Japanese Journal of Radiology最新文献

Purpose: The purpose is to evaluate the utility of whole-lesion and whole-prostate gland histogram and texture analysis based on biparametric MRI (bp-MRI) for differentiating clinically significant prostate cancer (csPCa) from non-clinically significant prostate cancer (ncsPCa). We further compared the diagnostic performance of these quantitative features with PI-RADS assessment, clinical parameters, and combined models.

Materials and methods: This retrospective study enrolled 337 patients (primary cohort, 260; validation cohort, 77) with pathologically proven prostate lesions. All patients underwent preoperative prostate bp-MRI [T2-weighted imaging and apparent diffusion coefficient (ADC) maps]. Histogram and texture features were extracted from both the whole lesion and the whole-prostate gland. Diagnostic models were constructed using multivariate logistic regression, incorporating PI-RADS scores, clinical parameters, and quantitative imaging features. Their performance was evaluated using the area under the receiver operating characteristic curve (AUC) and validated on an internal cohort.

Results: Multiple histogram and texture parameters from both whole-lesion and whole-prostate analyses significantly differed between csPCa and ncsPCa groups (p < 0.05), with ADC-derived features generally outperforming T2WI-derived ones. The combined model integrating texture features, clinical parameters, and PI-RADS (Texture&Clinics&PI-RADS) demonstrated the highest diagnostic performance for both whole-lesion analysis (AUCs: 0.938 for peripheral-zone or transitional-zone (PZ + TZ), 0.894 for peripheral-zone (PZ), 0.971 for transitional-zone (TZ) lesions) and whole-prostate analysis (AUCs: 0.926 for PZ + TZ, 0.804 for PZ, 0.981 for TZ lesions) in the primary cohort. This superior performance was consistently replicated in the validation cohort. Notably, no significant difference in diagnostic efficacy was found between whole-lesion and whole-prostate analyses for TZ lesions.

Conclusion: Both whole-lesion and whole-prostate histogram and texture analysis based on bp-MRI are promising non-invasive tools for identifying csPCa. The combination of texture features, clinical parameters, and PI-RADS scores achieved the best diagnostic performance. These findings indicate that whole-lesion and whole-prostate histogram and texture analyses may improve the detection of csPCa above conventional PI-RADS assessment.

Background: Accurate prediction of metachronous liver metastasis (MLM) within the 24 months remains a clinical challenge in rectal cancer. While radiomics offers noninvasive insights into tumor characteristics, few studies have investigated multi-sequence MRI-based habitat radiomics with interpretable modeling strategies.

Methods: This retrospective study enrolled 391 patients with pathologically confirmed rectal cancer. K-means clustering was applied to pretreatment T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MRI to generate tumor subregions. Radiomic features were extracted from both sequences, and clinical variables were also included. Support vector machine (SVM) classifiers were used to construct radiomics, habitat, and combined models. Model performance was assessed using area under the ROC curve (AUC) and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to interpret the contribution of individual features.

Results: The habitat model demonstrated superior predictive performance compared to conventional radiomics, achieving AUCs of 0.875 in the training cohort, 0.829 in the internal validation cohort, and 0.810 in the external test cohort. The combined model, incorporating clinical variables and habitat features, achieved the highest performance in the validation cohort (AUC = 0.870) and external test cohort (AUC = 0.862). SHAP analysis revealed complementary contributions from T1WI and T2WI features, highlighting the intratumoral heterogeneity interpretability of the multi-sequence habitat approach.

Conclusion: Multi-sequence MRI-based habitat radiomics demonstrated strong performance in predicting MLM, and the integration with clinical variables further improved accuracy, providing a practical tool for individualized risk assessment and treatment planning.

Purpose: To evaluate the image quality of pediatric portable chest radiographs processed using a deep learning-based noise reduction (NR) algorithm implemented in clinical radiography systems, which is designed to reduce image noise without altering radiation dose, both alone and with edge enhancement.

Materials and methods: This retrospective visual grading analysis included 101 pediatric patients (median age: 33 days; median weight: 2844 g) who underwent portable chest radiography. Each image was processed using four techniques: (1) standard (no processing), (2) edge enhancement only, (3) NR only, and (4) NR with edge enhancement. Image quality was assessed using five criteria: visualization of proximal bronchi, small peripheral airways, vertebrae, image noise, and overall image quality. In an anonymous, randomized review, two pediatric radiologists rated each criterion using a 5-point Likert scale. Statistical comparisons were conducted between processing methods.

Results: Images processed with NR and edge enhancement (NR + /Filter +) achieved the highest mean scores across all criteria. Structural visibility-particularly of small peripheral airways, proximal bronchi, and vertebrae-showed significant improvement with edge enhancement (p < 0.0001). No significant difference in image noise was observed between NR-only and NR + /Filter + groups (p = 0.482).

Conclusion: AI-based noise reduction significantly improves image quality by reducing noise. Although edge enhancement does not further suppress noise, it improves the visibility of delicate anatomical structures. This combined approach may enhance diagnostic confidence in neonatal chest radiography, particularly under low-dose conditions.

Adnexal torsion and pedunculated subserosal leiomyoma torsion are significant gynecological emergencies requiring prompt recognition and surgical intervention. Although ultrasound remains the primary imaging modality, cross-sectional imaging with computed tomography and magnetic resonance imaging plays a crucial role in diagnosis, particularly in complex cases. This review provides a comprehensive analysis of the imaging features of various types of torsion, with direct correlation with laparoscopic findings. We describe key imaging features across different modalities, focusing on specific manifestations of ovarian torsion variants, including massive ovarian edema, mature cystic teratoma, fibroma, and mucinous cystadenoma. Special attention was given to isolated fallopian tube torsion and its subtypes and the unique features of leiomyoma torsion. Understanding these imaging features and their correlation with laparoscopic findings is crucial for accurate diagnosis and appropriate surgical planning. This review emphasizes the importance of recognizing specific imaging patterns that can help guide clinical decision-making and improve patient outcomes through timely intervention.

Purpose: Low agreements among experts for differentiating usual interstitial pneumonia (UIP) from nonspecific interstitial pneumonia (NSIP) motivate the use of automated imaging diagnosis. Volume histogram analysis (VHA) of the lung parenchyma using computer-aided diagnostic software is more straightforward to perform and interpret than radiomics. To assess whether a predictive model generated by VHA (VHA model), using voxel data of each lung lobe obtained via whole-lung CT, can differentiate radiological UIP from NSIP, and to explore the relationship between VHA model outcomes and patient prognosis.

Materials and methods: This study included 74 patients from one university hospital (cohort A: 47 patients with idiopathic pulmonary fibrosis [IPF]/UIP and 27 with idiopathic NSIP [iNSIP] and connective tissue disease-associated NSIP [CTD-NSIP]) and 146 patients from another hospital (cohort B: 111 with IPF/UIP and 35 with iNSIP/CTD-NSIP), with diagnoses confirmed through multidisciplinary discussion. Using the VHA values obtained from each lung lobe in cohort A, a formula-based VHA model was developed. The regularization parameters were optimized using five-fold cross-validation to maximize the area under the receiver operating characteristic curve (AUC). This VHA model was externally validated in cohort B. The correlation between various parameters and prognosis was analyzed using Cox proportional hazards multivariate analysis.

Results: The mean AUC of the best VHA model that differentiated UIP patterns in cohort A was 0.91 (95% confidence interval [CI], 0.84-0.98), with a positive predictive value (PPV) of 0.97 (0.88-1.00). External validation of this model for cohort B revealed that the AUC for UIP differentiation was 0.81 (0.70-0.88), with a PPV of 0.94 (0.88-0.98). Multivariate analysis revealed that the values calculated by the VHA model were correlated with prognosis (hazard ratio, 1.60; 95% CI, 1.17-2.18; p = 0.003).

Conclusion: The VHA model could effectively differentiate radiological UIP patterns and may help predict the prognosis of patients with interstitial pneumonia. A formula-based model using CT volume histogram analysis (VHA) of each lung lobe was developed to differentiate usual interstitial pneumonia (UIP) from nonspecific interstitial pneumonia (NSIP). The VHA model demonstrated strong diagnostic performance, achieving an area under the curve of 0.81 in external validation, and also statistically correlated with patient prognosis.

Purpose: To investigate the effects of radiological, clinical and histological features in the radiological assessment of tumor size in breast cancer, with a particular focus on the effect of surrounding parenchymal features (SPFs).

Method: Patients with SPFs reported in the postoperative pathology reports were included in this retrospective multicenter study. Primary lesions were categorized as invasive, in situ (DCIS) or mixed (invasive + DCIS) carcinoma. Pathological tumor size was accepted as the gold standard and compared with tumor sizes measured on mammography (MMG), ultrasonography (US), and magnetic resonance imaging (MRI), according to the presence or absence of SPFs with or without atypia. The effects of other factors such as mammographic breast density, background parenchmal enhancement (BPE), lesion type, lesion size, tumor grade and patient age were also evaluated.

Results: There were SPFs in 402/473 patients (85%); and 228 of them (56.7%) had high-risk lesions, of which 196 (48.8%) were lesions with atypia. Overall MRI had the best correlation levels in the presence of SPFs. US had agreement levels close to MRI for invasive and mixed tumors, but not for DCIS. Presence of atypical high-risk lesions decreased the correlation levels of MMG (r = 0.193 vs r = 0.485) and MRI (r = 0.220 vs r = 0.679) in DCIS, and of MRI in mixed tumors (r = 0.718 vs r = 0.848). Correlation levels increased with high patient age, low breast density, low BPE, high nuclear grade for DCIS, and increasing tumor size.

Conclusion: This study showed that surrounding parenchymal findings and high-risk lesions adjacent to the tumor are not only a stimulus for malignant development, but also a biological factor that directly affects the accuracy of tumor size measurement in imaging modalities. The fact that MRI preserves the highest level of correlation with pathology, even in the presence of complex parenchymal structures and high-risk lesions, justifies its consideration as the primary modality in surgical planning.

Purpose: This study aims to conduct a comprehensive bibliometric analysis of global research in prostate-specific-membrane-antigen (PSMA)-targeted radiopharmaceuticals to map the field's development, identify key contributors, and determine emerging research directions. PSMA-targeted radiopharmaceuticals have revolutionized prostate cancer theranostics, yet comprehensive bibliometric analyses examining the global research landscape remain absent. Understanding research trends, collaborative networks, and emerging directions is crucial for optimizing resource allocation and guiding future development in this rapidly evolving field.

Materials and methods: This study examined 3,249 publications related to PSMA-targeted radiopharmaceuticals from 2005 to 2025 using the Web of Science Core Collection database. Data visualization and analysis were conducted using VOSviewer (version 1.6.19), CiteSpace (version 6.2.R3), and the biblioshiny R package.

Results: The analysis included 77 countries, 3,350 institutions, and 13,976 researchers across 430 journals. Germany leads with 869 publications and 40,615 citations, with Technical University Munich identified as the most influential institution (n = 199). The European Journal of Nuclear Medicine and Molecular Imaging has the highest number of publications (n = 316), while Journal of Nuclear Medicine has the most co-citations (14,245). Matthias Eiber is the most prolific author with 153 publications and an H-index of 57, while A. Afshar-Oromieh has the highest number of co-citations (n = 2248). The citation patterns showed strong growth after 2018 (surpassing 14,000 citations by 2024), reflecting the field's impact. The current research hotspots mainly include "⁶⁸ Ga-PSMA-PET/CT diagnostic imaging", "¹⁷⁷Lu-based radioligand therapy", "translational clinical applications", and "AI-driven technological innovation". Future research emphasizes "AI-enhanced theranostic optimization", "immunotherapy-radiopharmaceutical combinations", and "precision biomarker-guided patient stratification".

Conclusions: This analysis describes the evolution of PSMA radiopharmaceuticals research patterns and identifies emerging research directions that may inform future precision oncology development.

Background: Breast edema, characterized by increased signal intensity on T2-W sequence of magnetic resonance imaging (MRI), is associated more aggressive tumor biology and worse long-term survival in breast cancer patients.

Purpose: To evaluate the association between types of breast edema and the achievement of pathological complete response (pCR) in the breast and axillary lymph nodes following neoadjuvant chemotherapy (NAC) in patients with breast cancer.

Methods: Dynamic breast MRI images before NAC treatment from 561 patients with nonmetastatic, axillary lymph node positive, locally advanced invasive breast cancer were reviewed retrospectively. The location of breast edema was examined on T2W images and classified as peritumoral, subcutaneous (local or diffuse), prepectoral or parasternal. Dynamic T1-W sequences were used to evaluate mass and nonmass lesions, including shape, border characteristics, focality. Radiologic data were integrated with clinicopathological data. In addition, the pathological response to NAC was included in a multivariate logistic regression analysis (P < 0.05).

Results: All types of edema were significantly associated with pCR in the breast&axilla (combined) (26.9% vs. 17.3%; p = 0.025; OR, 1.8) following NAC. However, no significant association was identified when the presence of edema was separately analyzed in the breast and axilla in relation to NAC response (p = 0.12; OR, 1.4 and p = 0.52; OR, 1.4 respectively). Particularly peritumoral edema was associated with higher pCR rates than other types of edema both in the breast (31% vs. 21%; p = 0.010; OR, 1.8) and in the breast&axilla (29% vs. 16%; p = 0.001; OR, 2.1). Logistic regression identified peritumoral edema as an independent predictor of overall pCR, improving prediction with the breast&axilla response (p = 0.036; OR, 1.7).

Conclusion: The presence of peritumoral edema can predict pCR in the breast&axilla following NAC. Edema assessment could play a crucial role in guiding short-term treatment planning and support closer clinical follow-up.