Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor

Prasanna, Prateek; Tiwari, Pallavi; Madabhushi, Anant

doi:10.1038/srep37241

Cited by 112 publications

(101 citation statements)

References 31 publications

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“…Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) features13Apply Haralick metrics to dominant intensity gradient orientations within a 5-pixel × 5-pixel window, quantifying patterns of local gradient alignment [59, 60]. Some descriptors quantify homogeneity of gradient orientations (e.g., CoLlAGe information measure of correlation), whereas others compute their disorder (e.g., CoLlAGe entropy).CoLlAGe entropy has previously been demonstrated to be effective in distinguishing breast cancer subtypes [59, 60].

Abbreviations: CT Computed tomography, NAC Neoadjuvant chemotherapy, pCR Pathological complete response, TIL Tumor-infiltrating lymphocyte

…”

Section: Methodsmentioning

confidence: 99%

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

et al. 2017

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BackgroundIn this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).MethodsA total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2−) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.ResultsAmong all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2− breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.ConclusionsThrough a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.

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Abbreviations: CT Computed tomography, NAC Neoadjuvant chemotherapy, pCR Pathological complete response, TIL Tumor-infiltrating lymphocyte

…”

Section: Methodsmentioning

confidence: 99%

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

et al. 2017

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“…Radiomic features employed in this study were extracted on a per‐voxel basis from T 2 WI and ADC maps within the annotated cancerous regions. The choice of radiomic features for characterizing PCa lesions was motivated by their use in previous studies . We also included additional features that have recently been shown to be capable of distinguishing among subtly different disease types on imaging (Table ).…”

Section: Methodsmentioning

confidence: 99%

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

Shiradkar

Ghose

Jambor

et al. 2018

Magnetic Resonance Imaging

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110

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“…State-of-the-art radiomics features 16 quantifying shape, texture, heterogeneity (Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) 17 ) and intensity were extracted from the ROI. Texture features were constructed using the Gray Level Co-occurence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), Gabor filters and Local Binary Patterns (LBP).…”

Section: Radiomicsmentioning

confidence: 99%

Classification of malignant and benign liver tumors using a radiomics approach

Starmans

Miclea

Voort

et al. 2018

Medical Imaging 2018: Image Processing

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Correct diagnosis of the liver tumor phenotype is crucial for treatment planning, especially the distinction between malignant and benign lesions. Clinical practice includes manual scoring of the tumors on Magnetic Resonance (MR) images by a radiologist. As this is challenging and subjective, it is often followed by a biopsy. In this study, we propose a radiomics approach as an objective and non-invasive alternative for distinguishing between malignant and benign phenotypes. T2-weighted (T2w) MR sequences of 119 patients from multiple centers were collected. We developed an efficient semi-automatic segmentation method, which was used by a radiologist to delineate the tumors. Within these regions, features quantifying tumor shape, intensity, texture, heterogeneity and orientation were extracted. Patient characteristics and semantic features were added for a total of 424 features. Classification was performed using Support Vector Machines (SVMs). The performance was evaluated using internal random-split cross-validation. On the training set within each iteration, feature selection and hyperparameter optimization were performed. To this end, another cross validation was performed by splitting the training sets in training and validation parts. The optimal settings were evaluated on the independent test sets. Manual scoring by a radiologist was also performed. The radiomics approach resulted in 95% confidence intervals of the AUC of [0.75, 0.92], specificity [0.76, 0.96] and sensitivity [0.52, 0.82]. These approach the performance of the radiologist, which were an AUC of 0.93, specificity 0.70 and sensitivity 0.93. Hence, radiomics has the potential to predict the liver tumor benignity in an objective and non-invasive manner.

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Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor

Cited by 112 publications

References 31 publications

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings

Classification of malignant and benign liver tumors using a radiomics approach

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