Japanese Journal of Radiology最新文献

Purpose: This study evaluated the efficacy and safety of high-dose pelvic radiotherapy combined with chemotherapy, including bevacizumab and immune checkpoint inhibitors (ICI) in patients with stage IVB cervical cancer (CC) based on the 2018 International Federation of Gynecology and Obstetrics (FIGO) cervical cancer staging system.

Materials and methods: A retrospective analysis was conducted on 38 patients with stage IVB CC, as classified by the 2018 FIGO cervical cancer staging system, who received pelvic external beam radiotherapy (≥ 40 Gy) with or without brachytherapy and chemotherapy. Data were collected from two centers. The 2-year local control (LC), progression-free survival (PFS), and overall survival (OS) rates were analyzed using the Kaplan-Meier method. Symptom relief, including reductions in genital bleeding and pain from the primary lesion, was assessed. Acute and late adverse events were also evaluated.

Results: The median follow-up period was 17.5 months. The 2-year LC, PFS, and OS were 82%, 11%, and 47%, respectively. Although the evaluation method has limitations, most patients with genital bleeding and pain from the primary lesion showed improvement in symptoms. Late adverse events of grade ≥ 2 related to both pelvic radiotherapy and bevacizumab included one case of grade 3 gastrointestinal bleeding and two cases of grade 2 fistula.

Conclusion: This two-center study demonstrated that high-dose pelvic radiotherapy combined with chemotherapy, including bevacizumab and ICI, may achieve favorable local control and symptom relief in patients with stage IVB CC while maintaining an acceptable safety profile.

Modern breast cancer treatment has evolved significantly, integrating advanced imaging techniques and a variety of drug therapies, including endocrine therapy, chemotherapy, molecularly targeted drugs, and immune checkpoint inhibitors. This study discusses the interplay of drug treatments and imaging diagnostics in optimizing patient outcomes. Key findings emphasize the importance of personalized treatments based on intrinsic subtypes and imaging-guided decisions, as well as the management of treatment-related adverse effects.

Approximately 5% and 1% of global greenhouse gas emissions originate from the healthcare sector and medical imaging respectively. These are not insignificant. Despite this, the medical field has been slow to adopt sustainable practices. Green radiology is a sustainable, innovative, and responsible approach in radiological practice that focuses on minimising the negative environmental impact of our technologies and procedures. The Asian Oceanian Society of Radiology (AOSR) conducted a survey across Asia-Oceania to better understand current operations and identify opportunities for meaningful progress toward more sustainable radiological practices. From the 123 institutions that responded, it was found that collectively the carbon footprint of Asia-Oceania's radiological services are high in some of the countries/regions because of the high density of high energy consumption scanners (such as computed tomography and magnetic resonance imaging scanners). Our survey showed that less than half of the 15 societies that responded had specific academic or research activities related to sustainable radiology and less than a third reported that sustainability had been incorporated in the training curriculum. The AOSR is committed to facilitating the sharing of best practices in green radiology and recommends that Asia-Oceania use an "As Low As Reasonably Achievable" principle, traditionally used in radiation safety and extend this in the approach to the issues of environmental impact of radiological services. The AOSR as a cross-regional professional society, has the mandate to partner with our counterpart societies and industry stakeholders, to emphasise the importance of sustainability as a critical agenda, through our various activities. We foresee that influencing radiology leaders in the region to urgently formulate strategies, implement policies towards adopting eco-friendly approaches including reducing inappropriate imaging and encouraging academic efforts will help to reduce the environmental impact in each country/region. A checklist has been included to help kickstart the process.

Proper treatment of intra-articular tumor/tumor-like lesions (tenosynovial giant cell tumor, synovial chondromatosis, synovial hemangioma / intra-articular venous malformations, lipoma arborescens, etc.) depends on an accurate diagnosis. This review highlights the imaging findings of intra-articular tumor/tumor-like lesions and the other synovial diseases (gout, amyloid arthropathy, rheumatoid arthritis, ganglion, and postoperative intra-articular tumor) to determine whether they could help in establishing the correct diagnosis. Many synovial proliferative diseases have specific imaging characteristics and an awareness of these characteristics along with their pathological and anatomical features can allow for an accurate diagnosis. Even though a wide spectrum of diseases may involve the synovium, careful MRI assessment used in conjunction with clinical information can lead to a substantial narrowing of the differential diagnosis.

Background: Conventional ¹²³I-ioflupane dopamine-transporter SPECT requires 25-40 min of acquisition, causing patient discomfort and limiting throughput. This study assessed whether deep-learning (DL) reconstruction can yield diagnostic-quality images from a 5-min scan.

Methods: We retrospectively analysed 207 studies (1035 slices) obtained between April 2018 and June 2020. After cropping to 64 × 64 striatal regions, 600, 185 and 250 images from 120, 37 and 50 patients were used for training, validation and testing. Six convolutional architectures-U-Net (one-five depths), V-Net, U-Net +  + , R2U-Net, Attention U-Net and TransUNet-were trained to translate 5-min into virtual 25-min images. Image quality was assessed with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), analysed by Friedman and Dunn-Holm tests. A blinded reader study involved three nuclear medicine physicians grading 50 cases (100 striata) on a four-point scale; agreement with the 25-min consensus was measured by weighted κ and intra-/inter-observer intraclass correlation coefficients (ICC).

Results: All DL reconstructions significantly outperformed raw 5-min images in PSNR and SSIM (p < 0.01). The four-layer U-Net achieved the highest quality (PSNR 32.7 ± 1.7 dB, SSIM 0.842 ± 0.069), ≈1.8 dB and 0.13 higher than baseline, and statistically indistinguishable from 25-min images (p > 0.05). Reader concordance improved from fair with baseline (κ = 0.29-0.41) to substantial with the four-layer U-Net (κ = 0.62-0.70); intra-reader ICC was 0.84-0.93 and inter-reader ICC 0.73-0.75.

Conclusions: A compact four-layer U-Net restores diagnostic fidelity to 5-min ¹²³I-ioflupane SPECT, enabling an 80% reduction in scan time without loss of quantitative metrics or interpretability. DL-accelerated protocols may enhance comfort, reduce motion artefacts and increase throughput, warranting prospective multicentre validation.

Purpose: An artificial intelligence (AI) system for detecting interstitial lung abnormalities (ILA) was previously developed but requires external validation. This study aimed to examine the robustness across different populations and investigate associations between the system outputs and traction bronchiectasis/bronchiolectasis severity patterns.

Materials and methods: CT scans from population-based samples of the Rotterdam Study (2018-2019) and the Age Gene/Environment Susceptibility Reykjavik (AGES-Reykjavik) Study (baseline CT: 2002-2006, follow-up CT: 2007-2011) were used in this secondary analysis of the two cohorts. The AI system calculated ILA probability score (AI score) in the range from 0 to 1. Three experienced readers evaluated independently all CT scans for ILA, and two chest radiologists assessed traction bronchiectasis/bronchiolectasis using the 4-scale traction bronchiectasis/bronchiolectasis index (TBI) for severity by consensus. Receiver operating characteristic (ROC) analysis and Kruskal-Wallis test were used for statistical analysis.

Results: The system analyzed 932 CT scans of the Rotterdam Study (mean participant age, 79.6 years ± 4.3 (SD), 482 women) and 5242 CT scans of the AGES-Reykjavik Study (mean participant age, 76.4 years ± 5.6, 3032 women), and achieved area under the ROC curve of 0.841 (95% CI 0.804, 0.879) and 0.823 (95% CI 0.798, 0.847), respectively. AI scores correlated with readers' certainty, decreasing from unanimous ILA cases to No-ILA cases. Higher baseline AI scores correlated with greater severity of traction bronchiectasis/bronchiolectasis (TBI-3: 0.931 [IQR, 0.911-0.932], TBI-2: 0.738 [IQR, 0.406-0.880], TBI-1: 0.537 [IQR, 0.317-0.761], TBI-0: 0.250 [IQR, 0.136-0.455]).

Conclusion: The system demonstrated robust ILA detection performance across different populations, with AI scores showing associations with traction bronchiectasis/bronchiolectasis severity.