Acta radiologica最新文献

Background: Knees with anterior cruciate ligament (ACL) injury have distinct knee bone morphology. However, the correlation between ACL injury and morphology of the proximal tibiofibular joint has not been investigated.

Purpose: To compare proximal tibiofibular joint morphology in patients with injured and intact ACLs to aid in predicting and preventing ACL injuries.

Material and methods: A total of 50 patients with ACL injury and 50 individuals without structural knee damage (revealed by magnetic resonance imaging [MRI]) were included in this study. In the anteroposterior knee radiographs, the length between the proximal end of the fibular head and the medial and lateral tibia joint line were measured. In the axial knee MRI scans with the most proximal part of the fibular head, the distance calculation was performed between the most anterior point of the tibia plateau (MATP) and the most anterior point of fibular head (MAFH).

Results: The mean length from the proximal end of the fibular head and the medial and lateral tibial joint lines was 8.7 ± 2.8 mm and 13.2 ± 2.6 mm in the ACL-injured group, and 6.5 ± 2.8 mm and 11.1 ± 2.4 mm in the control group. The length for both medial and lateral sides was significantly greater in the ACL-injured group. The length between the MATP and MAFH was significantly larger in the ACL-injured group than the control group.

Conclusion: A large distance between the proximal end of the fibular head and the tibial joint line and knees with posterior fibular head placement would be another anatomic risk factor of ACL injury.

Background: Numerous studies have examined the prognosis of patients with relapsing-remitting multiple sclerosis (RRMS).

Purpose: To seek a relationship between the gray matter/white matter (GM/WM) volume ratio, clinical features, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) in RRMS.

Material and methods: A total of 61 patients aged 18-70 years with RRMS and 20 controls were included. Neurological examinations were noted at the first attack and last visit. Blood samples were taken in remission.

Results: Patients' WM volume was lower and the GM/WM volume ratio was larger than in the controls (P < 0.001). Patients' WM correlated with GM volume (P < 0.001, r = 0.608). WM correlated with GM/WM volume ratio (P < 0.001, r = -0.632). For NfL and GFAP, no difference was found between patients and controls. Patients' NfL correlated with GFAP (P < 0.001, r = 0.452). Age and disease duration were correlated with Gfap (P < 0.001, r = 0.434; P < 0.002, r = 0.381). No correlation was found between NfL/GFAP levels and all volumetric measurements of patients. Higher Expanded Disability Status Scale (EDSS), lower GM and WM volumes, and greater GM/WM volume ratio were detected in patients with the first pyramidal findings (P < 0.001).

Conclusion: Patients' NfL and GFAP levels were lower than in controls and did not correlate with all volumes. GFAP is more informative, particularly in patients of advanced age and those with longstanding diseases. Higher EDSS and decreased GM and WM volumes were seen in patients with pyramidal symptoms at the first attack.

Background: While the "swallow tail" sign observed in the substantia nigra (SN) on susceptibility map-weighted imaging (SMWI) serves as an effective marker for differentiating patients with Parkinson's disease (PD) from healthy individuals, its visual assessment proves inadequate in differentiating PD from atypical Parkinson syndromes (APS).

Purpose: To employ radiomic features extracted from multi-echo SMWI of the SN to distinguish between PD and APS.

Material and methods: SMWI data were acquired from 63 PD patients, 38 APS patients, and 89 healthy controls. The participants were randomly assigned to either training or test groups in a 7:3 proportion. Utilizing the PyRadiomics software, a set of radiomic features were extracted from SN for analysis. Features underwent standardization via the maximum-minimum method, with 166 statistically significant features identified through independent t-tests. To minimize the risk of overfitting, the least absolute shrinkage and selection operator (LASSO) algorithm was implemented to identify and select the five most significant features from the radiomic dataset. Five distinct machine-learning classifiers were developed to distinguish between PD, APS, and healthy controls. The SHapley Additive Explanations was employed to gain insights into and visualize the relative importance of each feature within these models.

Results: Morphological, first-order, texture, and wavelet transform features of the SN emerged as the most crucial determinants. The light gradient-boosting machine model demonstrated superior performance in distinguishing between PD, APS, and healthy controls.

Conclusion: Radiomic features of the SN derived from SMWI show promise in differentiating PD from APS, potentially enhancing diagnostic accuracy in clinical settings.

Background: Identification and staging of rectal cancer are mainly based on the difference in signal intensity (SI) between the tumor and normal structures of the intestinal wall on T2-weighted imaging. However, differentiating stage pT0-T1 from pT2 rectal tumors is difficult using routine magnetic resonance imaging (MRI) sequences.

Purpose: To summarize and explore whether MRI findings from routine imaging can help differentiate pT0-T1 from pT2 rectal tumors.

Material and methods: A total of 110 patients with pT0-T2 rectal cancer underwent preoperative pelvic MRI examinations and tumor resection without preoperative chemoradiotherapy. MRI findings of rectal lesions (including tumor location, shape, longitudinal length, maximum cross-section, submucosal high intensity [SHI], extramural fibrotic scarring, wall shrinkage, lesion-to-wall signal intensity ratio, and presence of lymph node with short axis over 3 mm) and clinical characteristics were analyzed by univariate and multivariate analyses to screen the independent factors associated with pathological results.

Results: Of all the lesions, 32 tumors were proved to be pT0-T1 and 78 tumors were pT2. Univariate and multivariate logistic regression analyses revealed that tumor shape (odds ratio [OR] = 24.607, P < 0.001), SHI (OR = 6.129, P = 0.002), and extramural fibrotic scarring (OR = 0.110, P = 0.007) were independent factors distinguishing pT0-T1 tumors from pT2 tumors. If the rectal lesion has a cauliflower-like shape with SHI and no extramural fibrotic scarring, it is more likely to be a pT0-T1 tumor.

Conclusion: The imaging features obtained from the routine MRI sequence showed potential value for differentiating pT0-T1 from pT2 rectal tumors.

Rater agreement is commonly assessed in radiologic studies concerning image quality. There are several methods of measuring rater agreement. To choose the appropriate method, the researcher needs to consider the scale of the outcome variable and the design of the study. This article provides a brief overview of available methods, focusing on the most practical and commonly used, including intraclass correlation, the Svensson method, variants of the kappa statistic, the agreement coefficient by Gwet (AC1/AC2), and Krippendorff's alpha. Additional methods that are not primarily intended for rater agreement analysis but are applied in some cases are also discussed.

Background: Renal cell carcinomas (RCCs) adjacent to critical structures can be safely treated using percutaneous cryoablation with adjunctive techniques. However, there are only a few reports describing the factors affecting local tumor control after percutaneous cryoablation for such RCCs.

Purpose: To evaluate the factors affecting local tumor control in the percutaneous cryoablation of RCCs adjacent to critical structures.

Material and methods: A total of 403 tumors from 328 patients were retrospectively reviewed. Patients with histologically proven RCCs adjacent to critical structures with at least 1 year of follow-up after treatment were included. Hereditary RCCs were excluded. Of the 403 tumors, 40 met the criteria. Owing to the tumor location, all procedures were performed using adjunctive techniques. Baseline, perioperative, and follow-up data were collected and factors affecting local tumor progression were evaluated. Comparisons between groups with and without local tumor progression were performed using Fisher's exact test or Wilcoxon's rank sum test.

Results: The median follow-up was 1289.5 days. Local tumor progression was observed in 7/40 (17.5%) patients. Univariate analysis revealed that significant factors affecting local tumor progression were situated adjacent to the ureter (P = 0.005), requiring adjunctive techniques other than hydro- and/or pneumo-dissection (P = 0.005), as well as the distance between tumors and critical structures (P = 0.04). The ice-ball margin tended to be smaller in the group with local tumor progression but this was not significant (P = 0.07).

Conclusion: Renal tumors adjacent to the ureter may result in local tumor progression after cryoablation compared with tumors adjacent to other organs.

Background: Digital breast tomosynthesis (DBT) has several limitations with respect to microcalcification depiction. The quality of DBT images may be improved by modifying technical parameters; however, the amount of raw data and postprocessing computational time is unacceptably huge. Therefore, once detected it still seems necessary to take additional spot images to characterize microcalcifications. Additional imaging increases cumulative dose. Radiation risks and protection are raising issues nowadays.

Purpose: To compare image quality of magnification tomosynthesis (t-spot) and synthetic spot magnification (s-spot) to conventional spot images in characterization of microcalcifications.

Material and methods: This prospective single institute reading study includes 45 patients with suspected microcalcifications initially found on mammography and referred to vacuum-assisted biopsy. Conventional spot and DBT were performed. Acquired DBT data were used to reconstruct t- and s-spots. All images were reviewed by four breast radiologists in two sessions. The morphology (BI-RADS) and extent of microcalcifications were recorded. Clinical performance was evaluated using area under the receiver operating characteristic curves (AUC) for BI-RADS and Bland-Altman plots for the maximum extent of microcalcifications.

Results: All four readers preferred t-spot to s-spot. The overall AUC for t-spot was 0.67 and for spot 0.69 with a difference lower than a non-inferiority margin (delta = 0.012, 95% confidence interval [CI] = -0.067-0.09, P = 0.772). Three readers underestimate the extent of microcalcifications on t-spots for both benign and malignant cases (P < 0.05). The discordance becomes more evident with the increasing extent of the lesion.

Conclusion: The performance of conventional and reconstructed spots was similar but reader-dependent and inconclusive.

Background: Emerging evidence suggests multiparametric prostate MRI's role in evaluating prostate cancer (PCa) aggressiveness.

Purpose: To evaluate the utility of quantitative sequences in prostate MRI for assessing peripheral zone PCa aggressiveness, focusing on added value of relaxation maps to ADC.

Material and methods: In total, 64 men undergoing preoperative bi-parametric prostate MRI with synthetic sequences were retrospectively enrolled. Radiologists delineated lesions on ADC maps and synthetic MRI, matched with digitized whole-mount specimens. PCa was graded as low, intermediate, or high based on Gleason grade. Statistical analyses assessed differences in T1, T2, PD, and ADC values across grades. For significant variables, AUC curves were generated. A logistic regression model evaluated the added diagnostic value of relaxation maps to ADC.

Results: Of the 95 PCa foci, 11 were low-grade, 45 intermediate-grade, and 39 high-grade. T1 (P = 0.4) and PD (P = 0.3) showed no significant differences, while T2 and ADC differed significantly (P < 0.001). Median T2 values were 98 ms (low-grade), 81 ms (intermediate), and 73 ms (high-grade), and median ADC values were 1006 mm²/s, 836 mm²/s, and 721 mm²/s, respectively. ADC yielded AUCs of 0.888 (95% CI=0.794-0.963; P < 0.0001) for low- versus intermediate-to-high-grade PCa, and 0.825 (95% CI=0.724-0.901; P < 0.0001) for low-to-intermediate versus high-grade. T2 AUCs were 0.770 (95% CI=0.605-0.908; P = 0.021) and 0.755 (95% CI=0.648-0.864; P = 0.0051), respectively. A logistic regression model combining T2 and ADC showed no significant improvement over ADC alone in differentiating low- versus intermediate-to-high-grade and low-to-intermediate versus high-grade PCa (P > 0.05).

Conclusion: T1 and PD maps from synthetic MRI showed limited utility in grading PCa. Although T2 values were effective, ADC values were superior in assessing PCa aggressiveness. The addition of T2 to ADC did not significantly improve diagnostic performance.

Background: Prostate cancer (PCa) is the most common malignant tumor that endangers the life and health of middle-aged and elderly men.

Purpose: To evaluate the significance of radiomic features from intralesional and perilesional regions in bi-parametric magnetic resonance imaging (MRI) for diagnosing PCa.

Material and methods: A total of 211 patients with suspected PCa who accepted prostate MRI scans were enrolled in this study. The region of interest (ROI) corresponding to the original lesion was manually delineated to define the intralesional ROI on bp-MRI maps. The original lesion ROI was then expanded by 2 mm, 4 mm, 6 mm, and 8 mm, while excluding the intralesional area to create the perilesional ROI. Features were extracted from each ROI, and a radiomics model was developed using logistic regression. The combined model integrated features from both intralesional and perilesional regions. Its predictive performance was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC) to evaluate its diagnostic efficacy for PCa.

Results: The comparison revealed that perilesional 4 mm model had the best performance among all perilesional models, its AUCs of 0.934 and 0.894 in the training testing set, respectively, outperformed the combined model of other regions. The clinical model, combined model for intralesional regions, and INTRAPERI model achieved AUCs of 0.911, 0.925, 0.931 in the training sets and 0.770, 0.867, 0.905 in the testing sets. The predictive performance of the INTRAPERI model is better than the clinical model and intralesional model.

Conclusion: The radiomic model combining intralesional and perilesional features from bi-parametric MRI shows strong predictive value for PCa and may enhance clinical decision-making.

Background: Spread through air spaces (STAS) in lung adenocarcinoma means different treatment and worse prognosis.

Purpose: To construct a radiomics model based on CT scans to predict the presence of STAS in stage IA lung adenocarcinoma, compared with the traditional clinical model.

Material and methods: The study included 317 patients (median age = 57.21 years; age range = 45.84-68.61 years) with pathologically confirmed stage IA lung adenocarcinoma. In total, 122 (38.5%) patients were diagnosed with STAS by pathology after the operation. Two experienced radiologists independently segmented the lesions using MITK software and extracted 1791 radiomics features using Python. Single-factor t-test or Mann-Whitney U-test and LASSO were used to screen for radiomics signatures related to STAS. This study constructed a radiomics model, a clinical model, and a combined model, combining radiomics and clinical features. Model performance was evaluated using the area under the curve (AUC).

Results: By single-factor analysis, four clinical features and 13 radiomics features were significantly associated with STAS. The three models (the clinical, radiomics, and combine models) achieved predictive efficacy, with an AUC of 0.849, 0.867, and 0.939, respectively, in the training set and 0.808, 0.848, and 0.876, respectively, in the testing set.

Conclusion: The combined model based on the radiomics and clinical features of preoperative chest CT could be used to preoperatively diagnose the presence of STAS in stage IA lung adenocarcinoma and has an excellent diagnostic performance.