MRI texture analysis of subchondral bone at the tibial plateau

Objectives

To determine the feasibility of MRI texture analysis as a method of quantifying subchondral bone architecture in knee osteoarthritis (OA).

Methods

Asymptomatic subjects aged 20–30 (group 1, n = 10), symptomatic patients aged 40–50 (group 2, n = 10) and patients scheduled for knee replacement aged 55–85 (group 3, n = 10) underwent high spatial resolution T1-weighted coronal 3T knee MRI.Regions of interest were created in the medial (MT) and lateral (LT) tibial subchondral bone from which 20 texture parameters were calculated. T2 mapping of the tibial cartilage was performed in groups 1 and 2. Mean parameter values were compared between groups using ANOVA. Linear discriminant analysis (LDA) was used to evaluate the ability of texture analysis to classify subjects correctly.

Results

Significant differences in 18/20 and 12/20 subchondral bone texture parameters were demonstrated between groups at the MT and LT respectively. There was no significant difference in mean MT or LT cartilage T2 values between group 1 and group 2.LDA demonstrated subject classification accuracy of 97 % (95 % CI 91–100 %).

Conclusion

MRI texture analysis of tibial subchondral bone may allow detection of alteration in subchondral bone architecture in OA. This has potential applications in understanding OA pathogenesis and assessing response to treatment.

Key Points

? Improved techniques to monitor OA disease progression and treatment response are desirable ? Subchondral bone (SB) may play significant role in the development of OA ? MRI texture analysis is a method of quantifying changes in SB architecture ? Pilot study showed that this technique is feasible and reliable ? Significant differences in SB texture were demonstrated between individuals with/without OA

     

CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer

Objectives

To determine if identifiable hepatic textural features are present at abdominal CT in patients with colorectal cancer (CRC) prior to the development of CT-detectable hepatic metastases.

Methods

Four filtration–histogram texture features (standard deviation, skewness, entropy and kurtosis) were extracted from the liver parenchyma on portal venous phase CT images at staging and post-treatment surveillance. Surveillance scans corresponded to the last scan prior to the development of CT-detectable CRC liver metastases in 29 patients (median time interval, 6 months), and these were compared with interval-matched surveillance scans in 60 CRC patients who did not develop liver metastases. Predictive models of liver metastasis-free survival and overall survival were built using regularised Cox proportional hazards regression.

Results

Texture features did not significantly differ between cases and controls. For Cox models using all features as predictors, all coefficients were shrunk to zero, suggesting no association between any CT texture features and outcomes. Prognostic indices derived from entropy features at surveillance CT incorrectly classified patients into risk groups for future liver metastases (p < 0.001).

Conclusions

On surveillance CT scans immediately prior to the development of CRC liver metastases, we found no evidence suggesting that changes in identifiable hepatic texture features were predictive of their development.

Key Points

? No correlation between liver texture features and metastasis-free survival was observed. ? Liver texture features incorrectly classified patients into risk groups for liver metastases. ? Standardised texture analysis workflows need to be developed to improve research reproducibility.

     

Bone susceptibility mapping with MRI is an alternative and reliable biomarker of osteoporosis in postmenopausal women

Objectives

To investigate the efficacy of quantitative susceptibility mapping (QSM) in the assessment of osteoporosis for postmenopausal women.

Methods

Between May and September 2017, a total of 70 postmenopausal women who underwent MRI-based QSM and quantitative computed tomography (QCT) were consecutively enrolled in this prospective study. The measurement of QSM and QCT values was performed on the L3 vertebrae body. On the basis of QCT value, all individuals were divided into three groups (normal, osteopenia and osteoporosis).

Results

On the basis of QCT, 18 individuals were normal (25.7%), 26 osteopenic (37.1%) and 26 osteoporotic (37.1%). The QSM value was age-related (p = 0.04) and significantly higher in the osteoporosis group than in either the normal or osteopenia group (for all, p < 0.001). In addition, the QSM value was highly correlated with QCT value (r = ? 0.720, p < 0.001). For QSM, the area under the curve (AUC), sensitivity and specificity for differentiating osteopenia from non-osteopenia were 0.88, 86.5% and 77.8%, respectively, and for differentiating osteoporosis from non-osteoporosis they were 0.86, 80.8% and 77.3%, respectively.

Conclusions

MRI-based QSM could be used for quantifying susceptibility in vertebrae and has the potential to be a new biomarker in the assessment of osteoporosis for postmenopausal women.

Key Points

? Osteoporosis significantly increases risk of fracture for postmenopausal women. ? QSM value was correlated with QCT value (r = ? 0.72, p < 0.001). ? QSM is feasible in the assessment of osteoporosis for postmenopausal women. ? QSM offers the quantification of susceptibility within bone.

     

Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma

Objectives

To apply a statistical clustering algorithm to combine information from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) into a single tumour map to distinguish high-grade from low-grade T1b clear cell renal cell carcinoma (ccRCC).

Methods

This prospective, Institutional Review Board -approved, Health Insurance Portability and Accountability Act -compliant study included 18 patients with solid T1b ccRCC who underwent pre-surgical DCE MRI. After statistical clustering of the parametric maps of the transfer constant between the intravascular and extravascular space (K ^trans ), rate constant (K _ep ) and initial area under the concentration curve (iAUC) with a fuzzy c-means (FCM) algorithm, each tumour was segmented into three regions (low/medium/high active areas). Percentages of each region and tumour size were compared to tumour grade at histopathology. A decision-tree model was constructed to select the best parameter(s) to predict high-grade ccRCC.

Results

Seven high-grade and 11 low-grade T1b ccRCCs were included. High-grade histology was associated with higher percent high active areas (p = 0.0154) and this was the only feature selected by the decision tree model, which had a diagnostic performance of 78% accuracy, 86% sensitivity, 73% specificity, 67% positive predictive value and 89% negative predictive value.

Conclusions

The FCM integrates multiple DCE-derived parameter maps and identifies tumour regions with unique pharmacokinetic characteristics. Using this approach, a decision tree model using criteria beyond size to predict tumour grade in T1b ccRCCs is proposed.

Key Points

? Tumour size did not correlate with tumour grade in T1b ccRCC. ? Tumour heterogeneity can be analysed using statistical clustering via DCE-MRI parameters. ? High-grade ccRCC has a larger percentage of high active area than low-grade ccRCCs. ? A decision-tree model offers a simple way to differentiate high/low-grade ccRCCs.

     

Pre-treatment magnetic resonance-based texture features as potential imaging biomarkers for predicting event free survival in anal cancer treated by chemoradiotherapy

Aim

To assess regular MRI findings and tumour texture features on pre-CRT imaging as potential predictive factors of event-free survival (disease progression or death) after chemoradiotherapy (CRT) for anal squamous cell carcinoma (ASCC) without metastasis.

Materials and methods

We retrospectively included 28 patients treated by CRT for pathologically proven ASCC with a pre-CRT MRI. Texture analysis was carried out with axial T2W images by delineating a 3D region of interest around the entire tumour volume. First-order analysis by quantification of the histogram was carried out. Second-order statistical texture features were derived from the calculation of the grey-level co-occurrence matrix using a distance of 1 (d1), 2 (d2) and 5 (d5) pixels. Prognostic factors were assessed by Cox regression and performance of the model by the Harrell C-index.

Results

Eight tumour progressions led to six tumour-specific deaths. After adjusting for age, gender and tumour grade, skewness (HR = 0.131, 95% CI = 0-0.447, p = 0.005) and cluster shade_d1 (HR = 0.601, 95% CI = 0-0.861, p = 0.027) were associated with event occurrence. The corresponding Harrell C-indices were 0.846, 95% CI = 0.697-0.993, and 0.851, 95% CI = 0.708-0.994.

Conclusion

ASCC MR texture analysis provides prognostic factors of event occurrence and requires additional studies to assess its potential in an “individual dose” strategy for ASCC chemoradiation therapy.

Key Points

? MR texture features help to identify tumours with high progression risk.? Texture feature maps help to identify intra-tumoral heterogeneity.? Texture features are a better prognostic factor than regular MR findings.

     

MRI texture analysis (MRTA) of T2-weighted images in Crohn’s disease may provide information on histological and MRI disease activity in patients undergoing ileal resection

Objectives

To associate MRI textural analysis (MRTA) with MRI and histological Crohn’s disease (CD) activity.

Methods

Sixteen patients (mean age 39.5 years, 9 male) undergoing MR enterography before ileal resection were retrospectively analysed. Thirty-six small (≤3 mm) ROIs were placed on T2-weighted images and location-matched histological acute inflammatory scores (AIS) measured. MRI activity (mural thickness, T2 signal, T1 enhancement) (CDA) was scored in large ROIs. MRTA features (mean, standard deviation, mean of positive pixels (MPP), entropy, kurtosis, skewness) were extracted using a filtration histogram technique. Spatial scale filtration (SSF) ranged from 2 to 5 mm. Regression (linear/logistic) tested associations between MRTA and AIS (small ROIs), and CDA/constituent parameters (large ROIs).

Results

Skewness (SSF?=?2 mm) was associated with AIS [regression coefficient (rc) 4.27, p?=?0.02]. Of 120 large ROI analyses (for each MRI, MRTA feature and SSF), 15 were significant. Entropy (SSF?=?2, 3 mm) and kurtosis (SSF?=?3 mm) were associated with CDA (rc 0.9, 1.0, ?0.45, p?=?0.006–0.01). Entropy and mean (SSF?=?2–4 mm) were associated with T2 signal [odds ratio (OR) 2.32–3.16, p?=?0.02–0.004], [OR 1.22–1.28, p?=?0.03–0.04]. MPP (SSF?=?2 mm) was associated with mural thickness (OR 0.91, p?=?0.04). Kurtosis (SSF?=?3 mm), standard deviation (SSF?=?5 mm) were associated with decreased T1 enhancement (OR 0.59, 0.42, p?=?0.004, 0.007).

Conclusions

MRTA features may be associated with CD activity.

Key Points

? MR texture analysis features may be associated with Crohn’s disease histological activity.? Texture analysis features may correlate with MR-dependent Crohn’s disease activity scores.? The utility of MR texture analysis in Crohn’s disease merits further investigation.

     

Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer?

Objective

To explore the utility of MR texture analysis (MRTA) for detection of nodal extracapsular spread (ECS) in oral cavity squamous cell carcinoma (SCC).

Methods

115 patients with oral cavity SCC treated with surgery and adjuvant (chemo)radiotherapy were identified retrospectively. First-order texture parameters (entropy, skewness and kurtosis) were extracted from tumour and nodal regions of interest (ROIs) using proprietary software (TexRAD). Nodal MR features associated with ECS (flare sign, irregular capsular contour; local infiltration; nodal necrosis) were reviewed and agreed in consensus by two experienced radiologists. Diagnostic performance characteristics of MR features of ECS were compared with primary tumour and nodal MRTA prediction using histology as the gold standard. Receiver operating characteristic (ROC) and regression analyses were also performed.

Results

Nodal entropy derived from contrast-enhanced T1-weighted images was significant in predicting ECS (p?=?0.018). MR features had varying accuracy: flare sign (70%); irregular contour (71%); local infiltration (66%); and nodal necrosis (64%). Nodal entropy combined with irregular contour was the best predictor of ECS (p?=?0.004, accuracy 79%).

Conclusion

First-order nodal MRTA combined with imaging features may improve ECS prediction in oral cavity SCC.

Key Points

? Nodal MR textural analysis can aid in predicting extracapsular spread (ECS). ? Medium filter contrast-enhanced T1 nodal entropy was strongly significant in predicting ECS. ? Combining nodal entropy with irregular nodal contour improves predictive accuracy.

     

Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker

Objectives

To investigate the association between preoperative texture analysis from multidetector computed tomography (MDCT) and overall survival in patients with gastric cancer.

Methods

Institutional review board approval and informed consent were obtained. Fifty-six patients with biopsy-proved gastric cancer were examined by MDCT and treated with surgery. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. The association with survival time was assessed using Kaplan–Meier and Cox analysis.

Results

The following parameters were significantly associated with a negative prognosis, according to different thresholds: energy [no filter] – Logarithm of relative risk (Log RR): 3.25; p?=?0.046; entropy [no filter] (Log RR: 5.96; p?=?0.002); entropy [filter 1.5] (Log RR: 3.54; p?=?0.027); maximum Hounsfield unit value [filter 1.5] (Log RR: 3.44; p?=?0.027); skewness [filter 2] (Log RR: 5.83; p?=?0.004); root mean square [filter 1] (Log RR: - 2.66; p?=?0.024) and mean absolute deviation [filter 2] (Log RR: - 4.22; p?=?0.007).

Conclusions

Texture analysis could increase the performance of a multivariate prognostic model for risk stratification in gastric cancer. Further evaluations are warranted to clarify the clinical role of texture analysis from MDCT.

Key points

? Textural analysis from computed tomography can be applied in gastric cancer.? Preoperative non-invasive texture features are related to prognosis in gastric cancer.? Texture analysis could help to evaluate the aggressiveness of this tumour.

     

Microflow imaging: New Doppler technology to detect low-grade inflammation in patients with arthritis

Aim

To assess the efficacy of microvascular imaging in detecting low-grade inflammation in arthritis compared with Power Doppler ultrasound (PDUS).

Method and materials

Patients presenting for ultrasound with arthralgia were assessed with grey-scale, PDUS and Superb Microvascular Imaging (SMI). Videoclips were stored for analysis at a later date. Three musculoskeletal radiologists scored grey-scale changes, signal on PDUS and/or SMI within these joints. If a signal was detected on both PDUS and SMI, the readers graded the conspicuity of vascular signal from the two Doppler techniques using a visual analogue scale.

Results

Eighty-three patients were recruited with 134 small joints assessed. Eighty-nine of these demonstrated vascular flow with both PD and SMI, whilst in five no flow was detected. In 40 joints, vascularity was detected with SMI but not with PDUS (p = 0.007). Out of the 89 joints with vascularity on both SMI and PDUS, 23 were rated as being equal; while SMI scored moderately or markedly better in 45 cases (p <0.001).

Conclusion

SMI is a new Doppler technique that increases conspicuity of Doppler vascularity in symptomatic joints when compared to PDUS. This allows detection of low grade inflammation not visualised with Power Doppler in patients with arthritis.

Key Points

? SMI detects vascularity with improved resolution and sensitivity compared to Power Doppler. ? SMI can detect low-grade inflammation not seen with Power Doppler. ? Earlier detection of active inflammation could have significant impact on treatment paradigms.

     

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

Objective

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach.

Methods

Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one.

Results

In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180).

Conclusion

Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels.

Key Points

? Texture analysis is a promising source of biomarkers for classifying brain neoplasms. ? MRI texture features of brain metastases could help identifying the primary cancer. ? Volumetric texture features are more discriminative than traditional 2D texture features.

     

Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging

Objectives

To investigate whether amide proton transfer (APT) MR imaging can differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) among gliomas without intense contrast enhancement (CE).

Methods

This retrospective study evaluated 34 patients (22 males, 12 females; age 36.0?±?11.3 years) including 20 with LGGs and 14 with HGGs, all scanned on a 3T MR scanner. Only tumours without intense CE were included. Two neuroradiologists independently performed histogram analyses to measure the 90th-percentile (APT₉₀) and mean (APT_mean) of the tumours’ APT signals. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were also measured. The parameters were compared between the groups with Student’s t-test. Diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis.

Results

The APT₉₀ (2.80?±?0.59 % in LGGs, 3.72?±?0.89 in HGGs, P?=?0.001) and APT_mean (1.87?±?0.49 % in LGGs, 2.70?±?0.58 in HGGs, P?=?0.0001) were significantly larger in the HGGs compared to the LGGs. The ADC and rCBV values were not significantly different between the groups. Both the APT₉₀ and APT_mean showed medium diagnostic performance in this discrimination.

Conclusions

APT imaging is useful in discriminating HGGs from LGGs among diffuse gliomas without intense CE.

Key Points

? Amide proton transfer (APT) imaging helps in grading non-enhancing gliomas ? High-grade gliomas showed higher APT signal than low-grade gliomas ? APT imaging showed better diagnostic performance than diffusion- and perfusion-weighted imaging

     

Evaluation of low-grade glioma structural changes after chemotherapy using DTI-based histogram analysis and functional diffusion maps

Objectives

To explore the role of diffusion tensor imaging (DTI)-based histogram analysis and functional diffusion maps (fDMs) in evaluating structural changes of low-grade gliomas (LGGs) receiving temozolomide (TMZ) chemotherapy.

Methods

Twenty-one LGG patients underwent 3T-MR examinations before and after three and six cycles of dose-dense TMZ, including 3D-fluid-attenuated inversion recovery (FLAIR) sequences and DTI (b?=?1000 s/mm², 32 directions). Mean diffusivity (MD), fractional anisotropy (FA), and tensor-decomposition DTI maps (p and q) were obtained. Histogram and fDM analyses were performed on co-registered baseline and post-chemotherapy maps. DTI changes were compared with modifications of tumour area and volume [according to Response Assessment in Neuro-Oncology (RANO) criteria], and seizure response.

Results

After three cycles of TMZ, 20/21 patients were stable according to RANO criteria, but DTI changes were observed in all patients (Wilcoxon test, P?≤?0.03). After six cycles, DTI changes were more pronounced (P?≤?0.005). Seventy-five percent of patients had early seizure response with significant improvement of DTI values, maintaining stability on FLAIR. Early changes of the 25th percentiles of p and MD predicted final volume change (R²?=?0.614 and 0.561, P?<?0.0005, respectively). TMZ-related changes were located mainly at tumour borders on p and MD fDMs.

Conclusions

DTI-based histogram and fDM analyses are useful techniques to evaluate the early effects of TMZ chemotherapy in LGG patients.

Key Points

? DTI helps to assess the efficacy of chemotherapy in low-grade gliomas. ? Histogram analysis of DTI metrics quantifies structural changes in tumour tissue. ? Functional diffusion maps (fDMs) spatially localize the changes of DTI metrics. ? Changes in DTI histograms and fDMs precede changes in conventional MRI. ? Early changes in DTI histograms and fDMs correlate with seizure response.

     

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT

Purpose

Image texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers.

Methods

We performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dose < 26 Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia.

Results

Seventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (p < 0.001, R2:0.664).

Conclusions

Xerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture analysis could enhance the normal dose constraints for the prediction of xerostomia.

     

Analysis of CT features and quantitative texture analysis in patients with thymic tumors: correlation with grading and staging

Objectives

To evaluate potential relationship between qualitative CT features, quantitative texture analysis (QTA), histology, WHO staging, Masaoka classification and myasthenic syndrome in patients with thymic tumors.

Materials and methods

Sixteen patients affected by histologically proven thymic tumors were retrospectively included in the study population. Clinical information, with special regard to myasthenic syndrome and serological positivity of anti-AchR antibodies, were recorded. Qualitative CT evaluation included the following parameters: (a) location; (b) tumor edges; (c) necrosis; (d) pleural effusion; (e) metastases; (f) chest wall infiltration; (g) tumor margins. QTA included evaluation of “Mean” (M), “Standard Deviation” (SD), “Kurtosis” (K), “Skewness” (S), “Entropy” (E), “Shape from Texture” (TX_sigma) and “average of positive pixels” (MPP). Pearson–Rho test was used to evaluate the relationship of continuous non-dichotomic parameters, whereas Mann–Whitney test was used for dichotomic parameters.

Results

Histological evaluation demonstrated thymoma in 12 cases and thymic carcinoma in 4 cases. Tumor necrosis was significantly correlated with QTA Mean (p = 0.0253), MPP (p = 0.0417), S (p = 0.0488) and K (p = 0.0178). WHO staging was correlated with Mean (p = 0.0193), SD (p = 0.0191) and MPP (p = 0.0195). Masaoka classification was correlated with Mean (p = 0.0322), MPP (p = 0.0315), skewness (p = 0.0433) and Kurtosis (p = 0.0083). Myasthenic syndrome was significantly associated with Mean (p = 0.0211) and MPP (p = 0.0261), whereas tumor size was correlated with Mean (p = 0.0241), entropy (p = 0.0177), MPP (p = 0.0468), skewness (p = 0.009) and Kurtosis (p = 0.006).

Conclusion

Our study demonstrates significant relationship between radiomics parameters, histology, grading and clinical manifestations of thymic tumors.

     

Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI

Objectives

To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T₂-weighted fat suppression (T₂-FS) and diffusion-weighted MRI (DWI).

Methods

We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T₂-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1–10) based on different combination of image features using stepwise forward method.

Results

For T₂-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T₂-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.

Conclusions

Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.

Key Points

? SLN biopsy to access breast cancer metastasis has multiple complications. ? Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. ? We combined T ₂ -FS and DWI textural features to predict SLN metastasis non-invasively.

     

Computed tomography compared to magnetic resonance imaging in occult or suspect hip fractures. A retrospective study in 44 patients

Background

Computed tomography (CT) for evaluation of occult and suspect hip fractures has been proposed as a good second-line investigation. The diagnostic precision compared to magnetic resonance imaging (MRI) is unclear.

Purpose

To compare the diagnostic performance of CT and MRI in a retrospective study on patients with suspect and occult hip fractures.

Material and methods

Forty-four elderly consecutive patients with low-energy trauma to the hip were identified where negative or suspect CT was followed by MRI. Primary reporting and review by two observers as well as the diagnostic performance of the two modalities were compared. Surgical treatment and clinical course were used as outcomes.

Results

Compared to the primary reports, the CT reviewers found fewer normal and no suspect cases. MRI changed the primary diagnoses in 27 cases, and in 14 and 15 cases, respectively, at review. There was no disagreement on MRI diagnoses.

Conclusion

In our patient population, MRI was deemed a more reliable modality for hip fracture diagnosis in comparison to CT. For clinical decision making, MRI seems to have a higher accuracy than CT. A negative CT finding cannot completely rule out a hip fracture in patients where clinical findings of hip fracture persevere.

Key Points

? Experience is highly influential in diagnosing occult or suspect hip fractures at CT ? Inconclusive hip CT shows high inter-rater reliability at experienced review ? There was low diagnostic accuracy via CT compared to MRI for all interpreters ? Hip fractures can readily be diagnosed at MRI regardless of radiological experience

     

MR imaging features and staging of neuroendocrine carcinomas of the uterine cervix with pathological correlations

Objective

To determine MR imaging features and staging accuracy of neuroendocrine carcinomas (NECs) of the uterine cervix with pathological correlations.

Methods

Twenty-six patients with histologically proven NECs, 60 patients with squamous cell carcinomas (SCCs), and 30 patients with adenocarcinomas of the uterine cervix were included. The clinical data, pathological findings, and MRI findings were reviewed retrospectively. MRI features of cervical NECs, SCCs, and adenocarcinomas were compared, and MRI staging of cervical NECs was compared with the pathological staging.

Results

Cervical NECs showed a higher tendency toward a homogeneous signal intensity on T2-weighted imaging and a homogeneous enhancement pattern, as well as a lower ADC value of tumour and a higher incidence of lymphadenopathy, compared with SCCs and adenocarcinomas (P?<?0.05). An ADC value cutoff of 0.90?×?10^-3 mm²/s was robust for differentiation between cervical NECs and other cervical cancers, with a sensitivity of 63.3 % and a specificity of 95 %. In 21 patients who underwent radical hysterectomy and lymphadenectomy, the overall accuracy of tumour staging by MR imaging was 85.7 % with reference to pathology staging.

Conclusion

Homogeneous lesion texture and low ADC value are likely suggestive features of cervical NECs and MR imaging is reliable for the staging of cervical NECs.

Key Points

? Cervical NECs show a tendency of lesion homogeneity and lymphadenopathy ? Low ADC values are found in cervical NECs ? MRI is an accurate imaging modality for the cervical NEC staging

     

Energy intake post-exercise is associated with enjoyment independently of exercise intensity

Purpose

The aim of the present study was to investigate the effect of exercise intensity on energy intake post-exercise and its relationship with enjoyment and rating of perceived exertion (RPE) generated by exercise.

Methods

For this, nine sedentary overweight men performed high-intensity intermittent exercise and steady-state exercise, totaling 30 min for both exercise sessions, and energy intake post-exercise was evaluated. Rating of perceived exertion and enjoyment scores were also measured immediately post-exercise.

Results

There was no difference in the amount of calories ingested post-exercise between conditions, enjoyment scores and RPE. There was a negative correlation between enjoyment and energy intake (r?=???0.552 [strong]; p?=?0.018).

Conclusions

These data demonstrated that independent of exercise intensity, enjoyment scores were related to post-exercise energy intake.

     

Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume

Objectives

To evaluate the added value of amide proton transfer (APT) imaging to the apparent diffusion coefficient (ADC) from diffusion tensor imaging (DTI) and the relative cerebral blood volume (rCBV) from perfusion magnetic resonance imaging (MRI) for discriminating between high- and low-grade gliomas.

Methods

Forty-six consecutive adult patients with diffuse gliomas who underwent preoperative APT imaging, DTI and perfusion MRI were enrolled. APT signals were compared according to the World Health Organization grade. The diagnostic ability and added value of the APT signal to the ADC and rCBV for discriminating between low- and high-grade gliomas were evaluated using receiver operating characteristic (ROC) analyses and integrated discrimination improvement.

Results

The APT signal increased as the glioma grade increased. The discrimination abilities of the APT, ADC and rCBV values were not significantly different. Using both the APT signal and ADC significantly improved discrimination vs. the ADC alone (area under the ROC curve [AUC], 0.888?vs.?0.910; P?=?0.007), whereas using both the APT signal and rCBV did not improve discrimination vs. the rCBV alone (AUC, 0.927?vs.?0.923; P?=?0.222).

Conclusions

APT imaging may be a useful imaging biomarker that adds value to the ADC for discriminating between low- and high-grade gliomas.

Key points

? Higher APT values were correlated with higher glioma grades. ? Adding the APT signal to the ADC improved glioma grading. ? Adding the APT signal to rCBV did not improve glioma grading. ? APT is a useful adjunct to the ADC for glioma grading.