Dynamic Contrast-enhanced Sequences Research Articles

Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model

Pancreatic cancer is a common malignant tumor with a dismal prognosis. Preoperative differentiation of extrapancreatic extension (EPE) based on radiomics will facilitate treatment decision-making. This research retrospectively recruited 156 patients from two medical centers. 122 patients from the center A were randomly divided into the training set and the internal test set in a 4:1 ratio. Additionally, 34 patients from the center B served as the external test set. Radiomics features were extracted from multiparametric MRI (MP-MRI), containing axial T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic contrast enhancement (DCE) sequences. The three-step method was used for feature extraction: SelecteKBest, least absolute shrinkage and selection operator (LASSO) algorithm, and recursive feature elimination based on random forest (RFE-RF). The model was constructed using six classifiers based on machine learning, and the classifier with the best performance was chosen. Finally, clinical factors associated with EPE were incorporated into the combined model. The classifier with the best performance was XGBoost, which obtained area under curve (AUC) values of 0.853 and 0.848 in the internal and external test sets, respectively. Through SelectKBest, the most relevant clinical factor for EPE was determined to be platelet, which was then added to the combined model, yielding AUC values of 0.880 and 0.848 in the internal and external test sets, respectively. Radiomics models had the potential to noninvasively and accurately predict EPE before surgery. Additionally, it would add value to personalized precision treatment.

Risk of recurrence after chemoradiotherapy identified by multimodal MRI and 18F-FDG-PET/CT in locally advanced cervical cancer

Background and purposeMRI, applying dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) sequences, and 18F-fluorodeoxyglucose (18F-FDG) PET/CT provide information about tumor aggressiveness that is unexploited in treatment of locally advanced cervical cancer (LACC). We investigated the potential of a multimodal combination of imaging parameters for classifying patients according to their risk of recurrence. Materials and methodsEighty-two LACC patients with diagnostic MRI and FDG-PET/CT, treated with chemoradiotherapy, were collected. Thirty-eight patients with MRI only were included for validation of MRI results. Endpoints were survival (disease-free, cancer-specific, overall) and tumor control (local, locoregional, distant). Ktrans, reflecting vascular function, apparent diffusion coefficient (ADC), reflecting cellularity, and standardized uptake value (SUV), reflecting glucose uptake, were extracted from DCE-MR, DW-MR and FDG-PET images, respectively. By applying an oxygen consumption and supply-based method, ADC and Ktrans parametric maps were voxel-wise combined into hypoxia images that were used to determine hypoxic fraction (HF). ResultsHF showed a stronger association with outcome than the single modality parameters. This association was confirmed in the validation cohort. Low HF identified low-risk patients with 95% precision. Based on the 50th SUV-percentile (SUV50), patients with high HF were divided into an intermediate- and high-risk group with high and low SUV50, respectively. This defined a multimodality biomarker, HF/SUV50. HF/SUV50 increased the precision of detecting high-risk patients from 41% (HF alone) to 57% and showed prognostic significance in multivariable analysis for all endpoints. ConclusionMultimodal combination of MR- and FDG-PET/CT-images improves classification of LACC patients compared to single modality images and clinical factors.

Contribution of Dynamic Contrast-enhanced and Diffusion MRI to PI-RADS for Detecting Clinically Significant Prostate Cancer.

Background Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 requires multiparametric MRI of the prostate, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging sequences; however, the contribution of DCE imaging remains unclear. Purpose To assess whether DCE imaging in addition to apparent diffusion coefficient (ADC) and normalized T2 values improves PI-RADS version 2.0 for prediction of clinically significant prostate cancer (csPCa). Materials and Methods In this retrospective study, clinically reported PI-RADS lesions in consecutive men who underwent 3-T multiparametric MRI (T2-weighted, DWI, and DCE MRI) from May 2015 to September 2016 were analyzed quantitatively and compared with systematic and targeted MRI-transrectal US fusion biopsy. The normalized T2 signal (nT2), ADC measurement, mean early-phase DCE signal (mDCE), and heuristic DCE parameters were calculated. Logistic regression analysis indicated the most predictive DCE parameters for csPCa (Gleason grade group ≥2). Receiver operating characteristic parameter models were compared using the Obuchowski test. Recursive partitioning analysis determined ADC and mDCE value ranges for combined use with PI-RADS. Results Overall, 260 men (median age, 64 years [IQR, 58-69 years]) with 432 lesions (csPCa [n = 152] and no csPCa [n = 280]) were included. The mDCE parameter was predictive of csPCa when accounting for the ADC and nT2 parameter in the peripheral zone (odds ratio [OR], 1.76; 95% CI: 1.30, 2.44; P = .001) but not the transition zone (OR, 1.17; 95% CI: 0.81, 1.69; P = .41). Recursive partitioning analysis selected an ADC cutoff of 0.897 × 10-3 mm2/sec (P = .04) as a classifier for peripheral zone lesions with a PI-RADS score assessed on the ADC map (hereafter, ADC PI-RADS) of 3. The mDCE parameter did not differentiate ADC PI-RADS 3 lesions (P = .11), but classified lesions with ADC PI-RADS scores greater than 3 with low ADC values (less than 0.903 × 10-3 mm2/sec, P < .001) into groups with csPCa rates of 70% and 97% (P = .008). A lesion size cutoff of 1.5 cm and qualitative DCE parameters were not defined as classifiers according to recursive partitioning (P > .05). Conclusion Quantitative or qualitative dynamic contrast-enhanced MRI was not relevant for Prostate Imaging Reporting and Data System (PI-RADS) 3 lesion risk stratification, while quantitative apparent diffusion coefficient (ADC) values were helpful in upgrading PI-RADS 3 and PI-RADS 4 lesions. Quantitative ADC measurement may be more important for risk stratification than current methods in future versions of PI-RADS. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Goh in this issue.

Differentiation of Skull Base Chondrosarcomas, Chordomas, and Metastases: Utility of DWI and Dynamic Contrast-Enhanced Perfusion MR Imaging.

Differentiation of skull base tumors, including chondrosarcomas, chordomas, and metastases, on conventional imaging remains a challenge. We aimed to test the utility of DWI and dynamic contrast-enhanced MR imaging for skull base tumors. Fifty-nine patients with chondrosarcomas, chordomas, or metastases between January 2015 and October 2021 were included in this retrospective study. Pretreatment normalized mean ADC and dynamic contrast-enhanced MR imaging parameters were calculated. The Kruskal-Wallis H test for all tumor types and the Mann-Whitney U test for each pair of tumors were used. Fifteen chondrosarcomas (9 men; median age, 62 years), 14 chordomas (6 men; median age, 47 years), and 30 metastases (11 men; median age, 61 years) were included in this study. Fractional plasma volume helped distinguish all 3 tumor types (P = .003, <.001, and <.001, respectively), whereas the normalized mean ADC was useful in distinguishing chondrosarcomas from chordomas and metastases (P < .001 and P < .001, respectively); fractional volume of extracellular space, in distinguishing chondrosarcomas from metastases (P = .02); and forward volume transfer constant, in distinguishing metastases from chondrosarcomas/chondroma (P = .002 and .002, respectively) using the Kruskal-Wallis H test. The diagnostic performances of fractional plasma volume for each pair of tumors showed areas under curve of 0.86-0.99 (95% CI, 0.70-1.0); the forward volume transfer constant differentiated metastases from chondrosarcomas/chordomas with areas under curve of 0.82 and 0.82 (95% CI, 0.67-0.98), respectively; and the normalized mean ADC distinguished chondrosarcomas from chordomas/metastases with areas under curve of 0.96 and 0.95 (95% CI, 0.88-1.0), respectively. DWI and dynamic contrast-enhanced MR imaging sequences can be beneficial for differentiating the 3 common skull base tumors.

The Additional Value of Tri-parametric MRI in Identifying Muscle-invasive Status in Bladder Cancer

Identification of muscle-invasive status (MIS) of bladder cancer (BCa) is critical for treatment decisions. The Vesical Imaging-Reporting and Data System (VI-RADS) has been widely used in preoperatively predicting MIS using tri-parametric MR imaging including T2-weighted (T2W), diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) sequences. While the diagnostic values of radiomics features from bi-parametric MRI such as T2W+DW to identification of MIS have been reported, whether the tri-parametric MRI could provide additional diagnostic value to the radiomics prediction task, and how to integrate DCE features into the radiomics model, which is the objectives of this study, remain unknown. Patients with postoperatively confirmed BCa lesions (150 in non-muscle-invasive BCa and 56 in muscle-invasive BCa groups) were retrospectively included. Their T2W, DW with apparent diffusion coefficient (ADC) maps, and DCE sequences were acquired using a 3.0T MR system. Regions of interest were manually depicted and VI-RADS scores were assessed by three radiologists. Three predictive models were developed by the radiomics features extracted from sequence combinations of T2W+DW (Model one), T2W+DCE (Model two), and T2W+DW+DCE (Model three), respectively, using the least absolute shrinkage and selection operator. The performance of each model was quantitatively assessed on both the training (n=165) and testing (n=41) cohorts. Then a 10 times five-fold cross validation was conducted to assess the overall performance. Three models achieved area under the curve of 0.888, 0.869, and 0.901 in the cross validation, respectively. The tri-parametric model performed significantly superior than the two bi-parametric models and VI-RADS. The analysis of feature coefficients derived from least absolute shrinkage and selection operator algorithm showed features from the tri-parametric MRI are effective in MIS discrimination. The tri-parametric MRI provides additional value to the radiomics-based identification of MIS.

Magnetic resonance imaging in the diagnosis of malignant gynaecological tumours

Magnetic resonance imaging (MRI) is a widely used imaging modality that depicts detailed information regarding morphological and functional characteristics of the human body. It is routinely used in gynaecologic oncology for female pelvis imaging because of the high spatial and soft-tissue contrast resolution. Furthermore, MRI is an important diagnostic tool for the assessment of common gynaecological malignancies - endometrial carcinoma, cervical carcinoma and malignant ovarian tumours. Novel technical developments enabled the multiparametric MRI approach in the diagnosis of respective tumours combining T1-weighted (T1W) sequences, T2-weighted (T2W) sequences, diffusion-weighted (DW) sequences with apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced (DCE) sequences. With highlighted novelties, MRI importance ranges from tumour detection to treatment response monitoring and early recurrent disease evaluation. This review discusses the value of MRI in the diagnostic assessment of the common gynaecological malignancies with an¬¬ emphasis on tumour staging.

Diagnosis and differential diagnosis of dermatofibrosarcoma protuberans: Utility of high-resolution dynamic contrast-enhanced (DCE) MRI.

Dermatofibrosarcoma protuberans (DFSP) is a kind of low-grade malignant spindle cell neoplasm, the diagnosis, and treatment, which have markedly attracted clinicians' attention for its repeated recurrence. High-resolution magnetic resonance imaging (HR-MRI) has shown unique capabilities in diagnosis of various cutaneous tumors. Data of 29 patients with clinically suspected DFSPs and undergoing dynamic contrast-enhanced (DCE) HR-MRI preoperatively were prospectively collected. The HR-MRI qualitative features were evaluated and compared. The DCE-associated quantitative parameters and the time-signal intensity curve (TIC) types were provided using DCE sequences. A total of 7 DFSPs, nine dermatofibromas (DF, including four cases of cellular variant [CDF]), 12 keloids, and one nodular fasciitis were enrolled. DFSP showed the largest major diameter and the deepest depth. Five DFSPs (71.4%) showed ill-defined margins as well as infiltration of peripheral adipose. All DFSPs showed irregular shape. Most DFSPs presented hyperintensity on T2 WI (71.4%) and iso-intensity on T1 WI (85.7%). Six cases (85.7%) had significant enhancement, and six cases (85.7%) had homogeneous enhancement. There were significant differences of Ktrans , Kep , Ve and iAUC values among DFSPs, DFs, and keloids, and DFSP had the highest values for these parameters. Six DFSPs (85.7%) and four CDFs (100%) showed type-III TICs, while the other lesions showed type-Ⅰor type-Ⅱ TICs. DCE-HR-MRI could show the growth characteristics of DFSPs, which was of great value for the diagnosis and differential diagnosis of DFSPs and was helpful for the determination of treatment options, thereby to improve the prognosis of patients.

Subchondral involvement in osteonecrosis of the femoral head: insight on local composition, microstructure and vascularization

To determine changes of subchondral bone composition, micro-structure, bone marrow adiposity and micro-vascular perfusion in end-stage osteonecrosis of the femoral head (ONFH) compared to osteoarthritis (OA) using a combined in vivo and ex vivo approach. Male patients up to 70 years old referred for total hip replacement surgery for end-stage ONFH were included (n=14). Fifteen patients with OA were controls. Pre-operative MRI was used to assess bone perfusion (dynamic contrast-enhanced (DCE) sequences) and marrow fat content (chemical shift imaging). Three distinct zones of femoral head subchondral bone - necrotic, sclerotic, distant - were compared between groups. After surgery, plugs were sampled in these zones and Raman spectroscopy was applied to characterize bone mineral and organic components (old and newly-formed), and contrast-enhanced micro-computed tomography (CE-μCT) to determine bone micro-structural parameters and volume of bone marrow adipocytes, using conventional 2D histology as a reference. In the necrotic zone of ONFH patients compared to OA patients: 1) the subchondral plate did not exhibit significant changes in composition nor structure; 2) the volume fraction of subchondral trabecular bone was significantly lower; 3) type-B carbonate substitution was less pronounced, 4) collagen maturity was more pronounced; and 5) bone marrow adipocytes were significantly depleted. The sclerotic zone from the ONFH group showed greater trabecular thickness, and higher DCE-MRI AUC and Ktrans. Volume fraction of subchondral bone, trabecular number, and Kep were significantly lower in the distant zone of the ONFH group. This study demonstrated alterations of subchondral bone microstructure, composition, perfusion and/or adipose content in all zones of the femoral head.

Radiomics Based on DCE-MRI Improved Diagnostic Performance Compared to BI-RADS Analysis in Identifying Sclerosing Adenosis of the Breast.

PurposeSclerosing adenosis (SA) is a benign lesion that could mimic breast carcinoma and be evaluated as malignancy by Breast Imaging-Reporting and Data System (BI-RADS) analysis. We aimed to construct and validate the performance of radiomic model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) compared to BI-RADS analysis to identify SA.MethodsSixty-seven patients with invasive ductal carcinoma (IDC) and 58 patients with SA were included in this retrospective study from two institutions. The 125 patients were divided into a training cohort (n= 88) from institution I and a validation cohort from institution II (n=37). Dynamic contrast-enhanced sequences including one pre-contrast and five dynamic post-contrast series were obtained for all cases with different 3T scanners. Single-phase enhancement, multi-phase enhancement, and dynamic radiomic features were extracted from DCE-MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation was performed to build the radscore of each single-phase enhancement and the final model combined multi-phase and dynamic radiomic features. The diagnostic performance of radiomics was evaluated by receiver operating characteristic (ROC) analysis and compared to the performance of BI-RADS analysis. The classification performance was tested using external validation.ResultsIn the training cohort, the AUCs of BI-RADS analysis were 0.71 (95%CI [0.60, 0.80]), 0.78 (95%CI [0.67, 0.86]), and 0.80 (95%CI [0.70, 0.88]), respectively. In single-phase analysis, the second enhanced phase radiomic signature achieved the highest AUC of 0.88 (95%CI [0.79, 0.94]) in distinguishing SA from IDC. Nine multi-phase radiomic features and two dynamic radiomic features showed the best predictive ability for final model building. The final model improved the AUC to 0.92 (95%CI [0.84, 0.97]), and showed statistically significant differences with BI-RADS analysis (p<0.05 for all). In the validation cohort, the AUC of the final model was 0.90 (95%CI [0.75, 0.97]), which was higher than all BI-RADS analyses and showed statistically significant differences with one of the BI-RADS analysis observers (p = 0.03).ConclusionsRadiomics based on DCE-MRI could show better diagnostic performance compared to BI-RADS analysis in differentiating SA from IDC, which may contribute to clinical diagnosis and treatment.

Multiparametric MRI of the prostate

Multiparametric magnetic resonance imaging (mpMRI) is an integral component of prostate cancer diagnostics. According to the S3 guidelines on prostate cancer, mpMRI should be used for the primary diagnostics of prostate cancer as well as in active surveillance (AS). Basically, mpMRI consists of high-resolution T2-weighted (T2w) sequences, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) sequences, which in turn are the basis for structured reporting according to the prostate imaging reporting and data system (PI-RADS) classification.

MRI Features Associated with Histology of Benign Prostatic Hyperplasia Nodules: Generation of a Predictive Model.

Background: Histologic phenotypic variation of benign prostatic hyperplasia (BPH) has been hypothesized to underlie response to medical therapy. We evaluate preoperative MRI of robot-assisted simple prostatectomy (RASP) specimens and determine imaging features associated with histologic phenotype. Materials and Methods: All patients undergoing RASP from November 2015 to November 2019 with a multiparametric MRI ≤1 year before RASP were included. Patients without identifiable BPH nodules on histologic specimens were excluded. Histology slides were obtained from whole mount adenoma specimens and corresponding MRI were reviewed and graded independently by a blinded expert in BPH histopathology (D.W.S.) and an experienced radiologist specializing in prostate imaging (D.N.C.), respectively. Each nodule was assigned a phenotypic score on a 5-point Likert scale (1 = predominantly glandular; 5 = predominantly stromal) by each reviewer. Scores were compared using the sign test and univariate analysis. Signal intensity relative to background transition zone and nodule texture were noted on T2, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging sequences. Univariate and multivariate stepwise linear regression analysis were conducted to identify MRI features associated with histology score. All analyses were performed using Statistical Analysis System (version 9.4). Results: A total of 99 prostate nodules in 29 patients were included. Median phenotypic scores by histology and MRI were comparable (2, interquartile range [IQR] 2-3 vs 2, IQR 2-4, respectively; p = 0.63). Histology scores were positively correlated with MRI scores (Pearson's correlation 0.84, p < 0.0001). Multivariate stepwise linear regression analysis showed that low apparent diffusion coefficient (ADC) signal intensity (p < 0.001) and DCE wash-in (p = 0.03) were positively associated with more stromal histology, whereas ADC standard deviation (p = 0.03), DCE wash-out (p = 0.001), and heterogeneous T2 texture (p = 0.003) were associated with more glandular histology. Conclusion: There is a strong correlation between MRI features and the histologic phenotype of BPH nodules. MRI may provide a noninvasive method to determine underlying BPH nodule histology.

Multiparametric Magnetic Resonance Imaging in Evaluation of Benign and Malignant Breast Masses with Pathological Correlation

BackgroundDynamic contrast-enhanced (DCE) MRI sequences plays a vital role in diagnosing breast masses with high sensitivity and specificity as compared to other diagnostic modalities. The addition of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values significantly improves diagnostic accuracy. This study aimed to study the breast masses on DCE-MRI, restricted diffusion on DWI, ADC values, and choline peak on spectroscopy in breast cancer diagnosis.Material and methodsThis study was a prospective observational study which involved subjects with breast lumps. Baseline data was collected from the patients along with pertinent clinical history and relevant laboratory investigations. MR mammography (MRM) was performed on a 1.5 Tesla MR Scanner (MAGNETOM® Avanto, Siemens AG, Munich Germany) using a dedicated double breast coil.ResultsForty-one subjects were included with a total of 54 breast masses in them. The mean age of the study population was 47.1±14.7 years. From the MRI final diagnosis, the majority (53.70%) were diagnosed as malignant lesions and 46.30% as benign. Out of 20 lesions diagnosed as benign on histopathology, only five percent had ADC value <1.3 ×10−3mm2/s, and the majority (95%) had ADC value >1.3 ×10−3mm2/s. All 20 lesions were circumscribed, ovoid, or round in shape showing no restricted diffusion on DWI, with corresponding ADC value of >1.3×10−3mm2/s, homogeneous post-contrast enhancement, or with dark internal septations, type I kinetic enhancement curve, and they showed no choline peak on spectroscopy. Out of 34 malignant lesions diagnosed on histopathology, the majority (85.29%) displayed restricted diffusion on DWI and had an ADC value of <1.3×10−3mm2/s, most of them had spiculated margins, type II/ III kinetic curve with choline peak on spectroscopy.ConclusionMultiparametric MR mammography, which included DCE-MRM, DWI, ADC values, and spectroscopy, correlated well with the histopathological diagnosis of benign and malignant breast masses.

Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer.

The overexpression of aquaporin-1 (AQP1) is associated with poor prognosis in rectal cancer. This study aimed to explore the value of multi-parameter diffusion and perfusion MRI and radiomics models in predicting AQP1 high expression. This prospective study was performed from July 2019 to February 2021, which included rectal cancer participants after preoperative rectal MRI, with diffusion-weighted imaging, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE) sequences. Radiomic features were extracted from MR images, and immunohistochemical tests assessed AQP1 expression. Selected quantitative MRI and radiomic features were analyzed. Receiver operating characteristic (ROC) curves evaluated the predictive performance. The nomogram performance was evaluated by its calibration, discrimen, and clinical utility. The intraclass correlation coefficient evaluated the interobserver agreement for the MRI features. 110 participants with the age of 60.7 ± 12.5years been enrolled in this study. The apparent diffusion coefficient (ADC), IVIM_D, DKI_diffusivity, and DCE_Ktrans were significantly higher in participants with high AQP1 expression than in those with low expression (P < 0.05). ADC (b = 1000, 2000, and 3000s/mm2), IVIM_D, DKI_diffusivity, and DCE_Ktrans were positively correlated (r = 0.205, 0.275, 0.37, 0.235, 0.229, and 0.227, respectively; P < 0.05), whereas DKI_Kurtosis was negatively correlated (r = - 0.22, P = 0.021) with AQP1 expression. ADC (b = 3000s/mm2), IVIM_D, DKI_ diffusivity, DKI_Kurtosis, and DCE_Ktrans had moderate diagnostic efficiencies for high AQP1 expression (AUC = 0.715, 0.636, 0.627, 0.633, and 0.632, respectively; P < 0.05). The radiomic features had excellent predictive efficiency for high AQP1 expression (AUC = 0.967 and 0.917 for training and validation). The model-based nomogram had C-indexes of 0.932 and 0.851 for the training and validation cohorts, which indicated good fitting to the calibration curves (p > 0.05). Diffusion and perfusion MRI can indicate the aquaporin-1 expression in rectal cancer, and radiomic features can enhance the predictive efficiency for high AQP1 expression. A nomogram for high aquaporin-1 expression will improve clinical decision-making.

An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions.

ObjectivesThe probability of Breast Imaging Reporting and Data Systems (BI-RADS) 4 lesions being malignant is 2%–95%, which shows the difficulty to make a diagnosis. Radiomics models based on magnetic resonance imaging (MRI) can replace clinicopathological diagnosis with high performance. In the present study, we developed and tested a radiomics model based on MRI images that can predict the malignancy of BI-RADS 4 breast lesions.MethodsWe retrospective enrolled a total of 216 BI-RADS 4 patients MRI and clinical information. We extracted 3,474 radiomics features from dynamic contrast-enhanced (DCE), T2-weighted images (T2WI), and diffusion-weighted imaging (DWI) MRI images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used to select features and build radiomics models based on different sequence combinations. We built eight radiomics models which were based on DCE, DWI, T2WI, DCE+DWI, DCE+T2WI, DWI+T2WI, and DCE+DWI+T2WI and a clinical predictive model built based on the visual assessment of radiologists. A nomogram was constructed with the best radiomics signature combined with patient characteristics. The calibration curves for the radiomics signature and nomogram were conducted, combined with the Hosmer-Lemeshow test.ResultsPearson’s correlation was used to eliminate 3,329 irrelevant features, and then LASSO and logistic regression were used to screen the remaining feature coefficients for each model we built. Finally, 12 related features were obtained in the model which had the best performance. These 12 features were used to build a radiomics model in combination with the actual clinical diagnosis of benign or malignant lesion labels we have obtained. The best model built by 12 features from the 3 sequences has an AUC value of 0.939 (95% CI, 0.884-0.994) and an accuracy of 0.931 in the testing cohort. The sensitivity, specificity, precision and Matthews correlation coefficient (MCC) of testing cohort are 0.932, 0.923, 0.982, and 0.791, respectively. The nomogram has also been verified to have calibration curves with good overlap.ConclusionsRadiomics is beneficial in the malignancy prediction of BI-RADS 4 breast lesions. The radiomics predictive model built by the combination of DCE, DWI, and T2WI sequences has great application potential.

THE ASSESSMENT OF MRE FINDINGS ACCORDING TO CDAI SCORE IN PATIENTS WITH CROHN’S DISEASE

Abstract INTRODUCTION Crohn’s disease (CD) is a chronic inflammatory bowel disease of the gastrointestinal tract. One of the diagnostic steps is to perform a magnetic resonance enterography (MRE). The purpose of the study was to compare MRE findings with clinical index of the disease activity - CDAI score, in order to verify the usefullness of magnetic resonance enterography. SUBJECTS/METHODS The study included 46 patients who underwent MRE according to a standard protocol, with dynamic contrast-enhanced sequences. All studies were performed in prone position, using mannitol as a positive oral contrast agent. The MRE severity grading was performed according to the consensus recommendations. CDAI score was assessed prior to examination by a specialist. RESULTS 32 patients had multifocal lesions, 14 presented with unilocular lesions and 14 had a history of previous bowel resection. Penetrating disease was diagnosed in 15 subjects. The results indicate for good correlation between MRE and CDAI assessment in groups of patients with moderate and severe clinical symptoms. However, there is a significant discrepancy in MRE findings in the group of patients with mild disease activity according to CDAI. Out of 23 patients with clinically mild symptoms, 1 exhibited MRE characteristics of severe, 9 of moderate and 7 of mild inflammatory intestinal lesions. CONCLUSION There is good accordance between MRE and clinical findings in patients with prominent disease symptoms, indicating that MR enterography might be a promising modality in terms of lesion activity grading. Nonetheless, there exist a significant group of patients with mild disease symptoms, in whom MRE tends to show full spectrum of disease severity.

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.

Abbreviated Gadoxetic Acid-Enhanced MRI for the Detection of Liver Metastases in Patients With Potentially Resectable Pancreatic Ductal Adenocarcinoma.

Gadoxetic acid-enhanced magnetic resonance imaging (MRI) is useful in detecting liver metastases from pancreatic ductal adenocarcinoma (PDAC). However, the long examination time limits its utility in the initial workup of patients with PDAC. To evaluate the incremental value of an abbreviated gadoxetic acid-enhanced MRI for the detection of liver metastases in patients with PDAC. Retrospective. Patients (N=130) with potentially resectable PDAC (women, 58 [44.6%]). 1.5 T and 3T; gradient dual-echo T1-weighted (in-phase and opposed-phase), fat-suppressed fast spin-echo T2-weighted, single-shot echo-planar diffusion-weighted, and three-dimensional fat-suppressed T1-weighted gradient-echo dynamic contrast-enhanced and hepatobiliary phase sequences, as well as contrast-enhanced computed tomography (CECT). Three radiologists independently reviewed three different image sets to detect liver metastases: set 1, CECT alone; set 2, CECT and abbreviated MRI comprising fat-suppressed T2-weighted, diffusion-weighted, and hepatobiliary phase images; and set 3, CECT and standard gadoxetic acid-enhanced MRI. Figure of merit (FOM) was compared using the jackknife alternative free-response receiver operating characteristics, and other per-lesion and per-patient diagnostic parameters for each image set were compared using McNemar's and Fisher's test. P < 0.05 was considered statistically significant. A total of 43 liver metastases were identified in 13 patients. Reader-averaged FOM to detect liver metastases were significantly higher for sets 2 (0.884) and 3 (0.886) than for set 1 (0.609), while they were comparable between sets 2 and 3 (P=0.96). The mean per-patient sensitivities, negative predictive values, and accuracies were significantly higher for sets 2 and 3 than for set 1, while those between sets 2 and 3 were not significantly different (not applicable, P > 0.99, and P > 0.99, respectively). Gadoxetic acid-enhanced MRI combined with CECT had higher diagnostic performance than CECT alone for the detection of liver metastases in patients with PDAC. The incremental values were comparable for the abbreviated MRI and standard MRI. 3 TECHNICAL EFFICACY: Stage 2.

Accuracy of fractal analysis and PI-RADS assessment of prostate magnetic resonance imaging for prediction of cancer grade groups: a clinical validation study

ObjectivesMultiparametric MRI with Prostate Imaging Reporting and Data System (PI-RADS) assessment is sensitive but not specific for detecting clinically significant prostate cancer. This study validates the diagnostic accuracy of the recently suggested fractal dimension (FD) of perfusion for detecting clinically significant cancer.Materials and methodsRoutine clinical MR imaging data, acquired at 3 T without an endorectal coil including dynamic contrast-enhanced sequences, of 72 prostate cancer foci in 64 patients were analyzed. In-bore MRI-guided biopsy with International Society of Urological Pathology (ISUP) grading served as reference standard. Previously established FD cutoffs for predicting tumor grade were compared to measurements of the apparent diffusion coefficient (25th percentile, ADC25) and PI-RADS assessment with and without inclusion of the FD as separate criterion.ResultsFractal analysis allowed prediction of ISUP grade groups 1 to 4 but not 5, with high agreement to the reference standard (κFD = 0.88 [CI: 0.79–0.98]). Integrating fractal analysis into PI-RADS allowed a strong improvement in specificity and overall accuracy while maintaining high sensitivity for significant cancer detection (ISUP > 1; PI-RADS alone: sensitivity = 96%, specificity = 20%, area under the receiver operating curve [AUC] = 0.65; versus PI-RADS with fractal analysis: sensitivity = 95%, specificity = 88%, AUC = 0.92, p < 0.001). ADC25 only differentiated low-grade group 1 from pooled higher-grade groups 2–5 (κADC = 0.36 [CI: 0.12–0.59]). Importantly, fractal analysis was significantly more reliable than ADC25 in predicting non-significant and clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75, p < 0.001). Diagnostic accuracy was not significantly affected by zone location.ConclusionsFractal analysis is accurate in noninvasively predicting tumor grades in prostate cancer and adds independent information when implemented into PI-RADS assessment. This opens the opportunity to individually adjust biopsy priority and method in individual patients.Key Points• Fractal analysis of perfusion is accurate in noninvasively predicting tumor grades in prostate cancer using dynamic contrast-enhanced sequences (κFD = 0.88).• Including the fractal dimension into PI-RADS as a separate criterion improved specificity (from 20 to 88%) and overall accuracy (AUC from 0.86 to 0.96) while maintaining high sensitivity (96% versus 95%) for predicting clinically significant cancer.• Fractal analysis was significantly more reliable than ADC25 in predicting clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75).

Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study

ObjectivesMultiparametric MRI has high diagnostic accuracy for detecting prostate cancer, but non-invasive prediction of tumor grade remains challenging. Characterizing tumor perfusion by exploiting the fractal nature of vascular anatomy might elucidate the aggressive potential of a tumor. This study introduces the concept of fractal analysis for characterizing prostate cancer perfusion and reports about its usefulness for non-invasive prediction of tumor grade.MethodsWe retrospectively analyzed the openly available PROSTATEx dataset with 112 cancer foci in 99 patients. In all patients, histological grading groups specified by the International Society of Urological Pathology (ISUP) were obtained from in-bore MRI-guided biopsy. Fractal analysis of dynamic contrast-enhanced perfusion MRI sequences was performed, yielding fractal dimension (FD) as quantitative descriptor. Two-class and multiclass diagnostic accuracy was analyzed using area under the curve (AUC) receiver operating characteristic analysis, and optimal FD cutoffs were established. Additionally, we compared fractal analysis to conventional apparent diffusion coefficient (ADC) measurements.ResultsFractal analysis of perfusion allowed accurate differentiation of non-significant (group 1) and clinically significant (groups 2–5) cancer with a sensitivity of 91% (confidence interval [CI]: 83–96%) and a specificity of 86% (CI: 73–94%). FD correlated linearly with ISUP groups (r2 = 0.874, p < 0.001). Significant groupwise differences were obtained between low, intermediate, and high ISUP group 1–4 (p ≤ 0.001) but not group 5 tumors. Fractal analysis of perfusion was significantly more reliable than ADC in predicting non-significant and clinically significant cancer (AUCFD = 0.97 versus AUCADC = 0.77, p < 0.001).ConclusionFractal analysis of perfusion MRI accurately predicts prostate cancer grading in low-, intermediate-, and high-, but not highest-grade, tumors.Key Points• In 112 prostate carcinomas, fractal analysis of MR perfusion imaging accurately differentiated low-, intermediate-, and high-grade cancer (ISUP grade groups 1–4).• Fractal analysis detected clinically significant prostate cancer with a sensitivity of 91% (83–96%) and a specificity of 86% (73–94%).• Fractal dimension of perfusion at the tumor margin may provide an imaging biomarker to predict prostate cancer grading.

Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences.

The aim of this study was to calculate MRI quantitative parameters extracted from chemical-shift (CS) and dynamic contrast-enhanced (DCE) T1-weighted (T1-WS) images of adrenal lesions (AL) with qualitative heterogeneous signal drop on CS T1-WS and compare them to those of AL with homogeneous or no signal drop on CS T1-WS. On 3 T MRI, 65 patients with a total of 72 AL were studied. CS images were qualitatively assessed for grouping AL as showing homogeneous (Group 1, n = 19), heterogeneous (Group 2, n = 23), and no (Group 3, n = 30) signal drop. Histopathology or follow-up data served as reference standard to classify AL. ROIs were drawn both on CS and DCE images to obtain adrenal CS signal intensity index (ASII), absolute (AWO), and relative washout (RWO) values. Quantitative parameters (QP) were compared with ANOVA analysis and post hoc Dunn’s test. The performance of QP to classify AL was assessed with receiver operating characteristic analysis. CS ASII values were significantly different among the three groups (p < 0.001) with median values of 71%, 53%, and 3%, respectively. AWO/RWO values were similar in Groups 1 (adenomas) and 2 (benign AL) but significantly (p < 0.001) lower in Group 3 (20 benign AL and 10 malignant AL). With cut-offs, respectively, of 60% (Group 1 vs. 2), 20% (Group 2 vs. 3), and 37% (Group 1 vs. 3), CS ASII showed areas under the curve of 0.85, 0.96, and 0.93 for the classification of AL, overall higher than AWO/RWO. In conclusion, AL with qualitative heterogeneous signal drop at CS represent benign AL with QP by DCE sequence similar to those of AL with homogeneous signal drop at CS, but different to those of AL with no signal drop at CS; ASII seems to be the only quantitative parameter able to differentiate AL among the three different groups.