Academic Radiology最新文献

Rationale and objectives: Post-transarterial chemoembolization liver failure (PTLF) is a potentially fatal complication of transarterial chemoembolization (TACE). Accurate preoperative prediction of PTLF is crucial for improving patient outcomes. This study aimed to develop and validate a prediction model based on the functional liver imaging score (FLIS) to assess the risk of PTLF.

Materials and methods: A total of 156 patients underwent Gadoxetic acid-enhanced MRI within four weeks before TACE. Two radiologists, unaware of the clinical data, independently assessed FLIS on hepatobiliary phase images to quantitatively assess liver function. Univariate and multivariate logistic regression analyses identified independent predictors of PTLF. A nomogram was developed and subjected to internal validation through bootstrap resampling of 1000 samples. The model's performance was conducted through the area under the curve (AUC), Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). P< 0.05 was considered statistically significant.

Results: PTLF occurred in 37.2% of patients (58/156). Significant differences were observed in factors such as portal vein thrombosis, albumin, aspartate transaminase, international normalized ratio (INR), model for end-stage liver disease scoring, albumin-bilirubin score, and FLIS. Multivariate analysis showed FLIS, portal vein thrombosis, and INR as independent predictors. The model achieved an AUC of 0.759, with 87.8% specificity and 56.9% sensitivity, and demonstrated good calibration (χ² = 7.101, P=0.526). Calibration curves and DCA confirmed its clinical utility.

Conclusion: This FLIS-based prediction model performs well in predicting PTLF, potentially serving as a practical clinical tool.

Rationale and objectives: The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop and validate a model that integrates deep learning and sub-regional radiomics from MRI imaging to predict pathological complete response (pCR) in patients with LARC.

Materials and methods: We retrospectively enrolled 768 eligible participants from three independent hospitals who had received neoadjuvant chemoradiotherapy followed by radical surgery. Pretreatment pelvic MRI scans (T2-weighted), were collected for annotation and feature extraction. The K-means approach was used to segment the tumor into sub-regions. Radiomics and deep learning features were extracted by the Pyradiomics and 3D ResNet50, respectively. The predictive models were developed using the radiomics, sub-regional radiomics, and deep learning features with the machine learning algorithm in training cohort, and then validated in the external tests. The models' performance was assessed using various metrics, including the area under the curve (AUC), decision curve analysis, Kaplan-Meier survival analysis.

Results: We constructed a combined model, named SRADL, which includes deep learning with sub-regional radiomics signatures, enabling precise prediction of pCR in LARC patients. SRADL had satisfactory performance for the prediction of pCR in the training cohort (AUC 0.925 [95% CI 0.894 to 0.948]), and in test 1 (AUC 0.915 [95% CI 0.869 to 0.949]) and in test 2 (AUC 0.902 [95% CI 0.846 to 0.945]). By employing optimal threshold of 0.486, the predicted pCR group had longer survival compared to predicted non-pCR group across three cohorts. SRADL also outperformed other single-modality prediction models.

Conclusion: The novel SRADL, which integrates deep learning with sub-regional signatures, showed high accuracy and robustness in predicting pCR to neoadjuvant chemoradiotherapy using pretreatment MRI images, making it a promising tool for the personalized management of LARC.

Objective: The purpose of this systematic review and meta-analysis was comparing diagnostic performance of ultrasound elastography (UE), strain UE and shear wave elastography (SWE), with magnetic resonance imaging (MRI) in differentiating benign and malignant breast lesions.

Methods: Literature search of MEDLINE, Web of Science, SCOPUS and Google Scholar was performed in June 2023. Included studies used Breast Imaging Reporting and Data System (BI-RADS) and histopathology as reference standard. A bivariate random-effects model was used to calculate sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios and area under the curve (AUC). Meta-regression subgroup analysis was performed.

Results: Nine studies and 536 lesions were included. Pooled sensitivity was not different between MRI vs UE [MRI: 94% (95% CI: 88.2%-96.9%) vs UE: 90% (95% CI: 84.7%-93.1%); P=0.153] but a difference was found for specificity [UE: 78% (95% CI: 66.3%-86.4%) vs MRI: 71.3% (95% CI: 52.1%-85%); P=0.0065]. Strain UE showed higher specificity and similar sensitivity to SWE [strain UE: 0.85 (95% CI: 0.71-0.93) vs SWE: 0.72 (95% 0.58-0.83); P=0.017 and strain UE: 0.87 (95% CI 0.79-0.93) vs SWE: 0.91 (95% CI 0.85-0.95); P=0.311, respectively]. AUC was similar between MRI vs UE [0.91 (95% CI 0.87-0.95) vs 0.92 (95% CI 0.88-0.95); P=0.452, respectively] as was DOR [MRI: 38.083 (95% CI: 12.401-116.957) vs UE: 30.395 (95% CI: 16.572-55.75); P>0.05]. Meta-regression analysis found no significant differences in the diagnostic accuracy between MRI, strain UE and SWE.

Conclusion: Our results show that UE when compared to MRI has adequate performance in differentiating benign and malignant breast lesions.

Rationale and objectives: Accurate preoperative mpMRI-based detection of extraprostatic extension (EPE) in prostate cancer (PCa) is critical for surgical planning and patient outcomes. This study aims to evaluate the impact of endorectal coil (ERC) use on the diagnostic performance of mpMRI in detecting EPE.

Materials and methods: This retrospective study with prospectively collected data included participants who underwent mpMRI and subsequent radical prostatectomy for PCa between 2007 and 2024. Participants were divided based on ERC use on mpMRI: MRI without ERC and with ERC. Surgical pathology reports were used to determine the patients with pathologic EPE on whole-mount histopathology. One radiologist evaluated mpMRI using an in-house (National Cancer Institute [NCI]) EPE grading system. Logistic regression (LR) analyses were conducted to identify significant predictors of pathologic EPE, including ERC use and NCI EPE grades.

Results: 934 men (median age: 62 years [IQR = 57-67]) were included. For NCI EPE grade≥1, ERC MRI group (n = 612) had higher NPV (91% [320/353] vs. 83% [166/200], p = 0.01) and sensitivity (75% [101/134] vs. 62% [56/90], p = 0.04) compared to non-ERC group (n = 322). For NCI EPE grade = 3, ERC MRI group had higher NPV (83% [452/546] vs. 75% [221/294], p = 0.01) and accuracy (80% [492/612] vs. 74% [238/322], p = 0.03). In multivariable LR, higher NCI EPE grades were strong independent predictors of pathologic EPE, irrespective of ERC use (NCI EPE grade 2 with ERC: odds ratio [OR] = 2.01, p = 0.04; without ERC: OR = 5.63, p<0.001, NCI EPE grade 3 with ERC: OR = 4.53, p<0.001; without ERC: OR = 5.22, p = 0.002).

Conclusion: ERC improves sensitivity, NPV, accuracy of EPE detection with mpMRI at different NCI EPE thresholds. NCI EPE grading system remains the stronger independent predictor of pathologic EPE regardless of ERC use.

Rationale and objectives: Isocitrate dehydrogenase (IDH) status, glioma subtypes and tumor proliferation are important for glioma evaluation. We comprehensively compare the diagnostic performance of amide proton transfer-weighted (APTw) MRI and its related metrics in glioma diagnosis, in the context of the latest classification.

Materials and methods: Totally 110 patients with adult-type diffuse gliomas underwent APTw imaging. The magnetization transfer ratio asymmetry (MTR_asym), magnetization transfer ratio normalized by reference signal (MTR_normref), and spillover-corrected magnetization transfer ratio yielding R_ex (MTR_Rex), and metrics based on Lorentzian fitting (Fit-amide, Fit-MTR_normref, and Fit-MTR_Rex) were calculated. Group differences were compared between IDH genotypes, and among three glioma subtypes. The diagnostic performances were assessed using the receiver operating characteristic (ROC) analysis and compared. The correlations with Ki-67 expression were also analyzed.

Results: All APTw-related metrics exhibited significantly higher values in IDH-wildtype gliomas than in IDH-mutant gliomas (all p < 0.001). Fit-MTR_normref had the best area under the curve (AUC) of 0.858. All APTw-related metrics in glioblastomas were significantly higher than oligodendrogliomas (all p < 0.01) and astrocytomas (all p < 0.001). No metrics had significant difference between oligodendrogliomas and astrocytomas. The highest AUCs was 0.870 for Fit-MTR_normref in distinguishing astrocytomas from glioblastomas, and 0.867 for Fit-MTR_Rex in distinguishing oligodendrogliomas from glioblastomas. Besides, Fit-MTR_normref had the highest correlation coefficient with Ki-67 expression of 0.578.

Conclusion: APTw-related metrics can effectively evaluate glioma IDH status, tumor subtypes and proliferation. The combination of Lorentzian fitting and the reference signal normalization could further improve the diagnostic performance, and perform better than MTR_asym.

Aim: To evaluate the efficacy and safety of radiofrequency ablation (RFA) and microwave ablation (MWA) for treating cervical lymph node metastasis (CLNM) from papillary thyroid carcinoma (PTC).

Methods: Medline, EMBASE, Web of Science, and Cochrane Library were searched for studies on the efficacy and safety of thermal ablations for treating CLNM from PTC until July 2024. Among 544 papers, 11 articles were reviewed involving 233 patients and 432 CLNM cases. Random- or fixed-effects models assessed pooled proportions of volume reduction rate (VRR), complete disappearance, recurrence, major and minor or other complications. Similarly, pooled estimates of changes in the largest diameter, volume, and serum thyroglobulin (Tg) were evaluated post-ablation. Subgroup analysis by treatment modality was performed. Study heterogeneity was analyzed using Q statistics and inconsistency index (I²). The quality of the studies was assessed using the MINORS scale.

Results: Eleven studies with 233 patients and 432 CLNM were analyzed. The pooled VRR was 95.24% [95% Confidence Interval (CI): 91.97- 98.51%], complete disappearance was 63.1%, and recurrence was 1.6%. Changes in largest diameter, volume, and serum Tg were 8.36 mm (95%CI: 6.46-10.26mm), 216.09mm³, and 6.12ng/ml, respectively. Major complications occurred at 3.0%, while minor complications were 25.6%. Significant heterogeneity was found for diameter, volume, VRR, Tg, and minor complications. Subgroup analysis showed that MWA had a higher VRR (97.18%) than RFA (93.84%) (P < 0.001).

Conclusion: Both RFA and MWA were effective and safe for treating CLNM from PTC. However, RFA showed lower volume reduction than MWA with significant heterogeneity in VRR.

Data availability statement: The original contributions revealed in the study are included in the article/Supplemental Material. Further inquiries can be made to the corresponding author.

Rationale and objectives: Radiology plays a crucial role in modern healthcare as it facilitates the diagnosis and treatment of various medical conditions in different specialties. Therefore, this study aimed to gain insights into the factors that contribute to medical students choosing radiology as a future career.

Materials and methods: This cross-sectional study used an online, self-administered questionnaire. Data were collected exclusively from medical students at Imam Abdulrahman bin Faisal University in Saudi Arabia from August to September 2023. The chi-square test was used to assess the factors associated with medical students' choices as a future specialty.

Results: A total of 431 eligible respondents completed the survey; 267 (61.9%) were female, and their ages ranged from 18-36 years. When asked about their specialty of choice, 209 (48.5%) were interested in surgery and internal medicine, whereas only 81 (18.8%) chose radiology. Regarding the factors influencing the choice of radiology, the majority (85.6%) reported the importance of lifestyle in their choice, followed by the impact on patient care (83.5%), work environment (82.1%), intellectual challenge (79.8%), presence of procedures (76.6%), degree of patient contact (76.1%), and pre-existing experience of radiology (75.9%).

Conclusion: Many factors influence medical students' choices of radiology as a future career. Predominantly, the working environment, current exposure, knowledge of the specialty, extent of patient contact, and work-life balance were chosen as the main factors affecting medical students' choices when considering radiology as a future specialty.

Rationale and objectives: In the USA over 1 million breast biopsies are performed annually. Approximately 9.6% diagnostic exams were given Breast Imaging Reporting and Data System (BI-RADS) ≥4A, most of which are 4A/4B. Contrast-enhanced mammography (CEM) may improve biopsy outcome prediction for this subpopulation, but machine learning-based analysis of CEM is largely unexplored. We aim to develop a machine learning-based analysis of CEM using computer-extracted radiomics and radiologist-assessed descriptors to predict breast biopsy outcomes of BI-RADS 4A/4B/4C or 5 lesions.

Materials and methods: This HIPPA-compliant, IRB-approved study included women in a single institution who had BI-RADS 4A/4B/4C or 5 lesions and underwent CEM imaging prior to biopsy. Logistic regression models were built to predict biopsy outcomes using radiomics features and four radiologist-assessed qualitative descriptors. A cohort of 201 patients was used for model development/training, and an independent group of 86 patients were used as an internal test set. AUC was used to measure model's performance. Positive predictive value (PPV) was assessed on subgroups of BI-RADS 4A or 4B lesions.

Results: Model AUC was 0.90 for radiomics, 0.81 for clinical descriptors and 0.88 for their combination. On patients with an initial BI-RADS 4A or 4B scores, model combining radiomics and clinical descriptors of pre-biopsy CEM increased PPV3 to 18% from the radiologist's 6% for 4A patients, and to 25% from the radiologist's 17% for 4B patients.

Conclusion: Machine learning models combining radiomics features and clinical descriptors on CEM can predict breast biopsy outcomes on women with BI-RADS 4A/4B/4C or 5 lesions.