Acta radiologica最新文献

BackgroundPreoperative identification of normal-sized lymph node metastases (LNM) remains clinically significant yet challenging in cervical cancer.PurposeTo investigate the value of super-resolution T2WI-derived intratumoral and peritumoral radiomics for normal-sized LNM prediction in cervical cancer.Material and MethodsA total of 257 patients from three sites of our hospital were divided into a development cohort (site 1, n = 97), a validation cohort (site 1, n = 42), and two internal test cohorts (site 2, n = 62; site 3, n = 56). Super-resolution reconstruction based on generative adversarial network was applied to all images. The volume of interest delineation encompassed primary tumor boundaries with outward expansions (1-5 mm increments) in super-resolution T2-weighted (T2W) imaging. Radiomics features were independently extracted from intratumoral and five peritumoral regions. The clinical, radiomics and combined models were built using multilayer perceptron. Model performance was evaluated through receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).ResultsThe IntraPeri3 mm radiomics model achieved superior discriminative performance compared to other radiomics models. The combined model integrated clinical variables (tumor size and squamous cell carcinoma antigen), intratumoral and peritumoral 3 mm radiomics features yielded optimal performance (AUC = 0.838 in the development cohort, 0.808 in the validation cohort, and 0.769 and 0.766 in the internal test cohorts). DCA confirmed the combined model's enhanced clinical utility across probability thresholds.ConclusionSuper-resolution T2W-based radiomics aids in predicting normal-sized LNM in cervical cancer, especially the combined model incorporating clinical information, intratumoral and peritumoral 3 mm radiomics features demonstrates optimal diagnostic performance.

BackgroundHepatic cystic echinococcosis (CE) is a major health concern, particularly in endemic regions. The modified catheterization technique (MoCaT) offers an effective option for the percutaneous treatment of hepatic CE.PurposeTo evaluate the effectiveness of MoCaT in the percutaneous treatment of all types of hepatic CE in 183 patients.Material and MethodsData from 183 patients who underwent percutaneous treatment using the MoCaT between February 2010 and April 2024 were retrospectively analyzed. Demographic data, cyst types, initial and final cyst volumes, complications, lengths of hospitalization, catheterization, and follow-up periods were documented for each patient.ResultsThis study included 183 patients with 211 hepatic hydatid cysts who were treated percutaneously with MoCaT. Among the 211 cysts, 20 (9.5%) were CE1, 37 (18%) were CE2, 23 (11%) were CE3a, 98 (46%) were CE3b, 16 (7.6%) were CE4, and 2 (0.9%) were CE5. In total, 15 (7%) patients presented with infected cyst cavities. No mortality was seen. A total of 29 (14%) major complications were observed. Recurrence was observed in 6 (3.85%) patients, and residual daughter vesicles remained in 1 (0.64%) patient. The mean follow-up period was 48.35 ± 36.42 months (range = 3-133 months). Mean volume reduction was 92.69%.ConclusionThe MoCaT is an effective, minimally invasive method with low morbidity and recurrence rates. In addition to its reported success in treating CE2 and CE3b cysts, we report that the MoCaT is also safe, reliable, and effective for the treatment of all types of hepatic CE.

BackgroundHigh-resolution magnetic resonance imaging (MRI) is a feasible method for identifying and measuring luminal thrombosis in the occluded middle cerebral artery (MCA) of stroke patients.PurposeTo evaluate the relationship between thrombus length in the middle cerebral artery (MCA) and clinical prognosis in stroke patients using high-resolution MRI (T1-weighted [T1W]-CUBE).Material and MethodsIn this retrospective study, patients with MCA thrombi were identified via high-resolution MRI. Thrombus length was measured using T1W-CUBE sequences, and clinical prognosis was assessed using modified Rankin scale (mRS) scores at a 3-month follow-up. Patients were categorized into good or poor prognosis groups based on mRS scores. Statistical analyses compared thrombus length and diameter between groups. Restricted cubic spline (RCS) analyses were performed to examine the relationship between thrombus length, diameter, and mRS scores. Multiple logistic regression was used to explore the association between thrombus length and prognosis.ResultsThe mean thrombus length was significantly longer in the poor prognosis group compared to the good prognosis group (25.69 ± 8.78 mm vs. 15.11 ± 6.91 mm; P <0.001). Thrombus diameter did not significantly differ between the groups (P = 0.961). RCS showed a monotonically increasing relationship between thrombus length and log OR of mRS. Multiple logistic regression indicated that each additional millimeter of thrombus length increased the risk of poor prognosis by 1.22 times (odds ratio = 1.22, 95% confidence interval = 1.11-1.40; P <0.05).ConclusionMCA thrombus length is a significant predictor of prognosis in stroke patients, with longer thrombi associated with worse clinical outcomes.

BackgroundIntracranial pressure is closely associated with pulsatile tinnitus (PT) caused by sigmoid sinus wall dehiscence (SSWD). Cerebrospinal fluid (CSF) plays a key role in regulating intracranial pressure; however, CSF alterations have not been reported in SSWD-PT patients.PurposeTo evaluate cardiac-driven CSF flow dynamics and volume changes in SSWD-PT patients with normal intracranial pressure.Material and MethodsSSWD-PT patients with normal intracranial pressure and age-, sex-, and handedness-matched healthy controls were prospectively enrolled and underwent MRI. Intracranial pressure was assessed using the index of transverse sinus stenosis and morphological changes. Cardiac-driven CSF flow dynamics were quantified by phase-contrast magnetic resonance imaging (MRI), and CSF volume was measured using ITK-SNAP segmentation software.ResultsThe study included 20 SSWD-PT patients and 35 controls. Compared with controls, the PT group showed a significant decrease in mean flux (MF) and a significant increase in regurgitant fraction (RF) (P = 0.043 and 0.008, respectively). No significant differences were observed in other parameters. The area under the curve (AUC), sensitivity, and specificity for MF and RF were 0.643, 100.0%, 31.4%, and 0.716, 50.0%, 88.6%, respectively. The combined diagnostic efficacy of MF and RF (AUC = 0.764) was higher than RF alone, though the difference was not significant (P = 0.390). The combined model and RF demonstrated significantly better diagnostic efficacy than MF (P = 0.025 and 0.045, respectively).ConclusionSSWD-PT patients exhibited altered cardiac-driven CSF flow dynamics, which may contribute to PT. The combination of MF and RF may serve as a complementary index for identifying the underlying etiology of SSWD-PT.

BackgroundComputed tomography with fistulography (CTF) using contrast material (CM) injected into a fistula is commonly used to visualize fistulous tracts (FT) in chronic osteomyelitis or chronic wounds. Typically, a CT scan is done twice: first without CM to identify sequestrum, followed by using CM to visualize the FT.PurposeTo develop a streamlined and effective CTF method that reduces patient radiation exposure and lowers examination costs.Material and MethodsThe method employs hydrogen peroxide (H₂O₂) as a contrast agent. When H₂O₂ enters the FT, it breaks down into water and oxygen, spreading under low pressure. Since gas has a significantly lower radiopacity than iodinated contrast agents, gas-filled fistulas appear black on the CT monitor, while CM, foreign bodies, and bones appear white. This technique has been used in over 200 patients for the lower and uppser extremities, pelvis, spine, and chest.ResultsThe images enabled a simultaneous investigation of the spatial relationship between the FT and sequestrum, irrespective of the body segment examined. Only a single scan was necessary. No complications or discomfort were reported by the patients.ConclusionThis method enhanced the topographic diagnosis of FT and the spatial positioning of sequestrum or foreign bodies, halving patient radiation exposure. It eliminates the need for expensive iodinated contrast agents and their associated complications, significantly reducing examination costs and time.

BackgroundIliocaval obstruction causes severe post-thrombotic syndrome (PTS) and impairs quality of life.PurposeTo evaluate endovascular treatment efficacy for symptomatic iliocaval obstruction in PTS.Material and MethodsA total of 17 patients underwent iliocaval venous reconstruction with stents between 2017 and 2022. All had bilateral iliac vein involvement; five presented with acute iliofemoral thrombosis and 12 with chronic iliocaval obstruction. All 17 patients had lower limb edema; nine patients reported lower limb pain (three acute iliofemoral thrombosis and six PTS) and four had active ulceration (all PTS). The technical results, adverse events, follow-up, and outcomes were documented.ResultsAll patients received successful iliocaval reconstruction with stents in a kissing configuration, with successful outcomes. Over a mean follow-up of 46.5 ± 16.7 months (final follow-up in April 2025), all four patients with baseline venous ulcers achieved complete healing, and no new or recurrent ulcerations were observed. Significant clinical improvement was observed, with mean Villalta scores reducing from 17.6 ± 5.1 at baseline to 3.9 ± 1.6 at 1 month postoperatively (P <0.001). At final follow-up, only two patients had symptom recurrence (mean Villalta score = 6.1 ± 4.7), with no significant change from the 1 month (P = 0.5). Reintervention for stent stenosis occurred in 35.3%. Primary patency rates were 82.4% (12 months), 70.6% (24 months), and 64.2% (36 months); secondary patency rates reached 94.1% (12 months), 94.1% (24 months), and 82.4% (36 months).ConclusionEndovascular interventions for iliocaval obstruction are safety and effectiveness, with excellent patency rates achieved.

BackgroundManual data curation was necessary to extract radiology reports due to the ambiguities of natural language.PurposeTo develop a fine-tuned large language model that classifies computed tomography (CT)-guided interventional radiology reports into technique categories and to compare its performance with that of the readers.Material and MethodsThis retrospective study included patients who underwent CT-guided interventional radiology between August 2008 and November 2024. Patients were chronologically assigned to the training (n = 1142; 646 men; mean age = 64.1 ± 15.7 years), validation (n = 131; 83 men; mean age = 66.1 ± 16.1 years), and test (n = 332; 196 men; mean age = 66.1 ± 14.8 years) datasets. In establishing a reference standard, reports were manually classified into categories 1 (drainage), 2 (lesion biopsy within fat or soft tissue density tissues), 3 (lung biopsy), and 4 (bone biopsy). The bi-directional encoder representation from the transformers model was fine-tuned with the training dataset, and the model with the best performance in the validation dataset was selected. The performance and required time for classification in the test dataset were compared between the best-performing model and the two readers.ResultsCategories 1/2/3/4 included 309/367/270/196, 30/42/40/19, and 75/124/78/55 patients for the training, validation, and test datasets, respectively. The model demonstrated an accuracy of 0.979 in the test dataset, which was significantly better than that of the readers (0.922-0.940) (P ≤0.012). The model classified reports within a 49.8-53.5-fold shorter time compared to readers.ConclusionThe fine-tuned large language model classified CT-guided interventional radiology reports into four categories demonstrating high accuracy within a remarkably short time.

BackgroundLimited data exist comparing the detection of microscopic fat in adrenal adenomas on two-dimensional chemical shift dual-echo (2D CSI) magnetic resonance imaging (MRI) and three-dimensional two-point Dixon techniques (3D Dixon).PurposeTo compare the sensitivity of 2D CSI versus 3D Dixon techniques for the diagnosis of adrenal adenomas.Material and MethodsA retrospective analysis was conducted of 33 patients with adrenal masses who underwent both 2D CSI and 3D Dixon sequences on a 1.5-T scanner. Two blinded radiologists measured and calculated signal intensity (SI) index (SII) (100×(SI in phase - SI out of phase)/SI in phase) of nodules on each technique. Reference standard diagnosis of 30 adrenal adenomas was established. Sensitivity for adrenal adenoma diagnosis was determined using a SII >16.5%.ResultsIn total, 33 nodules were investigated (mean size=22 mm, range=11-55 mm). Of the 30 adenomas, the mean SII on 2D CSI was 48% for reader 1 and 44% for reader 2, compared to 34% on 3D Dixon for both readers (P < 0.001). Sensitivity for the diagnosis of adenoma with 2D CSI was 90% (95% confidence interval [CI]=82-98) for both readers, while 3D Dixon demonstrated a sensitivity of 73% (95% CI=65-82) for reader 1 and 63% (95% CI=55-72) for reader 2.Conclusion2D dual gradient-echo CSI demonstrated a higher sensitivity for the diagnosis of adrenal adenoma than the 3D Dixon technique. Adrenal MRI evaluation of the adrenal glands at 1.5 T should include 2D dual gradient-echo CSI and not rely solely on 3D two-point Dixon techniques for the diagnosis of adrenal adenomas.

BackgroundThe use of artificial intelligence (AI) in screen-reading of mammograms has shown promising results for cancer detection. However, less attention has been paid to the false positives generated by AI.PurposeTo investigate mammographic features in screening mammograms with high AI scores but a true-negative screening result.Material and MethodsIn this retrospective study, 54,662 screening examinations from BreastScreen Norway 2010-2022 were analyzed with a commercially available AI system (Transpara v. 2.0.0). An AI score of 1-10 indicated the suspiciousness of malignancy. We selected examinations with an AI score of 10, with a true-negative screening result, followed by two consecutive true-negative screening examinations. Of the 2,124 examinations matching these criteria, 382 random examinations underwent blinded consensus review by three experienced breast radiologists. The examinations were classified according to mammographic features, radiologist interpretation score (1-5), and mammographic breast density (BI-RADS 5th ed. a-d).ResultsThe reviews classified 91.1% (348/382) of the examinations as negative (interpretation score 1). All examinations (26/26) categorized as BI-RADS d were given an interpretation score of 1. Classification of mammographic features: asymmetry = 30.6% (117/382); calcifications = 30.1% (115/382); asymmetry with calcifications = 29.3% (112/382); mass = 8.9% (34/382); distortion = 0.8% (3/382); spiculated mass = 0.3% (1/382). For examinations with calcifications, 79.1% (91/115) were classified with benign morphology.ConclusionThe majority of false-positive screening examinations generated by AI were classified as non-suspicious in a retrospective blinded consensus review and would likely not have been recalled for further assessment in a real screening setting using AI as a decision support.

BackgroundA timely assessment of local recurrence (LoR) risk in extremity high-grade osteosarcoma is crucial for optimizing treatment strategies and improving patient outcomes.PurposeTo explore the potential of machine-learning algorithms in predicting LoR in patients with osteosarcoma.Material and MethodsData from patients with high-grade osteosarcoma who underwent preoperative radiograph and multiparametric magnetic resonance imaging (MRI) were collected. Machine-learning models were developed and trained on this dataset to predict LoR. The study involved selecting relevant features, training the models, and evaluating their performance using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). DeLong's test was utilized for comparing the AUCs.ResultsThe performance (AUC, sensitivity, specificity, and accuracy) of four classifiers (random forest [RF], support vector machine, logistic regression, and extreme gradient boosting) using radiograph-MRI as image inputs were stable (all Hosmer-Lemeshow index >0.05) with the fair to good prognosis efficacy. The RF classifier using radiograph-MRI features as training inputs exhibited better performance (AUC = 0.806, 0.868) than that using MRI only (AUC = 0.774, 0.771) and radiograph only (AUC = 0.613 and 0.627) in the training and testing sets (P <0.05) while the other three classifiers showed no difference between MRI-only and radiograph-MRI models.ConclusionThis study provides valuable insights into the use of machine learning for predicting LoR in osteosarcoma patients. These findings emphasize the potential of integrating radiomics data with algorithms to improve prognostic assessments.