Radiomics Feature Analysis Using Native T1 Mapping for Discriminating Between Cardiac Tumors and Thrombi

Son, Jinwoo; Hong, Yoo Jin; Kim, Sujeong; Han, Kyunghwa; Lee, Hye Jeong; Hur, Jin; Kim, Young Jin; Choi, Byoung Wook

doi:10.1016/j.acra.2020.12.009

Cited by 10 publications

(8 citation statements)

References 41 publications

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“…Thrombi depict low T2 time as compared to both benign and malignant tumor. 20 This study shows 73.6% (28) thrombi and 62.5% (5) malignant tumors have T2 time less than 60ms and only 20% (2) benign lesions have less than 60ms T2 time. Majority of benign tumors show parametric T1 time ranging from 2000-2750ms, malignant tumors have T1 time in between 1450-1950ms whereas, thrombi lie within 600-1650ms.…”

Section: Discussionmentioning

confidence: 65%

Emerging Role of 3.0 Tesla Cardiac Magnetic Resonance Imaging in Differentiating Cardiac Masses

Saif

Maken²,

Kamran³

et al. 2022

PAFMJ

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Objective: To evaluate the role of Cardiac Magnetic Resonance Imaging in differentiating cardiac masses (thrombus vs tumor and benign tumor vs malignant tumor) in local population. Study Design: Analytical cross-sectional study. Place and Duration of Study: Tertiary Cardiac Care Center, Department of Cardiac Magnetic Resonance Imaging, Rawalpindi Pakistan, from Oct 2017 to Jun 2021. Methodology: This retrospective study included (n=56) patients via universal sampling, enrolled for Cardiovascular magnetic resonance imaging with a suspicion of cardiac masses either on echocardiography or Computed Tomography scan. Cardiovascular magnetic resonance imaging sequences were reviewed as SSFP cine images for mass location, size and mobility.T1 weighted turbo spin echo, T2 weighted turbo spin echo with and without fat saturation and TIRM sequences told their intensity as compared to normal myocardium, myomaps evaluated the relaxation time, while contrast first pass perfusion indicated the vascularity and delayed gadolinium enhancement images with standard and long TI were analyzed for contrast enhancement. Results: Total n=56 patients with a confirmed diagnosis of mass were included for the analysis. Mean age of participants was found to be 45.21(18.3%), height 168.4(8.69%) and weight 68.6(14.94%). There were 47(83.9%) males and females were 9(16.1%). 22(39.3%) had hypertension, 12(21.4%) had diabetes mellitus, 24(42.9%) had previous myocardial infarction. Maximum number of masses were found in LV 31(55.4%) followed by RV 11(19.6%). Sensitivity of T1 map to detect fibrosis intumor is 100%. Sensitivity of T2 map to detect edema in tumor is 82%. Conclusion: Cardiac masses like thrombi and tumors accurately diagnosed by cardiac MRI with etiology..

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Section: Discussionmentioning

confidence: 65%

Emerging Role of 3.0 Tesla Cardiac Magnetic Resonance Imaging in Differentiating Cardiac Masses

Saif

Maken²,

Kamran³

et al. 2022

PAFMJ

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“…Prior studies reported that radiomics might facilitate the prediction of the treatment response in several disease entities, such as hepatocellular carcinoma, pancreatic cancer, rectal cancer and gastric cancer [ 24 , 25 , 26 , 27 ]. As for the application of radiomics in cardiology, the study conducted by Son et al [ 22 ] revealed that a native T1 radiomics model could differentiate thrombi from tumors better than the mean T1 value (AUC 0.98 vs. 0.86). Ma et al [ 28 ] reported that native T1 mapping-based radiomics showed superior performance for the diagnosis of microvascular obstruction compared to T1 values.…”

Section: Discussionmentioning

confidence: 99%

“…The ROIs were delineated by the senior radiologist (15 years of experience in CMR evaluation) in 30 randomly selected patients (20 cases in the effective group and 10 cases in the ineffective group) for evaluation of the inter-observer variability. Complex pre-processing steps such as normalization or inhomogeneity correction were unnecessary for T1 mapping images before radiomics analysis given that each pixel in the map represents the objective corresponding T1 values of the tissues under the same scanning conditions [ 21 , 22 ]. The software enables the automatic extraction of radiomics features from the segmented ROIs.…”

Section: Methodsmentioning

confidence: 99%

Native T1 Mapping-Based Radiomics for Noninvasive Prediction of the Therapeutic Effect of Pulmonary Arterial Hypertension

Wang

et al. 2022

Diagnostics

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(1) Background: Novel markers for predicting the short-term therapeutic effect of pulmonary arterial hypertension (PAH) to assist in the prompt initiation of tailored treatment strategies are greatly needed and highly desirable. The aim of the study was to investigate the role of cardiac magnetic resonance (CMR) native T1 mapping radiomics in predicting the short-term therapeutic effect in PAH patients; (2) Methods: Fifty-five PAH patients who received targeted therapy were retrospectively included. Patients were subdivided into an effective group and an ineffective group by assessing the therapeutic effect after ≥3 months of treatment. All patients underwent CMR examinations prior to the beginning of the therapy. Radiomics features from native T1 mapping images were extracted. A radiomics model was constructed using the support vector machine (SVM) algorithm for predicting the therapeutic effect; (3) Results: The SVM radiomics model revealed favorable performance for predicting the therapeutic effect with areas under the receiver operating characteristic curve of 0.955 in the training cohort and 0.893 in the test cohort, respectively. With the optimal cutoff value, the radiomics model showed accuracies of 0.909 and 0.818 in the training and test cohorts, respectively; (4) Conclusions: The CMR native T1 mapping-based radiomics model holds promise for predicting the therapeutic effect in PAH patients.

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“…Moreover, the T2 values vary between sites and vendors [ 22 , 23 ]. On the other hand, many studies suggest radiomics as an interesting prospect to be studied regarding quantitative MRI images without these shortcomings aforementioned [ 24 ]. The superior performance of radiomics features to T2 values of cartilage in discerning ACLR patients and controls indicated that radiomics features showed higher sensitivity in assessing the degenerative changes of early-stage PTOA in knee cartilage after ACLR compared to the T2 values.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Feature Analysis of Cartilage and Subchondral Bone in Differentiating Knees Predisposed to Posttraumatic Osteoarthritis after Anterior Cruciate Ligament Reconstruction from Healthy Knees

Xie

Dan

Tao

et al. 2021

BioMed Research International

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Objectives. To introduce a new implementation of radiomics analysis for cartilage and subchondral bone of the knee and to compare the performance of the proposed models to classic T2 relaxation time in distinguishing knees predisposed to posttraumatic osteoarthritis (PTOA) after anterior cruciate ligament reconstruction (ACLR) and healthy controls. Methods. 114 patients following ACLR after at least 2 years and 43 healthy controls were reviewed and allocated to training ( n = 110 ) and testing ( n = 47 ) cohorts. Radiomics models are built for cartilage and subchondral bone regions of different compartments: lateral femur (LF), lateral tibia (LT), medial femur (MF), and medial tibia (MT) and combined models of four compartments on T2 mapping images. The model performance of discrimination between patients and controls was illustrated with the receiver operating characteristic curve and compared with a classic T2 value-based model. Results. The T2 value model of cartilage yielded moderate predictive performance in discerning patients and controls, with an AUC of 0.731 (95% confidence interval, 0.556–0.875) in the testing cohort, while the radiomics signature of cartilage and subchondral bone of different compartments demonstrated excellent performance, with AUCs of 0.864–0.979. Furthermore, the combined model reported an even better performance, with AUCs of 0.977 (95% confidence interval, 0.919–1.000) for the cartilage and 0.934 (95% confidence interval, 0.865–0.994) for the subchondral bone in the testing cohort. Conclusion. The radiomics features of the cartilage and subchondral bone may be able to provide powerful tools with more sensitive detection than T2 values in differentiating knees at risk for PTOA after ACLR from healthy knees.

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Radiomics Feature Analysis Using Native T1 Mapping for Discriminating Between Cardiac Tumors and Thrombi

Cited by 10 publications

References 41 publications

Emerging Role of 3.0 Tesla Cardiac Magnetic Resonance Imaging in Differentiating Cardiac Masses

Emerging Role of 3.0 Tesla Cardiac Magnetic Resonance Imaging in Differentiating Cardiac Masses

Native T1 Mapping-Based Radiomics for Noninvasive Prediction of the Therapeutic Effect of Pulmonary Arterial Hypertension

Radiomics Feature Analysis of Cartilage and Subchondral Bone in Differentiating Knees Predisposed to Posttraumatic Osteoarthritis after Anterior Cruciate Ligament Reconstruction from Healthy Knees

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