Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features.

PURPOSE: To evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models. METHODS: This study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on "real-world" radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with "real-world" radiomic training data to evaluate their impact on the predictive model's performance. RESULTS: A prediction model was generated using only "real-world" radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to "real-world" data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n_samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so. CONCLUSION: Synthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity.

Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients.

PURPOSE: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests. MATERIALS AND METHODS: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO). RESULTS: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19). CONCLUSION: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases.

BACKGROUND: Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM). METHODS: A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia. RESULTS: Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61-0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50-0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42-0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46-0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44-0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33-0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33-0.74, p = 0.415) images were not predictive of tumour hypoxia. CONCLUSIONS: T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms. CRITICAL RELEVANCE STATEMENT: Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM).

Colon cancer CT staging according to mismatch repair status: Comparison and suggestion of imaging features for high-risk colon cancer.

BACKGROUND: Neoadjuvant treatment with either chemotherapy or immunotherapy is gaining momentum in colon cancers (CC). To reduce over-treatment, increasing staging accuracy using computed tomography (CT) is of high importance. PURPOSE: To assess and compare CT imaging features of CC between mismatch repair-proficient (pMMR) and MMR-deficient (dMMR) tumours and identify CT features that can distinguish high-risk (pT3-4, N+) CC according to MMR status. METHODS: Primary staging CTs of 266 patients who underwent primary surgical resection of a colon tumour were retrospectively and independently evaluated by two radiologists. Logistic regression analysis was performed to identify significant associations between imaging features and positive lymph node status. Receiver operating characteristic (ROC) curves of significantly associated features were assessed and validated in an external cohort of 104 patients. RESULTS: Among pT3 tumours only, dMMR CC were significantly larger than pMMR CC in both length and thickness (length 59.39 ± 26.28 mm versus 48.70 ± 23.72, respectively, p = 0.031; thickness 20.54 mm ± 11.17 versus 16.34 ± 8.73, respectively, p = 0.027). For pMMR tumours, nodal internal heterogeneity on CT was significantly associated with a positive lymph node status (odds ratio (OR) = 2.66, p = 0.027), while for dMMR tumours, the largest short diameter of the nodes was associated with lymph node status (OR = 2.01, p = 0.049). The best cut-off value of the largest short diameter of involved nodes was 10.4 mm for dMMR and 7.95 mm for pMMR. In the external validation cohort, AUCs for predicting involved nodes based on the largest short diameter was 0.764 for dMMR tumours using 10 mm size cut-off and 0.624 for pMMR tumours using 7 mm cut-off. CONCLUSION: These data show that CT imaging features of primary CC differ between dMMR and pMMR tumours, suggesting that the assessment of CT-based CC staging should take MMR status into consideration, especially for lymph node status, and thus may help in selecting patients for neoadjuvant treatment.

Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist's performance for T staging?

PURPOSE: To assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer. METHODS: We included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging. RESULTS: The model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720-0.839 for combined model and AUC = 0.697, 95% CI = 0.538-0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621-0.777 for combined model and AUC = 0.628, 95% CI = 0.558-0.689 for CT staging only, Boot CI = 0.099). CONCLUSION: CT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.

CT for lymph node staging of Colon cancer: not only size but also location and number of lymph node count.

PURPOSE: To evaluate the diagnostic accuracy of imaging features to predict lymph node status of colon cancer using CT. METHODS: This was a retrospective study from 2 tertiary hospitals in South Korea and Netherlands. 317 Colon cancer patients who underwent primary surgical treatment were included. Number of lymph nodes according to the anatomical location, size, cluster, degree of attenuation, shape, presence of internal heterogeneity and ill-defined margin of the lymph node were assessed and compared according to histological lymph node status. RESULTS: The largest short diameter of lymph node and presence of internal heterogeneity of lymph node showed significant association with malignant lymph node status (P < 0.001 and P = 0.041, respectively). The ROC curve analysis revealed AUC of 0.703 for the largest short diameter of lymph node (P < 0.001), and AUC of the presence of internal heterogeneity was 0.630 (P < 0.001). In addition, our study showed that a total number of lymph nodes, regardless of size, (P = 0.022) and number of lymph nodes in peritumoral area (P < 0.001) and along the mesenteric vessels (P < 0.001) on CT demonstrated significant association with malignant status of lymph nodes in colon cancer. CONCLUSIONS: There were significant associations between lymph node status and imaging features of lymph nodes on CT in colon cancer patients. The largest short diameter of lymph node and presence of internal heterogeneity can be used to predict the malignant status of lymph node in colon cancer patients. Also, the number of lymph nodes near the colonic tumor should be considered in assessment of colon cancer lymph node involvement on CT.

Comparison of Genetic Profiles and Prognosis of High-Grade Gliomas Using Quantitative and Qualitative MRI Features: A Focus on G3 Gliomas.

OBJECTIVE: To evaluate the association of MRI features with the major genomic profiles and prognosis of World Health Organization grade III (G3) gliomas compared with those of glioblastomas (GBMs). MATERIALS AND METHODS: We enrolled 76 G3 glioma and 155 GBM patients with pathologically confirmed disease who had pretreatment brain MRI and major genetic information of tumors. Qualitative and quantitative imaging features, including volumetrics and histogram parameters, such as normalized cerebral blood volume (nCBV), cerebral blood flow (nCBF), and apparent diffusion coefficient (nADC) were evaluated. The G3 gliomas were divided into three groups for the analysis: with this isocitrate dehydrogenase (IDH)-mutation, IDH mutation and a chromosome arm 1p/19q-codeleted (IDHmut1p/19qdel), IDH mutation, 1p/19q-nondeleted (IDHmut1p/19qnondel), and IDH wildtype (IDHwt). A prediction model for the genetic profiles of G3 gliomas was developed and validated on a separate cohort. Both the quantitative and qualitative imaging parameters and progression-free survival (PFS) of G3 gliomas were compared and survival analysis was performed. Moreover, the imaging parameters and PFS between IDHwt G3 gliomas and GBMs were compared. RESULTS: IDHmut G3 gliomas showed a larger volume (p = 0.017), lower nCBF (p = 0.048), and higher nADC (p = 0.007) than IDHwt. Between the IDHmut tumors, IDHmut1p/19qdel G3 gliomas had higher nCBV (p = 0.024) and lower nADC (p = 0.002) than IDHmut1p/19qnondel G3 gliomas. Moreover, IDHmut1p/19qdel tumors had the best prognosis and IDHwt tumors had the worst prognosis among G3 gliomas (p < 0.001). PFS was significantly associated with the 95th percentile values of nCBV and nCBF in G3 gliomas. There was no significant difference in neither PFS nor imaging features between IDHwt G3 gliomas and IDHwt GBMs. CONCLUSION: We found significant differences in MRI features, including volumetrics, CBV, and ADC, in G3 gliomas, according to IDH mutation and 1p/19q codeletion status, which can be utilized for the prediction of genomic profiles and the prognosis of G3 glioma patients. The MRI signatures and prognosis of IDHwt G3 gliomas tend to follow those of IDHwt GBMs.

Locoregional CT staging of colon cancer: does a learning curve exist?

PURPOSE: To evaluate the learning curve for locoreginal staging of colon cancer in radiologist trainees. METHODS: Eighty-eight cases of colon cancer CT were included in this retrospective study. Four senior radiology residents staged the CTs according to TNM classification. Two out of four radiologists received feedback after reading every 20 cases. Radiologic staging was compared with pathologic staging and the learning curve, diagnostic performance, reader confidence and reading time were evaluated and compared between the two groups (feedback vs. no feedback). Generalized estimating equations logistic regression, QICu statistic, ANOVA and t test/Mann-Whitney test were utilized. RESULTS: Radiologists demonstrated a significant increase in their performance to distinguish between ≤ T2 and ≥ T3 and reached an inflection point at 38 cases, with a significant association with increased number of cases reviewed (P < 0.001). Sensitivity (P < 0.001), specificity (P = 0.030) and NPV (P = 0.002) demonstrated significant associations with increased experience. The overall reader's confidence was significantly higher in the group which received feedback (P < 0.001). There was no significant improvement in performance nor in reader's confidence for N staging (N0 vs. ≥ N1) for all readers. Reading time decreased with experience and showed a significant negative association with experience (P < 0.001). CONCLUSION: Diagnostic performance of senior radiology trainees in differentiating between T2 and T3 colon cancer on CTs improved with increased experience. In contrast, evaluation of lymph node involvement did not improve with more experience. Feedback had no significant effect on improvement of diagnostic performances.

Prognostic Value of Dynamic Contrast-Enhanced MRI-Derived Pharmacokinetic Variables in Glioblastoma Patients: Analysis of Contrast-Enhancing Lesions and Non-Enhancing T2 High-Signal Intensity Lesions.

OBJECTIVE: To evaluate pharmacokinetic variables from contrast-enhancing lesions (CELs) and non-enhancing T2 high signal intensity lesions (NE-T2HSILs) on dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in glioblastoma (GBM) patients. MATERIALS AND METHODS: Sixty-four GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. We analyzed the pharmacokinetic variables of the volume transfer constant (Ktrans) and volume fraction of extravascular extracellular space within the CEL and NE-T2HSIL of the entire tumor. Univariate and multivariate Cox regression analyses were performed using preoperative clinical characteristics, pharmacokinetic variables of DCE MR imaging, and postoperative molecular biomarkers to predict PFS. RESULTS: The increased mean Ktrans of the CEL, increased 95th percentile Ktrans of the CELs, and absence of methylated O6-methylguanine-DNA methyltransferase promoter were relevant adverse variables for PFS in the univariate analysis (p = 0.041, p = 0.032, and p = 0.083, respectively). The Kaplan-Meier survival curves demonstrated that PFS was significantly shorter in patients with a mean Ktrans of the CEL > 0.068 and 95th percentile Ktrans of the CEL>0.223 (log-rank p = 0.038 and p = 0.041, respectively). However, only mean Ktrans of the CEL was significantly associated with PFS (p = 0.024; hazard ratio, 553.08; 95% confidence interval, 2.27-134756.74) in the multivariate Cox proportional hazard analysis. None of the pharmacokinetic variables from NE-T2HSILs were significantly related to PFS. CONCLUSION: Among the pharmacokinetic variables extracted from CELs and NE-T2HSILs on preoperative DCE MR imaging, the mean Ktrans of CELs exhibits potential as a useful imaging predictor of PFS in GBM patients.

Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas.

OBJECTIVES: Prediction of progression-free survival (PFS) and overall survival (OS) and early identification of molecular biomarkers with prognostic information are clinically important in glioblastoma (GBM) patients. We aimed to explore the utility of arterial spin labeling perfusion-weighted imaging (ASL-PWI) in the prediction of molecular biomarkers and survival in GBM patients. METHODS: We retrospectively analyzed 149 consecutive GBM patients, who had undergone maximal surgical resection or biopsy followed by concurrent chemoradiotherapy and adjuvant chemotherapy using temozolomide between November 2010 and June 2016. On preoperative ASL-PWI, cerebral blood flow (CBF) within contrast-enhancing (CE) and nonenhancing (NE) portions were evaluated both qualitatively (perfusion pattern[CE] and perfusion pattern[NE]) and quantitatively (nCBFCE and nCBFNE). ASL-PWI findings were correlated with molecular biomarkers, including isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) methylation statuses, and survival, using the Mann-Whitney U-test, Spearman rank correlation, Kaplan-Meier analysis, and receiver operating characteristics analysis. RESULTS: nCBFCE was significantly higher in the IDH wild-type group than in the IDH mutant group (p = .013) and in the MGMT unmethylated group than in the methylated group (p = .047). Areas under the receiver operating characteristic curve were 0.678 for IDH mutation (p = .022) and 0.601 for MGMT promoter methylation (p = .043). Hyperperfusion was associated with the shortest median PFS for both perfusion pattern[CE] (7.6 months) and perfusion pattern[NE] (4.0 months). The perfusion pattern[NE] remained an independent predictor for PFS and OS even after adjusting for clinical and molecular predictors, unlike perfusion pattern[CE]. CONCLUSIONS: ASL-PWI can aid to predict survival and molecular biomarkers including IDH mutation and MGMT promoter methylation statuses in GBM patients. KEY POINTS: • ASL-PWI can aid to predict survival in GBM patients. • ASL-PWI can aid to predict IDH and MGMT promoter methylation statuses in GBM.

Assessment of hepatic sinusoidal obstruction syndrome with intravoxel incoherent motion diffusion-weighted imaging: An experimental study in a rat model.

BACKGROUND: Intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters may reflect perfusion and diffusion changes in hepatic sinusoidal obstruction syndrome (SOS). PURPOSE: To investigate the feasibility of IVIM-DWI in the noninvasive assessment of hepatic SOS in an experimental rat model. STUDY TYPE: Animal study. POPULATION/SUBJECTS: Forty-four rats were administered different doses (90 or 160 mg/kg) of monocrotaline by gavage either 48 or 72 hours before MRI to induce different degrees of hepatic SOS, and another 10 rats served as controls. FIELD STRENGTH/SEQUENCE: 3T scanner, IVIM-DWI using nine b values (0-800 sec/mm2 ). ASSESSMENT: Histologically, rats were classified as having none (n = 10), mild (n = 8), moderate (n = 19), or severe SOS (n = 17). The apparent diffusion coefficient (ADC) and IVIM-derived parameters (D: true diffusion coefficient, D*: pseudo-diffusion coefficient, and f: perfusion fraction) of the liver parenchyma were measured. STATISTICAL TESTS: IVIM-DWI parameters were compared according to histologic grades of SOS (none, mild, moderate, and severe), and receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic accuracy. RESULTS: ADC, D, and f of the liver parenchyma were significantly different according to SOS severity groups (Ps < 0.01) and significantly decreased as SOS severity increased (rho = -0.323, -0.313, and -0.700; P = 0.017, 0.021, and <0.001, respectively). Means of f in none, mild, moderate, and severe SOS were 17.2%, 13.3%, 12.3%, and 11.1%, respectively. Among ADC and IVIM-derived parameters, f provided the highest area under the ROC curves for detecting ≥mild, ≥moderate, and severe SOS (0.991, 0.890, and 0.803, respectively). DATA CONCLUSION: IVIM-DWI may be useful in the diagnosis and severity assessment of hepatic SOS. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:81-89.

Deep Learning for Chest Radiograph Diagnosis in the Emergency Department.

BackgroundThe performance of a deep learning (DL) algorithm should be validated in actual clinical situations, before its clinical implementation.PurposeTo evaluate the performance of a DL algorithm for identifying chest radiographs with clinically relevant abnormalities in the emergency department (ED) setting.Materials and MethodsThis single-center retrospective study included consecutive patients who visited the ED and underwent initial chest radiography between January 1 and March 31, 2017. Chest radiographs were analyzed with a commercially available DL algorithm. The performance of the algorithm was evaluated by determining the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity at predefined operating cutoffs (high-sensitivity and high-specificity cutoffs). The sensitivities and specificities of the algorithm were compared with those of the on-call radiology residents who interpreted the chest radiographs in the actual practice by using McNemar tests. If there were discordant findings between the algorithm and resident, the residents reinterpreted the chest radiographs by using the algorithm's output.ResultsA total of 1135 patients (mean age, 53 years ± 18; 582 men) were evaluated. In the identification of abnormal chest radiographs, the algorithm showed an AUC of 0.95 (95% confidence interval [CI]: 0.93, 0.96), a sensitivity of 88.7% (227 of 256 radiographs; 95% CI: 84.1%, 92.3%), and a specificity of 69.6% (612 of 879 radiographs; 95% CI: 66.5%, 72.7%) at the high-sensitivity cutoff and a sensitivity of 81.6% (209 of 256 radiographs; 95% CI: 76.3%, 86.2%) and specificity of 90.3% (794 of 879 radiographs; 95% CI: 88.2%, 92.2%) at the high-specificity cutoff. Radiology residents showed lower sensitivity (65.6% [168 of 256 radiographs; 95% CI: 59.5%, 71.4%], P < .001) and higher specificity (98.1% [862 of 879 radiographs; 95% CI: 96.9%, 98.9%], P < .001) compared with the algorithm. After reinterpretation of chest radiographs with use of the algorithm's outputs, the sensitivity of the residents improved (73.4% [188 of 256 radiographs; 95% CI: 68.0%, 78.8%], P = .003), whereas specificity was reduced (94.3% [829 of 879 radiographs; 95% CI: 92.8%, 95.8%], P < .001).ConclusionA deep learning algorithm used with emergency department chest radiographs showed diagnostic performance for identifying clinically relevant abnormalities and helped improve the sensitivity of radiology residents' evaluation.Published under a CC BY 4.0 license.Online supplemental material is available for this article.See also the editorial by Munera and Infante in this issue.

Diagnostic value of computed tomography combined with ultrasonography in detecting cervical recurrence in patients with thyroid cancer.

BACKGROUND: To determine the diagnostic role of CT added to ultrasound for the diagnosis of recurrent differentiated thyroid cancer (DTC) and to evaluate potential benefits for patients. METHODS: A total of 193 patients with recurrent DTC were retrospectively included. The diagnostic performances of ultrasound and combination of ultrasound and CT (ultrasound/CT) in detecting recurrence were compared. Benefits of CT were assessed based on the presence of any recurrence detected only with additional CT. RESULTS: In detecting cervical recurrence, ultrasound/CT showed higher sensitivity (P = .001) and lower specificity (P < .001) than ultrasound alone, overall resulting in higher area under the curve (P < .001). Seventy-nine patients (40.9%) benefited from additional CT in detecting recurrence. CONCLUSION: For reoperation of cervical recurrence in patients with DTC, addition of CT to ultrasound offers better surgical planning by enhancing detection of recurrent cancers that were overlooked with ultrasound alone.

Persistent/Recurrent Differentiated Thyroid Cancer: Clinical and Radiological Characteristics of Persistent Disease and Clinical Recurrence Based on Computed Tomography Analysis.

BACKGROUND: The natural course of persistent/recurrent differentiated thyroid cancer (DTC) has not been fully elucidated. The purpose of this study was to assess the relative incidence and clinico-radiological characteristics of persistent disease and clinical recurrence based on computed tomography (CT) analysis in patients with persistent/recurrent DTC. METHODS: From January 2005 to December 2016, this retrospective study included 107 patients (M:F = 28:79; Mage = 53.5 years) with surgically proven cervical locoregional recurrence of DTC. Two neck CT examinations (median interval 1.92 years; range 0.17-7.58 years) before the last thyroid cancer surgery within the study period were reevaluated. Based on the presence of the lesion on the first CT and its progression on the second CT, the locoregional recurrence was classified into the following categories: stable persistence (decrease, no change, or increase by <2 mm in short dimension on the second CT), progressive persistence (increase by ≥2 mm), and clinical recurrence (newly appeared on the second CT). Clinical and radiological characteristics of the three groups were compared using univariate and multivariate logistic regression analyses. RESULTS: The relative incidences of stable persistence, progressive persistence, and clinical recurrence were 56.1% (60/107), 15.0% (16/107), and 29.0% (31/107), respectively. Multivariate analysis between the clinical recurrence (29.0%) and persistence (71.0%) groups revealed various independent factors for prediction of clinical recurrence. These included longer interval between the two CT examinations (median 2.67 vs. 1.79 years; p = 0.021), a smaller number of thyroid surgeries (1.16 ± 0.45 vs. 1.55 ± 0.81; p = 0.002), and a history of neck dissection at the location of the largest locoregional recurrence (70.0% vs. 31.4%; p < 0.001). There was no significant independent factor for differentiation between the stable persistence (78.9%; 60/76) and progressive persistence (21.1%; 16/76) groups. The results may have been influenced by selection bias because this study included only surgically proven cases. CONCLUSIONS: With regard to cervical locoregional recurrence of DTC, active surveillance may be favored because more than a half of the cases are structurally persistent and stable. However, meticulous evaluation is necessary to detect progressive persistence and clinical recurrence, considering various clinical factors.

Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma.

OBJECTIVES: To assess the association between MR imaging features and major genomic profiles in glioblastoma. METHODS: Qualitative and quantitative imaging features such as volumetrics and histogram analysis from normalised CBV (nCBV) and ADC (nADC) were evaluated based on both T2WI and CET1WI. The imaging parameters of different genetic profile groups were compared and regression analyses were used for identifying imaging-molecular associations. Progression-free survival (PFS) was analysed by a Kaplan-Meier test and Cox proportional hazards model. RESULTS: An IDH mutation was observed in 18/176 patients, and ATRX loss was positive in 17/158 of the IDH-wt cases. The IDH-mut group showed a larger volume on T2WI and a higher volume ratio between T2WI and CET1WI than the IDH-wt group (p < 0.05). In the IDH-mut group, higher mean nADC values were observed compared with the IDH-wt tumours (p < 0.05). Among the IDH-wt tumours, IDH-wt, ATRX-loss tumours revealed higher 5th percentile nADC values than the IDH-wt, ATRX-noloss tumours (p = 0.03). PFS was the longest in the IDH-mut group, followed by the IDH-wt, ATRX-loss groups and the IDH-wt, ATRX-noloss groups, consecutively (p < 0.05). We found significant associations of PFS with the genetic profiles and imaging parameters. CONCLUSION: Major genetic profiles of glioblastoma showed a significant association with MR imaging features, along with some genetic profiles, which are independent prognostic parameters for GBM. KEY POINTS: • Significant correlation exists between radiological parameters such as volumetric and ADC values and major genomic profiles such as IDH mutation and ATRX loss status • Radiological parameters such as the ADC value were feasible predictors of glioblastoma patients' prognosis • Imaging features can predict major genomic profiles of the tumours and the prognosis of glioblastoma patients.

Accurate measurements of liver stiffness using shear wave elastography in children and young adults and the role of the stability index.

PURPOSE: The purpose of this study was to evaluate the usefulness of the stability index (SI) in liver stiffness measurements using shear wave elastography (SWE) in children. METHODS: A total of 29 children and young adults (mean age, 16.1 years; range, 8 to 28 years; 11 boys and 18 girls) who underwent liver stiffness measurements using SWE under free-breathing and breath-holding conditions were included in our study. Ten SWE measurements were acquired in each of four groups: free-breathing and breath-holding, and with and without using the SI. The failure rate of acquisition of SI values over 90% was calculated in each group. To evaluate variability in the SWE measurements, the standard deviation, coefficient of variation, and percentage of unreliable measurements were compared. Intraobserver agreement and the optimal minimal number of measurements were calculated using intraclass correlation coefficients. RESULTS: A failure to acquire SI values over 90% was observed in 17% of the scans in the free-breathing group and in 7% of the scans in the breath-holding group. In both groups, utilizing the SI led to a significantly lower standard deviation and coefficient of variation. When using the SI, the percentage of unreliable measurements decreased from 16.7% to 8.3% in the free-breathing group and 14.8% to 0% in the breath-holding group. With the use of the SI, intraobserver agreement increased and the optimal minimal number of repeated measurements decreased in both the free-breathing and breath-holding groups. CONCLUSION: Utilization of the SI in the measurement of liver SWE in children reduced measurement variability and increased reliability in both free-breathing and breath-holding conditions.

Identifying characteristic back shapes from anatomical scans of wheelchair users to improve seating design.

Spinal deformities are common in people who require the use of a wheelchair for mobility as a result of spinal cord injuries and other disabilities. Sitting positions vary between individuals with disabilities who use wheelchairs and individuals without disabilities. In individuals with spinal cord injury, spinal deformities can result in the development of back contours that deviate from the shape of standard rigid back support shells. The purpose of this study was to distinguish and classify various back contours of wheelchair users by utilizing digital anatomic scanning technology in order to inform the future development of back supports that would enhance postural support for those with spinal deformities. The three dimensional (3D) locations of bony landmarks were digitized when participants were in position, using a mechanical wand linked to the FastScan(tm) system commonly used to measure surface contours. Raw FastScan(tm) data were transformed according to bony landmarks. A total of 129 individuals participated in this study. A wide range of back contours were identified and categorized. Although participant characteristics (e.g., gender, diagnosis) were similar amongst the contour groups; no one characteristic explained the contours. Participants who were seated in a forward lean position had a higher amount of pelvic obliquity compared to those seated in an upright position; however, participants' back contour was not correlated with pelvic obliquity. In conclusion, an array of different back shapes were classified in our cohort through 3D laser scanning technology. The methods and technology applied in this study could be replicated in future studies to categorize ranges of back shapes in larger populations of people with spinal cord injuries. Preliminary evidence indicates that customized postural support may be warranted to optimize positioning and posture when a standard rigid shell does not align with contours of a person's back. To optimize positioning, a range of contoured rigid backrests as well as height and angle adjustability are likely needed.

Design, testing and evaluation of angle-adjustable backrest hardware.

PURPOSE: The purpose of this study was to design and evaluate a final design prototype of angle-adjustable backrest hardware. METHODS: A traditional iterative design development protocol was undertaken and completed. Before evaluation by a focus group, testing of the prototype was performed in strict accordance with ISO standards. Focus group participants were between 18 and 80 years of age, used a manual wheelchair as their primary means of mobility, and transferred independently. Individuals with pressure sores or who required of the use of specialized or custom seating for trunk support were excluded from the study. A questionnaire was administered to elicit participants' opinions on the adjustability, function and appearance of the angle-adjustable backrest device. RESULTS: The prototype successfully met the ISO testing standards. Wheelchair users (n = 8) who evaluated the device in a focus group had an overall positive response. Things they most liked about the prototype were comfort, support (function/activities) and adjustability, while things they most disliked about the prototype were problems with string and reaching back position to adjust. CONCLUSIONS: The prototype had a positive impression from participants, however, improvements on the operation method and usability were suggested. Implications for Rehabilitation The adjustable backrest is in need of development to provide function, comfort and support for manual wheelchair users. Manual wheelchair users will benefit by using the angle adjustment as they maintain their active lifestyles. Balance control while performing pressure relief in a wheelchair would be increased.

Comfort and stability of wheelchair backrests according to the TAWC (tool for assessing wheelchair discomfort).

PURPOSE: The goal of this study was thus to determine if people with different types of wheelchair backrests on their personal wheelchairs reported different levels of comfort as measured by the Tool for Assessing Wheelchair disComfort (TAWC). METHODS: Participants were between 18 and 80 years of age and were manual wheelchair users. The TAWC was used to assess the participants' wheelchair seating discomfort levels with the wheelchair and seating systems. RESULTS: We surveyed 131 wheelchair users to assess the comfort of their backrests on their personal wheelchairs and found a trend suggesting that rigid backrests are were less comfortable as compared with sling backrests. This finding was statistically significant in a subgroup of participants with tetraplegia. CONCLUSIONS: Although many clinicians expect rigid backrests to be more comfortable because they may provide more support, the higher discomfort ratings among rigid backrest users with tetraplegia may be due to sub-optimal shape, fit, adjustment or user preferences. Implications for Rehabilitation Development of a measure for long-term seating discomfort is needed. Design and development of better rigid backrests that are functional but provide adequate comfort are in need.

Non small cell carcinoma metastasis to meningioma.

"Tumor-to-tumor" metastasis is a rare event; meningioma has been reported as the most common primary intracranial tumor to harbor cancer metastases. Several hypotheses have been previously proposed to explain this occurrence, but the exact mechanism by which these metastases develop into meningiomas is not yet understood. Magnetic resonance imaging and spectroscopy have been valuable diagnostic tools, but preoperative diagnosis of metastasis to meningioma remains highly difficult. We present a case report of a metastasis of non-small cell lung cancer into an intracranial meningioma.