Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.

PURPOSE: To evaluate the ability of MRI radiomics to categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer (OEC) patients. METHOD: A total of 286 patients with pathologically proven adnexal tumor were retrospectively included in this study. We evaluated diagnostic performance of the signatures derived from MRI radiomics in differentiating (1) between benign adnexal tumors and malignancies and (2) between type I and type II OEC. The least absolute shrinkage and selection operator method was used for radiomics feature selection. Risk scores were calculated from the Lasso model and were used for survival analysis. RESULT: For the classification between benign and malignant masses, the MRI radiomics model achieved a high accuracy of 0.90 in the leave-one-out (LOO) cross-validation cohort and an accuracy of 0.87 in the independent validation cohort. For the classification between type I and type II subtypes, our method made a satisfactory classification in the LOO cross-validation cohort (accuracy = 0.93) and in the independent validation cohort (accuracy = 0.84). Low-high-high short-run high gray-level emphasis and low-low-high variance from coronal T2-weighted imaging (T2WI) and eccentricity from axial T1-weighted imaging (T1WI) images had the best performance in two classification tasks. The patients with higher risk scores were more likely to have poor prognosis (hazard ratio = 4.1694, p = 0.001). CONCLUSION: Our results suggest radiomics features extracted from MRI are highly correlated with OEC classification and prognosis of patients. MRI radiomics can provide survival estimations with high accuracy. KEY POINTS: • The MRI radiomics model could achieve a higher accuracy in discriminating benign ovarian diseases from malignancies. • Low-high-high short-run high gray-level emphasis, low-low-high variance from coronal T2WI, and eccentricity from axial T1WI had the best performance outcomes in various classification tasks. • The ovarian cancer patients with high-risk scores had poor prognosis.

Study on diagnostic value of quantitative parameters of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in prostate cancer.

OBJECTIVE: To investigate the diagnostic value of quantitative parameters of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in prostate cancer. METHODS: From January 2019 to June 2020, 96 patients with prostatic tumor admitted in the department of urological surgery of our hospital were selected as subjects. Magnetic resonance imaging data of 48 cases of benign prostatic hyperplasia and 48 cases of prostate cancer were retrospectively analyzed. The patients included in this study received conventional MRI and IVIM-DWI examinations. Quantitative parameters of IVIM-DWI including D value, D* value, apparent diffusion coefficient (ADC) value and f value in lesions of prostatic tumor were calculated through the double exponential model fitting algorithm. D value, D* value, ADC value and f value were compared between benign prostatic hyperplasia (BPH) group and prostate cancer group. Quantitative parameters of IVIM-DWI were also compared among patients from different Gleason scores groups. The correlation of quantitative parameters of IVIM-DWI with Gleason scores and PSA concentration was analyzed. Diagnostic efficiency of quantitative parameters of IVIM-DWI for prostate cancer was evaluated by ROC curve. RESULTS: Compared with those in BPH group, D value, ADC value and f value in prostate cancer group were significantly lower, but D* value was obviously higher. With the Gleason score increased, D value, ADC value and f value gradually decreased, while D* value gradually increased. The diagnostic efficiency of parameters ADC and D was higher among other parameters. D value, ADC value and f value of prostate cancer were negatively correlated with Gleason score and PSA concentration, respectively (all P<0.05), while D* value was positively correlated with Gleason score and PSA concentration. CONCLUSIONS: Quantitative parameters of IVIM-DWI could be used for the diagnosis and evaluation of prostate cancer, and quantitative parameters of IVIM-DWI were associated with Gleason score and PSA concentration.

Integration of quantitative diffusion kurtosis imaging and prostate specific antigen in differential diagnostic of prostate cancer.

ABSTRACT: This study aimed to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) and prostate-specific antigen (PSA) biomarkers in differentiating prostate cancer (PCa) and benign prostatic hyperplasia (BPH).A total of 43 cases of prostate diseases verified by pathology were enrolled in the present study. These cases were assigned to the BPH group (n = 20, 68.85±10.81 years old) and PCa group (n = 23, 74.13 ±â€Š7.37 years old). All patients underwent routine prostate magnetic resonance imaging and DKI examinations, and the mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA) values were calculated. Three serum indicators (PSA, free PSA [fPSA], and f/t PSA) were collected. We used univariate logistic regression to analyze the above quantitative parameters between the 2 groups, and the independent factors were further incorporated into the multivariate logistic regression model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic efficacy of the single indicator and combined model.The difference in PSA, f/t PSA, MK, and FA between PCa and BPH was statistically significant (P < .05). The AUC for the combined model (f/t PSA, MK, and FA) of 0.972 (95% confidence interval [CI]: 0.928, 1.000) was higher than the AUC of 0.902 (95% CI: 0.801, 1.000) for f/t PSA, 0.833 (95% CI: 0.707, 0.958) for MK, and 0.807 (95% CI: 0.679, 0.934) for FA.The MK and FA values for DKI and f/t PSA effectively identify PCa and BPH, compared to the PSA indicators. Combining DKI and PSA derivatives can further improve the diagnosis efficiency and might help in the clinical setting.