Combined T2-weighted Imaging Research Articles

Synthetic double inversion recovery imaging for rectal cancer T staging evaluation: imaging quality and added value to T2-weighted imaging

ObjectiveTo assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients.MethodsA total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients.ResultsNo significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07–0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938).ConclusionThe combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection.Critical relevance statementSyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration.Key PointsSynthetic double inversion recovery (SyDIR) and T2WI have comparable image quality.SyDIR provides rectal cancer anatomical features for extramural infiltration detections.The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer.Graphical

Diagnostic significance of multisequence MRI radiomics models in distinguishing benign and malignant spinal fractures

PurposeTo investigate the diagnostic value of multi-sequence magnetic resonance imaging (MRI) in benign and malignant spinal fractures. Materials and methodsMRI data of patients with pathologically confirmed malignant vertebral fractures were retrospectively collected and compared with those with benign vertebral fractures. The image omics features of T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) were extracted respectively, and were randomly divided into training and test sets at 8:2. The training set adopted recursive feature elimination method and minimum absolute contraction and selection operator (LASSO) regression to screen the variables. Three image omics models, T1WI, T2WI and the combination of two sequences, were constructed by logistic regression, and the diagnostic efficiency of each model was verified by the test set. The receiver operating curve evaluated the diagnostic efficiency of the model. ResultsA total of 111 vertebrae with fracture were included in 97 patients, 55 with benign fracture and 56 with malignant fracture. In the training set, the AUC, sensitivity and specificity of the T1WI model were 0.892, 88.9% and 81.4%, respectively. In T2WI model, the results were 0.924, 82.2% and 95.3%, respectively. The combined models of the two sequences were 0.934, 90.0% and 87.5%, respectively. In the test set, the AUC, sensitivity and specificity of T1WI model were 0.877, 80.0% and 92.3%, respectively. In T2WI model, the results were 0.923, 90.0% and 92.3%, respectively. The combined models were 0.933, 100.0% and 87.5%, respectively. The diagnostic efficiency of the two-sequence combined image omics model was better than that of the radiologist. ConclusionThe combined model, incorporating T1WI and T2WI, outperformed single-sequence models in terms of diagnostic accuracy. The integration of multisequence radiomic features provided enhanced texture information, contributing to superior accuracy and efficiency in diagnosing malignant spinal fractures.

The Utility of Apparent Water Diffusion Coefficient Maps for Evaluating the Presence of Myometrial Invasion in Patients with Endometrial Cancer.

To assess the utility of apparent diffusion coefficient maps (ADC) for diagnosing myometrial invasion (MI) in endometrial cancer (EC). This retrospective study included 164 patients (mean age, 56 years; range, 25-89 years) who underwent preoperative MRI for EC with <1/2 MI or no MI between April 2016 and July 2023. Five sequences were evaluated: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), ADC, dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI), and contrast-enhanced T1WI (CE-T1WI). Three experienced radiologists independently assessed the sequences for MI. For ADC, MI was determined if the endometrial-myometrial junction-tumor boundary had disappeared. Additionally, the assessment of MI was performed using the combination of T2WI, DWI, and ADC, as well as T2WI, DCE-T1WI, and CE-T1WI. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the presence of MI were calculated and compared between the sequences and combinations. Inter-reader agreement was assessed using kappa (κ) statistics. The sensitivity of ADC was significantly higher than T2WI (P < 0.001) and DCE-T1WI (P = 0.018) for one reader and significantly higher than CE-T1WI (P = 0.045 and 0.043) for two readers. The specificity of ADC was significantly lower than T2WI (P = 0.015 and < 0.001) and CE-T1WI (P = 0.031 and 0.01) for two readers and significantly lower than DCE-T1WI (P = 0.031) for one reader. The AUC of ADC was significantly higher than T2WI (P = 0.048) and DCE-T1WI (P = 0.049) for one reader. The combination including ADC showed higher positive predictive value for all three readers compared to any sequence or combination including contrast enhancement. Additionally, ADC demonstrated the highest agreement rates. ADC had high sensitivity for MI and the highest agreement rate among all sequences. Thus, this sequence, combined with other sequences, can be crucial for a comprehensive evaluation of MI.

A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer

ObjectiveThe aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients. MethodsThis was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets. ResultsThe model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0. ConclusionCombining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.

Radiomics analysis of T1WI and T2WI magnetic resonance images to differentiate between IgG4-related ophthalmic disease and orbital MALT lymphoma

BackgroundPreoperative differentiation between IgG4-related orbital disease (IgG4-ROD) and orbital mucosa-associated lymphoid tissue (MALT) lymphoma has a significant impact on clinical decision-making. Our research aims to construct and evaluate a magnetic resonance imaging (MRI)-based radiomics model to assist clinicians to better identify IgG4-ROD and orbital MALT lymphoma and make better preoperative medical decisions.MethodsMR images and clinical data from 20 IgG4-ROD patients and 30 orbital MALT lymphoma patients were classified into a training (21 MALT; 14 IgG4-ROD) or validation set (nine MALT; six IgG4-ROD). Radiomics features were collected from T1-weighted (T1WI) and T2-weighted images (T2WI). Student’s t-test, the least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) were conducted to screen and select the radiomics features. Support vector machine (SVM) classifiers developed from the selected radiomic features for T1WI, T2WI and combined T1WI and T2WI were trained and tested on the training and validation set via five-fold cross-validation, respectively. Diagnostic performance of the classifiers were evaluated with area under the curve (AUC) readings of the receiver operating characteristic (ROC) curve, and readouts for precision, accuracy, recall and F1 score.ResultsAmong 12 statistically significant features from T1WI, four were selected for SVM modelling after LASSO analysis. For T2WI, eight of 51 statistically significant features were analyzed by LASSO followed by PCA, with five features finally used for SVM. Combined analysis of T1WI and T2WI features selected two and four, respectively, for SVM. The AUC values for T1WI and T2WI classifiers separately were 0.722 ± 0.037 and 0.744 ± 0.027, respectively, while combined analysis of T1WI and T2WI classifiers further enhanced the classification performances with AUC values ranging from 0.727 to 0.821.ConclusionThe radiomics model based on features from both T1WI and T2WI images is effective and promising for the differential diagnosis of IgG4-ROD and MALT lymphoma. More detailed radiomics features and advanced techniques should be considered to further explore the differences between these diseases.

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 pm 0.25, 0.76 pm 0.18, and 0.60 pm 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists’ assessment.

Practical MRI of Endometrial Neoplasms, Part 2: MRI and FIGO Staging

MRI is the method of choice for the evaluation of endometrial carcinomas (ECs), from detection to evaluation of recurrent disease. Although the staging of ECs is done surgically using the system of the International Federation of Gynecology and Obstetrics (FIGO), preoperative assessment based on MRI findings helps in risk stratification and decision-making. The main parameters evaluated include the degree and depth of myometrial invasion, parametrial extent, and cervical stromal infiltration. The combination of T2-weighted imaging, dynamic contrast-enhanced MRI, and diffusion-weighted imaging increases the accuracy of EC diagnosis and staging. A proposed step-by-step guide was developed to help the radiologist formulate hypotheses and guide clinical reasoning from the approach of the patient to the preparation of the final report. A structured report is also proposed with the objective of improving report quality and facilitating communication with the treating physicians.

Practical MRI of Endometrial Neoplasms, Part 1: Background and Imaging Protocols

MRI is the method of choice for the evaluation of endometrial carcinomas (ECs), from detection to evaluation of recurrent disease. Although the staging of ECs is done surgically using the system of the International Federation of Gynecology and Obstetrics (FIGO), preoperative assessment based on imaging findings helps in risk stratification and decision-making. The main parameters evaluated include the degree and depth of myometrial invasion, parametrial extent, and cervical stromal infiltration. The combination of T2-weighted imaging, dynamic contrast-enhanced MRI, and diffusion-weighted imaging increases the accuracy of EC diagnosis and staging.

Contrast-enhanced MRI for T Restaging of Locally Advanced Rectal Cancer Following Neoadjuvant Chemotherapy and Radiation Therapy.

Background Accurate restaging of rectal cancer is crucial in the selection of candidates for local excision after neoadjuvant chemotherapy and radiation therapy (NCRT). The conventional approach of combined T2-weighted imaging and diffusion-weighted imaging (DWI) at MRI has been found to have limitations in restaging. Purpose To determine the diagnostic performance of contrast-enhanced MRI in distinguishing between pathologic stage ypT0-1 and ypT2-4 rectal cancer after NCRT compared with T2-weighted imaging and DWI by using surgical pathologic specimens as the reference standard. Materials and Methods This retrospective study evaluated MRI scans in all consecutive patients with locally advanced rectal cancer who underwent total mesorectal excision after NCRT in Peking University Cancer Hospital (Beijing, China) from January 2014 to October 2018. All MRI features obtained before and after NCRT were evaluated by two experienced radiologists, independently and blinded to personal, clinical, and histopathologic information. The post-NCRT yT stage was assigned based on high b value (b = 1000 sec/mm2) DWI with T2-weighted imaging (protocol 1) in the first round and on contrast-enhanced MRI scans (protocol 2) in a second round. The diagnostic accuracies for the differentiation of pathologic stage ypT0-1 from ypT2-4 tumors with the two protocols were compared. Multivariable regression analysis was used to explore the independent predictors of pathologic stage ypT0-1 tumors. Results A total of 328 patients (mean age, 57 years ± 10 [SD]; 227 men; 69%) were enrolled. The area under the receiver operating characteristic curve of the contrast-enhanced MRI protocol in predicting pathologic stage ypT0-1 tumors was 0.81 (95% CI: 0.77, 0.85), which was better than that of the T2-weighted DWI protocol (0.66; 95% CI: 0.60, 0.71; P < .001). Multivariable logistic regression analysis showed that yT stage after NCRT on contrast-enhanced MRI scans was the only independent predictor of pathologic stage ypT0-1 tumors (P < .001). Conclusion Contrast-enhanced MRI provides accurate differentiation of ypT0-1 from ypT2-4 tumors after neoadjuvant chemotherapy and radiation therapy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Zins and Santiago in this issue.

Relationship Between Arterial Stiffness Index, Pulse Pressure, and Magnetic Resonance Imaging Markers of White Matter Integrity: A UK Biobank Study.

BackgroundAlzheimer’s disease and dementia in general constitute one of the major public health problems of the 21st century. Research in arterial stiffness and pulse pressure (PP) play an important role in the quest to reduce the risk of developing dementia through controlling modifiable risk factors.ObjectiveThe aim of the study is to investigate the association between peripheral PP, arterial stiffness index (ASI) and brain integrity, and to discover if ASI is a better predictor of white matter integrity than peripheral PP.Materials and Methods17,984 participants 63.09 ± 7.31 from the UK Biobank were used for this study. ASI was estimated using infrared light (photoplethysmography) and peripheral PP was calculated by subtracting the diastolic from the systolic brachial blood pressure value. Measure of fractional anisotropy (FA) was obtained from diffusion imaging to estimate white matter microstructural integrity. White matter hyperintensities were segmented from the combined T1 and T2-weighted FLAIR images as a measure of irreversible white matter damage.ResultsAn important finding is that peripheral PP better predicts white matter integrity when compared to ASI. This finding is consistent until 75 years old. Interestingly, no significant relationship is found between either peripheral PP or ASI and white matter integrity after 75 years old.ConclusionThese results suggest that ASI from plethysmography should not be used to estimate cerebrovascular integrity in older adults and further question the relationship between arterial stiffness, blood pressure, and white matter damage after the age of 75 years old.

Magnetic resonance imaging-based radiomics analysis for the assessment of hepatic alveolar echinococcosis biological activity

OBJECTIVE: The objective of this study was to develop and evaluate predictive models based on a combination of T2-weighted images (T2WI) and different machine learning algorithms, and to explore the value of hepatic alveolar echinococcosis (HAE) activity assessment by magnetic resonance imaging (MRI) radiomics. MATERIALS AND METHODS: This retrospective study included 136 patients diagnosed with HAE at the First Affiliated Hospital of Xinjiang Medical University between 2012 and 2020. All subjects underwent MRI and positron emission tomography–computed tomography (PET-CT) before surgery. Taking the PET-CT examination results as the reference standard, patients were divided into active (90 cases) and inactive groups (46 cases). The volume of interest of the lesion was manually delineated on T2WI, and quantitative radiomics features were extracted. Synthetic Minority Oversampling Technology was used to balance the number of patients in the categories. To control for redundancy, the least absolute shrinkage and selection operator was used for feature screening after normalization, and ten optimal features were obtained based on correlation coefficient screening. Three machine learning classifiers were trained using five-fold cross-validation and their performance was compared to establish an optimal HAE activity assessment model. The performance of the classifier was evaluated by area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy (ACC). The ten optimal features selected from each fold were combined using three machine learning algorithms: logistic regression, multilayer perceptron (MLP), and support vector machine, to establish an HAE activity prediction model. RESULTS: The three machine learning classifiers all showed good prediction performance with a mean AUC on the test set of more than 0.80, and the MLP showing the best performance (AUC = 0.830 ± 0.053, ACC = 0.817, sensitivity = 0.822, and specificity = 0.811). CONCLUSION: HAE activity can be accurately evaluated by a radiomics method using a combination of quantitative T2WI features and machine learning.

Accuracy and Challenges in the Vesical Imaging-Reporting and Data System for Staging Bladder Cancer.

The Vesical Imaging-Reporting and Data System (VI-RADS) scoring system has been widely used to stage bladder cancer (BCa) since 2018. To describe the characteristics of cases with discordant T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) scores in patients with BCa and further verify the accuracy of the VI-RADS scoring system and the necessity of dynamic contrast-enhanced (DCE) sequence. Retrospective. A total of 106 patients (include 16.5% female) with bladder cancer. T2WI (fast spin echo), DWI (echo planer imaging), and DCE (gradient echo). Some cases are difficult to score according to the system, mainly when the T2WI (category 4) and DWI (category 2) sequence scores are discordant, termed the discordant group below. The complementary group will be termed concordant group. Each MRI sequence was reviewed respectively according to the 5-point VI-RADS scoring system by three observers. The diagnostic ability of sequences for evaluating muscle invasion by BCa was calculated using histopathology as the reference standards. Receiver operating characteristic (ROC) curve, DeLong test, intraclass correlation coefficient. A P value of 0.05 or less was considered statistically significant. Fourteen cases (13.2%) had discordant VI-RADS scoring system. In the discordant group, the area under the ROC curve (AUC) of DCE was 0.875, while the T2WI and DWI showed limited diagnostic performance (AUCs=0.50). In the concordant group, there was no significant difference in diagnostic efficacy between the overall VI-RADS (AUC: 0.950) and the combination of T2WI and DWI (AUC: 0.946) (P=0.56). Among all patients, the AUC of overall VIRADS was 0.939 with a 3 or greater cutoff value. The DCE was crucial in the discordant group for evaluating muscle-invasiveness, while DCE may not be necessary for the concordant group. The VI-RADS scoring system performed with overall good diagnostic performance in evaluating muscle-invasiveness in BCa patients. 4 TECHNICAL EFFICACY: Stage 3.

MRI-Based Radiomics for Differentiating Orbital Cavernous Hemangioma and Orbital Schwannoma.

Aim: The purpose of this work was to develop and evaluate magnetic resonance imaging (MRI)-based radiomics for differentiation of orbital cavernous hemangioma (OCH) and orbital schwannoma (OSC).Methods: Fifty-eight patients (40 OCH and 18 OSC, confirmed pathohistologically) screened out from 216 consecutive patients who presented between 2015 and 2020 were divided into a training group (28 OCH and 12 OSC) and a validation group (12 OCH and 6 OSC). Radiomics features were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). T-tests, the least absolute shrinkage and selection operator (LASSO), and principal components analysis (PCA) were used to select features for use in the classification models. A logistic regression (LR) model, support vector machine (SVM) model, decision tree (DT) model, and random forest (RF) model were constructed to differentiate OCH from OSC. The models were evaluated according to their accuracy and the area under the receiver operator characteristic (ROC) curve (AUC).Results: Six features from T1WI, five features from T2WI, and eight features from combined T1WI and T2WI were finally selected for building the classification models. The models using T2WI features showed superior performance on the validation data than those using T1WI features, especially the LR model and SVM model, which showed accuracy of 93% (85–100%) and 92%, respectively, The SVM model showed high accuracy of 93% (91–96%) on the combined feature group with an AUC of 98% (97–99%). The DT and RF models did not perform as well as the SVM model.Conclusion: Radiomics analysis using an SVM model achieved an accuracy of 93% for distinguishing OCH and OSC, which may be helpful for clinical diagnosis.

Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma

Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC. One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm2). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis. Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively. T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.

MRI-based radiomics as response predictor to radiochemotherapy for metastatic cervical lymph node in nasopharyngeal carcinoma.

To establish and substantiate MRI-based radiomic models to predict the treatment response of metastatic cervical lymph node to radiochemotherapy in patients with nasopharyngeal carcinoma (NPC). A total of 145 consecutive patients with NPC were enrolled including 102 in primary cohort and 43 in validation cohort. Metastatic lymph nodes were diagnosed according to radiologic criteria and treatment response was evaluated according to the Response Evaluation Criteria in Solid Tumors. A total of 2704 radiomic features were extracted from contrast-enhanced T1 weighted imaging (CE- T1WI) and T2 weighted imaging (T2WI) for each patient, and were selected to construct radiomic signatures for CE-T1WI, T2WI, and combined CE-T1WI and T2WI, respectively. The area under curve (AUC) of receiver operating characteristic, sensitivity, specificity, and accuracy were used to estimate the performance of these radiomic models in predicting treatment response of metastatic lymph node. No significant difference of AUC was found among radiomic signatures of CE-T1WI, T2WI, and combined CE-T1WI and T2WI in the primary and validation cohorts (all p > 0.05). For combined CE-T1WI and T2WI data set, 12 features were selected to develop the radiomic signature. The AUC, sensitivity, specificity, and accuracy were 0.927 (0.878-0.975), 0.911 (0.804-0.970), 0.826 (0.686-0.922), and 0.872 (0.792-0.930) in primary cohort, and were 0.772 (0.624-0.920), 0.792 (0.578-0.929), 0.790 (0.544-0.939), and 0.791 (0.640-0.900) in validation cohort. MRI-based radiomic models were developed to predict the treatment response of metastatic cervical lymph nodes to radiochemotherapy in patients with NPC, which might facilitate individualized therapy for metastatic lymph nodes before treatment. Predicting the response in patients with NPC before treatment may allow more individualizing therapeutic strategy and avoid unnecessary side-effects and costs. Radiomic features extracted from metastatic cervical lymph nodes showed promising application for predicting the treatment response in NPC.

How to Combine Diffusion-Weighted and T2-Weighted Imaging for MRI Assessment of Pathologic Complete Response to Neoadjuvant Chemoradiotherapy in Patients with Rectal Cancer?

ObjectiveAdequate methods of combining T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) to assess complete response (CR) to chemoradiotherapy (CRT) for rectal cancer are obscure. We aimed to determine an algorithm for combining T2WI and DWI to optimally suggest CR on MRI using visual assessment.Materials and MethodsWe included 376 patients (male:female, 256:120; mean age ± standard deviation, 59.7 ± 11.1 years) who had undergone long-course CRT for rectal cancer and both pre- and post-CRT high-resolution rectal MRI during 2017–2018. Two experienced radiologists independently evaluated whether a tumor signal was absent, representing CR, on both post-CRT T2WI and DWI, and whether the pre-treatment DWI showed homogeneous hyperintensity throughout the lesion. Algorithms for combining T2WI and DWI were as follows: ‘AND,’ if both showed CR; ‘OR,’ if any one showed CR; and ‘conditional OR,’ if T2WI showed CR or DWI showed CR after the pre-treatment DWI showed homogeneous hyperintensity. Their efficacies for diagnosing pathologic CR (pCR) were determined in comparison with T2WI alone.ResultsSixty-nine patients (18.4%) had pCR. AND had a lower sensitivity without statistical significance (vs. 62.3% [43/69]; 59.4% [41/69], p = 0.500) and a significantly higher specificity (vs. 87.0% [267/307]; 90.2% [277/307], p = 0.002) than those of T2WI. Both OR and conditional OR combinations resulted in a large increase in sensitivity (vs. 62.3% [43/69]; 81.2% [56/69], p < 0.001; and 73.9% [51/69], p = 0.008, respectively) and a large decrease in specificity (vs. 87.0% [267/307]; 57.0% [175/307], p < 0.001; and 69.1% [212/307], p < 0.001, respectively) as compared with T2WI, ultimately creating additional false interpretations of CR more frequently than additional identification of patients with pCR.ConclusionAND combination of T2WI and DWI is an appropriate strategy for suggesting CR using visual assessment of MRI after CRT for rectal cancer.

Clinical utility of combined T2-weighted imaging and T2-mapping in the detection of prostate cancer: a multi-observer study.

To evaluate the clinical utility of combined T2-weighted imaging and T2-mapping for the detection of prostate cancer. Forty patients underwent multiparametric magnetic resonance imaging (mpMRI) and T2-mapping of the prostate. Three readers each reviewed two sets of images: T2-weighted fast spin-echo (FSE) sequence (standard T2), and standard T2 in combination with T2-mapping. Each reader assigned probability scores for malignancy to each zone [peripheral zone (PZ) or transition zone (TZ)]. Inter-observer variability for standard T2 and combined standard T2 with T2-mapping were assessed. Diagnostic accuracy was compared between standard T2 and combined standard T2 with T2-mapping. There was fair agreement between all three readers for standard T2 [intraclass correlation coefficient (ICC) =0.56] and combined standard T2 with T2-mapping (ICC =0.58). There was no significant difference in the area under the receiver operator characteristics curve for standard T2 compared to combined standard T2 with T2-mapping (0.89 vs. 0.82, P=0.31). Sensitivity (Sn) for combined standard T2 with T2-mapping was significantly higher compared to standard T2 alone (73.0% vs. 49.2%, P=0.006). Specificity (Sp) for combined standard T2 with T2-mapping was borderline significantly lower compared to standard T2 alone (89.3% vs. 94.9%, P=0.05). There was no significant differences between the negative predictive values (NPVs) and positive predictive values (PPVs) (P=0.07, P=0.45). Combination of T2-weighted imaging and T2-mapping could potentially increase Sn for prostate malignancy compared to T2-weighted imaging alone.

Role of abbreviated MRI protocol for screening of HCC in HCV related cirrhotic patients prior to direct-acting antiviral treatment

BackgroundChronic HCV infection is a global health problem causing progressive hepatic fibrosis, cirrhosis, and ultimately hepatocellular carcinoma (HCC). Recent advantage of direct-acting antiviral drugs (DAA) with a high sustained virologic response (SVR) reduces overall HCV-related morbidity and mortality, yet recent studies report a high recurrence rate of HCC after DAA; this calls availability of a reliable screening method to properly exclude HCC before DAA treatment.The primary objective of our cohort study was to assess the feasibility of an abbreviated MRI protocol as a screening tool for the detection of hepatic focal lesions/early HCC in patients with HCV-related liver cirrhosis.The study included 41 patients with HCV-related cirrhosis candidates to DAA therapy. All patients underwent routine screening for HCC by combined abdominal ultrasound and serum alfa-fetoprotein. An abbreviated MRI protocol (Abr-MR) including combined T2-weighted image and diffusion-weighted imaging (DWI) followed by dynamic contrast-enhanced MRI (CE-MRI) was performed for all subjects, assessing for presence and characterization of focal lesions.ResultsFor all included 41patients, no elevation of the alpha-fetoprotein was shown. Ultrasound detected a single focal lesion in one patient. Abbreviated MR demonstrated 15 focal lesions; for detected lesions, 1 lesion shows “shine though” and the rest showing moderate and high degrees of restriction. CE-MRI characterized lesions as 1 lesion = LIRADS-1, 3 lesions = LR-M, and 11 lesions = LR-5.The standard screening using combined ultrasound and alpha-fetoprotein had sensitivity, specificity, PPV, and NPV of 6.6 % (95% CI = 0.0034–0.29), 100% (95% CI = 0.89–1.000), 100% (95% CI = 0.051–1.000), and 68.8% (95% CI = 0.54–0.80). Abr-MR protocol showed sensitivity, specificity, PPV, and NPV of 100% for all parameters (95% CI = 0.79–1.000, 0.89–1.000, 0.79–1.000, and 0.89–1.000 respectively).ConclusionIn our study, we demonstrate the superiority of a proposed cost-effective Abr-MR protocol in the detection of hepatic focal lesions and small-sized HCC compared to routine screening using alpha-fetoprotein and ultrasound in HCV-related liver cirrhosis.

The association between MRI findings and breast cancer subtypes: focused on the combination patterns on diffusion-weighted and T2-weighted images.

To assess morphology on diffusion-weighted imaging (DWI) and intratumoral signal intensity (SI) on T2-weighted images (T2WI) of breast carcinomas, and to evaluate the association between the combined DWI and T2WI findings and breast cancer subtypes. Two hundred and eighty breast cancer patients who underwent breast MRI prior to therapy were included in this retrospective study. All had invasive carcinomas, which were classified into five subtypes: Luminal A-like (n = 149), Luminal B-like (n = 63), Hormone receptor-positive HER2 (n = 31), Hormone receptor-negative HER2 (n = 13), or Triple-negative (TN) (n = 24). Based on the morphology on DWI, the tumors were classified into two patterns: DWI-homogeneous or DWI-heterogeneous. If DWI-heterogeneous, an assessment of intratumoral SI on T2WI was performed: tumors with intratumoral high/low SI on T2WI were classified as Hete-H/Hete-L, respectively. The associations between (1) the morphological patterns on DWI and the five subtypes, and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous were evaluated. There was a significant association between (1) the morphological patterns on DWI and the five subtypes (p < 0.0001), and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous (p < 0.0001). DWI-homogeneous was dominant in Luminal A-like (67.1%), and Hete-H was dominant in TN type (75%). Hete-H, suggesting the presence of intratumoral necrosis, included high proliferative and/or aggressive subtypes more frequently (80%) than Hete-L, suggesting the presence of fibrotic focus. Fibrotic focus was seen more commonly in the luminal subtypes. The combined findings on DWI and T2WI revealed breast carcinomas that were associated with particular subtypes.

Characterizing Computed Tomography-Detected Arterial Hyperenhancing-Only Lesions in Patients at Risk of Hepatocellular Carcinoma: Can Non-Contrast Magnetic Resonance Imaging Be Used for Sequential Imaging?

ObjectiveTo test the feasibility of non-contrast magnetic resonance imaging (MRI) in a sequential imaging study for characterizing computed tomography (CT)-detected arterial-enhancing nodules that do not washout in patients at risk of hepatocellular carcinoma (HCC).Materials and MethodsIn this retrospective study, 134 patients (mean age ± standard deviation, 56.8 ± 10.0 years) with 151 arterial enhancing-only nodules measuring up to 2 cm during multiphasic CT that were subsequently evaluated using gadoxetic acid-enhanced MRI in treatment-naïve at-risk patients from three tertiary referral centers were included. Tentative diagnostic criteria for HCC and hepatic malignancy were defined as the presence of one of eight MRI features favoring HCC in combinations of the following sequences: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), T1-weighted dual gradient-echo in-phase and out-of-phase imaging (Dual-GRE), and hepatobiliary phase imaging (HBP). Typical hemangiomas and arterioportal shunts were excluded from the analysis. Diagnostic performance for HCC and hepatic malignancy was calculated and compared between the abbreviated MRI and full-sequence gadoxetic acid-enhanced MRI.ResultsOf 151 nodules (mean size, 1.2 cm) 68 HCCs and 83 non-HCC benignities and malignancies were included. The combination of T2WI, DWI, and Dual-GRE showed per-lesion sensitivity, specificity, and accuracy of 88.2%, 90.4%, and 89.4%, respectively, comparable to those of full-sequence MRI. Applying the same sequence combination to diagnose hepatic malignancy had per-lesion sensitivity, specificity, and accuracy of 86.8%, 97.3%, and 92.1%. In nodules < 1 cm, adding HBP increased sensitivity by up to 13% without compromising the specificity or accuracy.ConclusionThe non-contrast MRI protocol comprising T2WI, DWI, and Dual-GRE showed reasonable and comparable performance to full-sequence MRI for discriminating HCC and primary liver malignancies in CT-detected indeterminate arterial enhancing-only nodules in at-risk patients, and can be potentially used for sequential imaging in place of a full-sequence MRI. In nodules < 1 cm, HBP may still be needed to preserve sensitivity.