Radiology advances最新文献

Purpose: Accurately predicting the expected duration of time until total knee replacement (time-to-TKR) is crucial for patient management and health care planning. Predicting when surgery may be needed, especially within shorter windows like 3 years, allows clinicians to plan timely interventions and health care systems to allocate resources more effectively. Existing models lack the precision for such time-based predictions. A survival analysis model for predicting time-to-TKR was developed using features from medical images and clinical measurements.

Methods: From the Osteoarthritis Initiative dataset, all knees with clinical variables, MRI scans, radiographs, and quantitative and semiquantitative assessments from images were identified. This resulted in 895 knees that underwent TKR within the 9-year follow-up period, as specified by the Osteoarthritis Initiative study design, and 786 control knees that did not undergo TKR (right-censored, indicating their status beyond the 9-year follow-up is unknown). These knees were used for model training and testing. Additionally, 518 and 164 subjects from the Multi-Center Osteoarthritis Study and internal hospital data were used for external testing, respectively. Deep learning models were utilized to extract features from radiographs and MR scans. Extracted features, clinical variables, and image assessments were used in survival analysis with Lasso Cox feature selection and a random survival forest model to predict time-to-TKR.

Results: The proposed model exhibited strong discrimination power by integrating self-supervised deep learning features with clinical variables (eg, age, body mass index, pain score) and image assessment measurements (eg, Kellgren-Lawrence grade, joint space narrowing, bone marrow lesion size, cartilage morphology) from multiple modalities. The model achieved an area under the curve of 94.5 (95% CI, 94.0-95.1) for predicting the time-to-TKR.

Conclusions: The proposed model demonstrated the potential of self-supervised learning and multimodal data fusion in accurately predicting time-to-TKR that may assist physicians to develop personalize treatment strategies.

Background: Sentinel lymph node biopsy (SLNB) is an important cancer diagnostic staging procedure. Conventional SLNB procedures with ^99mTc radiotracers and scintigraphy are constrained by tracer half-life and, in some cases, insufficient image resolution. Here, we explore an alternative magnetic (nonradioactive) image-guided SLNB procedure.

Purpose: To demonstrate that magnetic particle imaging (MPI) lymphography can sensitively, specifically, and quantitatively identify and map sentinel lymph modes (SLNs) in murine models in multiple regional lymphatic basins.

Materials and methods: Iron oxide nanoparticles were administered intradermally to healthy C57BL/6 mice (male, 12-week-old, n = 5). The nanoparticles (0.675 mg Fe/kg) were injected into the tongue, forepaw, base of tail, or hind footpad, then detected by 3-dimensional MPI at multiple timepoints between 1 hour and 4 to 6 days. In this mouse model, the SLN is represented by the first lymph node draining from the injection site. SLNs were extracted to verify the MPI signal ex vivo and processed using Perl's Prussian iron staining. Paired t-test was conducted to compare MPI signal from SLNs in vivo vs. ex vivo and considered significant if P < .05.

Results: MPI lymphography identified SLNs in multiple lymphatic pathways, including the cervical SLN draining the tongue, axillary SLN draining the forepaw, inguinal SLN draining the tail, and popliteal SLN draining the footpad. MPI signal in lymph nodes was present after 1 hour and stable for the duration of the study (4-6 days). Perl's Prussian iron staining was identified in the subcapsular space of excised SLNs.

Conclusion: Our data support the use of MPI lymphography to specifically detect SLN(s) using a magnetic tracer for a minimum of 4 to 6 days, thereby providing information required to plan the SLN approach in cancer surgery. As clinical-scale MPI is developed, translation will benefit from a history of using iron-oxide nanoparticles in human imaging and recent regulatory-approvals for use in SLNB.

Background: Sodium (²³Na) MRI of prostate cancer (PCa) is a novel but underdocumented technique conventionally acquired using an endorectal coil. These endorectal coils are associated with challenges (e.g., a nonuniform sensitivity profile, limited prostate coverage, patient discomfort) that could be mitigated with an external ²³Na MRI coil.

Purpose: To quantify tissue sodium concentration (TSC) differences within the prostate of participants with PCa and healthy volunteers using an external ²³Na MRI radiofrequency coil at 3 T.

Materials and methods: A prospective study was conducted from January 2022 to June 2024 in healthy volunteers and participants with biopsy-proven PCa. Prostate ²³Na MRI was acquired on a 3-T PET/MRI scanner using a custom-built 2-loop (diameter, 18 cm) butterfly surface coil tuned for the ²³Na frequency (32.6 MHz). The percent difference in TSC (ΔTSC) between prostate cancer lesions and surrounding noncancerous prostate tissue of the peripheral zone (PZ) and transition zone (TZ) was evaluated using a 1-sample t-test. TSC was compared to apparent diffusion coefficient (ADC) measurements as a clinical reference.

Results: Six healthy volunteers (mean age, 54.5 years ± 12.7) and 20 participants with PCa (mean age, 70.7 years ± 8.3) were evaluated. A total of 31 lesions were detected (21 PZ, 10 TZ) across PCa participants. Compared to noncancerous prostate tissue, prostate cancer lesions had significantly lower TSC (ΔTSC, -14.1% ± 18.2, P = .0002) and ADC (ΔADC, -26.6% ± 18.7, P < .0001).

Conclusion: We used an external ²³Na MRI coil for whole-gland comparison of TSC in PCa and noncancerous prostate tissue at 3 T. PCa lesions presented with lower TSC compared to surrounding noncancerous PZ and TZ tissue. These findings demonstrate the feasibility of an external ²³Na MRI coil to quantify TSC in the prostate and offer a promising, noninvasive approach to PCa diagnosis and management.

Background: Medial meniscus root tears often lead to knee osteoarthritis. The extent of meniscal tissue changes beyond the localized root tear is unknown.

Purpose: To evaluate if 7 Tesla 3D T2*-mapping can detect intrasubstance meniscal degeneration in patients with arthroscopically verified medial meniscus posterior root tears (MMPRTs), and assess if tissue changes extend beyond the immediate site of the posterior root tear detected on surface examination by arthroscopy.

Methods: In this prospective study we acquired 7 T knee MRIs from patients with MMPRTs and asymptomatic controls. Using a linear mixed model, we compared T2* values between patients and controls, and across different meniscal regions. Patients underwent arthroscopic assessment before MMPRT repair. Changes in pain levels before and after repair were calculated using Knee Injury & Osteoarthritis Outcome Score (KOOS). Pain changes and meniscal extrusion were correlated with T2* using Pearson correlation (r).

Results: Twenty patients (mean age 53 ± 8; 16 females) demonstrated significantly higher T2* values across the medial meniscus (anterior horn, posterior body and posterior horn: all P < .001; anterior body: P = .007), and lateral meniscus anterior (P = .024) and posterior (P < .001) horns when compared to the corresponding regions in ten matched controls (mean age 53 ± 12; 8 females). Elevated T2* values were inversely correlated with the change in pain levels before and after repair. All patients had medial meniscal extrusion of ≥2 mm. Arthroscopy did not reveal surface abnormalities in 70% of patients (14 out of 20).

Conclusions: Elevated T2* values across both medial and lateral menisci indicate that degenerative changes in patients with MMPRTs extend beyond the immediate vicinity of the posterior root tear. This suggests more widespread meniscal degeneration, often undetected by surface examinations in arthroscopy.