GPU-Accelerated GLRLM Algorithm for Feature Extraction of MRI

Zhang, Hanyu; Hung, Che Lun; Min, Geyong; Guo, Jhih Peng; Liu, Meiyuan; Hu, Xiaoye

doi:10.1038/s41598-019-46622-w

Cited by 35 publications

(37 citation statements)

References 17 publications

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“…A semi-automated process also requires time for curation. With the development of parallel graphics processing unit, studies have employed artificial neural networks to identify and distinguish texture pattern [35]. Artificial intelligence could be a possible new paradigm for time-consuming work [35].…”

Section: Discussionmentioning

confidence: 99%

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer

et al. 2020

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Objectives This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC). Methods Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival. Results In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046). Conclusions Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence. Key Points• Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence. • Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer. • Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer.

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Section: Discussionmentioning

confidence: 99%

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer

et al. 2020

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“…In this study, the proportions of GLRLM (8/16) and GLCM (5/16) features were the largest in the final constructed model. The GLRLM is broadly utilized to extract statistical features [ 37 ], whose entries record distributions and relationships of image pixels, which can better reflect regional heterogeneous differences. The GLCM provides a second-order technique to generate texture features for determining associations among combinations of gray levels in image indexes [ 38 ], which can reflect internal spatial heterogeneity of the lesions.…”

Section: Discussionmentioning

confidence: 99%

Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer

Wei

Wang

et al. 2021

BMC Med Imaging

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Background To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically. Methods The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model’s performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve. Results Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively. Conclusions Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.

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“…In this study, the proportions of GLRLM(8/16) and GLCM 5/16 features were the largest in the nal constructed model. The GLRLM is broadly utilized to extract statistical features [40], whose entries record distributions and relationships of image pixels, which can better re ect regional heterogeneous differences. The GLCM provides a second-order technique to generate texture features for determining associations among combinations of gray levels in image indexes [41], which can re ect internal spatial heterogeneity of the lesions.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Based on Multiparametric MRI for Extrathyroidal Extension Feature Prediction in Papillary Thyroid Cancer

Wei

Wang

et al. 2020

Preprint

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Purpose: To determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically.Methods: The present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator (LASSO) were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model’s performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic (ROC) curve. Results: Totally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively.Conclusions: Our study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.

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GPU-Accelerated GLRLM Algorithm for Feature Extraction of MRI

Cited by 35 publications

References 17 publications

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer

MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer

Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer

Radiomics Based on Multiparametric MRI for Extrathyroidal Extension Feature Prediction in Papillary Thyroid Cancer

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