BMC Medical Imaging最新文献

Objective: This study aims to evaluate the clinical utility of using 3D Slicer software for volumetric measurement of the cranial cavity and cerebral ventricular system, particularly in hydrocephalus patients. We also provide detailed steps for performing the measurements.

Methods: Volumetric measurements were performed on 186 healthy volunteers, 117 hydrocephalus patients with intact skulls, and 72 hydrocephalus patients with incomplete skulls using 3D Slicer based on computed tomography (CT) data. CT scans were performed using a GE Discovery750 scanner and analyzed with 3D Slicer software (version 5.0.2). Cranial cavity volumes were measured using two methods: the Swiss Skull Stripper module and the Segment Editor tool. Ventricular volumes were assessed by segmenting the ventricles and periventricular structures with anatomical markers. Data were analyzed for consistency and accuracy using SPSS version 25.0, with statistical significance set at p ≤ 0.05.

Results: Intracranial volume measurements showed no significant differences between healthy controls and HANPH patients, nor between different measurement methods. In healthy controls, males had larger ventricular volumes than females, and older individuals had larger volumes, except for the fourth ventricle. The left lateral ventricle was larger than the right. No discrepancies were found between measurements taken by two neurosurgeons.

Conclusion: The volumetric measurement of cranial cavity and cerebral ventricular system with 3D Slicer software based on CT data are accurate, repeatable and consistent, providing methodological and technical support for hydrocephalus research, especially for incomplete skull patients, the third ventricle and the fourth ventricle.

Clinical trial number: Not applicable.

Background: To develop a predictive nomogram for breast cancer lympho-vascular invasion (LVI), based on digital breast tomography (DBT) data obtained from intra- and peri-tumoral regions.

Methods: One hundred ninety-two breast cancer patients were enrolled in this retrospective study from 2 institutions, in which Institution 1 served as the basis for training (n = 113) and testing (n = 49) sets, while Institution 2 served as the external validation set (n = 30). Tumor regions of interest (ROI) were manually-delineated on DBT images, in which peri-tumoral ROI was defined as 1 mm around intra-tumoral ROI. Radiomics features were extracted, and logistic regression was used to construct intra-, peri-, and intra- + peri-tumoral radiomics models. Patient clinical data was analyzed by both uni- and multi-variable logistic regression analyses to identify independent risk factors for the non-radiomics clinical imaging model, and the combination of both the most optimal radiomics and clinical imaging models comprised the comprehensive model. The best-performing model out of the 3 types (radiomics, clinical imaging, comprehensive) was identified using receiver operating characteristic (ROC) curve analysis, and used to construct the predictive nomogram.

Results: The most optimal radiomics model was the intra- + peri-tumoral model, and 3 independent risk factors for LVI, maximum tumor diameter (odds ratio [OR] = 1.486, 95% confidence interval [CI] = 1.082-2.041, P = 0.014), suspicious malignant calcification (OR = 2.898, 95% CI = 1.232 ~ 6.815, P = 0.015), and axillary lymph node (ALN) metastasis (OR = 3.615, 95% CI = 1.642-7.962, P < 0.001) were identified by the clinical imaging model. Furthermore, the comprehensive model was the most accurate in predicting LVI occurrence, with areas under the curve (AUCs) of 0.889, 0.916, and 0.862, for, respectively, the training, testing and external validation sets, compared to radiomics (0.858, 0.849, 0.844) and clinical imaging (0.743, 0.759, 0.732). The resulting nomogram, incorporating radiomics from the intra- + peri-tumoral model, as well as maximum tumor diameter, suspicious malignant calcification, and ALN metastasis, had great correspondence with actual LVI diagnoses under the calibration curve, and was of high clinical utility under decision curve analysis.

Conclusions: The predictive nomogram, derived from both radiomics and clinical imaging features, was highly accurate in identifying future LVI occurrence in breast cancer, demonstrating its potential as an assistive tool for clinicians to devise individualized treatment regimes.

Background: To investigate the clinical value of fetal heart quantification (fetal HQ) in the evaluation of right ventricular size, morphology and cardiac function in fetuses with redundancy foramen ovale flap (RFOF).

Methods: 31 fetuses diagnosed with RFOF through echocardiography from September 2021 to December 2023 were selected as the control group, and 62 healthy fetuses that matched the age and gestational period of the pregnant women in the RFOF group were chosen as the control group. Fetal HQ software provided by GE Voluson E10 was employed to automatically track endocardial parameters of the right ventricle in 24 segments.

Results: The internal diameter of foramen ovale in RFOF group was significantly smaller than that of normal fetal diameter in control group, with statistical significance (P < 0.05). Comparing the morphological parameters of the fetuses in the RFOF group and the control group, there was no statistically significant difference in the GSI scores (P > 0.05), but the RV-LED of the fetuses in the RFOF group in the segments of 1-24 were higher than the fetuses in the normal control group (both P < 0.05), and the RV-SI was lower than that in the normal control group (all P < 0.05).

Conclusions: The Fetal HQ technique enables accurate localisation of the site of the RFOF foetal lesion by rapid quantitative analysis of morphological and functional parameters of the right ventricle of the foetal heart.

Background: To compare the influence of rectal susceptibility artifacts on the subjective evaluation and deep learning (DL) in prostate cancer (PCa) diagnosis.

Methods: This retrospective two-center study included 1052 patients who underwent MRI and biopsy due to clinically suspected PCa between November 2019 and November 2023. The extent of rectal artifacts in these patients' images was evaluated using the Likert four-level method. The PCa diagnosis was performed by six radiologists and an automated PCa diagnosis DL method. The performance of DL and radiologists was evaluated using the area under the receiver operating characteristic curve (AUC) and the area under the multi-reader multi-case receiver operating characteristic curve, respectively.

Results: Junior radiologists and DL demonstrated statistically significantly higher AUCs in patients without artifacts compared to those with artifacts (R1: 0.73 vs. 0.64; P = 0.01; R2: 0.74 vs. 0.67; P = 0.03; DL: 0.77 vs. 0.61; P < 0.001). In subgroup analysis, no statistically significant differences in the AUC were observed among different grades of rectal artifacts for both all radiologists (0.08 ≤ P ≤ 0.90) and DL models (0.12 ≤ P ≤ 0.96). The AUC for DL without artifacts significantly exceeded those with artifacts in both the peripheral zone (PZ) and transitional zone (TZ) (DL_PZ: 0.78 vs. 0.61; P = 0.003; DL_TZ: 0.73 vs. 0.59; P = 0.011). Conversely, there were no statistically significant differences in AUC with and without artifacts for all radiologists in PZ and TZ (0.08 ≤ P ≤ 0.98).

Conclusions: Rectal susceptibility artifacts have significant negative effects on subjective evaluation of junior radiologists and DL.

Clinical trial number: Not applicable.

Background: Vesical Imaging-Reporting and Data System (VI-RADS) based on multiparametric magnetic resonance imaging (mp-MRI) performed well in diagnosing muscle-invasive bladder cancer (MIBC). However, certain cases may present challenges in determining the final VI-RADS score using only T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, especially in the absence of dynamic contrast-enhanced (DCE) imaging. This study aims to evaluates whether biparametric MRI (bp-MRI) achieve comparable diagnostic performance to mp-MRI for predicting MIBC and seeks to identify the most suitable bp-MRI criterion by establishing four specific conditions based on T2WI and DWI.

Methods: A retrospective analysis was conducted on 447 patients who underwent preoperative mp-MRI. Images were evaluated according to the VI-RADS protocol by three independent readers. In the bp-DWI and bp-DWI Plus criteria, DWI was the primary sequence used for lesion assessment, while T2WI was the primary sequence for bp-T2WI and bp-T2WI Plus criteria. The Plus criteria (bp-DWI Plus and bp-T2WI Plus) assigned a final VI-RADS score of 4 when both T2WI and DWI scores were 3. The gold standard for diagnosis was histopathological evaluation after surgery. Diagnostic performance was evaluated by comparing the area under the curve (AUC), sensitivity, specificity, and inter-reader agreement using Cohen's kappa analysis.

Results: Among 447 patients, 304 confirmed as NMIBC and 143 as MIBC. The kappa values were 0.876, 0.873, 0.873, 0.642, and 0.642 for mp-MRI, bp-DWI, bp-DWI Plus, bp-T2WI, and bp-T2WI Plus, respectively, when VI-RADS cutoff > 2. Similarly, when cutoff > 3, the kappa values were 0.848, 0.811, 0.873, 0.811, and 0.873. No significant differences were observed between mp-MRI and bp-DWI (AUC: 0.916 vs. 0.912, p = 0.498), but mp-MRI and bp-DWI had higher AUCs compared to bp-DWI Plus, bp-T2WI, and bp-T2WI Plus.

Conclusions: Both mp-MRI and bp-DWI demonstrate excellent performance in predicting MIBC, with bp-DWI being an alternative to mp-MRI.

Trial registration: retrospectively.

Background: ¹⁸F fluoro-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) pharmacokinetics is an approach for efficiently quantifying perfusion and metabolic processes in the liver, but the conventional single-individual optimization algorithms and single-population optimization algorithms have difficulty obtaining reasonable physiological characteristics from estimated parameters. A prior-based multi-population multi-objective optimization (p-MPMOO) approach using two sub-populations based on two categories of prior information was preliminarily proposed for estimating the ¹⁸F-FDG PET/CT pharmacokinetics of patients with hepatocellular carcinoma.

Methods: PET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60 min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (K₁, k₂, k₃, k₄, f_a, and [Formula: see text]) were used to quantify the metabolic information. The single-individual Levenberg-Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters.

Results: The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K₁ and k₄ (P < 0.05 in the DeLong test) and the single population optimization in k₂ and k₃ (P < 0.05), and did not differ from other methods in f_a and v_b (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K₁, k₂, k₃, and k₄. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k₂, k₃, k₄, and f_a. The p-MPMODE is implemented on K₁, k₂, k₃, k₄, and f_a; The p-MPMOGA does it on all six parameters.

Conclusions: The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.

Background: Recently, pediatric hip joint diseases have received increasing attention, however, most researches focus on conventional ultrasound. The aim of our study is to explore the application of real-time shear wave elastography (SWE) technology in different tissue structures of healthy pediatric hip joints to distinguish between the normal and pathological states, and provide a normal reference range for shear wave Young's moduli for clinical practices and subsequent scientific researches.

Methods: According to the selection criteria, 189 healthy full-term infants with 378 hip joints were enrolled, including 102 males and 87 females aged 2-90 days. They were divided into three groups based on age: 0-30 days (61 patients), 31-60 days (63 patients), and 61-90 days (65 patients). All the subjects underwent routine ultrasound examination to perform Graf typing, and then subjected to the SWE. The Young's moduli of the femoral head, acetabular lip, acetabular cartilage apex, gluteus medius, gluteus minimus, and iliacus were recorded. The differences in various parts among the three groups, between the left and right sides, and between males and females were compared. The 95% medical reference value range for each part was obtained and consistency test was conducted.

Results: There was no statistically significant difference in various parts between the left and right hip joints (P > 0.05) or between males and females (P > 0.05). There were significant differences in the femoral head, acetabular lip, and acetabular cartilage apex among the three groups (P < 0.05). The Young's moduli of the femoral head, acetabular lip, and acetabular cartilage apex were positively correlated with age (r₁ = 0.56, P < 0.05; r₂ = 0.51, P < 0.05; r₃ = 0.58, P < 0.05). The Young's moduli of the gluteus medius, gluteus minimus, and iliacus were not correlated with age (P > 0.05). The intra- and inter-observer evaluation results both had a high correlation, and the 95% Confidence Interval (95% CI) of both were relatively concentrated.

Conclusion: Real-time SWE technology can be used to obtain the Young's moduli of healthy pediatric hip joints and surrounding tissues, and distinguish between healthy and pathological states. This can provide a normal reference range for shear wave Young's moduli for clinical practices and subsequent scientific researches.

Background: Both meningiomas and schwannomas are the most common Meckel's cave (MC) tumors in terms of distinct imaging features. When they are small, they may present with similar imaging characteristics that make their diagnosis difficult. The aim of this study was to diagnose small meningiomas and schwannomas of the MC on the basis of their clinical and MRI findings.

Methods: The clinical data of 33 patients who were diagnosed with small MC tumors (SMCTs) (17 schwannomas, 16 meningiomas) between August 2002 and August 2023 were retrospectively evaluated. SMCTs were defined as MC tumors that were less than 3 cm in size. We analyzed their clinical and MRI findings, including demographic features, lesion morphologies and changes in adjacent structures.

Results: The rate of subtotal resection of meningiomas less than 3 cm in size was significantly lower than that of schwannomas less than 3 cm in size (43.8% vs. 100%, p = 0.032). The MRI features of meningiomas and schwannomas were as follows: 1) a prominent dura tail sign (8/16 [50%] vs. 0/17 [0%], p < 0.001); 2) few cystic components (0/16 [0%] vs. 9/17 [52.94%], p < 0.001); 3) lower minimum ADC (ADCmin) values (820.575 ± 302.545 [86.1-1144.4] vs. 1372.424 ± 561.337 [355.7-2616.6], p < 0.001); and 4) minimal ipsilateral masticatory muscle atrophy (-6.71% ± 22.43% [-85.71% ~ 13.79%] vs. 11.24% ± 11.98% [-14% ~ 38%], p < 0.001). Very small MC tumors (VSMCTs) were ≤ 2 cm in size, and the subgroup analysis of very small meningiomas and schwannomas revealed no differences in terms of ipsilateral masticatory muscle atrophy (p = 0.078), prominence of the dural tail (p = 0.236), or the presence of cystic components (p = 0. 364). However, the ADCmin values were significantly lower for very small meningiomas than for very small schwannomas (p = 0.009).

Conclusion: MRI features such as a prominent dural tail appearance, the presence of fewer cystic components, and less masticatory muscle atrophy may aid in differentiating meningiomas from schwannomas less than 3 cm in size. The ADC and DWI parameters provided additional critical insights, particularly for VSMCTs, thus facilitating preoperative diagnoses.

Purpose: To develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC) and olfactory neuroblastoma (ONB) and to evaluate whether the DL models could improve the diagnostic performance of Senior radiologist (SR) and Junior radiologist (JR).

Methods: This retrospective analysis consisted of 465 patients (229 sinonasal SCCs, 128 ACCs and 108 ONBs). The training and validation cohorts included 325 and 47 patients and the independent external testing cohort consisted of 93 patients. MRI images included T2-weighted image (T2WI), contrast-enhanced T1-weighted image (CE-T1WI) and apparent diffusion coefficient (ADC). We analyzed the conventional MRI features to choose the independent predictors and built the conventional MRI model. Then we compared the macro- and micro- area under the curves (AUCs) of different sequences and different DL networks to formulate the best DL model [artificial intelligence (AI) model scheme]. With AI assistance, we observed the diagnostic performances between SR and JR. The diagnostic efficacies of SR and JR were assessed by accuracy, Recall, precision, F1-Score and confusion matrices.

Results: The independent predictors of conventional MRI included intensity on T2WI and intracranial invasion of sinonasal malignancies. With ExtraTrees (ET) classier, the conventional MRI model owned AUC of 78.8%. For DL models, ResNet101 network showed better performance than ResNet50 and DensNet121, especially for the mean fusion sequence (macro-AUC = 0.892, micro-AUC = 0.875, Accuracy = 0.810), and also good for the ADC sequence (macro-AUC = 0.872, micro-AUC = 0.874, Accuracy = 0.814). Grad-CAM showed that DL models focused on solid component of lesions. With the best AI scheme (ResNet101-mean sequence-based DL model) assistance, the diagnosis performances of SR (accuracy = 0.957, average Recall = 0.962, precision = 0.955, F1-Score = 0.957) and JR (accuracy = 0.925, average Recall = 0.917, precision = 0.931, F1-Score = 0.923) were significantly improved.

Conclusion: The ResNet101 network with mean sequence based DL model could effectively differential between sinonasal SCC, ACC and ONB and improved the diagnostic performances of both senior and junior radiologists.

Purpose: This study aimed to determine whether adding pointwise encoding time reduction with radial acquisition (PETRA) images to conventional magnetic resonance (MR) imaging improves the depiction and characterization of traumatic fractures in pediatric patients.

Methods: Twenty-nine pediatric subjects with fractures and a control group of twenty individuals without fractures were included. Two independent observers assessed conventional MR, PETRA, and combined MRI + PETRA images, documenting the presence of fractures, bone fragments, callus formation, displacement, size, and angles of fractures.

Results: Diagnostic accuracy was higher for combined conventional MR with PETRA images than for conventional MR or PETRA alone in detecting fractures (area under curve (AUC): 0.86 for conventional MR, 0.97 for PETRA, 1.00 for combined), callus formation (AUC: 0.67 conventional MR, 0.82 PETRA, 0.86 combined), and bone fragments (AUC: 0.89 conventional MR, 0.96 PETRA, 0.97 combined). PETRA images improved agreement in detecting fractures, especially in the ulna/radius (κ = 0.46 conventional MR, 1.00 PETRA, 1.00 combined) and fibula/talus (κ = 0.42 conventional MR, 1.00 PETRA, 1.00 combined), compared to CT. PETRA also enhanced agreement in characterizing callus formation, bone fragments, displacement, size and fracture angles (intraclass correlation > 0.88 for all comparisons), compared to CT. Addition of PETRA images revealed that the differences in the measurements of fragment displacement, size, and fracture angle compared to CT, were not statistically significant (all p > 0.05).

Conclusion: Adding PETRA images to conventional MR enhances diagnostic accuracy and reliability in detecting fractures among pediatric patients compared to conventional MR alone.

Clinical trial number: Not applicable.