An Overview of the Role of Multiparametric MRI in the Investigation of Testicular Tumors

Discriminating Malignant from Benign Testicular Masses Using Multiparametric Magnetic Resonance Imaging-A Prospective Single-Center Study

Objective: The objective of this study was to prospectively assess the extent to which magnetic resonance imaging (MRI) can differentiate malignant from benign lesions of the testis. Materials and Methods: All included patients underwent multiparametric testicular MRI, including diffusion-weighted imaging (DWI) and subtraction dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Subsequently, all patients underwent a histopathological examination via orchiectomy or testicular biopsy/partial resection. The Kolmogorov-Smirnov test, t-test, Mann-Whitney U test, Fisher's exact test, and logistic regression were applied for statistical analysis. Results: We included 48 male patients (median age 37.5 years [range 18-69]) with testicular tumors. The median tumor size on MRI was 2.0 cm for malignant tumors and 1.1 cm for benign tumors (p < 0.05). A statistically significant difference was observed for the type (type 0-III curve, p < 0.05) and pattern of enhancement (homogeneous, heterogeneous, or rim-like, p < 0.01) between malignant and benign tumors. The minimum apparent diffusion coefficient (ADC) value was 0.9 for benign tumors and 0.7 for malignant tumors (each ×103 mm2/s, p < 0.05), while the mean ADC was 0.05. The mean ADC value was significantly lower for malignant tumors; the mean ADC value was 1.1 for benign tumors and 0.9 for malignant tumors (each ×103 mm2/s, p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of multiparametric MRI for differentiating malignant from benign testicular lesions were 94.3%, 76.9%, 91.7%, and 83.3%, respectively. The surgical procedures performed included orchiectomy (n = 33; 71.7%) and partial testicular resection (n = 11; 23.9%). Histopathology (HP) revealed malignancy in 35 patients (72.9%), including 26 with seminomas and 9 with non-seminomatous germ cell tumors (NSGCTs). The HP was benign in 13 (27.1%) patients, including 5 with Leydig cell tumors. Conclusions: Malignant and benign tumors differ in MRI characteristics in terms of the type and pattern of enhancement and the extent of diffusion restriction, indicating that MRI can be an important imaging modality for the accurate diagnosis of testicular lesions.

Multimodal ultrasound diagnosis of epididymo-orchitis with secondary testicular infarction: A case report

We report a case of a 48-year-old man with testicular infarction caused by epididymo-orchitis (EO). Multimodal ultrasound showed extensive necrosis of the testis, and the patient underwent right orchiectomy. Postoperative pathology confirmed extensive necrosis of the testis. After 3 months of follow-up, the examination of scrotal ultrasound showed that the left testis and epididymis had no obvious abnormality.

Elastography of the Male Pelvic Region-Perspectives on Malignant Lesions

Ultrasound elastography is widely used to assess tissue stiffness for lesion characterization, including differentiation between benign and malignant lesions. This study focuses on the use of elastography in the male pelvis, including the prostate, testicles, and rectum, by comparing elastography types (shear wave and strain). This article provides a summary of the existing literature on the use of elastography in the male pelvic region and outlines the clinical perspective. Ultrasound elastography is a good technique for evaluating and monitoring lesions in the male pelvic region.

Differentiation of testicular seminomas from nonseminomas based on multiphase CT radiomics combined with machine learning: A multicenter study

BACKGROUND

Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose.

METHODS

In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility.

RESULTS

Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas.

CONCLUSION

The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.

Apparent diffusion coefficient histogram in the differentiation of benign and malignant testicular tumors

Purpose: This retrospective study assessed the value of histogram parameters of the apparent diffusion coefficient (ADC) map (HA) in differentiating between benign and malignant testicular tumors. We compared the diagnostic performance of two different volume-of-interest (VOI) placement methods: VOI 1, the entire tumor; VOI 2, the tumor excluding its cystic, calcified, hemorrhagic, and necrotic portions. Materials and methods: We retrospectively evaluated 45 patients with testicular tumors examined with scrotal contrast-enhanced magnetic resonance imaging. These patients underwent surgery with the pathological result of seven benign and 39 malignant tumors. We calculated the HA parameters, including mean, median, maximum, minimum, kurtosis, skewness, entropy, standard deviation (SD), mean of positive pixels, and uniformity of positive pixels by the two different VOI segmentation methods. We compared these parameters using the chi-square test, Mann-Whitney U test, and area under the receiver operating characteristic curve (AUC) to determine their optimal cut-off, sensitivity (Se), and specificity (Sp). Result: This study included 45 patients with 46 testicular lesions (seven benign and 39 malignant tumors), one of which had bilateral testicular seminoma. With the VOI 1 method, benign lesions had significantly lower maximum ADC (p = 0.002), ADC skewness (p = 0.017), and ADC variance (p = 0.000) than malignant lesions. In contrast, their minimum ADC was significantly higher ADC (p = 0.000). With the VOI 2 method, the benign lesions had significantly higher ADC SD (p = 0.048) and maximum ADC (p = 0.015) than malignant lesions. In contrast, their minimum ADC was significantly lower (p = 0.000). With the VOI 1 method, maximum ADC, ADC variance, and ADC skewness performed well in differentiating benign and malignant testicular lesions with cut-offs (Se, Sp, AUC) of 1846.000 (74.4%, 100%, 0.883), 39198.387 (79.5%, 85.7%, 0.868), and 0.893 (48.7%, 100%, 0.758). Conclusion: The HA parameters showed value in differentiating benign and malignant testicular neoplasms. The entire tumor VOI placement method was preferable to the VOI placement method excluding cystic, calcified, hemorrhagic, and necrotic portions in measuring HA parameters. Using this VOI segmentation, maximum ADC performed best in discriminating benign and malignant testicular lesions, followed by ADC variance and skewness.