Background and Purpose: Differentiation between functioning and nonfunctioning pituitary adenomas/pituitary neuroendocrine tumors (PAs) is clinically relevant. The goal of this study was to determine the feasibility of using time‐dependent diffusion MRI (dMRI) for microstructural characterization of PAs. Methods: The study included 54 participants, 24 with functioning PA and 30 with nonfunctioning PA. Time‐dependent dMRI of the pituitary gland was performed using an inner field‐of‐view echo‐planar imaging based on 2‐dimensional‐selective radiofrequency excitations with oscillating gradient and pulsed gradient preparation (effective diffusion time: 7.1 and 36.3 ms) at b ‐values of 0 and 1000 seconds/mm ² . Each tumor had its apparent diffusion coefficients (ADCs) measured at two diffusion times (ADC _7.1 ms and ADC _36.3 ms ), its ADC change (cADC), and relative ADC change. The mean values of diffusion parameters were compared between functioning and nonfunctioning PAs. We compared the diffusion parameters of nonfunctioning PAs with those of each type of hormone‐producing PAs. The diagnostic performances of the diffusion parameters were assessed. Results: The cADC was significantly higher in functioning PAs than nonfunctioning PAs ( p = .0124). The receiver operating characteristic (ROC) curve analysis revealed that cADC (area under the ROC curve [AUC] = .677, p = .017) is effective in distinguishing between functioning and nonfunctioning PAs. The cADC was significantly higher in growth hormone (GH)‐producing PAs compared to nonfunctioning PAs ( p = .006). The ROC curve analysis indicated that cADC (AUC = .771, p < .001) effectively distinguishes between GH‐producing and nonfunctioning PAs. Conclusions: The cADC derived from time‐dependent dMRI could distinguish between functioning and nonfunctioning PAs, particularly those producing GH.

Background and Objectives: The causes and clinical outcomes of renal perfusion abnormalities occurring after para-aortic lymphadenectomy (PANDx) for gynecologic malignancies are unknown. We investigated the potential involvement of accessory renal artery (ARA) obstruction in their development by reassessing perioperative contrast-enhanced computed tomography (CECT). Materials and Methods: This retrospective study investigated a clinical database to identify urinary contrast defects using CECT in all patients who had undergone PANDx between January 2020 and December 2024. The perfusion defects in the kidney detected by CECT were extracted by a gynecologic oncologist and evaluated by a radiologist and urologist for suspected obstruction of ARAs. Results: Postoperative renal contrast defects were observed in 3.8% (6/157) of patients. Renal parenchymal fibrosis, cortical atrophy, and parenchymal thinning were observed as universal findings in all patients showing renal contrast defects. In five of the six cases, ARAs supplying the infarcted renal segments were identified on preoperative CECT, and arterial obstruction was confirmed on postoperative imaging. The remaining case was considered to be latent pyelonephritis. All five patients underwent laparotomy, and preoperative CECT failed to detect ARAs. The median resected para-aortic lymph node was 23 nodes (range: 15–33) in five patients, showing no statistically significant difference compared to patients without perfusion abnormalities (p = 0.19). Postoperative serum creatinine levels remained stable. Conclusions: ARA obstruction appears to be a risk factor for segmental renal infarction after para-aortic lymphadenectomy in gynecological malignancies; however, the clinical impact on urinary function may be limited. Awareness of this potential complication is essential for gynecologic oncologists performing PANDx.

Purpose: To evaluate MRI-defined parametrial venous invasion (mrPMVI) for detecting pathological parametrial venous invasion (pPMVI) and its associations with pretreatment lymph node metastasis (LNM) and survival outcomes in locally advanced cervical cancer. Materials and methods: This retrospective study included 42 patients with cervical cancer with parametrial invasion on MRI or histopathology, corresponding to International Federation of Gynecology and Obstetrics 2018 stage IIB or higher disease. Fifteen patients underwent surgery without preoperative therapy and were included in the pathological validation analysis. Image interpretation was primarily based on T2-weighted and diffusion-weighted imaging. Two radiologists independently assessed mrPMVI using a five-point scoring system. Scores ≥ 3 were predefined as mrPMVI-positive. Diagnostic performance for pPMVI and interobserver agreement were evaluated. Univariable logistic and Cox regression analyses were performed for pretreatment LNM and survival outcomes, respectively. Results: pPMVI was present in 10 of 15 patients. The five-point mrPMVI score showed an area under the receiver operating characteristic curve of 0.87 (95% confidence interval [CI], 0.60–0.98) for detecting pPMVI. The predefined threshold (≥ 3) yielded 30.0% sensitivity and 100% specificity. Interobserver agreement for the five-point mrPMVI score was substantial (linearly weighted κ = 0.75). mrPMVI positivity was associated with pretreatment LNM (odds ratio = 4.00, 95% CI: 1.05–15.26, p = 0.042). During a median follow-up of 24.0 months, 16 patients experienced disease progression and 10 died. mrPMVI positivity was associated with shorter progression-free survival (hazard ratio = 34.25, 95% CI: 4.35–269.91; p < 0.001) and overall survival (hazard ratio = 10.16; 95% CI: 1.28–80.61, p = 0.028). Conclusion: The mrPMVI score showed potential for identifying pPMVI, although the predefined threshold was highly specific but insensitive. mrPMVI positivity was associated with pretreatment LNM and survival outcomes, suggesting potential complementary value for risk characterization in locally advanced cervical cancer.

Objectives: The potential use of electron density (ED) and effective atomic number (Zeff) derived from dual-energy computed tomography (DECT) as novel quantitative imaging biomarkers for differentiating malignant brain tumors was investigated. Methods: Data pertaining to 136 patients with a pathological diagnosis of brain metastasis (BM), glioblastoma, and primary central nervous system lymphoma (PCNSL) were retrospectively reviewed. The 10th percentile, mean and 90th percentile values of conventional 120-kVp CT value (CTconv), ED, Zeff, and relative apparent diffusion coefficient derived from diffusion-weighted magnetic resonance imaging (rADC: ADC of lesion divided by ADC of normal-appearing white matter) within the contrast-enhanced tumor region were compared across the three groups. Furthermore, machine learning (ML)-based diagnostic models were developed to maximize diagnostic performance for each tumor classification using the indices of DECT parameters and rADC. Machine learning models were developed using the AutoGluon-Tabular framework with rigorous patient-level data splitting into training (60%), validation (20%), and independent test sets (20%). Results: The 10th percentile of Zeff was significantly higher in glioblastomas than in BMs (p = 0.02), and it was the only index with a significant difference between BMs and glioblastomas. In the comparisons including PCNSLs, all indices of CTconv, Zeff, and rADC exhibited significant differences (p < 0.001–0.02). DECT-based ML models exhibited high area under the receiver operating characteristic curves (AUC) for all pairwise differentiations (BMs vs. Glioblastomas: AUC = 0.83; BMs vs. PCNSLs: AUC = 0.91; Glioblastomas vs. PCNSLs: AUC = 0.82). Combined models of DECT and rADC demonstrated excellent diagnostic performance between BMs and PCNSLs (AUC = 1) and between Glioblastomas and PCNSLs (AUC = 0.93). Conclusion: This study suggested the potential of DECT-derived ED and Zeff as novel quantitative imaging biomarkers for differentiating malignant brain tumors.