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Abstract MRI-guided targeted biopsy (MRGB) was recommended as part of biopsy paradigm of prostate cancers by current guidelines. This study aimed to analyze the diagnostic efficacy of MRGB and systemic biopsy (SB), and to compare diagnostic capabilities within subgroups of MRGB: MRI-cognitive biopsy (MRCB) and MRI-fusion biopsy (MRFB). We retrospectively enrolled patients who underwent MRGB for suspicious malignant lesion(s) identified on MRI in a single tertiary center, sample size was 74 patients. An mpMRI was performed prior to biopsy and reviewed by an experienced radiologist specialized in prostate cancer. Per-person results of MRGB and each concomitant SB were analyzed as independent biopsies for its positive biopsy rate and positive core percentage. Per-lesion results of MRFB and MRCB were compared for the detection rate. Variables of interest were analyzed with t-test, chi-squared test, and logistic regression analysis. Statistical analyses were performed with IBM Statistical Product and Service Solutions (SPSS), Version 23 (IBM, Armonk, New York). Total of 74 patients fulfilled the inclusion criteria and were enrolled. MRFB had higher PCa detection rate comparing to both MRCB and SB (56.1%, 30.3%, and 33.9% respectively, p value = 0.036); clinically significant prostate cancer (csPCa) detection rate was also significantly higher in MRFB group (43.9%, 24.2%, and 16.9% in each group respectively, p value = 0.011). In per-lesion analysis, MRCB and MRFB had no significant difference in PCa and csPCa detection rate (41.0% vs. 26.2% and 29.5% vs. 16.7% respectively, p value = 0.090 and 0.103). In the lesion ≦ 1.3 cm group, MRFB could achieve higher PCa detection rate, comparing to MRCB (36.4% vs. 14.3%, p value = 0.047); there were also higher positive rates for PCa and csPCa per biopsied cores (22.1% vs. 6.8% and 15.6% vs. 2.7%, p value = 0.029 and 0.028, respectively). Further logistic regression of multi-variate analysis in subgroup of lesion ≦ 1.3 cm revealed that PIRADS score and biopsy method were significant predictors of positive biopsy result for PCa (p value = 0.045 and 0.026, respectively) and for csPCa (p value = 0.043 and 0.025, respectively). In patients receiving trans-perineal prostate biopsy, MRFB had higher cancer detection rate than MRCB and SB. In per lesion comparison, MRFB and MRCB had similar diagnostic accuracy. However, in lesions with diameter less than 1.3 cm, MRFB can provided better diagnose value for PCa and csPCa than MRCB.

Background/Objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients. Methods: In this retrospective study, a deep learning model is developed, based on aortic computed tomography angiography (CTA) scans of 498 patients using training, validation and test sets of 398, 50 and 50 patients, respectively. An independent test set of 316 patients is used to validate and evaluate its performance. Results: Our model comprises two components. The first one is an objection detection model, which can identify the aorta from CTA. The second one is a dissection classification model, which can automatically detect the presence of aortic dissection and determine its type based on Stanford classification. Overall, the sensitivity and specificity for Type A AD were 0.969 and 0.982, for Type B AD were 0.946 and 0.996 and for normal cases were 0.988 and 1.000, respectively. The average processing time per CTA scan was 7.9 ± 2.8 s. (mean ± standard deviation). Conclusions: This deep learning automatic model can accurately and quickly detect type A AD patients, and could serve as an imaging triage in an emergency setting and facilitate early intervention and surgery to decrease the mortality rates of type A AD patients.

Abstract Background To investigate the value of [18F]FDG-PET/MRI in predicting treatment response and survival in patients with primary M0 esophageal squamous cell carcinoma. Methods Patients with esophageal squamous cell carcinoma received [18F]FDG-PET/MRI at baseline and during neoadjuvant or definitive chemoradiotherapy. The treatment response was classified according to the Response Evaluation Criteria for Solid Tumors 1.1. We used Kaplan-Meier and Cox regression analyses to assess the association between PET/MRI parameters and overall survival (OS) or progression-free survival (PFS). Results We included 40 M0 patients in the final analysis. The volume transfer constant (K trans ) from baseline PET/MRI (area under the curve (AUC) = 0.688, P = 0.034) and total lesion glycolysis (TLG) from baseline PET/MRI (AUC = 0.723, P = 0.006) or interim PET/MRI (AUC = 0.853, P

Background: The International Society of Urological Pathology (ISUP) grade and positive surgical margins (PSMs) after radical prostatectomy (RP) may reflect the prognosis of prostate cancer (PCa) patients. This study aimed to investigate whether DCE-MRI parameters (i.e., Ktrans, kep, and IAUC) could predict ISUP grade and PSMs after RP. Method: Forty-five PCa patients underwent preoperative DCE-MRI. The clinical characteristics and DCE-MRI parameters of the 45 patients were compared between the low- and high-risk (i.e., ISUP grades III-V) groups and between patients with or without PSMs after RP. Multivariate logistic regression analysis was used to identify the significant predictors of placement in the high-risk group and PSMs. Results: The DCE parameter Ktrans-max was significantly higher in the high-risk group than in the low-risk group (p = 0.028) and was also a significant predictor of placement in the high-risk group (odds ratio [OR] = 1.032, 95% confidence interval [CI] = 1.005–1.060, p = 0.021). Patients with PSMs had significantly higher prostate-specific antigen (PSA) titers, positive biopsy core percentages, Ktrans-max, kep-median, and kep-max than others (all p < 0.05). Of these, positive biopsy core percentage (OR = 1.035, 95% CI = 1.003–1.068, p = 0.032) and kep-max (OR = 1.078, 95% CI = 1.012–1.148, p = 0.020) were significant predictors of PSMs. Conclusion: Preoperative DCE-MRI parameters, specifically Ktrans-max and kep-max, could potentially serve as preoperative imaging biomarkers for postoperative PCa prognosis based on their predictability of PCa risk group and PSM on RP, respectively.

PurposeTo elucidate the usefulness of intravoxel incoherent motion (IVIM)/apparent diffusion coefficient (ADC) parameters in preoperative risk stratification using International Society of Urological Pathology (ISUP) grades.Materials and MethodsForty-five prostate cancer (PCa) patients undergoing radical prostatectomy (RP) after prostate multiparametric magnetic resonance imaging (mpMRI) were included. The ISUP grades were categorized into low-risk (I-II) and high-risk (III-V) groups, and the concordance between the preoperative and postoperative grades was analyzed. The largest region of interest (ROI) of the dominant tumor on each IVIM/ADC image was delineated to obtain its histogram values (i.e., minimum, mean, and kurtosis) of diffusivity (D), pseudodiffusivity (D*), perfusion fraction (PF), and ADC. Multivariable logistic regression analysis of the IVIM/ADC parameters without and with preoperative ISUP grades were performed to identify predictors for the postoperative high-risk group.ResultsThirty-two (71.1%) of 45 patients had concordant preoperative and postoperative ISUP grades. Dmean, D*kurtosis, PFkurtosis, ADCmin, and ADCmean were significantly associated with the postoperative ISUP risk group (all p < 0.05). Dmean and D*kurtosis (model I, both p < 0.05) could predict the postoperative ISUP high-risk group with an area under the curve (AUC) of 0.842 and a 95% confidence interval (CI) of 0.726–0.958. The addition of D*kurtosis to the preoperative ISUP grade (model II) may enhance prediction performance, with an AUC of 0.907 (95% CI 0.822–0.992).ConclusionsThe postoperative ISUP risk group could be predicted by Dmean and D*kurtosis from mpMRI, especially D*kurtosis. Obtaining the biexponential IVIM parameters is important for better risk stratification for PCa.