T2-weighted Imaging Sequences Research Articles

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer. <P> Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model. <P> Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value. <P> Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

Implementation of a Survey Spine Magnetic Resonance Imaging Protocol for Cord Compression in the Emergency Department: Experience at a Level-1 Trauma Center.

Imaging stewardship in the emergency department (ED) is vital in ensuring patients receive optimized care. While suspected cord compression (CC) is a frequent indication for total spine MRI in the ED, the incidence of CC is low. Recently, our level-I trauma center introduced a survey spine MRI protocol to evaluate for suspected CC while reducing exam time to avoid imaging overutilization. This study aims to evaluate the time savings, frequency of ordering patterns of the survey, and the symptoms and outcomes of patients undergoing the survey. This retrospective study examined patients who received a survey spine MRI in the ED at our institution between 2018 and 2022. All exams were performed on a 1.5T GE scanner using our institutional CC survey protocol, which includes sagittal T2 and STIR sequences through the cervical, thoracic, and lumbar spine. Exams were read by a blinded, board-certified neuroradiologist. A total of 2,002 patients received a survey spine MRI protocol during the study period. Of these patients, 845 (42.2%, mean age 57 ± 19 years, 45% female) received survey spine MRI exams for the suspicion of CC, and 120 patients (14.2% positivity rate) had radiographic CC. The survey spine MRI averaged 5 minutes and 50 seconds (79% faster than routine MRI). On multivariate analysis, trauma, back pain, lower extremity weakness, urinary or bowel incontinence, numbness, ataxia, and hyperreflexia were each independently associated with CC. Of the 120 patients with CC, 71 underwent emergent surgery, 20 underwent non-emergent surgery, and 29 were managed medically. The survey spine protocol was positive for CC in 14% of patients in our cohort and acquired at a 79% faster rate compared to routine total spine. Understanding the positivity rate of CC, the clinical symptoms that are most associated with CC, and the subsequent care management for patients presenting with suspected cord compression who received the survey spine MRI may better inform the broad adoption and subsequent utilization of survey imaging protocols in emergency settings to increase throughput, improve allocation of resources, and provide efficient care for patients with suspected CC.ABBREVIATIONS: CC, cord compression; ED, emergency department; MRI, magnetic resonance imaging; T2; T2-weighted imaging sequence; STIR, short TI inversion recovery.

Effect of contrast agent on T2-weighted fat-suppressed imaging and diffusion-weighted imaging in the diagnosis of breast tumors.

Although previous studies have shown that the injection of contrast agents can improve image quality, the specific impact of this on T2-weighted fat-suppressed (T2 FS) and diffusion-weighted imaging (DWI) sequences in the diagnosis of breast cancer remains incompletely understood. In particular, there is insufficient research on how contrast agents affect the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values within these sequences, and how these changes influence the diagnosis of benign and malignant breast tumors. Breast magnetic resonance images (MRI) were obtained from 178 consecutive patients on a 3T scanner. The SNR and CNR of lesions on T2 FS sequence were calculated before and after contrast agent injection and compared. Differences between pre- and post-contrast ADC in identifying different tumor types were compared using the Kruskal-Wallis H-test and the paired comparison test. The accuracy of ADC values between pre- and post-contrast in distinguishing benign and malignant breast masses was assessed using receiver operating characteristic (ROC) curves. The SNR and CNR of T2 FS sequence increased after contrast injection, and especially for invasive cancer and benign tumor, the increase was significant. For DWI, there was a slight increase or decrease of ADC values after contrast injection, but the ADC values before and after contrast had a similar effect in identifying different types of tumors. In the ROC curve analysis for assessing benign and malignant breast tumors, the area under the curve (AUC) before and after contrast showed similar results. Contrast agent injection can improve the SNR and CNR of T2 FS sequence, thus providing higher quality images for the diagnosis of breast lesions. Furthermore, injection of contrast agent had little effect on the ability of ADC values to identify different types of lesions and both ADC values before and after the contrast agent were able to distinguish between benign and malignant tumors with almost the same accuracy.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study.

Perineural invasion (PNI) has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer (RC). Preoperative prediction of PNI status is helpful for individualized treatment of RC. Recently, several radiomics studies have been used to predict the PNI status in RC, demonstrating a good predictive effect, but the results lacked generalizability. The preoperative prediction of PNI status is still challenging and needs further study. To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients. This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers. The patients underwent pre-operative high-resolution magnetic resonance imaging (MRI) between May 2019 and August 2022. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI) and contrast-enhanced T1WI (T1CE) sequences. The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared (T2WI, T1CE and T2WI + T1CE fusion sequences). A clinical-radiomics (CR) model was established by combining the radiomics features and clinical risk factors. The internal and external validation groups were used to validate the proposed models. The area under the receiver operating characteristic curve (AUC), DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the model performance. Among the radiomics models, the T2WI + T1CE fusion sequences model showed the best predictive performance, in the training and internal validation groups, the AUCs of the fusion sequence model were 0.839 [95% confidence interval (CI): 0.757-0.921] and 0.787 (95%CI: 0.650-0.923), which were higher than those of the T2WI and T1CE sequence models. The CR model constructed by combining clinical risk factors had the best predictive performance. In the training and internal and external validation groups, the AUCs of the CR model were 0.889 (95%CI: 0.824-0.954), 0.889 (95%CI: 0.803-0.976) and 0.894 (95%CI: 0.814-0.974). Delong test, NRI, and IDI showed that the CR model had significant differences from other models (P < 0.05). Calibration curves demonstrated good agreement, and DCA revealed significant benefits of the CR model. The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively, which facilitates individualized treatment of RC patients.

Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study

PurposeSpinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them. MethodsEnrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score. ResultsBased on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively. ConclusionsThe radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.

Time-Saving 3D MR Imaging Protocols with Millimeter and Submillimeter Isotropic Spatial Resolution for Face and Neck Imaging as Implemented at a Single-Site Major Referral Center.

MR imaging has become the routine technique for staging nasopharyngeal carcinoma, evaluating perineural tumor spread, and detecting cartilage invasion in laryngeal carcinoma. However, these protocols traditionally require in the range of 25 to 35 minutes of acquisition time. 3D sequences offer the potential advantage of time savings through the acquisition of 1-mm or submillimeter resolution isotropic data followed by multiplanar reformats that require no further imaging time. We have iteratively optimized vendor product 3D T1-weighted MR imaging sequences for morphologic face and neck imaging, reducing the average acquisition time of our 3T protocols by 9 minutes 57 seconds (40.9%) and of our 1.5T protocols by 9 minutes 5 seconds (37.0%), while simultaneously maintaining or improving spatial resolution. This clinical report describes our experience optimizing and implementing commercially available 3D T1-weighted MR imaging pulse sequence protocols for clinical face and neck MR imaging examinations using illustrative cases. We provide protocol details to allow others to replicate our implementations, and we report challenges we faced along with our solutions.

SMMF: a self-attention-based multi-parametric MRI feature fusion framework for the diagnosis of bladder cancer grading.

Multi-parametric magnetic resonance imaging (MP-MRI) may provide comprehensive information for graded diagnosis of bladder cancer (BCa). Nevertheless, existing methods ignore the complex correlation between these MRI sequences, failing to provide adequate information. Therefore, the main objective of this study is to enhance feature fusion and extract comprehensive features from MP-MRI using deep learning methods to achieve an accurate diagnosis of BCa grading. In this study, a self-attention-based MP-MRI feature fusion framework (SMMF) is proposed to enhance the performance of the model by extracting and fusing features of both T2-weighted imaging (T2WI) and dynamic contrast-enhanced imaging (DCE) sequences. A new multiscale attention (MA) model is designed to embed into the neural network (CNN) end to further extract rich features from T2WI and DCE. Finally, a self-attention feature fusion strategy (SAFF) was used to effectively capture and fuse the common and complementary features of patients' MP-MRIs. In a clinically collected sample of 138 BCa patients, the SMMF network demonstrated superior performance compared to the existing deep learning-based bladder cancer grading model, with accuracy, F1 value, and AUC values of 0.9488, 0.9426, and 0.9459, respectively. Our proposed SMMF framework combined with MP-MRI information can accurately predict the pathological grading of BCa and can better assist physicians in diagnosing BCa.

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma.

To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma

BackgroundXerostomia is one of the most common side effects in nasopharyngeal carcinoma (NPC) patients after chemoradiotherapy. To establish a Delta radiomics model for predicting xerostomia secondary to chemoradiotherapy for NPC based on magnetic resonance T1-weighted imaging (T1WI) sequence and evaluate its diagnostic efficacy.MethodsClinical data and Magnetic resonance imaging (MRI) data before treatment and after induction chemotherapy (IC) of 255 NPC patients with stage III-IV were collected retrospectively. Within one week after CCRT, the patients were divided into mild (92 cases) and severe (163 cases) according to the grade of xerostomia. Parotid glands in T1WI sequence images before and after IC were delineated as regions of interest for radiomics feature extraction, and Delta radiomics feature values were calculated. Univariate logistic analysis, correlation, and Gradient Boosting Decision Tree (GBDT) methods were applied to reduce the dimension, select the best radiomics features, and establish pretreatment, post-IC, and Delta radiomics xerostomia grading predictive models. The receiver operating characteristic (ROC) curve and decision curve were drawn to evaluate the predictive efficacy of different models.ResultsFinally, 15, 10, and 12 optimal features were selected from pretreatment, post-IC, and Delta radiomics features, respectively, and a xerostomia prediction model was constructed with AUC values of 0.738, 0.751, and 0.843 in the training set, respectively. Only age was statistically significant in the clinical data of both groups (P < 0.05).ConclusionDelta radiomics can predict the degree of xerostomia after chemoradiotherapy for NPC patients and it has certain guiding significance for clinical early intervention measures.

Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study.

To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH). This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets. Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets. The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.

Time-Saving 3D MR Imaging Protocols with Millimeter and Submillimeter Isotropic Spatial Resolution for Face and Neck Imaging as Implemented at a Single-Site Major Referral Center.

MR imaging has become the routine technique for staging nasopharyngeal carcinoma, evaluating perineural tumor spread, and detecting cartilage invasion in laryngeal carcinoma. However, these protocols traditionally require in the range of 25 to 35 minutes of acquisition time. 3D sequences offer the potential advantage of time savings through the acquisition of 1-mm or submillimeter resolution isotropic data followed by multiplanar reformats that require no further imaging time. We have iteratively optimized vendor product 3D T1-weighted MR imaging sequences for morphologic face and neck imaging, reducing the average acquisition time of our 3T protocols by 9 minutes 57 seconds (40.9%) and of our 1.5T protocols by 9 minutes 5 seconds (37.0%), while simultaneously maintaining or improving spatial resolution. This clinical report describes our experience optimizing and implementing commercially available 3D T1-weighted MR imaging pulse sequence protocols for clinical face and neck MR imaging examinations using illustrative cases. We provide protocol details to allow others to replicate our implementations, and we report challenges we faced along with our solutions.

Feasibility of deep learning-reconstructed thin-slice single-breath-hold HASTE for detecting pancreatic lesions: A comparison with two conventional T2-weighted imaging sequences

ObjectiveThe objective of this study was to evaluate the clinical feasibility of deep learning reconstruction-accelerated thin-slice single-breath-hold half-Fourier single-shot turbo spin echo imaging (HASTEDL) for detecting pancreatic lesions, in comparison with two conventional T2-weighted imaging sequences: compressed-sensing HASTE (HASTECS) and BLADE. MethodsFrom March 2022 to January 2023, a total of 63 patients with suspected pancreatic-related disease underwent the HASTEDL, HASTECS, and BLADE sequences were enrolled in this retrospectively study. The acquisition time, the pancreatic lesion conspicuity (LCP), respiratory motion artifact (RMA), main pancreatic duct conspicuity (MPDC), overall image quality (OIQ), signal-to-noise ratio (SNR), and contrast-noise-ratio (CNR) of the pancreatic lesions were compared among the three sequences by two readers. ResultsThe acquisition time of both HASTEDL and HASTECS was 16 s, which was significantly shorter than that of 102 s for BLADE. In terms of qualitative parameters, Reader 1 and Reader 2 assigned significantly higher scores to the LCP, RMA, MPDC, and OIQ for HASTEDL compared to HASTECS and BLADE sequences; As for the quantitative parameters, the SNR values of the pancreatic head, body, tail, and lesions, the CNR of the pancreatic lesion measured by the two readers were also significantly higher for HASTEDL than for HASTECS and BLADE sequences. ConclusionsCompared to conventional T2WI sequences (HASTECS and BLADE), deep-learning reconstructed HASTE enables thin slice and single-breath-hold acquisition with clinical acceptable image quality for detection of pancreatic lesions.

Diffusion-weighted imaging does not seem to be a predictor of consistency in pituitary adenomas.

To prospectively evaluate the usefulness of T1-weighted imaging (T1WI) and diffusion-weighted imaging (DWI) sequences in predicting the consistency of macroadenomas. In addition, to determine their values ​​as prognostic factors of surgical outcomes. Patients with pituitary macroadenoma and surgical indication were included. All patients underwent pre-surgical magnetic resonance imaging (MRI) that included the sequences T1WI before and after contrast administration and DWI with the apparent diffusion coefficient (ADC) map. Post-surgical MRI was performed at least 3months after surgery. The consistency of the macroadenomas was evaluated at surgery, and they were grouped into soft and intermediate/hard adenomas. Mean ADC values, signal on T1WI and the ratio of tumor ADC values ​​to pons (ADCR) were compared with tumor consistency and grade of surgical resection. A total of 80 patients were included. A softened consistency was found at surgery in 53 patients and hardened in 27 patients. The median ADC in the soft consistency group was 0.532 × 10-3 mm2/sec (0.306-1.096 × 10-3 mm2/sec), and in the intermediate/hard consistency group was 0.509 × 10-3 mm2/sec (0.308-0.818 × 10-3 mm2/sec). There was no significant difference between the median values ​​of ADC, ADCR and signal on T1W between the soft and hard tumor groups, or between patients with and without tumor residue. Our results did not show usefulness of the DWI and T1WI for assessing the consistency of pituitary macroadenomas, nor as a predictor of the degree of surgical resection.

Non-contrast enhanced MRI for efficiency evaluation of high-intensity focused ultrasound in adenomyosis ablation

Objective To investigate the value of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in evaluating the therapeutic effect of high-intensity focused ultrasound (HIFU) in adenomyosis ablation. Material and methods One hundred eighty-nine patients with adenomyosis were treated with HIFU. The ablation areas on T2WI and DWI sequences were classified into different types: type I, relatively ill-defined rim or unrecognizable; subtype IIa, well-defined rim with hyperintensity; subtype IIb, well-defined rim with hypointensity. The volume of ablation areas on T2WI (VT2WI) and DWI (VDWI) was measured and compared with the non-perfused volume (NPV), and linear regression was conducted to analyze their correlation with NPV. Results The VT2WI of type I and type II (subtype IIa and subtype IIb) were statistically different from the corresponding NPV (p = 0.004 and 0.024, respectively), while no significant difference was found between the VDWI of type I and type II with NPV (p = 0.478 and 0.561, respectively). In the linear regression analysis, both VT2WI and VDWI were positively correlated with NPV, with R 2 reaching 0.96 and 0.97, respectively. Conclusions Both T2WI and DWI have the potential for efficient evaluation of HIFU treatment in adenomyosis, and DWI can be a replacement for CE-T1WI to some extent.

Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases

BackgroundKirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction. MethodsThis retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models. ResultsThe four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort). ConclusionsRadiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype.

Comparison of 3D Magnetization-transfer- and Spectral-presaturation-with-inversion-recovery-based Neuromelanin Imaging.

Neuromelanin is visualized by optimizing the conditions of longitudinal relaxation (T1)-weighted imaging (T1WI). Although it was originally developed in 2D imaging, 3D imaging has been also reported, and T1WI sequences with magnetization transfer (MT) pulses are now widely used in 3D gradient echo (GRE) sequences. In this study, we assert that the use of spectral presaturation with inversion recovery (SPIR) may also be useful as an alternative to MT pulses, and we optimize SPIR and compare it with MT. Neuromelanin images with MT pulse and SPIR (flip angles [FAs] = 19º, 22º, and 25º) were acquired from 30 healthy volunteers. To achieve the same acquisition time of 5 min, the slab thickness of the MT images was less than 1/3 of those of the SPIR images; the acquisition areas for MT and SPIR were the brainstem and the whole brain, respectively. Visual and quantitative evaluation was performed and compared on the four sequences acquired for the substantia nigra pars compacta (SNc) and the locus coeruleus (LC). For visual assessment, we used the mean score from a 3-point scale by two evaluators. For quantitative evaluation, the contrast ratios of SNc and LC were calculated in comparison with the background tissue signal. In visual assessments, the mean scores of the SPIR FA19º and FA22º images were better than others in the SNc. Regarding LC, the SPIR FA22º image yielded the best mean score. In quantitative evaluations, the MT image was significantly lower than the other three images in SNc. Regarding LC, there were no significant differences among the four acquired images (MT and SPIR FA19º, FA22º, and FA25º). Detection of neuromelanin in SNc and LC was improved by the use of SPIR compared to MT pulse in 3D neuromelanin imaging.

MRI-based interpretable radiomics nomogram for discrimination between Brucella spondylitis and Pyogenic spondylitis

BackgroundPyogenic spondylitis (PS) and Brucella spondylitis (BS) are commonly seen spinal infectious diseases. Both types can lead to vertebral destruction, kyphosis, and long-term neurological deficits if not promptly diagnosed and treated. Therefore, accurately diagnosis is crucial for personalized therapy. Distinguishing between PS and BS in everyday clinical settings is challenging due to the similarity of their clinical symptoms and imaging features. Hence, this study aims to evaluate the effectiveness of a radiomics nomogram using magnetic resonance imaging (MRI) to accurately differentiate between the two types of spondylitis. MethodsClinical and MRI data from 133 patients (2017–2022) with pathologically confirmed PS and BS (68 and 65 patients, respectively) were collected. We have divided patients into training and testing cohorts. In order to develop a clinical diagnostic model, logistic regression was utilized to fit a conventional clinical model (M1). Radiomics features were extracted from sagittal fat-suppressed T2-weighted imaging (FS-T2WI) sequence. The radiomics features were preprocessed, including scaling using Z-score and undergoing univariate analysis to eliminate redundant features. Furthermore, the Least Absolute Shrinkage and Selection Operator (LASSO) was employed to develop a radiomics score (M2). A composite model (M3) was created by combining M1 and M2. Subsequently, calibration and decision curves were generated to evaluate the nomogram's performance in both training and testing groups. The diagnostic performance of each model and the indication was assessed using the receiver operating curve (ROC) with its area under the curve (AUC). Finally, we used the SHapley Additive exPlanations (SHAP) model explanations technique to interpret the model result. ResultsWe have finally selected 9 significant features from sagittal FS-T2WI sequences. In the differential diagnosis of PS and BS, the AUC values of M1, M2, and M3 in the testing set were 0.795, 0.859, and 0.868. The composite model exhibited a high degree of concurrence with the ideal outcomes, as evidenced by the calibration curves. The nomogram's possible clinical application values were indicated by the decision curve analysis. By using SHAP values to represent prediction outcomes, our model’s prediction results are more understandable. ConclusionsThe implementation of a nomogram that integrates MRI and clinical data has the potential to significantly enhance the accuracy of discriminating between PS and BS within clinical settings.

Preoperative Noninvasive Evaluation of Tumor Budding in Rectal Cancer Using Multiparameter MRI Radiomics

To assess the value of a multiparametric magnetic resonance imaging (MRI)-based model integrating radiomics features with clinical and MRI semantic features for preoperative evaluation of tumor budding (TB) in rectal cancer. A total of 120 patients with pathologically confirmed rectal cancer were retrospectively analyzed. The patients were randomized into training and validation cohorts in a 6:4 ratio. Radiomics features were extracted and selected from preoperative T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1CE) sequences, after which the corresponding radiomics score (RS) was calculated, and the radiomics models (T2WI model, DWI model, and T1CE model) were constructed. Logistic regression analysis was selected to develop a combined model integrated RST2WI, RSDWI, RST1CE, and clinical and MRI semantic features. The efficacy of each model in diagnosing TB grade was observed by the receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was used to assess the clinical benefits of the models. Seven features were extracted and selected from each T2WI, DWI, and T1CE sequence to calculate the corresponding RS and construct the corresponding radiomics model. MRI reported N stagewas an independent risk factor for TB. The area under the ROC curveof the combined model was 0.961 and 0.891 in the training and validation cohorts, respectively. The combined model showed better performance than the other models. DCA showed that the net benefit of the combined model was better than that of the other models in the vast majority of threshold probabilities. A combined model integrating radiomics features and MRI semantic features allows for noninvasive preoperative evaluation of TB grading in patients with rectal cancer.

Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases.

Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics. This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort. There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort. The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.