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Objectives: To explore the potential of CT quantitative parameters in differentiating adrenal lipid-poor adenoma (LPA) from nodular hyperplasia and evaluate diagnostic performance., Materials and Methods: Patients with LPA or nodular hyperplasia who underwent contrast-enhanced CT before adrenalectomy were analyzed retrospectively. The study included 128 patients (83 with LPA and 45 with nodular hyperplasia). Each lesion's unenhanced attenuation, portal-venous phase attenuation (CTp), and the portal-venous phase attenuation of the abdominal aorta were evaluated. We subsequently calculated absolute enhancement [a lesion's portal-venous phase attenuation minus unenhanced attenuation (in HUs)], relative enhancement (absolute enhancement divided by unenhanced attenuation), and the relative enhancement ratio [(absolute enhancement divided by abdominal aorta's portal-venous phase attenuation) ×100%]. Lesion number and size were recorded. Volume was assessed by ITK-snap software and the ratio of lesion volume to ipsilateral adrenal volume (volume ratio) was determined. Intergroup differences were analyzed using Student's t-test and chi-squared test. Logistic regression models were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC), sensitivity, and specificity. The model's performance was then compared against radiologists' subjective assessments, and the inter- and intra-reader agreement values among radiologists were calculated., Results: Portal-venous phase attenuation, volume ratio, and lesion number were independent predictors of LPA. The logistic regression model incorporating CTp, volume ratio, and lesion number achieved an AUC of 0.835, with a sensitivity of 73.5% and a specificity of 80.0%. The radiologists' diagnostic specificity and accuracy appeared to be inferior to the model. The inter-reader agreement among radiologists ranged from 0.082 to 0.535, and the intra-reader agreement of two radiologists were 0.734 and 0.583., Conclusion: The portal-venous phase CT demonstrated potential in distinguishing LPA from nodular hyperplasia. The diagnostic performance of the model integrating CTp, volume ratio, and lesion number outperformed radiologists in terms of variability and reproducibility., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To investigate whether preoperative CT features can be used to predict risk stratification of non-muscle invasive bladder cancer (NMIBC)., Methods: The 168 patients with pathologically confirmed NMIBC who underwent preoperative CT urography were retrospectively analyzed and were divided into training (n = 117) and testing (n = 51) sets. According to the European Association of Urology Guidelines, patients were classified into low-risk (n = 50), medium-risk (n = 23), and high-risk (n = 95) groups. A random over-sample was performed to handle the offset caused by the unbalanced groups. We measured some CT features that may help stratify which for modeling were determined using an F-test-based feature selection with a tenfold cross-validation procedure, and the Gaussian Naive Bayes model was trained on the entire training set. In the testing set, the performance of the model was evaluated., Results: The selected CT features were the maximum and the minimum diameter of the largest tumor, whether the largest tumor is located at the trigone, and tumor number. In the testing set, the model reached a macro- and micro- AUC of 0.783 and 0.745 with an accuracy of 0.529. As for the one-vs-rest problem, the model was most effective in identifying low-risk individuals, with an AUC, accuracy, sensitivity, and specificity of 0.870, 0.647, 1.000, and 0.438, respectively; the medium-risk group reached 0.814, 0.882, 0.250, and 0.936, respectively; the identification of the high-risk group was harder, going 0.665, 0.529, 0.250, and 0.870, respectively., Conclusion: It is feasible to predict the risk stratification of NMIBC using preoperative CT features., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To compare and analyse the diagnostic value of PI-RADS v2.1 when used with biparametric MRI (bpMRI) versus multiparametric MRI (mpMRI), DWI versus T2WI to detect peripheral-zone prostate cancer (pzPCa) and clinically significant peripheral-zone prostate cancer (cs-pzPCa)., Methods: The diagnostic efficiencies of mpMRI and bpMRI as well as DWI and T2WI in pzPCa and cs-pzPCa were compared using a PI-RADS score of ≥ 4 as the positive threshold and prostate biopsy and radical prostatectomy as the reference standards., Results: A total of 307 prostate cases were included in the study, including 142 in the non-pzPCa group, 165 in the pzPCa group, and 130 in the cs-pzPCa group. The AUCs of mpMRI and bpMRI were 0.717 and 0.733 (P = 0.317), respectively, for the diagnosis of pzPCa (sensitivities: 89.1% and 81.8%; specificities: 54.2% and 64.8%, both P < 0.001) and 0.594 and 0.602 (P = 0.756), respectively, for the diagnosis of cs-pzPCa (sensitivities: 93.1% and 86.2%, P = 0.004; specificities: 25.7% and 34.3%, P = 0.250). The AUCs of DWI and T2WI were 0.733 and 0.749 (P = 0.308), respectively, for the diagnosis of pzPCa (sensitivities: 81.8% and 84.2%; specificities: 64.8% and 66.2%, both P > 0.05) and 0.602 and 0.581 (P = 0.371), respectively, for the diagnosis of cs-pzPCa (sensitivities: 86.2% and 87.7%; specificities: 34.3% and 28.6%, both P > 0.05)., Conclusion: mpMRI and bpMRI as well as DWI and T2WI using PI-RADS v2.1 exhibited similar diagnostic efficiency in pzPCa and cs-pzPCa., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To predict the diagnosis of pelvic lipomatosis (PL) by assessing CT imaging parameters., Methods: Fifty clinically proven PL patients and 50 controls were included. Two radiologists recorded the types of bladder shapes and measured the following CT imaging parameters: (1) pelvic fat volume (PFV); (2) a set of morphological indices of bladder and rectosigmoid, including the ratio of craniocaudal to anterior-posterior length of bladder (CC/AP), angle between anterior and posterior wall (AAP), relative length of posterior urethra (rLPU), right and left angles between bladder and seminal vesicle (RABS and LABS), distance from verumontanum to anterior wall of rectum (DVR) and rectosigmoid morphological index (RMI). Secondary complications were also noted., Results: The results were evaluated by an unpaired t test, ROC analysis and logistic regression. All CT imaging parameters except RMI (p = 1.000) indicated a statistical difference between PL and controls (p < 0.01). The AUCs of these parameters were between 0.724 (for rLPU) and 0.993 (for AAP). The sensitivity and specificity were 94% and 98% for AAP and 92% and 94% for the combined model, respectively. 23 patients were pathologically diagnosed with cystitis glandular. The incidence of hydronephrosis in the PL group was higher than that in the control group (p < 0.01)., Conclusion: CT imaging parameters can predict the diagnosis of PL. The combined model of objective parameters including PFV, rLPU and DVR showed the best diagnostic performance for PL.

Purpose: To compare the diagnostic performance of PI-RADS version 2.1 (PI-RADS v2.1) and PI-RADS v2 for transition zone prostate cancer (TZPC), and analyse its performance for readers with different experience levels., Methods: Eighty-five patients with suspected prostate cancer who underwent biopsy after MRI scan between January and December 2017 were retrospectively enrolled. One junior radiologist (reader 1, 1 year of experience in using PI-RADS v2) and one senior radiologist (reader 2, 6 years of experience) independently reviewed and assigned a score for each lesion according to PI-RADS v2.1 and v2. The template-guided transperineal prostate biopsy was used for standard of reference. To compare the diagnostic performance of the two methods, the AUC was calculated. The sensitivity, specificity, and accuracy were calculated at predefined positive values (PI-RADS ≥ 3). The interreader agreement and frequency of prostate cancer for each PI-RADS category were also calculated., Results: Among the 85 patients, 27 had prostate cancers, and 25 were clinically significant prostate cancer (csPCa). The AUC values for diagnosing clinically significant prostate cancer significantly increased with PI-RADS v2.1 for reader 2 (0.766 vs. 0.902, P = 0.009). The specificity and accuracy for both readers also increased with PI-RADS v2.1 (specificity: reader 1, 41.7% vs. 78.3% and reader 2, 33.3% vs. 81.7%; accuracy: reader 1, 52.9% vs. 76.5% and reader 2, 48.2% vs. 83.5%, all P < 0.05). The interreader agreement was good for both versions. The percentage of prostate cancer decreased in lower PI-RADS categories (PI-RADS 2) and increased in higher PI-RADS categories (PI-RADS 3 ~ 4)., Conclusion: Compared with PI-RADS v2, PI-RADS v2.1 may improve radiologists' diagnostic performance for TZPC.