Radiology. Imaging cancer最新文献

Hematologic malignancies are among the most common cancers worldwide, with incidence nearly doubling between 1990 and 2019. Compared with the general population, patients with these malignancies are at significantly increased risk for invasive fungal disease (IFD) due to prolonged immunosuppression and neutropenia from chemotherapy, hematopoietic stem cell transplant, or the underlying disease itself. IFD contributes substantially to morbidity and infection-related mortality in this population, with reported mortality rates ranging from 29% to 90%. Among IFD infections, invasive fusariosis is one of the most lethal, with case fatality rates reaching 100% in certain organ systems. It frequently presents as a debilitating, multiorgan infection affecting the skin, bone, central nervous system, and cardiovascular structures. This review highlights the diverse clinical and imaging manifestations of invasive fusariosis in patients with hematologic malignancies, providing a multimodal overview of disease presentations, organ involvement, and treatment approaches based on published cases. Current diagnostic challenges are discussed, along with future strategies aimed at improving early recognition and management. Given the severity and poor outcomes associated with invasive fusariosis, greater awareness and education are essential to improve diagnosis, treatment, and outcomes for this vulnerable population of patients with hematologic malignancies. Keywords: CT, Echocardiography, PET/CT, Ultrasound, MR-Imaging, Cardiac, CNS, Soft Tissues/Skin, Skeletal-Appendicular, Oncology, Leukemia, Infection, Fusariosis, Hematologic Malignancies, Acute Myelogenous Leukemia, Acute Lymphocytic Leukemia © RSNA, 2025.

Purpose To compare incremental cancer detection rate (ICDR) by breast tissue density (BTD) and determine whether breast MRI adds diagnostic value in high-risk individuals with low-density breast tissue. Materials and Methods This retrospective review included 10 233 screening breast MRI examinations performed between 2012 and 2022. For each examination, the Breast Imaging Reporting and Data System BTD category was recorded. Patient charts were reviewed to identify patients diagnosed with breast cancer (BC) during the screening period. The nearest mammograms were reviewed to assess whether cancers were also visible at mammography. Results Among 10 233 female individuals screened, 91 cancers were detected (cancer detection rate [CDR] = 8.90 per 1000 examinations), with no evidence of a difference across BTD categories (P = .39). By BTD category, seven cancers (7.7%) were in category A, 34 (37.4%) in B, 40 (44.0%) in C, and 10 (11.0%) in D. Among 90 BCs detected at MRI, only 18 (20.0%) had a corresponding abnormality identified on the nearest screening mammogram. Of the 872 MRI examinations in individuals with category A BTD, six of seven cancers (85.7%) were detected at MRI alone and one (14.3%) was detected at both mammography and MRI. No evidence of a difference in incremental CDR was found across BTD categories (A = 6.9 per 1000 examinations; B = 5.9 per 1000; C = 8.3 per 1000; D = 7.5 per 1000; P > .50). Conclusion In high-risk individuals with low-density breasts (category A BTD), supplemental screening with breast MRI remained necessary despite improved mammographic sensitivity. Keywords: Mammography, MRI, Breast, Primary Neoplasms, Screening © RSNA, 2025 Supplemental material is available for this article.

Purpose To evaluate the ability of habitat imaging to noninvasively assess high-grade prostate cancer (PCa). Materials and Methods This retrospective, multicenter study included patients with PCa undergoing MRI examination and subsequent radical prostatectomy (RP) between January 2018 and June 2024. Following the 2019 International Society of Urological Pathology (ISUP) guidelines, patients were categorized into low- to medium-grade (ISUP ≤ 3) and high-grade (ISUP ≥ 4) groups, using RP results as the reference. After integrating multimodal imaging data of each voxel, lesions were clustered into k habitat subregions. RP specimens were matched to these subregions, and each subregion's ISUP grade was evaluated to calculate the detection rate of high-grade lesions. Logistic regression identified high-grade PCa-related variables, forming the habitat imaging-clinical imaging (HICI) predictive model. The model's performance was validated using the area under the receiver operating characteristic curve (AUC). Results This study enrolled 359 male patients with PCa (median age, 68 years) divided into training (159 patients), internal test (69 patients), and external test (131 patients) sets. Habitat 1, which featured high cellular density, blood perfusion, and tissue structural complexity, showed a 92.6% (87 of 94) detection rate for high-grade PCa. Logistic regression identified the proportion of habitat 1 (odds ratio [OR], 3.18; P < .001), the prostate-specific antigen level (OR, 2.71; P = .004), and the Prostate Imaging Reporting and Data System score (OR, 1.69; P = .04) as independent risk factors. The HICI model (AUC, 0.87) outperformed the clinical imaging model (AUC, 0.81; P = .01). Conclusion The HICI model can noninvasively assess high-grade PCa. Keywords: MR-Diffusion Weighted Imaging, Prostate, MR-Imaging Supplemental material is available for this article. © RSNA, 2025.

Decreased tobacco use has resulted in substantial declines in the prevalence of upper aerodigestive tract malignancies. However, the prevalence of oral cavity squamous cell carcinomas has been steadily increasing despite decreases in tobacco-related malignancies both within the United States and worldwide. The cause driving the increasing prevalence is unknown and may reflect a combination of viral, environmental, and genetic mechanisms. Radiologists must be familiar with the imaging appearance of oral cavity carcinomas to achieve proper staging and to guide surgical and/or radiation therapy management. This article will review the emerging trends in oral cavity carcinoma, the basics of oral cavity anatomy relevant to subsites of cancer involvement, the imaging appearance of this entity, and the information critical for appropriate staging to direct surgical management, medical treatment, and/or radiation therapy. Keywords: Ear/Nose/Throat, Head/Neck, Tongue, Neoplasms-Primary, Oncology, CT, MR Imaging, Diagnosis, PET/CT, PET/MRI © RSNA, 2025.

As the consequences of climate change have become increasingly evident and the environmental impact of cancer care continues to grow, there is a clear need for practice guidelines that integrate sustainability into the radiologic assessment and management of cancer. The use of imaging and image-guided procedures in cancer care has expanded substantially, contributing to improved patient outcomes, but also increased emissions and waste. This review examines the current environmental impact of cancer imaging and image-guided therapy, outlines a vision for sustainable cancer care, and proposes actionable steps to achieve a future that co-benefits patients and the planet. Keywords: Sustainability, Environmental Equity, Climate Resilience, Green Labs Supplemental material is available for this article. © RSNA, 2025.

Purpose To evaluate quantitative MRI parameters for assessing bone marrow composition and fibrosis in individuals with myelofibrosis (MF), as a noninvasive alternative to biopsy. Materials and Methods This prospective, single-site study (ClinicalTrials.gov identifier no. NCT01973881) included participants with MF and with non-MF myeloproliferative neoplasms (MPNs) and healthy controls who underwent MRI scans from November 2016 to January 2024. Different MRI sequences assessed fat content (proton density fat fraction), cellularity (apparent diffusion coefficient, ADC), and cellularity/macromolecular structure (magnetization transfer ratio, MTR) across lumbar vertebrae, ilium, and femoral heads. The authors used linear discriminant analysis to classify the extent of bone marrow fibrosis for each participant based on ADC values. Results This study included 66 participants (45 with MF and 15 with other MPNs [34 female] and six healthy controls (four male)]. The median age was 63 years among participants with MF and other MPNs and 62 years among healthy controls. Participants in the MF subgroup showed elevated ADCs and MTRs with lower bone marrow fat than healthy controls. Individual bone marrow MRI metrics generally correlated across anatomic sites (Pearson r = 0.57-0.89). ADC in the ilium showed the highest correlation with pathologic grade of bone marrow fibrosis (Kendall τ_B = 0.44, P = .01). ADC values near the linear discriminant analysis threshold in two to three anatomic sites correlated with increased risk of overt bone marrow fibrosis (odds ratio = 5.81, P = .01). Conclusion Quantitative bone marrow MRI parameters, particularly ADC, correlated with bone marrow fibrosis and disease severity in MF. Keywords: MR Imaging, Hematologic Supplemental material is available for this article. © RSNA, 2025.

Purpose To develop a deep learning (DL) model based on contrast-enhanced US (CEUS) to help radiologists diagnose intrahepatic cholangiocarcinoma (iCCA). Materials and Methods In this retrospective study (July 2017-December 2023), CEUS examinations from 49 centers were used to train and validate a DL model using four algorithms (BNInception, MobileNet-v2, ResNet-50, and VGG-19). External test set A, collected from two independent centers, was used to evaluate model performance. External test set B, collected from 51 centers, was used to compare the DL model's performance on iCCA diagnosis with that of three CEUS radiologists and one MRI radiologist and to assess the effect of DL-assisted interpretation on radiologist performance. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Results A total of 1148 CEUS examinations were divided into training (n = 804) and validation (n = 344) sets. External test sets A and B included 153 (mean age, 55.52 years ± 11.12 [SD]; 120 male patients) and 240 (mean age, 55.03 years ± 11.25; 184 male patients) CEUS examinations, respectively. Among the four evaluated algorithms, ResNet-50 achieved the best performance (AUC, 0.92) and robustness (coefficient of variation, 5.1) in external test set A. In external test set B, the DL model achieved a higher AUC than did the junior (0.91 vs 0.72, P < .01) and midlevel (0.91 vs 0.78, P < .01) CEUS radiologists and performance similar to that of the senior CEUS radiologist (0.91 vs 0.87, P = .32) and senior MRI radiologist (0.91 vs 0.89, P = .56). With DL assistance, diagnostic performance of the junior and midlevel CEUS radiologists improved significantly (from 0.72 to 0.89 [P < .01] and from 0.78 to 0.90 [P < .01], respectively), reaching performance similar to that of the senior CEUS radiologist (P = .50 for junior radiologist and P = .94 for midlevel radiologist). Conclusion A CEUS-based DL model demonstrated diagnostic performance similar to that of a senior CEUS radiologist and improved the performance of junior and midlevel CEUS radiologists. Keywords: Applications-Ultrasound, Deep Learning, Ultrasound-Contrast, Abdomen/GI, Liver, Oncology ClinicalTrials.gov identifier no. NCT04682886 Supplemental material is available for this article. © RSNA, 2025.