T2-weighted Images Research Articles

Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction.

The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volumetry. This retrospective study collected 3D T1-weighted data using 3T from 42 participants for the simulated acceleration dataset and 48 for the validation dataset. The simulated acceleration dataset consists of three sets at different simulated acceleration levels (Simul-Accel) corresponding to level 1 (65% undersampling), 2 (70%), and 3 (75%). These images were then subjected to deep learning-based reconstruction (Simul-Accel-DL). Conventional images (Conv) without acceleration and DL were set as the reference. In the validation dataset, DICOM images were collected from Conv and accelerated scan with DL-based reconstruction (Accel-DL). The image quality of Simul-Accel-DL was evaluated using quantitative error metrics. Volumetric measurements were evaluated using intraclass correlation coefficients (ICCs) and linear regression analysis in both datasets. The volumes were estimated by two software, NeuroQuant and DeepBrain. Simul-Accel-DL across all acceleration levels revealed comparable or better error metrics than Simul-Accel. In the simulated acceleration dataset, ICCs between Conv and Simul-Accel-DL in all ROIs exceeded 0.90 for volumes and 0.77 for normative percentiles at all acceleration levels. In the validation dataset, ICCs for volumes > 0.96, ICCs for normative percentiles > 0.89, and R2 > 0.93 at all ROIs except pallidum demonstrated good agreement in both software. DL-based reconstruction achieves clinical feasibility of 3D T1 brain volumetric MRI by up to 75% acceleration relative to full-sampled acquisition.

Fast diffusion kurtosis imaging for venous stroke caused by cerebral venous thrombosis

Background: Diffusion kurtosis imaging (DKI) has been found to be more precise than diffusion weighted imaging (DWI) for detecting irreversible infarction in acute ischemic stroke. This study aimed to evaluate whether fast DKI has distinctive advantages in detecting venous stroke caused by cerebral venous thrombosis (CVT). Methods: All data from patients diagnosed with venous stroke due to CVT and qualified for mechanical recanalization treatment were collected. Fast DKI and DWI were obtained both before and after revascularization therapy, and lesions were measured. Lesion volumes on T2 weighted imaging (T2WI) were followed up at six months. Results: A total of 11 patients were recruited. Compared to the contralateral brain, ADC values of the lesions in pre-operation increased significantly (1340.12±235.42 vs 919.75±128.98, P < 0.05), while MK decreased (0.59±0.11 vs 0.81±0.12, P < 0.05). And the same changing trend about ADC values (1258.94±185.08 vs 949.81±148.52, P < 0.05) and MK values (0.64±0.12 vs 0.83±0.12, P < 0.05) in post-operation. There was no significant difference in the volume of lesions between DKI and DWI during the same examination period (26.97 vs 29.28, Ppre > 0.05; 13.34 vs 13.14, Ppost > 0.05). However, the lesion volume after revascularization was significantly reduced compared to the first DKI and DWI examinations (26.97 vs 13.34, P < 0.05), and the same as DWI (29.28 vs 13.14, P < 0.05). Volume of T2WI lesions after the 6th month diminished significantly compared with both DKI lesions and DWI lesions before treatment (7.25 vs 26.97, P < 0.05; 7.25 vs 31.19, P < 0.05). Fast DKI had a higher signal to noise ratio (SNR) than traditional DKI and DWI. Conclusion: The MK of the venous stroke lesions decreased significantly in the subacute stage of CVT, suggesting reversible infarction. Fast DKI has more distinctive superiority in the evaluation of venous stroke. Fast DKI approach may hold great promise for patients in clinical setting because of a higher SNR than DWI. But the sample need to be further expanded because of only 11 patients recruited.

Bacillus Calmette-Guérin Instillations May Mimic Prostate Cancer on Multiparametric Magnetic Resonance Imaging.

Granulomatous prostatitis (GP) is a rare and benign inflammatory condition of the prostate, often mimicking prostate cancer (PCa) in clinical and radiological evaluations. This study examines the characteristics and diagnostic challenges of GP in a cohort of 12 patients who received Bacillus Calmette-Guérin (BCG) therapy following treatment for non-muscle-invasive bladder urothelial carcinoma. In this case series, we analyze their clinical presentations, MRI findings, and histopathological results. Patients presented with elevated PSA levels and firm or nodular prostates on digital rectal examination, complicating the differentiation from PCa. Multiparametric MRI showed lesions with hypointensity on T2-weighted images, hypersignal on diffusion-weighted imaging and hyposignal on the apparent diffusion coefficient map, further mimicking malignancy. Histopathological examination remains the gold standard for diagnosing GP, distinguishing it from PCa through the identification of granulomas and associated inflammatory cells. This study underscores the importance of awareness and accurate diagnosis of GP to avoid unnecessary biopsies and treatments, highlighting the need for a multidisciplinary approach combining clinical, imaging, and pathological data.

Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma

PurposeTo evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC).Materials and methodsA retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test.ResultsIn predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807–0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1).ConclusionsThe combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.

Preoperative prediction of lymph node metastasis in endometrial cancer patients via an intratumoral and peritumoral multiparameter MRI radiomics nomogram.

While lymph node metastasis (LNM) plays a critical role in determining treatment options for endometrial cancer (EC) patients, the existing preoperative methods for evaluating the lymph node state are not always satisfactory. This study aimed to develop and validate a nomogram based on intra- and peritumoral radiomics features and multiparameter magnetic resonance imaging (MRI) features to preoperatively predict LNM in EC patients. Three hundred and seventy-four women with histologically confirmed EC were divided into training (n = 220), test (n = 94), and independent validation (n = 60) cohorts. Radiomic features were extracted from intra- and peritumoral regions via axial T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) mapping. A radiomics model (annotated as the Radscore) was established using the selected features from different regions. The clinical parameters were statistically analyzed. A nomogram was developed by combining the Radscore and the most predictive clinical parameters. Decision curve analysis (DCA) and the net reclassification index (NRI) were used to assess the clinical benefit of using the nomogram. Nine radiomics features were ultimately selected from the intra- and peritumoral regions via ADC mapping and T2WI. The nomogram combining the Radscore, serum CA125 level, and tumor area ratio achieved the highest AUCs in the training, test and independent validation sets (nomogram vs. Radscore vs. clinical model: 0.878 vs. 0.850 vs. 0.674 (training), 0.877 vs. 0.838 vs. 0.668 (test), and 0.864 vs. 0.836 vs. 0.618 (independent validation)). The DCA and NRI results revealed the nomogram had greater diagnostic performance and net clinical benefits than the Radscore alone. The combined intra- and peritumoral region multiparameter MRI radiomics nomogram showed good diagnostic performance and could be used to preoperatively predict LNM in patients with EC.

Comparison of different MRI-based unsupervised segmentation algorithms in predicting sarcopenia

PurposeTo compare the performance of MRI-based Gaussian mixture model (GMM), K-means clustering, and Otsu unsupervised algorithms in predicting sarcopenia and to develop a combined model by integrating clinical indicators. MethodsRetrospective analysis was conducted on clinical and lumbar MRI data from 118 patients diagnosed with sarcopenia and 222 patients without the sarcopenia. All patients were randomly divided into training and validation groups in a 7:3 ratio. Regions of interest (ROI), specifically the paravertebral muscles at the L3/4 intervertebral disc level, were delineated on axial T2-weighted images (T2WI). The Gaussian mixture model (GMM), K-means clustering, and Otsu’s thresholding algorithms were employed to automatically segment muscle and adipose tissues at both the cohort and case levels. Subsequently, the mean signal intensity, volumes, and percentages of these tissues were calculated and compared. Logistic regression analyses were conducted to construct models and identify independent predictors of sarcopenia. An combined model was developed by combining the optimal magnetic resonance imaging (MRI) model and clinical predictors. The performance of the constructed model was assessed using receiver operating characteristic (ROC) curve analysis. ResultsAge, BMI, and serum albumin were identified as independent clinical predictors of sarcopenia. The cohort-level GMM demonstrated the best predictive performance both in the training group (AUC=0.840) and validation group (AUC=0.800), while the predictive performance of the other models was lower than that of the clinical model both in the training and validation groups. After combining the cohort-level GMM with the independent clinical predictors, the AUC of the training and validation groups increased to 0.871 and 0.867, respectively. ConclusionThe cohort-level GMM shows potential in predicting sarcopenia, and the incorporation of independent clinical predictors further increased the performance.

Repeated intrathecal injections of peripheral nerve-derived stem cell spheroids improve outcomes in a rat model of traumatic brain injury

BackgroundTraumatic brain injury (TBI) is a major cause of disability and mortality worldwide. However, existing treatments still face numerous clinical challenges. Building on our prior research showing peripheral nerve-derived stem cell (PNSC) spheroids with Schwann cell-like phenotypes can secrete neurotrophic factors to aid in neural tissue regeneration, we hypothesized that repeated intrathecal injections of PNSC spheroids would improve the delivery of neurotrophic factors, thereby facilitating the restoration of neurological function and brain tissue repair post-TBI.MethodsWe generated PNSC spheroids from human peripheral nerve tissue using suspension culture techniques. These spheroids were characterized using flow cytometry, immunofluorescence, and reverse-transcription polymerase chain reaction. The conditioned media were evaluated in SH-SY5Y and RAW264.7 cell lines to assess their effects on neurogenesis and inflammation. To simulate TBI, we established a controlled cortical impact (CCI) model in rats. The animals were administered intrathecal injections of PNSC spheroids on three occasions, with each injection spaced at a 3-day interval. Recovery of sensory and motor function was assessed using the modified neurological severity score (mNSS) and rotarod tests, while histological (hematoxylin and eosin, Luxol fast blue staining) and T2-weighted magnetic resonance imaging analyses, alongside immunofluorescence, were conducted to evaluate the recovery of neural structures and pathophysiology.ResultsPNSC spheroids expressed high levels of Schwann cell markers and neurotrophic factors, such as neurotrophin-3 and Ephrin B3. Their conditioned medium was found to promote neurite outgrowth, reduce reactive oxygen species-mediated cell death and inflammation, and influence M1-M2 macrophage polarization. In the CCI rat model, rats receiving repeated triple intrathecal injections of PNSC spheroids showed significant improvements in sensory and motor function, with considerable neural tissue recovery in damaged areas. Notably, this treatment promoted nerve regeneration, axon regrowth, and remyelination. It also reduced glial scar formation and inflammation, while encouraging angiogenesis.ConclusionOur findings suggest that repeated intrathecal injections of PNSC spheroids can significantly enhance neural recovery after TBI. This effect is mediated by the diverse neurotrophic factors secreted by PNSC spheroids. Thus, the strategy of combining therapeutic cell delivery with multiple intrathecal injections holds promise as a novel clinical treatment for TBI recovery.

Association between subcortical nuclei volume changes and cognition in preschool-aged children with tetralogy of Fallot after corrective surgery: a cross-sectional study

BackgroundNeurocognitive disorders frequently occur in patients with cyanotic congenital heart disease (CCHD) because of the hemodynamic abnormalities induced by preoperative cardiac structural changes. We aimed to evaluate subcortical nuclei volume changes and cognition in postoperative tetralogy of Fallot (TOF) children, and analyze their relationship with preoperative cardiac structural changes.MethodsThis case-control study involved thirty-six children with repaired TOF and twenty-nine healthy controls (HCs). We utilized three-dimensional (3D) T1-weighted high-resolution structural images alongside the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) to evaluate the cognitive differences between the TOF and HC group.ResultsWe observed notable differences in subcortical nuclei volume between the TOF and HC group, specifically in the left amygdala nucleus (LAM, TOF: 1292.60 ± 155.57; HC: 1436.27 ± 140.62, p < 0.001), left thalamus proper nucleus (LTHA, TOF: 6771.54 ± 666.03; HC: 7435.36 ± 532.84, p < 0.001), and right thalamus proper nucleus (RTHA, TOF: 6514.61 ± 715.23; HC: 7162.94 ± 554.60, p < 0.001). Furthermore, a diminished integrity of LAM ( β:-19.828, 95% CI: -36.462, -3.193), which showed an inverse relationship with the size of the preoperative ventricular septal defect (VSD), correlated with lower working memory indices in children with TOF.ConclusionsOur findings indicate that subcortical nuclei structural injuries possibly potentially stemming from cardiac anatomical abnormalities, are associated with impaired working memory in preschool-aged children with TOF. The LAM in particular may serve as a potential biomarker for neurocognitive deficits in TOF, offering predictive value for future neurodevelopmental outcomes, and shedding light on the neurophysiological mechanisms of these cognitive impairments.

Multi-Chambered Core/Shell Supraparticles for Real-Time, Full-Time Diagnosis and Treatment Integration of Tumors.

To a certain extent, theranostic nanoplatforms promote tumor treatment efficiency. However, timely monitoring of the critical stages and signal sustainability of the entire process is challenging. In this study, multi-chambered core/shell magnetic nanoparticles (MC-MNPs) as drug and imaging agent multi-loaded nanocarriers with a synergistic release function are reported. Supraparticles with stable chambers are formed by the supercooling self-assembly of several core/shell magnetic nanoparticles composed of amphiphilic copolymers as the core and hydrophilic magnetic iron oxide nanoparticles as the shell. Desalinized doxorubicin and coumarin 6 are stored in different cavities of nanocarriers, and chitosan is used as an outer encapsulation layer. Based on their construction properties, MC-MNPs can exhibit gradient-degraded and steady-released controllability in the tumor environment. Furthermore, real-time accumulation situations and full-time diagnostic signals of nanocarriers are thoroughly demonstrated using fluorescence imaging and T2-weighted magnetic resonance imaging before and after magnetic hyperthermia in targeted tumors under an alternating magnetic field. Thus, MC-MNPs as theranostic nanocarriers exhibit great potential for the timely monitoring and full-time guidance of tumor treatment.

Evaluation of Inflammatory Disease Activity in the Sacroiliac Joints Using Magnetic Resonance Imaging: A Comparative Analysis of Short-Tau Inversion Recovery, Post-Contrast, and Diffusion-Weighted Imaging –Preliminary Study

Introduction: It is essential to detect sacroiliitis earlier to decrease morbidity and unwanted complications such as ankylosing of sacroiliac joints. In recent studies, magnetic resonance imaging (MRI) serves as a key diagnostic tool for identifying sacroiliitis and is now included in the diagnostic criteria. We aim to detect the utility of diffusion-weighted imaging on the MRI in diagnosing sacroiliitis and measure ADC values for the response to treatment in future studies. Materials -methods: There were 39 patients (16 male, 20 female). A 1.5 Tesla MR device with a pelvic coil was used. The sequences were T1 (before and after intravenous contrast administration of gadolinium), T2-weighted fast field echo, short tau inversion recovery (STIR), diffusion-weighted (DW) images, and apparent diffusion coefficient (ADC) mapping. Sacroiliac joints were divided into four quadrants, and two radiologists interpreted all the sequences by giving scores from 0 to 3 depending on bone marrow edema from none to severe. The concordance of scores assigned to STIR, T1-weighted gadolinium-enhanced images, ADC mapping, and diffusion-weighted MR images was evaluated by calculating intraclass correlation coefficients. Results: A statistically significant positive correlation was observed between the activity scores derived from short tau inversion recovery (STIR) and other magnetic resonance (MR) sequences, namely T1-weighted gadolinium-enhanced images, apparent diffusion coefficient (ADC) mapping, and diffusion-weighted MR images. Conclusion: Active bone marrow abnormalities were identified with comparable efficacy using STIR imaging and other MR sequences (T1W-Gad, ADC mapping, DW-MR). In our study, we tried to emphasize the role of the diffusion-weighted images near the other sequences. Diffusion is a fast, easy-applied sequence. After increased experience in the diffusion-weighted images, routine application of this sequence will increase, and even becoming one of the diagnostic criteria will be inevitable. The difference in DWI from the other sequences is that it is measurable. The response to treatment may be followed up by ADC values.

Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI

This study assessed pretreatment breast MRI coupled with machine learning for predicting early clinical responses to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), focusing on identifying non-responders. A retrospective analysis of 135 TNBC patients (107 responders, 28 non-responders) treated with NAC from January 2015 to October 2022 was conducted. Non-responders were defined according to RECIST guidelines. Data included clinicopathologic factors and clinical MRI findings, with radiomics features from contrast-enhanced T1-weighted images, to train a stacking ensemble of 13 machine learning models. For subgroup analysis, propensity score matching was conducted to adjust for clinical disparities in NAC response. The efficacy of the models was evaluated using the area under the receiver-operating-characteristic curve (AUROC) before and after matching. The model combining clinicopathologic factors and clinical MRI findings achieved an AUROC of 0.752 (95% CI 0.644–0.860) for predicting non-responders, while radiomics-based models showed 0.749 (95% CI 0.614–0.884). An integrated model of radiomics, clinicopathologic factors, and clinical MRI findings reached an AUROC of 0.802 (95% CI 0.699–0.905). After propensity score matching, the hierarchical order of key radiomics features remained consistent. Our study demonstrated the potential of using machine learning models based on pretreatment MRI to non-invasively predict TNBC non-responders to NAC.

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer.

Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Advancements in opportunistic intracranial aneurysm screening: The impact of a deep learning algorithm on radiologists' analysis of T2-weighted cranial MRI

(1) Background: Unruptured Intracranial Aneurysms (UIAs) are common blood vessel malformations, occurring in up to 3 % of healthy adults. Magnetic Resonance Angiography (MRA) is frequently used for the screening of UIAs due to its high resolution in vascular anatomy. However, T2-Weighted Magnetic Resonance Imaging (T2WI) is a standard sequence utilized for the majority of outpatient head scans. By employing a sequence such as T2WI, there is a possible shift towards early detection of UIAs through opportunistic screening. Here, we assess a Deep Learning Algorithm (DLA) developed to assist radiologists in identifying and reporting UIAs on T2WI in a routine clinical setting. (2) Methods: A DLA was trained on a set of 110 patients undergoing an MR head scan with the gold standard set by two radiology experts. Eight radiologists were given a cohort of 50 cranial T2WI studies and asked for a routine report. After a 10-day washout period, they reviewed the same cases randomized and supported by the DLA predictions. We assessed changes in sensitivity, specificity, accuracy, and false positives. (3) Results: During routine reporting, the models’ assistance improved the sensitivity of the eight participants by an average of 36.19 and the accuracy by 10.00 percentage points. (4) Conclusion: Our results indicate the potential benefit of deep learning to improve radiologists' detection of UIAs during routine reporting. From this, we can infer that the combination of T2WI with our DLA supports opportunistic screening, suggesting potential approaches for future research and application.

The Utility of Magnetic Resonance Imaging-based Vertebral Bone Quality Scores as a Predictor of Cage Subsidence Following Transforaminal and Posterior Lumbar Interbody Fusion.

Retrospective study. The objectives were to determine whether vertebral bone quality (VBQ) scores are associated with interbody cage subsidence following transforaminal (TLIF) and posterior (PLIF) lumbar interbody fusions and whether there is a clinically sensitive threshold for subsidence. Interbody cage subsidence following lumbar fusion is a complication that can generate poor surgical outcomes. Prior research has correlated cage subsidence with bone mineral density. VBQ scores derived from magnetic resonance imaging (MRI) have been proposed as a tool for measuring bone mineral density, offering a potential new and convenient preoperative risk assessment tool for subsidence. The study involved patients undergoing single-level PLIF or TLIF between 2007 and 2022. Exclusions were for nondegenerative diagnoses, multilevel/revision surgeries, inadequate radiographs, missing immediate postoperative radiographs, and preoperative MRI studies older than 1 year. VBQ was calculated at L1-L4 from preoperative T1-weighted MRI images. Subsidence was assessed by changes in disc height (DH; >2mm difference) and segmental lordosis (SL; >5 degrees difference) between immediate weight-bearing postoperative and latest postoperative lateral radiographs. Statistical analysis included descriptive and inferential statistics. Subsidence was observed in 27% (SL parameter) and 47% (DH parameter) of 51 total patients. VBQ scores were significantly associated with cage subsidence based on both SL (odds ratio = 7.750, P = 0.012; correlation coefficient = 0.382, P = 0.006) and DH (odds ratio = 4.074, P = 0.026; correlation coefficient = 0.258, P = 0.057) in the combined TLIF/PLIF cohorts. In the cohort of 36 patients undergoing TLIF, a VBQ of 2.70 yielded 100.0% sensitivity and 46.2% specificity in detecting subsidence with SL measurement (area under the curve = 0.812, P < 0.001) and 86.7% sensitivity and 47.6% specificity with the DH measurement (area under the curve = 0.692, P = 0.033). We found that MRI-based VBQ scores are effective predictors of cage subsidence following TLIF surgery. A VBQ score of 2.70 demonstrated a reliable model and high sensitivity for doing so, identifying a potential clinical threshold for preoperative subsidence risk assessment. Level III.

Clinical feasibility of deep learning–accelerated single-shot turbo spin echo sequence with enhanced denoising for pancreas MRI at 3 Tesla

PurposeTo assess the feasibility of the single-shot turbo spin echo sequence using deep learning-based reconstruction (DLR) (HASTEDL) with enhanced denoising for pancreas MRI. MethodsPatients who underwent pancreas MRI from March to April 2021 were included. Four T2-weighted images (non-FS conventional HASTE vs. HASTEDL with enhanced denoising and FS HASTEDL with enhanced denoising vs. HASTEDL) were acquired. Two readers independently assessed the image quality parameters of the two non-FS image sets using a 4-point Likert scale. The signal-to-noise ratio (SNR) of the cystic lesions and pancreatic parenchyma and the contrast-to-noise ratio between the cystic lesion and pancreatic parenchyma were calculated for all four image sets. The size of the largest cystic lesion and the diameter of pancreatic duct were measured. ResultsA total of 63 patients were included, 48 (76.2 %) of whom had 136 pancreatic cystic lesion(s). The acquisition times of conventional HASTE and HASTEDL were 69 and 18 sec, respectively. All image quality parameters except artifacts for reader 2 were significantly better for HASTEDL with enhanced denoising. Those images also received scores for overall image quality that were significantly higher than those for the conventional HASTE (3.26 ± 0.54 vs. 2.47 ± 0.56, p < 0.001). The SNR of the pancreatic cystic lesion and pancreatic parenchyma was significantly higher in the HASTEDL with enhanced denoising (p < 0.001 for both). Inter-reader variability for measuring the pancreatic cyst size (ICC, 0.999 vs. 0.995; 95 % LoA, −0.13481 to 0.14743 vs. −0.24097 to 0.27404) and duct diameter (ICC, 0.994 vs. 0.969; 95 % LoA, −0.11684 to 0.36026 vs. −0.45544 to 0.44664) was lower in HASTEDL with enhanced denoising than in the conventional HASTE. ConclusionHASTEDL with enhanced denoising could be useful for reducing the acquisition time of pancreas MRI while improving the image quality for the evaluation of pancreatic cystic lesions.

Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.

To develop and compare various preoperative cervical stromal invasion (CSI) prediction models, including radiomics, three-dimensional (3D) deep transfer learning (DTL), and integrated models, using single-sequence and multiparametric MRI. Data from 466 early-stage endometrial carcinoma (EC) patients from three centers were collected. Radiomics models were constructed based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) mapping, contrast-enhanced T1-weighted imaging (CE-T1WI), and four combined sequences as well as 3D DTL models. Two integrated models were created using ensemble and stacking algorithms based on optimal radiomics and DTL models. Model performance and clinical benefits were assessed using area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination index (IDI), and the Delong test for model comparisons. Multiparametric MRI models were superior to single-sequence models for radiomics or DTL models. Ensemble and stacking integrated models displayed excellent performance. The stacking model had the highest average AUC (0.908) and accuracy (0.883) in external validation groups 1 and 2 (AUC = 0.965 and 0.851, respectively) and emerged as the best predictive model for CSI. All models significantly outperformed the radiologist (P < 0.05). In terms of net benefits, all models demonstrated favorable outcomes in DCA, NRI, and IDI, with the stacking model yielding the highest net benefit. Multiparametric MRI-based radiomics combined with 3D DTL can be used to noninvasively predict CSI in EC patients with greater diagnostic accuracy than the radiologist. Stacking integrated models showed significant potential utility in predicting CSI. Which helps to provide new treatment strategy for clinicians to treat early-stage EC patients.

Predictive value of mono-exponential and multiple mathematical models in locally advanced rectal cancer response to neoadjuvant chemoradiotherapy.

This prospective study aimed to assess the predictive value of mono-exponential and multiple mathematical diffusion-weighted imaging (DWI) models in determining the response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). The study included 103 LARC patients scheduled for preoperative chemoradiotherapy between December 2021 and June 2023 Magnetic resonance imaging (MRI) scans were performed using a 3.0-T MR scanner, encompassing sagittal, axial, and oblique coronal T2-weighted images without fat saturation, along with DWI perpendicular to the rectum's long axis. Various DWI parameters, including apparent diffusion coefficient (ADC), stretched exponential model (SEM), continuous-time random-walk model (CTRW), and fractional-order calculus model (FROC), were measured. The pathologic complete response (pCR) rate and tumor downstaging (T-downstage) rate were determined. After nCRT, SEM-α, SEM-DDC, CTRW-α, CTRW-β, CTRW-D, FROC-β, and ADC values were significantly higher in the pCR group compared to the non-pCR group (all P < 0.05). SEM-DDC, CTRW-α, CTRW-D, FROC-β, FROC-µ, and ADC values were significantly higher in the T-downstage group (ypT0-1) than in the non-T-downstage group (ypT2-4) (P < 0.05). The combination of CTRW (α + β + D) exhibited the best diagnostic performance for assessing pCR after nCRT (AUC = 0.840, P < 0.001). Pre-nCRT CTRW (α + β) demonstrated a predictive AUC of 0.652 (95%CI: 0.552-0.743), 90.3% sensitivity, and 43.1% specificity for pCR. Regarding T-downstage assessment after nCRT, the combination of CTRW (α + D) yielded the best diagnostic performance (AUC = 0.877, P = 0.048). In LARC patients, imaging markers derived from CTRW show promise in predicting tumor response before nCRT and assessing pCR after nCRT.

Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor

BackgroundThis study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.MethodsThis study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists.ResultsThe single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists.ConclusionsThe combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Focally Enlarged Perivascular Spaces in Pediatric and Adolescent Patients with Polymicrogyria-an MRI Study.

Polymicrogyria (PMG) is acortical malformation frequently associated with epilepsy. Our aim was to investigate the frequency and conspicuity of enlarged perivascular spaces (EPVS) underneath dysplastic cortex as apotentially underrecognized feature of PMG in pediatric and adolescent patients undergoing clinical magnetic resonance imaging (MRI). We analyzed data from 28pediatric and adolescent patients with PMG and amatched control group, ranging in age from 2days to 21years, who underwent MRI at 1.5T or 3T. T2-weighted MR images were examined for the presence of EPVS underneath the dysplastic cortex. The quantity of EPVS was graded from 0to 4(0: none, 1: < 10, 2: 11-20, 3: 21-40, 4: > 40 EPVS). We then compared the presence and quantity of EPVS to the matched controls in terms of total EPVS scores, and EPVS scores underneath the dysplastsic cortex depending on the age groups, the localization of PMG, and the MRI field strength. In 23/28 (82%) PMG patients, EPVS spatially related to the dysplastic cortex were identified. EPVS scores were significantly higher in PMG patients compared to controls, independent from age or PMG location. No significant differences were observed in EPVS scores in patients examined at 1.5T compared to those examined at 3T. EPVS underneath the dysplastic cortex were identified in 82% of patients. EPVS may serve as an important clue for PMG and amarker for cortical malformation.

Magnetic resonance imaging-based radiomics for predicting infiltration levels of CD68+ tumor-associated macrophages in glioblastomas.

Tumor-associated macrophages (TAMs) are important biomarkers of tumor invasion and prognosis in patients with glioblastoma. We combined the imaging and radiomics features of preoperative MRI to predict CD68+ macrophage infiltration. Clinical, MRI image, and pathology data of 188 patients with glioblastoma were analyzed. Overall, 143 patients were included in the training (n = 101) and validation (n = 42) sets, whereas 45patients were included in an independent test set. The optimal cut-off value (14.8%) was based on the minimum p-value formed by the Kaplan-Meier survival analysis and log-rank tests which divided patients into groups with high CD68+ TAMs(≥ 14.8%) and low CD68+ TAMs (< 14.8%). Regions of interest and radiomics features extraction were based on contrast-enhanced T1-weighted images (CE-T1WI) and T2WI. Multi-parameter stepwise regression was used to create the clinical, radiomics, and combined models, each evaluated using the receiver operating characteristic curve. Decision curve analysis was used to assess the clinical applicability of the nomogram. Aclinical model based on the minimum apparent diffusion coefficient (ADCmin) revealed an area under the curve (AUC) of 0.768, 0.764, and 0.624 for the training set, validation set, and test set, respectively. The 2D radiomics model, based on two features, revealed an AUC of 0.783, 0.724, and 0.789 for the training, validation, and test sets, respectively. The 3D radiomics model, based on three features, revealed AUCs of 0.823, 0.811, and 0.787 for the training, validation, and test sets, respectively. The combined model, with ADCmin and radiomics features, showed the best performance, with AUCs of 0.865, 0.822, and 0.776 for the training, validation, and test sets, respectively. The calibration curve of the combined model nomogram showed good agreement between the estimated and actual probabilities. The combined model constructed using ADCmin, aquantitative imaging parameter, combined with five key radiomics features can be used to evaluate the extent of CD68+ macrophages before surgery.