Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: a retrospective multicenter study.

OBJECTIVES: Gastrointestinal stromal tumors (GISTs) carrying different KIT exon 11 (KIT-11) mutations exhibit varying prognoses and responses to Imatinib. Herein, we aimed to determine whether computed tomography (CT) radiomics can accurately stratify KIT-11 mutation genotypes to benefit Imatinib therapy and GISTs monitoring. METHODS: Overall, 1143 GISTs from 3 independent centers were separated into a training cohort (TC) or validation cohort (VC). In addition, the KIT-11 mutation genotype was classified into 4 categories: no KIT-11 mutation (K11-NM), point mutations or duplications (K11-PM/D), KIT-11 557/558 deletions (K11-557/558D), and KIT-11 deletion without codons 557/558 involvement (K11-D). Subsequently, radiomic signatures (RS) were generated based on the arterial phase of contrast CT, which were then developed as KIT-11 mutation predictors using 1408 quantitative image features and LASSO regression analysis, with further evaluation of its predictive capability. RESULTS: The TC AUCs for K11-NM, K11-PM/D, K11-557/558D, and K11-D ranged from 0.848 (95% CI 0.812-0.884), 0.759 (95% CI 0.722-0.797), 0.956 (95% CI 0.938-0.974), and 0.876 (95% CI 0.844-0.908), whereas the VC AUCs ranged from 0.723 (95% CI 0.660-0.786), 0.688 (95% CI 0.643-0.732), 0.870 (95% CI 0.824-0.918), and 0.830 (95% CI 0.780-0.878). Macro-weighted AUCs for the KIT-11 mutant genotype ranged from 0.838 (95% CI 0.820-0.855) in the TC to 0.758 (95% CI 0.758-0.784) in VC. TC had an overall accuracy of 0.694 (95%CI 0.660-0.729) for RS-based predictions of the KIT-11 mutant genotype, whereas VC had an accuracy of 0.637 (95%CI 0.595-0.679). CONCLUSIONS: CT radiomics signature exhibited good predictive performance in estimating the KIT-11 mutation genotype, especially in prediction of K11-557/558D genotype. RS-based classification of K11-NM, K11-557/558D, and K11-D patients may be an indication for choice of Imatinib therapy.

Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis.

OBJECTIVES: Carotid artery stenting (CAS) is an established treatment for local stenosis. The most common complication is new ipsilateral ischemic lesions (NIILs). This study aimed to develop models considering lesion morphological and compositional features, and radiomics to predict NIILs. MATERIALS AND METHODS: One hundred and forty-six patients who underwent brain MRI and high-resolution vessel wall MR imaging (hrVWI) before and after CAS were retrospectively recruited. Lumen and outer wall boundaries were segmented on hrVWI as well as atherosclerotic components. A traditional model was constructed with patient clinical information, and lesion morphological and compositional features. Least absolute shrinkage and selection operator algorithm was performed to determine key radiomics features for reconstructing a radiomics model. The model in predicting NIILs was trained and its performance was tested. RESULTS: Sixty-one patients were NIIL-positive and eighty-five negative. Volume percentage of intraplaque hemorrhage (IPH) and patients' clinical presentation (symptomatic/asymptomatic) were risk factors of NIILs. The traditional model considering these two features achieved an area under the curve (AUC) of 0.778 and 0.777 in the training and test cohorts, respectively. Twenty-two key radiomics features were identified and the model based on these features achieved an AUC of 0.885 and 0.801 in the two cohorts. The AUCs of the combined model considering IPH volume percentage, clinical presentation, and radiomics features were 0.893 and 0.842 in the training and test cohort respectively. CONCLUSIONS: Compared with traditional features (clinical and compositional features), the combination of traditional and radiomics features improved the power in predicting NIILs after CAS. KEY POINTS: • Volume percentage of IPH and symptomatic events were independent risk factors of new ipsilateral ischemic lesions (NIILs). • Radiomics features derived from carotid artery high-resolution vessel wall imaging had great potential in predicting NIILs after CAS. • The combination model with radiomics and traditional features further improved the diagnostic performance than traditional features alone.

Detection of mural inflammation with low b-value diffusion-weighted imaging in patients with active Takayasu Arteritis.

OBJECTIVES: To evaluate the performance of low b-value diffusion-weighted imaging (DWI) for detection of inflamed vessels in active Takayasu arteritis (TA). METHODS: Forty patients with active TA involving the thoracic aorta and its super-aortic branches underwent low b-value (50 s/mm2) DWI, T2-weighted imaging (T2WI), and delayed enhancement T1-weighted imaging (DEI). Corresponding images on these 3 sequences at the same diseased level were evaluated qualitatively and quantitatively using Friedman and Kruskal-Wallis test, and the agreement between them in detection of inflamed vessels was assessed using Cochran's Q test. RESULTS: The overall image quality of DEI, DWI, and T2WI was scored 7.97 ± 1.15, 7.32 ± 1.73, and 6.51 ± 1.69 respectively. The score of DEI and DWI was higher than that of T2WI (p < 0.001). The quality of blood suppression was rated higher in DWI than T2WI and DEI (p < 0.001). Both the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the diseased vessel walls measured on DEI and DWI were significantly higher than those on T2WI (p < 0.001). However, there was no significant difference in SNR and CNR between DEI and DWI (p = 0.283 and 0.063). In detection of mural inflammation, significant advantage was observed when comparing the findings from DEI/DWI to those from T2WI (p < 0.001). But no significant difference was found between the findings of DWI and DEI (p > 0.99). CONCLUSIONS: Low b-value DWI may be used as a promising alternative to DEI for detecting inflamed vessels in active TA. KEY POINTS: • Currently, the most widely used imaging modality in detection of mural inflammation is contrast-enhanced MRI. • Low b-value DWI is shown comparable to contrast-enhanced MRI and superior to T2WI in identifying mural inflammation in patients with active Takayasu arteritis. • Low b-value DWI is a fast and unenhanced MRI technique which may potentially replace contrast-enhanced MRI in identifying disease activity of Takayasu arteritis.

Identification of high-risk carotid plaque with MRI-based radiomics and machine learning.

OBJECTIVES: We sought to build a high-risk plaque MRI-based model (HRPMM) using radiomics features and machine learning for differentiating symptomatic from asymptomatic carotid plaques. MATERIALS AND METHODS: One hundred sixty-two patients with carotid stenosis were randomly divided into training and test cohorts. Multi-contrast MRI including time of flight (TOF), T1- and T2-weighted imaging, and contrast-enhanced imaging was done. Radiological characteristics of the carotid plaques were recorded and calculated to build a traditional model. After extracting the radiomics features on these images, we constructed HRPMM with least absolute shrinkage and selection operator algorithm in the training cohort and evaluated its performance in the test cohort. A combined model was also built using both the traditional and radiomics features. The performance of all the models in the identification of high-risk carotid plaque was compared. RESULTS: Intraplaque hemorrhage and lipid-rich necrotic core were independently associated with clinical symptoms and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.825 versus 0.804 in the training and test cohorts. The HRPMM and the combined model achieved an AUC of 0.988 versus 0.984 and of 0.989 versus 0.986 respectively in the two cohorts. Both the radiomics model and combined model outperformed the traditional model, whereas the combined model showed no significant difference with the HRPMM. CONCLUSIONS: Our MRI-based radiomics model can accurately distinguish symptomatic from asymptomatic carotid plaques. It is superior to the traditional model in the identification of high-risk plaques. KEY POINTS: • Carotid plaque multi-contrast MRI stores other valuable information to be further exploited by radiomics analysis. • Radiomics analysis can accurately distinguish symptomatic from asymptomatic carotid plaques. • The radiomics model is superior to the traditional model in the identification of high-risk plaques.

Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage

PURPOSE: Early monitoring and intervention for patients with novel coronavirus disease-2019 (COVID-19) will benefit both patients and the medical system. Chest computed tomography (CT) radiomics provide more information regarding the prognosis of COVID-19. METHODS: A total of 833 quantitative features of 157 COVID-19 patients in the hospital were extracted. By filtering unstable features using the least absolute shrinkage and selection operator algorithm, a radiomic signature was built to predict the prognosis of COVID-19 pneumonia. The main outcomes were the area under the curve (AUC) of the prediction models for death, clinical stage, and complications. Internal validation was performed using the bootstrapping validation technique. RESULTS: The AUC of each model demonstrated good predictive accuracy [death, 0.846; stage, 0.918; complication, 0.919; acute respiratory distress syndrome (ARDS), 0.852]. After finding the optimal cut-off for each outcome, the respective accuracy, sensitivity, and specificity were 0.854, 0.700, and 0.864 for the prediction of the death of COVID-19 patients; 0.814, 0.949, and 0.732 for the prediction of a higher stage of COVID-19; 0.846, 0.920, and 0.832 for the prediction of complications of COVID-19 patients; and 0.814, 0.818, and 0.814 for ARDS of COVID-19 patients. The AUCs after bootstrapping were 0.846 [95% confidence interval (CI): 0.844-0.848] for the death prediction model, 0.919 (95% CI: 0.917-0.922) for the stage prediction model, 0.919 (95% CI: 0.916-0.921) for the complication prediction model, and 0.853 (95% CI: 0.852-0.0.855) for the ARDS prediction model in the internal validation. Based on the decision curve analysis, the radiomics nomogram was clinically significant and useful. CONCLUSION: The radiomic signature from the chest CT was significantly associated with the prognosis of COVID-19. A radiomic signature model achieved maximum accuracy in the prognosis prediction. Although our results provide vital insights into the prognosis of COVID-19, they need to be verified by large samples in multiple centers.

Circumferential degree of carotid calcification is associated with new ischemic brain lesions after carotid artery stenting.

BACKGROUND: The relationship between plaque calcification and new ischemic brain lesions after carotid artery stenting (CAS) remains controversial. The purpose of this study was to determine if the circumferential degree of carotid calcification is associated with new ischemic brain lesions on diffusion-weighted imaging (DWI) after CAS. METHODS: A total of 96 patients with carotid stenosis of ≥50% who underwent CAS were enrolled in the study. All patients underwent preoperative carotid computed tomography (CT), and preoperative and postoperative brain MRI. The brain MRI sequences included T1WI, T2WI, T2-fluid-attenuated inversion recovery (FLAIR), and DWI. The location, circumferential degree, volume, percentage volume, maximum density, mean density, Agatston score of carotid calcification, and total plaque volume were assessed and compared between patients with and without new ischemic brain lesions after CAS. Univariate and multivariate analyses were performed to evaluate predictors of new ischemic brain lesions. RESULTS: All of the 96 patients (67.8±6.8 years of age, 83.3% men) were included in the analysis. New ischemic brain lesions on DWI were observed in 40 patients (41.7%). Patients with new ischemic brain lesions after CAS had a larger circumferential degree of calcification than those without new ischemic brain lesions (P<0.001). There was only a possible trend toward significance for the percentage volume of calcification between the two groups with and without new brain ischemic lesions (P=0.07). No significant differences were found regarding the location (P=0.18), volume (P=0.37), maximum density (P=0.44), mean density (P=0.39), Agatston score (P=0.28), and total plaque volume (P=0.33) of carotid calcification between the DWI+ and DWI- groups. In the multivariate analysis, an increased risk of new ischemic brain lesions was observed in patients with a high score for the circumferential degree of calcification [score 3; odds ratio (OR): 10.7, P<0.001; score 4, OR: 11.7, P=0.038]. CONCLUSIONS: The circumferential degree of carotid calcification was associated with new ischemic brain lesions after CAS. CAS should be avoided if possible for carotid stenosis with large circumferential calcified plaques.

Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort.

BACKGROUND AND AIM: To develop and validate radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHOD: A total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki-67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. RESULTS: The radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632-0.801], 0.765 (95% CI 0.683-0.847), and 0.754 (95% CI 0.666-0.842) in the prediction of high Ki-67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726-0.876), 0.828 (95% CI 0.681-0.974), and 0.784 (95% CI 0.701-0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. CONCLUSIONS: The radiomic signature from CE-CT was significantly associated with Ki-67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki-67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions.

Comparison of malignancy-prediction efficiency between contrast and non-contract CT-based radiomics features in gastrointestinal stromal tumors: A multicenter study.

This work seeks the development and validation of radiomics signatures from nonenhanced computed tomography (CT, NE-RS) to preoperatively predict the malignancy degree of gastrointestinal stromal tumors (GISTs) and the comparison of these signatures with those from contrast-enhanced CT. A dataset for 370 GIST patients was collected from four centers. This dataset was divided into cohorts for training, as well as internal and external validation. The minimum-redundancy maximum-relevance algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm were used to filter unstable features. (a) NE-RS and radiomics signature from contrast-enhanced CT (CE-RS) were built and compared for the prediction of malignancy potential of GIST based on the area under the receiver operating characteristic curve (AUC). (b) The radiomics model was also developed with both the tumor size and NE-RS. The AUC values were comparable between NE-RS and CE-RS in the training (.965 vs .936; P = .251), internal validation (.967 vs .960; P = .801), and external validation (.941 vs .899; P = .173) cohorts in diagnosis of high malignancy potential of GISTs. We next focused on the NE-RS. With 0.185 selected as the cutoff of NE-RS for diagnosis of the malignancy potential of GISTs, accuracy, sensitivity, and specificity for diagnosis high-malignancy potential GIST was 90.0%, 88.2%, and 92.3%, respectively, in the training cohort. For the internal validation set, the corresponding metrics are 89.1%, 94.9%, and 80.0%, respectively. The corresponding metrics for the external cohort are 84.6%, 76.1%, and 91.0%, respectively. Compared with only NE-RS, the radiomics model increased the sensitivity in the diagnosis of GIST with high-malignancy potential by 5.9% (P = .025), 2.5% (P = .317), 10.5% (P = .008) for the training set, internal validation set, and external validation set, respectively. The NE-RS had comparable prediction efficiency in the diagnosis of high-risk GISTs to CE-RS. The NE-RS and radiomics model both had excellent accuracy in predicting malignancy potential of GISTs.