, median, 10th and 90th) and ROI-based ADCs (mean) showed dominant relationships with ordinal GS of Pca (ρ = -0.225 to -0.406, p < 0.05). All above imaging indices reflected significant difference between LG-PCa and HG-PCa (all p values <0.01). Histogram 10th ADCs had dominantly high Az (0.738), Youden index (0.415), and positive likelihood ratio (LR+, 2.45) in stratifying tumor GS against mean, median and 90th ADCs, and ROI-based ADCs. Histogram mean, median, and 10th ADCs showed higher specificity (65.3%-74.1% vs. 44.9%, p < 0.01), but lower sensitivity (57.1%-71.3% vs. 84.4%, p < 0.05) than ROI-based ADCs in differentiating LG-PCa from HG-PCa. CONCLUSIONS: DWI-associated histogram analysis had higher specificity, Az, Youden index, and LR+ for differentiation of PCa Gleason grade than ROI-based approach.

Dual-energy CT quantitative parameters to evaluate occult peritoneal metastasis in advanced gastric cancer preoperatively.

PURPOSE: To explore whether dual-energy CT (DECT) quantitative parameters could provide analytic value for the diagnosis of patients with occult peritoneal metastasis (OPM) in advanced gastric cancer preoperatively. MATERIALS AND METHODS: This retrospective study included 219 patients with advanced gastric cancer and DECT scans. The patient's clinical data and DECT related iodine concentration (IC) parameters and effective atomic number (Zeff) were collated and analyzed among noun-peritoneal metastasis (NPM), OPM and radiologically peritoneal metastasis (RPM) groups. The predictive performance of the DECT parameters was compared with that of the conventional CT features and clinical characteristics through evaluating area under curve of the precision-recall (AUC-PR), F1 score, balanced accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: Borrmann IV type diagnosed on CT and serum tumor indicator CA125 index were statistically different between the NPM and OPM groups. DECT parameters included IC, normalized IC (NIC), and Zeff of PM group were lower than the NPM group. The DECT predictive nomogram combined three independent DECT parameters produced a better diagnostic performance than the conventional CT feature Borrmann IV type and serum CA125 index in AUC-PR with 0.884 vs 0.368 vs 0.189, but similar to the combined indicator which was based on the DECT parameters, the conventional CT feature, and serum CA125 index in AUC-PR with 0.884 vs 0.918. CONCLUSION: The lower quantitative NIC, IC ratio, and Zeff on DECT was associated with peritoneal metastasis in advanced gastric cancer and was promising to identify patients with OPM noninvasively.

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T2-weighted MR imaging (T2WI) for gastric cancer (GC). METHODS: 112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-T2WI and BLADE-T2WI of stomach were scanned with same spatial resolution. Three radiologists independently evaluated the image qualities using a 5-scale Likert scales (IQS) in terms of lesion delineation, gastric wall boundary conspicuity, and overall image quality. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in measurable lesions. T staging was conducted based on the results of both sequences for GC patients with gastrectomy. Pairwise comparisons between DLSB-T2WI and BLADE-T2WI were performed using the Wilcoxon signed-rank test, paired t-test, and chi-squared test. Kendall's W, Fleiss' Kappa, and intraclass correlation coefficient values were used to determine inter-reader reliability. RESULTS: Against BLADE, DLSB reduced total acquisition time of T2WI from 495 min (mean 4:42 per patient) to 33.6 min (18 s per patient), with better overall image quality that produced 9.43-fold, 8.00-fold, and 18.31-fold IQS upgrading against BALDE, respectively, in three readers. In 69 measurable lesions, DLSB-T2WI had higher mean SNR and higher CNR than BLADE-T2WI. Among 71 patients with gastrectomy, DLSB-T2WI resulted in comparable accuracy to BLADE-T2WI in staging GCs (P > 0.05). CONCLUSIONS: DLSB-T2WI demonstrated shorter acquisition time, better image quality, and comparable staging accuracy, which could be an alternative to BLADE-T2WI for gastric cancer imaging.

Development and validation of a dual-energy CT-based model to estimate the malignant probability of distal gastric wall thickening.

Background: This study developed and validated a viable model for the preoperative diagnosis of malignant distal gastric wall thickening based on dual-energy spectral computed tomography (DEsCT). Methods: The imaging data of 208 patients who were diagnosed with distal gastric wall thickening using DEsCT were retrospectively collected and divided into a training cohort (n=151) and a testing cohort (n=57). The patient's clinical data and pathological information were collated. The multivariable logistic regression model was built using 5 selected features, and subsequently, a 10-fold cross-validation was performed to identify the optimal model. A nomogram was established based on the training cohort. Finally, the diagnostic performance of the best model was compared to the existing conventional CT scheme through evaluating the discrimination ability in the testing cohort in terms of the receiver operating characteristic curve (ROC), calibration, and clinical usefulness. Results: Stepwise regression analysis identified 5 candidate variables with the smallest Akaike information criteria (AIC), namely, the venous phase spectral curve [VP_ SC; odds ratio (OR) 8.419], focal enhancement (OR 3.741), arterial phase mixed (OR 1.030), tumor site (OR 0.573), and diphasic shape change (DP_shape change; OR 2.746). The best regression model with 10-fold cross-validation consisting of VP_SC and focal enhancement was built using the 5 candidate variables. The average area under the ROC curve (AUC) of the model from the 10-fold cross-validation was 0.803 (sensitivity of 69.2%, specificity of 94.1%, and accuracy of 74.8%). In the testing cohort, the DEsCT model identified using the regression model performed better (AUC 0.905, sensitivity 81.3%, specificity 85.4%, and accuracy 84.2%) than did the conventional CT scheme (AUC 0.852, sensitivity 80.0%, specificity 76.6%, and accuracy 77.2%). The nomogram based on the DEsCT model showed good calibration and provided a better net benefit for predicting malignancy of distal gastric wall thickening. Conclusions: Comprehensive assessment with the DEsCT-based model can be used to facilitate the individualized diagnosis of malignancy risk in patients presenting with distal gastric wall thickening.

SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables.

Radiological images have shown promising effects in patient prognostication. Deep learning provides a powerful approach for in-depth analysis of imaging data and integration of multi-modal data for modeling. In this work, we propose SurvivalCNN, a deep learning structure for cancer patient survival prediction using CT imaging data and non-imaging clinical data. In SurvivalCNN, a supervised convolutional neural network is designed to extract volumetric image features, and radiomics features are also integrated to provide potentially different imaging information. Within SurvivalCNN, a novel multi-thread multi-layer perceptron module, namely, SurvivalMLP, is proposed to perform survival prediction from censored survival data. We evaluate the proposed SurvivalCNN framework on a large clinical dataset of 1061 gastric cancer patients for both overall survival (OS) and progression-free survival (PFS) prediction. We compare SurvivalCNN to three different modeling methods and examine the effects of various sets of data/features when used individually or in combination. With five-fold cross validation, our experimental results show that SurvivalCNN achieves averaged concordance index 0.849 and 0.783 for predicting OS and PFS, respectively, outperforming the compared state-of-the-art methods and the clinical model. After future validation, the proposed SurvivalCNN model may serve as a clinical tool to improve gastric cancer patient survival estimation and prognosis analysis.

Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging.

OBJECTIVES: Lymphovascular invasion (LVI) is a factor significantly impacting treatment and outcome of patients with gastric cancer (GC). We aimed to investigate prognostic aspects of a preoperative LVI prediction in GC using radiomics and deep transfer learning (DTL) from contrast-enhanced CT (CECT) imaging. METHODS: A total of 1062 GC patients (728 training and 334 testing) between Jan 2014 and Dec 2018 undergoing gastrectomy were retrospectively included. Based on CECT imaging, we built two gastric imaging (GI) markers, GI-marker-1 from radiomics and GI-marker-2 from DTL features, to decode LVI status. We then integrated demographics, clinical data, GI markers, radiologic interpretation, and biopsies into a Gastric Cancer Risk (GRISK) model for predicting LVI. The performance of GRISK model was tested and applied to predict survival outcomes in GC patients. Furthermore, the prognosis between LVI (+) and LVI (-) patients was compared in chemotherapy and non-chemotherapy cohorts, respectively. RESULTS: GI-marker-1 and GI-marker-2 yield similar performance in predicting LVI in training and testing dataset. The GRISK model yields the diagnostic performance with AUC of 0.755 (95% CI 0.719-0.790) and 0.725 (95% CI 0.669-0.781) in training and testing dataset. Patients with LVI (+) trend toward lower progression-free survival (PFS) and overall survival (OS). The difference of prognosis between LVI (+) and LVI (-) was more noticeable in non-chemotherapy than that in chemotherapy group. CONCLUSION: Radiomics and deep transfer learning features on CECT demonstrate potential power for predicting LVI in GC patients. Prospective use of a GRISK model can help to optimize individualized treatment decisions and predict survival outcomes.