Contrast-enhanced T1-weighted Magnetic Resonance Research Articles

Contrast-Enhanced T1-Weighted Magnetic Resonance Cholangiography Using Gadoxetate Disodium in Potential Living Liver Donors: Qualitative and Quantitative Improvement with 3-hour Delayed Imaging

BackgroundContrast-enhanced T1-weighted magnetic resonance cholangiography (CE-T1-MRC) after gadoxetate disodium administration can be used for preoperative evaluation of the bile ducts in live liver donors. This study aimed to determine whether CE-T1-MRC with 3-hour delayed imaging improves bile duct visualization both qualitatively and quantitatively compared with 20-minute delayed imaging in potential living liver donors. MethodsWe retrospectively identified 33 potential living liver donors (mean age, 30.1 years; 18 men and 15 women) who underwent preoperative CE-T1-MRC with both 20-minute delayed and 3-hour delayed imaging in a single session. The radiologist scored biliary visualization for right and left hepatic ducts (RHD and LHD), their secondary confluences and segmental bile ducts, common hepatic duct (CHD), and cystic duct (CD), and measured relative contrast ratio (rC) and relative signal intensity (rS) for RHD, LHD, and CHD. The data were analyzed using Wilcoxon's signed-rank test and paired t-test. ResultsIn qualitative analysis, duct visualization scores for RHD and LHD, their secondary confluences and segmental bile ducts, CHD, and CD were significantly higher on CE-T1-MRC with 3-hour delayed imaging than with 20-minute delayed imaging (all, P ≤ .046). In quantitative analysis, both rC and rS of RHD, LHD, and CHD were significantly higher on CE-T1-MRC with 3-hour delayed imaging than with 20-minute delayed imaging (all, P < .001). ConclusionsCE-T1-MRC with 3-hour delay imaging improves bile duct visualization both qualitatively and quantitatively in potential living liver donors.

Contrast-enhanced pelvic magnetic resonance imaging (MRI) for the prediction of treatment response in mucinous rectal cancer.

In mucinous rectal cancer, it can be difficult to differentiate between cellular and acellular mucin. The purpose of this study was to evaluate, in patients with mucinous rectal cancer, the value of static enhancement (enh) and pharmacokinetic parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting pathologic complete response. This retrospective cross-sectional study performed at Memorial Sloan Kettering Cancer Center included 43 patients (24 males and 19 females; mean age, 57 years) with mucinous rectal cancer who underwent MRI at baseline as well as after neoadjuvant chemoradiotherapy but before surgical resection between 2008 and 2019. Two radiologists independently segmented tumors on contrast-enhanced axial 3D T1-weighted images and sagittal DCE magnetic resonance images. On contrast-enhanced axial T1-weighted images, the static parameters enh and relative enhancement (renh) were estimated. On DCE images, the pharmacokinetic parameters Ktrans, kep, relative Ktrans (rKtrans), and relative kep (rkep) were estimated. Associations between all parameters with pathologic complete response were tested using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis was performed to assess the area under the curve (AUC) for each parameter. Of the 43 patients who were included in the study, 42/43 (98%) had evaluable contrast-enhanced axial T1-weighted images and 35/43 (81%) had evaluable DCE images. Of the patients with evaluable contrast-enhanced axial T1-weighted images, 9/42 (21%) had pathologic complete response and 33/42 (79%) did not have pathologic complete response. For reader 1, enh(pre-neoadjuvant chemotherapy), enh(post-neoadjuvant chemotherapy), and renh were significant predictors of pathologic complete response [P=0.045 (AUC =0.73), 0.039 (AUC =0.74), and 0.0042, respectively]. For reader 2, enh(pre-neoadjuvant chemotherapy) and renh were significant predictors [P=0.021 (AUC =0.77) and 0.002, respectively]. For renh, the AUC was 0.83 for reader 1, and 0.82 for reader 2. Meanwhile, of those patients with evaluable DCE images, 9/35 (26%) had pathologic complete response and 26/35 (74%) did not have pathologic complete response. Ktrans(pre-neoadjuvant chemotherapy), kep(pre-neoadjuvant chemotherapy), and rkep were significant predictors [P=0.016 (AUC =0.73), 0.00057 (AUC =0.81), and 0.0096 (AUC =0.74), respectively]. Static and pharmacokinetic parameters of contrast-enhanced MRI show promise to predict neoadjuvant treatment response. Static enh parameters, which are simpler to assess, showed the strongest prediction.

Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration

This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p < 0.001) and T2w MR (4.41 vs. 7.42, p < 0.001).

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas

PurposePreoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved.MethodsContrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers.ResultsAfter comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71–0.84, 95% CI) based on T1-weighted images, 0.79 (0.72–0.84, 95% CI) for T2-weighted images, 0.88 (0.83–0.92, 95% CI) for CE-T1 images, and 0.88 (0.83–0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66–0.78, 95% CI), 0.84 (0.78–0.89, 95% CI) based on T2-WI, 0.74 (0.67–0.80, 95% CI) for CE-T1, and 0.81 (0.76–0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87.ConclusionsCNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Intravascular papillary endothelial hyperplasia of the maxillary sinus extending into the contralateral nasal cavity

IntroductionIntravascular papillary endothelial hyperplasia (IPEH) predominantly occurs in the subcutaneous and dermal regions and rarely originates from the sinonasal mucosa.Case presentationWe report on the case of a 58-year-old male patient who presented with progressive bilateral nasal obstruction, left-sided epiphora, and intermittent epistaxis. Computed tomography revealed a soft tissue opacity in the left maxillary sinus with intersinusoidal nasal wall demineralization, extending into the surrounding ethmoid cells and the right nasal cavity through a contralateral deviation of the nasal septum. Contrast-enhanced T1-weighted magnetic resonance imaging further confirmed these findings. The IPEH originating from the maxillary sinus extended into the contralateral nasal cavity, and it was successfully removed using an endoscopic endonasal approach, avoiding overly aggressive treatment.ConclusionThis case report highlights the diagnostic challenges of IPEH in the sinonasal region and the importance of considering IPEH as a differential diagnosis in patients presenting with nasal obstruction, epiphora, and intermittent epistaxis.

A MRI-based radiomics combined prediction model for radiation-induced temporal lobe injury in nasopharyngeal carcinoma patients

PurposeRadiation-induced temporal lobe injury (RTLI) is a common radiation-induced injury in patients with nasopharyngeal carcinoma (NPC). Our aim is to evaluate the potential factors associated with RTLI and to develop a predictive model to assess RTLI. MethodA total of 393 NPC patients who had received full course radiotherapy at two institutions were included. Patients from institution I were divided into a training cohort and a testing cohort according to the time of first-line treatment. Patients from institution II were used for external validation. The pre-treatment T1-weighted, T2-weighted and contrast-enhanced T1-weighted (T1c-weighted) magnetic resonance imaging (MRI) images before treatment were retrieved for radiomics feature extraction. The left and right temporal regions were separately defined and delineated as regions of interest (ROIs) by the MIM segmentation system. The relative contributions of different MRI sequences to the radiomics (Rad) score evaluation were compared. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to calculate Rad scores and develop comprehensive models. ResultsThe radiomics model consisting of all MRI features (combined T1-weighted, T2-weighted and T1c-weighted) was most associated with RTLI. The AUCs were 0.77 (95% CI: 0.69–0.85) in the testing cohort and 0.74 (95% CI: 0.70–0.79) in the external validation cohort. The AUC for the clinical-only model (including clinical stage and D0.5 cc) was 0.52 (95% CI: 0.31–0.74), whereas models including clinical factors (clinical stage), dosimetric factors (D0.5 cc) and Rad scores from all MRI sequences were more optimal. The AUC was 0.76 (95% CI: 0.67–0.84). ConclusionWe generated a feasible RTLI prediction model with radiomics, clinical and dosimetric factors.

Long-term neurocognitive and psychological outcomes in meningioma survivors: Individual changes over time and radiation dosimetry.

This study investigates long-term changes in neurocognitive performance and psychological symptoms in meningioma survivors and associations with radiation dose to circumscribed brain regions. We undertook a retrospective study of meningioma survivors who underwent longitudinal clinical neurocognitive assessments. Change in neurocognitive performance or psychological symptoms was assessed using reliable change indices. Radiation dosimetry, if prescribed, was evaluated based on treatment-planning computerized tomography co-registered with contrast-enhanced 3D T1-weighted magnetic resonance imaging. Mixed effects analyses were used to explore whether incidental radiation to brain regions outside the tumor influences neurocognitive and psychological outcomes. Most (range = 41%-93%) survivors demonstrated stable-albeit often below average-neurocognitive and psychological trajectories, although some also exhibited improvements (range = 0%-31%) or declines (range = 0%-36%) over time. Higher radiation dose to the parietal-occipital region (partial R2 = 0.462) and cerebellum (partial R2 = 0.276) was independently associated with slower visuomotor processing speed. Higher dose to the hippocampi was associated with increases in depression (partial R2 = 0.367) and trait anxiety (partial R2 = 0.236). Meningioma survivors experience neurocognitive deficits and psychological symptoms many years after diagnosis, and a proportion of them decline over time. This study offers proof of concept that incidental radiation to brain regions beyond the tumor site may contribute to these sequelae. Future investigations should include radiation dosimetry when examining risk factors that contribute to the quality of survivorship in this growing population.

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm

Introduction and importanceAccurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms. Case presentation and methodsCE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations. Clinical discussionThe approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods. ConclusionThe MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Temporal muscle thickness has no prognostic relevance in patients with high-grade glioma compared to functional scales.

GBM research is constantly assessing potential valuable prognostic biomarkers to better understand the disease and prognosticate future outcomes. Measuring temporal muscle thickness (TMT) has appeared to be a promising new surrogate marker for skeletal muscle mass and sarcopenia, which further indicates frailty and predicts overall survival (OS). The aim of this study was to determine its usefulness as a prognostic marker in patients with high-grade glioma compared to functional status scales. TMT was measured in preoperative axial T1-weighted contrast-enhanced magnetic resonance images in 277 patients who received surgical treatment of newly diagnosed WHO III and IV gliomas in our institution between 2015 and 2020. Clinical Frailty Scale (CFS) and Karnofsky Performance Scale (KPS) were assessed preoperatively and during a follow-up visit. Female gender has shown significant correlation with TMT, while TMT did not correlate with preoperative and follow-up functional scores, age, WHO classification, IDH mutation, MGMT promoter methylation, EGFR and ATRX expression, or 1p/19q co-deletion. No significant prognostic value of TMT could be shown in 6, 12, and 24 months OS, while changes in CFS and KPS proved to have a significant impact. Only female gender, but no other clinical, histological, or molecular marker showed any interrelation with TMT. Functional scores outclass measuring TMT as a reliable prognostic factor for predicting OS in patients with high-grade glioma.

Automated Radiomic Analysis of Vestibular Schwannomas and Inner Ears Using Contrast-Enhanced T1-Weighted and T2-Weighted Magnetic Resonance Imaging Sequences and Artificial Intelligence.

To objectively evaluate vestibular schwannomas (VSs) and their spatial relationships with the ipsilateral inner ear (IE) in magnetic resonance imaging (MRI) using deep learning. Cross-sectional study. A total of 490 adults with VS, high-resolution MRI scans, and no previous neurotologic surgery. MRI studies of VS patients were split into training (390 patients) and test (100 patients) sets. A three-dimensional convolutional neural network model was trained to segment VS and IE structures using contrast-enhanced T1-weighted and T2-weighted sequences, respectively. Manual segmentations were used as ground truths. Model performance was evaluated on the test set and on an external set of 100 VS patients from a public data set (Vestibular-Schwannoma-SEG). Dice score, relative volume error, average symmetric surface distance, 95th-percentile Hausdorff distance, and centroid locations. Dice scores for VS and IE volume segmentations were 0.91 and 0.90, respectively. On the public data set, the model segmented VS tumors with a Dice score of 0.89 ± 0.06 (mean ± standard deviation), relative volume error of 9.8 ± 9.6%, average symmetric surface distance of 0.31 ± 0.22 mm, and 95th-percentile Hausdorff distance of 1.26 ± 0.76 mm. Predicted VS segmentations overlapped with ground truth segmentations in all test subjects. Mean errors of predicted VS volume, VS centroid location, and IE centroid location were 0.05 cm 3 , 0.52 mm, and 0.85 mm, respectively. A deep learning system can segment VS and IE structures in high-resolution MRI scans with excellent accuracy. This technology offers promise to improve the clinical workflow for assessing VS radiomics and enhance the management of VS patients.

A radiomics model enables prediction venous sinus invasion in meningioma.

Preoperative prediction of meningioma venous sinus invasion would facilitate the selection of surgical approaches and predicting the prognosis. To predict venous sinus invasion in meningiomas, we used radiomic signatures to construct a model based on preoperative contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging. In total, 599 patients with pathologically confirmed meningioma were retrospectively enrolled. For each patient enrolled in this study, 1595 radiomic signatures were extracted from T1C and T2 image sequences. Pearson correlation analysis and recursive feature elimination were used to select the most relevant signatures extracted from different image sequences, and logistic regression algorithms were used to build a radiomic model for risk prediction of meningioma sinus invasion. Furthermore, a nomogram was built by incorporating clinical characteristics and radiomic signatures, and a decision curve analysis was used to evaluate the clinical utility of the nomogram. Twenty radiomic signatures that were significantly related to venous sinus invasion were screened from 3190 radiomic signatures. Venous sinus invasion was associated with tumor position, and the clinicoradiomic model that incorporated the above characteristics (20 radiomic signatures and tumor position) had the best discriminating ability. The areas under the curve for the training and validation cohorts were 0.857 (95% confidence interval [CI], 0.824-0.890) and 0.824 (95% CI, 0.752-0.8976), respectively. The clinicoradiomic model had good predictive performance for venous sinus invasion in meningioma, which can aid in devising surgical strategies and predicting prognosis.

Evaluation of Arachnoid Granulations in Cranial Dural Sinuses with Contrast-Enhanced 3-Dimensional T1-Weighted Magnetic Resonance Imaging.

Several studies in the literature have used contrast-enhanced magnetic resonance imaging to investigate arachnoid granulations protruding into the cranial dural sinuses. The current study aimed to investigate the protrusion of arachnoid granulations into the superior sagittal sinus, transverse sinus, straight sinus, and confluence of sinuses and determine the frequency of brain herniation into giant arachnoid granulations using contrast-enhanced 3-dimensional T1-weighted magnetic resonance imaging. Images of 550 patients with intra-sinus arachnoid granulations who underwent contrast-enhanced 3-dimensional T1-weighted thin-slice magnetic resonance imaging were retrospectively re-evaluated. Only 300 patients with at least 1 intra-sinus arachnoid granulation were included in the study. The protrusion of arachnoid granulations into superior sagittal sinus, transverse sinus, straight sinus, and confluence of sinuses was investigated. In addition, large arachnoid granulations and brain herniations into arachnoid granulations were also noted. A total of 889 focal filling defects of arachnoid granulations, at least 1 in the dural sinus, were detected. Of the filling defects of arachnoid granulations, 183 were in the right transverse sinus, 222 in the left transverse sinus, 265 in superior sagittal sinus, 185 in straight sinus, and 34 in confluence of sinuses. Brain herniation into arachnoid granulations was detected in 8 (2.7%) of the patients included in the study. All the filling defects detected in the dural sinuses on post-contrast 3-dimensional T1-weighted images were isointense with cerebrospinal fluid and had round, oval, or lobulated contours. A positive weak correla- tion was found between patient age and the size and number of arachnoid granulations (r = 0.181, P < .01 and r=0.207, P < .001, respectively). It was observed that the size and number of arachnoid granulations increased as the age of the patients increased. The distribution, shape, number, and size of intra-sinus arachnoid granulations can vary considerably. Brain herniation into arachnoid granulation can also be seen. Three-dimensional cranial magnetic resonance imaging sequences can be safely used in the evaluation of arachnoid granulations.

Granulomatous inflammation mimicking a hematoma around the replaced ascending aorta in magnetic resonance imaging: a case report

BackgroundGranulomatous inflammation results from various causes including infections and allergic reactions. It can appear as high signal intensity in T2-weighted or contrast-enhanced T1-weighted magnetic resonance imaging (MRI). Here, we describe a case of granulomatous inflammation looking like a hematoma on an ascending aortic graft in MRI.Case presentationA 75-year-old female was undergoing assessment for chest pain. She had a history of hemi-arch replacement for aortic dissection 10 years earlier. The initial chest computed tomography and subsequent chest MRI were suggestive of a hematoma, implying a pseudoaneurysm of the thoracic aorta, which is associated with high mortality in reoperation. Through redo median sternotomy, severe adhesion was found in the retrosternal space. A sac in the pericardial space contained yellowish and pus-like material, confirming that there was no hematoma around the ascending aortic graft. The pathologic finding was chronic necrotizing granulomatous inflammation. Microbiological tests including polymerase chain reaction analysis were negative.ConclusionOur experience indicates that an MRI finding of a hematoma at the site long after cardiovascular surgery suggests that there may be granulomatous inflammation.

Development of Predictive Models for the Response of Vestibular Schwannoma Treated with Cyberknife®: A Feasibility Study Based on Radiomics and Machine Learning.

to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images. patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment. Clinical and treatment data were collected contextually. Treatment responses were assessed considering the VS volume variation based on pre- and post-radiosurgery MR images at both time points. Tumors were semi-automatically segmented and radiomic features were extracted. Four ML algorithms (Random Forest, Support Vector Machine, Neural Network, and extreme Gradient Boosting) were trained and tested for treatment response (i.e., increased or non-increased tumor volume) using nested cross-validation. For training, feature selection was performed using the Least Absolute Shrinkage and Selection Operator, and the selected features were used as input to separately build the four ML classification algorithms. To overcome class imbalance during training, Synthetic Minority Oversampling Technique was used. Finally, trained models were tested on the corresponding held out set of patients to evaluate balanced accuracy, sensitivity, and specificity. 108 patients treated with Cyberknife® were retrieved; an increased tumor volume was observed at 24 months in 12 patients, and at 36 months in another group of 12 patients. The Neural Network was the best predictive algorithm for response at 24 (balanced accuracy 73% ± 18%, specificity 85% ± 12%, sensitivity 60% ± 42%) and 36 months (balanced accuracy 65% ± 12%, specificity 83% ± 9%, sensitivity 47% ± 27%). radiomics may predict VS response to radiosurgery avoiding long-term follow-up as well as unnecessary treatment.

LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn's disease: utility in noise reduction and image quality improvement.

This study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic resonance enterography (MRE) images reconstructed with vendor-supplied deep-learning reconstruction (DLR) with those reconstructed conventionally in terms of image quality. A total of 35 patients who underwent MRE for Crohn's disease between August 2021 and February 2022 were included in this retrospective study. The enteric phase CE-T1W MRE images of each patient were reconstructed with conventional reconstruction and no image filter (original), with conventional reconstruction and image filter (filtered), and with a prototype version of AIRTM Recon DL 3D (DLR), which were then reformatted into the axial plane to generate six image sets per patient. Two radiologists independently assessed the images for overall image quality, contrast, sharpness, presence of motion artifacts, blurring, and synthetic appearance for qualitative analysis, and the signal-to-noise ratio (SNR) was measured for quantitative analysis. The mean scores of the DLR image set with respect to overall image quality, contrast, sharpness, motion artifacts, and blurring in the coronal and axial images were significantly superior to those of both the filtered and original images (P < 0.001). However, the DLR images showed a significantly more synthetic appearance than the other two images (P < 0.05). There was no statistically significant difference in all scores between the original and filtered images (P > 0.05). In the quantitative analysis, the SNR was significantly increased in the order of original, filtered, and DLR images (P < 0.001). Using DLR for near-isotropic CE-T1W MRE improved the image quality and increased the SNR.

A new biomarker combining multimodal MRI radiomics and clinical indicators for differentiating inverted papilloma from nasal polyp invaded the olfactory nerve possibly.

Inverted papilloma (IP) and nasal polyp (NP), as two benign lesions, are difficult to distinguish on MRI imaging and clinically, especially in predicting whether the olfactory nerve is damaged, which is an important aspect of treatment and prognosis. We plan to establish a new biomarker to distinguish IP and NP that may invade the olfactory nerve, and to analyze its diagnostic efficacy. A total of 74 cases of IP and 55 cases of NP were collected. A total of 80% of 129 patients were used as the training set (59 IP and 44 NP); the remaining were used as the testing set. As a multimodal study (two MRI sequences and clinical indicators), preoperative MR images including T2-weighted magnetic resonance imaging (T2-WI) and contrast-enhanced T1-weighted magnetic resonance imaging (CE-T1WI) were collected. Radiomic features were extracted from MR images. Then, the least absolute shrinkage and selection operator (LASSO) regression method was used to decrease the high degree of redundancy and irrelevance. Subsequently, the radiomics model is constructed by the rad scoring formula. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model have been calculated. Finally, the decision curve analysis (DCA) is used to evaluate the clinical practicability of the model. There were significant differences in age, nasal bleeding, and hyposmia between the two lesions (p < 0.05). In total, 1,906 radiomic features were extracted from T2-WI and CE-T1WI images. After feature selection, using 12 key features to bulid model. AUC, sensitivity, specificity, and accuracy on the testing cohort of the optimal model were, respectively, 0.9121, 0.828, 0.9091, and 0.899. AUC on the testing cohort of the optimal model was 0.9121; in addition, sensitivity, specificity, and accuracy were, respectively, 0.828, 0.9091, and 0.899. A new biomarker combining multimodal MRI radiomics and clinical indicators can effectively distinguish between IP and NP that may invade the olfactory nerve, which can provide a valuable decision basis for individualized treatment.

DeSeg: auto detector-based segmentation for brain metastases

Delineation of brain metastases (BMs) is a paramount step in stereotactic radiosurgery treatment. Clinical practice has specific expectation on BM auto-delineation that the method is supposed to avoid missing of small lesions and yield accurate contours for large lesions. In this study, we propose a novel coarse-to-fine framework, named detector-based segmentation (DeSeg), to incorporate object-level detection into pixel-wise segmentation so as to meet the clinical demand. DeSeg consists of three components: a center-point-guided single-shot detector to localize the potential lesion regions, a multi-head U-Net segmentation model to refine contours, and a data cascade unit to connect both tasks smoothly. Performance on tiny lesions is measured by the object-based sensitivity and positive predictive value (PPV), while that on large lesions is quantified by dice similarity coefficient (DSC), average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD95). Besides, computational complexity is also considered to study the potential of method in real-time processing. This study retrospectively collected 240 BM patients with Gadolinium injected contrast-enhanced T1-weighted magnetic resonance imaging (T1c-MRI), which were randomly split into training, validating and testing datasets (192, 24 and 24 scans, respectively). The lesions in the testing dataset were further divided into two groups based on the volume size (small S: ≤1.5 cc, N = 88; large L: > 1.5 cc, N = 15). On average, DeSeg yielded a sensitivity of 0.91 and a PPV of 0.77 on S group, and a DSC of 0.86, an ASSD 0f 0.76 mm and a HD95 of 2.31 mm on L group. The results indicated that DeSeg achieved leading sensitivity and PPV for tiny lesions as well as segmentation metrics for large ones. After our clinical validation, DeSeg showed competitive segmentation performance while kept faster processing speed comparing with existing 3D models.

Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model.

To develop a deep-learning-based multi-task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast-enhanced T1-weighted (ceT1) magnetic resonance images (MRIs). Initial ceT1 MRIs for VS patients meeting the inclusion/exclusion criteria of this study were retrospectively collected. VSs on the initial MRIs and their first follow-up scans were manually contoured. Tumor volume and enlargement ratio were measured based on expert contours. A DMT model was constructed for jointly TS and TEP. The manually segmented VS volume on the initial scan and the tumor enlargement label (≥20% volumetric growth) were used as the ground truth for training and evaluating the TS and TEP modules, respectively. We performed 5-fold cross-validation with the eligible patients (n=103). Median segmentation dice coefficient, prediction sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were measured and achieved the following values: 84.20%, 0.68, 0.78, 0.72, and 0.77, respectively. The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, p=0.03) and marginally lower than the state-of-the-art segmentation model nnU-Net (dice coefficient of 86.45%, p=0.16). The TEP performance is significantly better than the single-task prediction model (AUC=0.60, p=0.01) and marginally better than a radiomics-based prediction model (AUC=0.70, p=0.17). The proposed DMT model is of higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management. NA Laryngoscope, 133:2754-2760, 2023.

A multi-perspective information aggregation network for automated T-staging detection of nasopharyngeal carcinoma

Accurate T-staging is important when planning personalized radiotherapy. However, T-staging via manual slice-by-slice inspection is time-consuming while tumor sizes and shapes are heterogeneous, and junior physicians find such inspection challenging. With inspiration from oncological diagnostics, we developed a multi-perspective aggregation network that incorporated various diagnosis-oriented knowledge which allowed automated nasopharyngeal carcinoma T-staging detection (TSD Net). Specifically, our TSD Net was designed in multi-branch architecture, which can capture tumor size and shape information (basic knowledge), strongly correlated contextual features, and associations between the tumor and surrounding tissues. We defined the association between the tumor and surrounding tissues by a signed distance map which can embed points and tumor contours in higher-dimensional spaces, yielding valuable information regarding the locations of tissue associations. TSD Net finally outputs a T1–T4 stage prediction by aggregating data from the three branches. We evaluated TSD Net by using the T1-weighted contrast-enhanced magnetic resonance imaging database of 320 patients in a three-fold cross-validation manner. The results show that the proposed method achieves a mean area under the curve (AUC) as high as 87.95%. We also compared our method to traditional classifiers and a deep learning-based method. Our TSD Net is efficient and accurate and outperforms other methods.

Increased relative biological effectiveness and periventricular radiosensitivity in proton therapy of glioma patients

PurposeCurrently, there is an intense debate on variations in intra-cerebral radiosensitivity and relative biological effectiveness (RBE) in proton therapy of primary brain tumours. Here, both effects were retrospectively investigated using late radiation-induced brain injuries (RIBI) observed in follow-up after proton therapy of patients with diagnosed glioma. MethodsIn total, 42 WHO grade 2–3 glioma patients out of a consecutive patient cohort having received (adjuvant) proton radio(chemo)therapy between 2014 and 2017 were eligible for analysis. RIBI lesions (symptomatic or clinically asymptomatic) were diagnosed and delineated on contrast-enhanced T1-weighted magnetic resonance imaging scans obtained in the first two years of follow-up. Correlation of RIBI location and occurrence with dose (D), proton dose-averaged linear energy transfer (LET) and variable RBE dose parameters were tested in voxel- and in patient-wise logistic regression analyses. Additionally, anatomical and clinical parameters were considered. Model performance was estimated through cross-validated area-under-the-curve (AUC) values. ResultsIn total, 64 RIBI lesions were diagnosed in 21 patients. The median time between start of proton radio(chemo)therapy and RIBI appearance was 10.2 months. Median distances of the RIBI volume centres to the cerebral ventricles and to the clinical target volume border were 2.1 mm and 1.3 mm, respectively. In voxel-wise regression, the multivariable model with D, D × LET and periventricular region (PVR) revealed the highest AUC of 0.90 (95 % confidence interval: 0.89–0.91) while the corresponding model without D × LET revealed a value of 0.84 (0.83–0.86). In patient-level analysis, the equivalent uniform dose (EUD11, a = 11) in the PVR using a variable RBE was the most prominent predictor for RIBI with an AUC of 0.63 (0.32–0.90). ConclusionsIn this glioma cohort, an increased radiosensitivity within the PVR was observed as well as a spatial correlation of RIBI with an increased RBE. Both need to be considered when delivering radio(chemo)therapy using proton beams.