CT-based radiomics for differentiating renal tumours: a systematic review

Bhandari, Abhishta; Ibrahim, Muhammad; Sharma, Chinmay; Liong, Rebecca; Gustafson, Sonja; Prior, Marita

doi:10.1007/s00261-020-02832-9

Cited by 54 publications

(28 citation statements)

References 37 publications

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“…Nonetheless, up to 30% of AIs do not fulfil the well-established criteria of a benign lesion, and novel approaches are needed. Recently, image-based texture analysis from CT and MR provides quantitative parameters that may be useful to measure the presence of necrosis, haemorrhage, calcifications, and intracellular lipid content, allowing to differentiate benign from malignant tumours [ 13 , 14 , 31 , 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review

Crimì

Quaia

Cabrelle

et al. 2022

IJMS

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Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review

Crimì

Quaia

Cabrelle

et al. 2022

IJMS

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“…When they had a disagreement, a third physician participated, and the three physicians negotiated and determined together. Current researches mainly focus on the relationship between CT, MR texture analysis, and ccRCC Fuhrman nuclear grade [22][23][24][25][26][27]. Since PET texture analysis can better reflect the heterogeneity of tumors than conventional radiomics, the emergence of PET radiomics provides new possibilities for the prediction of ccRCC Fuhrman nuclear grade.…”

Section: Discussionmentioning

confidence: 99%

18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma

et al. 2021

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Purpose This article analyzes the image heterogeneity of clear cell renal cell carcinoma (ccRCC) based on positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) texture parameters, and provides a new objective quantitative parameter for predicting pathological Fuhrman nuclear grading before surgery. Methods A retrospective analysis was performed on preoperative PET/CT images of 49 patients whose surgical pathology was ccRCC, 27 of whom were low grade (Fuhrman I/II) and 22 of whom were high grade (Fuhrman III/IV). Radiological parameters and standard uptake value (SUV) indicators on PET and computed tomography (CT) images were extracted by using the LIFEx software package. The discriminative ability of each texture parameter was evaluated through receiver operating curve (ROC). Binary logistic regression analysis was used to screen the texture parameters with distinguishing and diagnostic capabilities and whose area under curve (AUC) > 0.5. DeLong's test was used to compare the AUCs of PET texture parameter model and PET/CT texture parameter model with traditional maximum standardized uptake value (SUVmax) model and the ratio of tumor SUVmax to liver SUVmean (SUL)model. In addition, the models with the larger AUCs among the SUV models and texture models were prospectively internally verified. Results In the ROC curve analysis, the AUCs of SUVmax model, SUL model, PET texture parameter model, and PET/CT texture parameter model were 0.803, 0.819, 0.873, and 0.926, respectively. The prediction ability of PET texture parameter model or PET/CT texture parameter model was significantly better than SUVmax model (P = 0.017, P = 0.02), but it was not better than SUL model (P = 0.269, P = 0.053). In the prospective validation cohort, both the SUL model and the PET/CT texture parameter model had good predictive ability, and the AUCs of them were 0.727 and 0.792, respectively. Conclusion PET and PET/CT texture parameter models can improve the prediction ability of ccRCC Fuhrman nuclear grade; SUL model may be the more accurate and easiest way to predict ccRCC Fuhrman nuclear grade. Graphic abstract

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“…Orange Canvas® software processed all texture features with different methods of machine learning: Stochastic Gradient Descent (SGD), Naive Bayes (NB) [10], and Support Vector Machine (SVM) [11,12].…”

Section: Machine Learning Classificationmentioning

confidence: 99%

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

et al. 2021

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Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation.

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CT-based radiomics for differentiating renal tumours: a systematic review

Cited by 54 publications

References 37 publications

Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review

Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review

18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

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