Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.

Background: Testicular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements. Purpose: Based on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV. Materials and methods: The study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model. Results: The deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV. Conclusion: The MRI-based deep learning model is an accurate and reliable tool for measuring TV.

Noninvasive Prediction of Sperm Retrieval Using Diffusion Tensor Imaging in Patients with Nonobstructive Azoospermia.

Microdissection testicular sperm extraction (mTESE) is the first-line treatment plan for nonobstructive azoospermia (NOA). However, studies reported that the overall sperm retrieval rate (SRR) was 43% to 63% among men with NOA, implying that nearly half of the patients fail sperm retrieval. This study aimed to evaluate the diagnostic performance of parameters derived from diffusion tensor imaging (DTI) in predicting SRR in patients with NOA. Seventy patients diagnosed with NOA were enrolled and classified into two groups based on the outcome of sperm retrieval during mTESE: success (29 patients) and failure (41 patients). Scrotal magnetic resonance imaging was performed, and the DTI parameters, including mean diffusivity and fractional anisotropy, were analyzed between groups. The results showed that there was a significant difference in mean diffusivity values between the two groups, and the area under the curve for mean diffusivity was calculated as 0.865, with a sensitivity of 72.2% and a specificity of 97.5%. No statistically significant difference was observed in fractional anisotropy values and sex hormone levels between the two groups. This study demonstrated that the mean diffusivity value might serve as a useful noninvasive imaging marker for predicting the SRR of NOA patients undergoing mTESE.

Contrasts Between Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MR in Diagnosing Malignancies of Breast Nonmass Enhancement Lesions Based on Morphologic Assessment.

BACKGROUND: Nonmass enhancement (NME) breast lesions are considered to be the leading cause of unnecessary biopsies. Diffusion-weighted imaging (DWI) or dynamic contrast-enhanced (DCE) sequences are typically used to differentiate between benign and malignant NMEs. It is important to know which one is more effective and reliable. PURPOSE: To compare the diagnostic performance of DCE curves and DWI in discriminating benign and malignant NME lesions on the basis of morphologic characteristics assessment on contrast-enhanced (CE)-MRI images. STUDY TYPE: Retrospective. SUBJECTS: A total of 180 patients with 184 lesions in the training cohort and 75 patients with 77 lesions in the validation cohort with pathological results. FIELD STRENGTH/SEQUENCE: A 3.0 T/multi-b-value DWI (b values = 0, 50, 1000, and 2000 sec/mm2 ) and time-resolved angiography with stochastic trajectories and volume-interpolated breath-hold examination (TWIST-VIBE) sequence. ASSESSMENT: In the training cohort, a diagnostic model for morphology based on the distribution and internal enhancement characteristics was first constructed. The apparent diffusion coefficient (ADC) model (ADC + morphology) and the time-intensity curves (TIC) model (TIC + morphology) were then established using binary logistic regression with pathological results as the reference standard. Both models were compared for sensitivity, specificity, and area under the curve (AUC) in the training and the validation cohort. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve analysis and two-sample t-tests/Mann-Whitney U-test/Chi-square test were performed. P < 0.05 was considered statistically significant. RESULTS: For the TIC/ADC model in the training cohort, sensitivities were 0.924/0.814, specificities were 0.615/0.615, and AUCs were 0.811 (95%, 0.727, 0.894)/0.769 (95%, 0.681, 0.856). The AUC of the TIC-ADC combined model was significantly higher than ADC model alone, while comparable with the TIC model (P = 0.494). In the validation cohort, the AUCs of TIC/ADC model were 0.799/0.635. DATA CONCLUSION: Based on the morphologic analyses, the performance of the TIC model was found to be superior than the ADC model for differentiating between benign and malignant NME lesions. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

A glycosylation signature for predicting the progression and immunotherapeutic response of prostate cancer.

BACKGROUND: Glycosylation has been proposed as a new cancer hallmark. However, focusing on specific glycans or glycoproteins may lose much data relevant to glycosylation alterations. The present study aimed to first comprehensively investigate the expression and mutation profiles of glycosylation-related genes (GRgenes) in prostate cancer (PCa) and then develop a glycosylation signature and explore its role in predicting the progression and immunotherapeutic response of PCa. METHODS: Based on The Cancer Genome Atlas database, we comprehensively screened potential prognostic GRgenes and analyzed their expression and mutation profiles in PCa. Through consensus clustering analysis, the study cohort was classified to investigate the effect of glycosylation patterns on the prognosis of PCa. Next, we developed a glycosylation signature (i.e., the glycosylation score [Gly_score]) using the differentially expressed genes between glycosylation pattern groups and evaluated its role in predicting the progression and immunotherapeutic response of PCa. RESULTS: We identified two distinct glycosylation patterns in PCa and found that GRgene expression patterns rather than mutations are associated with the prognosis of PCa. The high Gly_score group had significantly shorter progression-free survival, lower PD-L1 levels, less infiltration of immune cells and lower immunophenoscores than the low Gly_score group. When the patients were grouped according to both the Gly_score and PD-L1 level, patients with a combination of low Gly_score and low PD-L1 expression had the best survival outcomes. CONCLUSIONS: In the present study, for the first time, we developed a glycosylation signature and demonstrated that the proposed glycosylation signature is a promising tool for predicting the prognosis and immunotherapeutic response of PCa.

Prevention of alloimmune rejection using XBP1-deleted bone marrow-derived dendritic cells in heart transplantation.

BACKGROUND: Genetically modified dendritic cells (DCs) modulate the alloimmunity of T lymphocytes by regulating antigen presentation. METHODS: We generated mice with specific deletion of the X-box-binding protein 1 (XBP1) allele in bone marrow cells and cultured bone marrow-derived DCs (Xbp1-/- BMDCs) from these animals. We then tested the phenotype of Xbp1-/- BMDCs, evaluated their capability to activate allogeneic T cells and investigated their mechanistic actions. We developed a mouse model of allogeneic heart transplantation in which recipients received PBS, Xbp1-/- BMDCs, a suboptimal dose of cyclosporine A (CsA), or Xbp1-/- BMDCs combined with a suboptimal dose of CsA to evaluate the effects of Xbp1-/- BMDC transfusion on alloimmunity and on the survival of heart allografts. RESULTS: The deletion of XBP1 in BMDCs exploited the IRE1-dependent decay of TAPBP mRNA to reduce the expression of MHC-I on the cell surface, altered the capability of BMDCs to activate CD8+ T cells, and ultimately suppressed CD8+ T-cell-mediated allogeneic rejection. The adoptive transfer of Xbp1-/- BMDCs inhibited CD8+ T-cell-mediated rejection. In addition, XBP1-deficient BMDCs were weak stimulators of allogeneic CD4+ T cells despite expressing high levels of MHC-II and costimulatory molecules on their cell surface. Moreover, the adoptive transfer of Xbp1-/- BMDCs inhibited the production of circulating donor-specific IgG. The combination of Xbp1-/- BMDCs and CsA treatment significantly prolonged the survival of allografts compared to CsA alone. CONCLUSIONS: The deletion of XBP1 induces immunosuppressive BMDCs, and treatment with these immunosuppressive BMDCs prevents alloimmune rejection and improves the outcomes of heart transplantation. This finding provides a promising therapeutic target in combating transplant rejection and expands knowledge of inducing therapeutic DCs.

A potential biomarker based on clinical-radiomics nomogram for predicting survival and adjuvant chemotherapy benefit in resected node-negative, early-stage lung adenocarcinoma.

BACKGROUND: We aimed to construct a clinical-radiomics nomogram to predict disease-free survival (DFS) and the added survival benefit of adjuvant chemotherapy (ACT) for node-negative, early-stage (I-II) lung adenocarcinoma (ADC). METHODS: In this retrospective study including 310 patients from two independent cohorts, the CT-derived radiomics features were selected by least absolute shrinkage and selection operator Cox regression to generate a radiomics signature associated with DFS. The radiomics signature was incorporated to construct a clinical-radiomics nomogram along with the independent clinical risk predictors. The model performance was evaluated with reference to discrimination quantified by Harrell concordance index (C-index), integrated discrimination improvement (IDI) and net reclassification index (NRI), calibration and clinical utility. The risk score (RS) for clinical-radiomics nomogram was calculated. The association between ACT and survival benefit was assessed in high and low RS subgroup. RESULTS: The clinical-radiomics nomogram achieved the highest C-index of 0.822 [95% confidence interval (CI): 0.769, 0.876] in training cohort and 0.802 (95% CI: 0.716, 0.888) in validation cohort. The incorporation of radiomics signature into clinical-radiomics nomogram showed an incremental benefit over clinical nomogram according to the improved NRI and IDI. The calibration curves and decision curve analysis further verified the clinical utility of clinical-radiomics nomogram. Further, patients with high RS based on clinical-radiomics nomogram were more prone to benefit from ACT. CONCLUSIONS: The clinical-radiomics nomogram approach can feasibly conduct risk prediction and have potential to identify the beneficiaries of ACT among patients with node-negative, early-stage ADC, which might serve as a helpful tool in informing therapeutic decision-making.

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values.

PURPOSE: To develop a machine-learning-based radiomics signature of ADC for discriminating between benign and malignant testicular masses and compare its classification performance with that of minimum and mean ADC. METHODS: A total of ninety-seven patients with 101 histopathologically confirmed testicular masses (70 malignancies, 31 benignities) were evaluated in this retrospective study. Eight hundred fifty-one radiomics features were extracted from the preoperative ADC map of each lesion. The mean and minimum ADC values are part of the radiomics features. Thirty lesions were randomly selected to estimate the reliability of the features. The redundant features were eliminated using univariate analysis (independent t test and Mann-Whitney U test, where appropriate) and Spearman's rank correlation. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection and radiomics signature generation. The classification performance of the radiomics signature and minimum and mean ADC values were evaluated by receiver operating characteristic (ROC) curve analysis and compared by DeLong's test. RESULTS: The whole lesion-based mean ADC showed no difference between benign and malignant testicular masses (P = 0.070, training cohort; P = 0.418, validation cohort). Compared with the minimum ADC, the ADC-based radiomics signature yielded a higher area under the curve (AUC) in both the training (AUC: 0.904, 95% confidence interval [CI]: 0.832-0.975) and validation cohorts (AUC: 0.868, 95% CI: 0.728-1.00). CONCLUSIONS: Conventional mean ADC values are not always helpful in discriminating between testicular benignities and malignancies. The minimum ADC and radiomics signature might be better alternatives, with the radiomics signature performing better than the minimum ADC.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset.

BACKGROUND: The aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge. METHODS: A total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected. The severity of the survivors' dyspnea was determined by follow-up questionnaire. The evolution of the CT findings from the peak period to discharge and throughout follow-up and the abilities of CT findings and clinical parameters to predict survival with and without dyspnea were analyzed. RESULTS: Ninety-one COVID-19 survivors still had dyspnea at follow-up. The age, comorbidity score, duration of hospital stays, receipt of hormone administration, receipt of immunoglobulin injections, intensive care unit (ICU) admission, receipt of mechanical ventilation, laboratory parameters, clinical classifications and parameters associated with lesion volume of the survivors with dyspnea were significantly different from those of survivors without dyspnea. Among the clinical parameters and CT parameters used to identify dyspnea, parameters associated with lesion volume showed the largest area under the curve (AUC) values, with lesion volume at discharge showing the largest AUC (0.820). Lesion volume decreased gradually from the peak period to discharge and through follow-up, with a notable decrease observed after discharge. Absorption of lesions continued 6 months after discharge. CONCLUSIONS: Among the clinical parameters and subjective CT findings, CT findings associated with lesion volume were the best predictors of post-discharge dyspnea in COVID-19 survivors.

Value of Intra-Perinodular Textural Transition Features from MRI in Distinguishing Between Benign and Malignant Testicular Lesions.

PURPOSE: To compare the performance of histogram analysis and intra-perinodular textural transition (Ipris) for distinguishing between benign and malignant testicular lesions. PATIENTS AND METHODS: This retrospective study included 76 patients with 80 pathologically confirmed testicular lesions (55 malignant, 25 benign). All patients underwent preoperative T2-weighted imaging (T2WI) on a 3.0T MR scanner. All testicular lesions were manually segmented on axial T2WI, and histogram and Ipris features were extracted. Thirty enrolled patients were randomly selected to estimate the robustness of the features. We used intraclass correlation coefficients (ICCs) to evaluate intra- and interobserver agreement of features, independent t-test or Mann-Whitney U-test to compare features between benign and malignant lesions, and receiver operating characteristic curve analysis to evaluate the diagnostic performance of features. RESULTS: Eighteen histogram features and forty-eight Ipris features were extracted from T2WI of each lesion. Most (60/66) histogram and Ipris features had good robustness (ICC of both intra- and interobserver variabilities >0.6). Three histogram and nine Ipris features were significantly different between the benign and malignant groups. The area under the curve values for Energy, TotalEnergy, and Ipris_shell1_id_std were 0.807, 0.808, and 0.708, respectively, which were relatively higher than those of other features. CONCLUSION: Ipris features may be useful for identifying benign and malignant testicular tumors but have no significant advantage over conventional histogram features.

Noninvasive Differentiation of Obstructive Azoospermia and Nonobstructive Azoospermia Using Multimodel Diffusion Weighted Imaging.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of parameters derived from multimodel diffusion weighted imaging (monoexponential, stretched-exponential diffusion weighted imaging and diffusion kurtosis imaging [DKI]) from noninvasive magnetic resonance imaging in distinguishing obstructive azoospermia (OA) from nonobstructive azoospermia (NOA). MATERIALS AND METHODS: Forty-six patients with azoospermia were prospectively enrolled and classified into two groups (21 OA patients and 25 NOA patients). The multimodel parameters of diffusion-weighted imaging (DWI; apparent diffusion coefficient [ADC], distributed diffusion coefficient [DDC], diffusion heterogeneity [α], diffusion kurtosis diffusivity [Dapp], and diffusion kurtosis coefficient [Kapp]) were derived. The diagnostic performance of these parameters for the differentiation of OA and NOA patients were evaluated using receiver operating characteristic analysis. The area under the curve (AUC) was calculated to evaluate the diagnostic accuracy of each parameter. RESULTS: All the parameters (ADC, α, DDC, Dapp, and Kapp) values were significantly different between OA and NOA (P < 0.001 for all). For the differentiation of OA from NOA, Kapp showed the highest AUC value (0.965), followed by DDC (0.946), Dapp (0.933), ADC (0.922), and α (0.887). Kapp had a significantly higher AUC than the conventional ADC (P < 0.05). CONCLUSION: Parameters derived from multimodels of DWI have the potential for the noninvasive differentiation of OA and NOA. The Kapp value derived from the DKI model might serve as a useful imaging marker for the differentiation of azoospermia.

Discrimination between benign and malignant testicular lesions using volumetric apparent diffusion coefficient histogram analysis.

PURPOSE: This study aimed to evaluate the role of volumetric apparent diffusion coefficient (ADC) histogram analysis in discriminating between benign and malignant testicular masses. METHODS: In this retrospective study, fifty-nine patients with 61 pathologically confirmed testicular masses were consecutively enrolled, including 18 benign lesions and 43 malignant lesions. All patients conducted preoperative magnetic resonance imaging (MRI) with diffusion-weighted imaging. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. Comparisons were conducted by an independent t-test or Mann-Whitney U test, where appropriate. The classification performance of the parameters that showed significant differences between benign and malignant testicular disease were evaluated via receiver operating characteristic (ROC) curve analysis. RESULTS: Among the 18 histogram parameters we extracted, the energy, total energy, and range of ADC of testicular malignancies were all significantly increased compared with those of benignities. The minimum ADC and 10th percentile ADC of testicular malignancies were both significantly reduced compared with those of benignities. The minimum ADC value achieved the highest diagnostic performance in distinguishing between testicular benignities and malignancies, with an area under the ROC curve (AUC) of 0.822, sensitivity of 81.40 %, and specificity of 77.78 %. CONCLUSIONS: Volumetric ADC histogram analysis might be a useful tool to preoperatively discriminate between benign and malignant testicular masses.

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps.

OBJECTIVE: To evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2). MATERIALS AND METHODS: Fifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann-Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed. RESULTS: Six texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750-0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732-0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort. CONCLUSION: A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma.

Objective: To evaluate the performance of a T2-weighted image (T2WI)-based radiomics signature for differentiating between seminomas and nonseminomas. Materials and Methods: In this retrospective study, 39 patients with testicular germ-cell tumors (TGCTs) confirmed by radical orchiectomy were enrolled, including 19 cases of seminomas and 20 cases of nonseminomas. All patients underwent 3T magnetic resonance imaging (MRI) before radical orchiectomy. Eight hundred fifty-one radiomics features were extracted from the T2WI of each patient. Intra- and interclass correlation coefficients were used to select the features with excellent stability and repeatability. Then, we used the minimum-redundancy maximum-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms for feature selection and radiomics signature development. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of the radiomics signature. Results: Five features were selected to build the radiomics signature. The radiomics signature was significantly different between the seminomas and nonseminomas (p < 0.01). The area under the curve (AUC), sensitivity, and specificity of the radiomics signature for discriminating between seminomas and nonseminomas were 0.979 (95% CI: 0.873-1.000), 90.00 (95% CI: 68.3-98.8), and 100.00 (95% CI: 82.4-100.0), respectively. Conclusion: The T2WI-based radiomics signature has the potential to non-invasively discriminate between seminomas and nonseminomas.

Validation of SE-EPI-based T2 mapping for characterization of prostate cancer: a new method compared with the traditional CPMG method.

PURPOSE: We aim to compare the results of spin echo-echo planar imaging (SE-EPI)-based T2 mapping with those of the conventional Carr-Purcell-Meiboom-Gill (CPMG) method and to investigate the potential validity of SE-EPI-T2 mapping for the characterization of prostate cancer (PCa). METHODS: Our retrospective study included 42 PCa patients and 42 noncancer patients who underwent 3.0T MRI with b values ranging from 0 to 2000 s/mm2 and echo times (TEs) ranging from 32 to 100 ms before biopsies. Bland-Altman analysis was used to compare the agreement between the two methods. The correlations between CPMG-T2 values and SE-EPI-T2 values at different b values were determined by Spearman's rho analysis or Pearson analysis. The Mann-Whitney U test and two-sample t tests were used to analyze the differences between the cancerous and noncancerous groups. RESULTS: Substantial agreement regarding the measurements was observed between the two methods. The average correlation between the CPMG-T2 values and SE-EPI-T2 values was moderate and positive, and the best correlations were found at b = 200 s/mm2 in the noncancer group (r = 0.557, P = 0.000) and at b = 100 s/mm2 in the cancer group (r = 0.537, P = 0.000). In addition, statistically significant differences were found between the noncancer and cancer groups in T2 values and ADC values (diff TEs) (P = 0.000). CONCLUSIONS: Substantial agreement in the measurements was found between the SE-EPI method and CPMG method. SE-EPI-based T2 mapping has potential clinical value for the prostate and can be considered an alternative to the traditional CPMG-T2 mapping method.