Imaging Pearls ❯ Adrenal ❯ Texture Analysis
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- “Compared with traditional black and white two-dimensional images and three-dimensional volume rendering (VR) images, CR images were more colorful, layered, and closer to the truth. CR has potential in diagnosing and preoperative planning of adrenal tumors, allowing vivid and realistic visualization of tumor location, morphology, different components (solid, cystic, fat, calcification, etc.), the pattern of enhancement, and the relationship with surrounding tissues and organs.”
Virtual or real: lifelike cinematic rendering of adrenal tumors
Lei Tang et al.
Quant Imaging Med Surg 2021;11(8):3854-3866 - "CR simulates the physical transmission of light in different environments in the real world, giving colorful vitality to the black-and-white image world. It presents the fine anatomical structures of different parts of the human body realistically and vividly, giving clinicians and patients a deeper perception. It also shows good potential application value in adrenal tumor localization, diagnosis, differential diagnosis, preoperative planning, teaching, and doctor- patient communication, which needs to be further verified in future studies.”
Virtual or real: lifelike cinematic rendering of adrenal tumors
Lei Tang et al.
Quant Imaging Med Surg 2021;11(8):3854-3866 - “Machine learning and radiomic features extraction can differentiate between benign and malignant indeterminate adrenal tumors and can be used to direct further workup with high sensitivity and specificity.”
Machine learning‐based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans
Ahmed W. Moawad et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03136-2 - “Adrenal “incidentalomas” are adrenal lesions that are accidentally discovered during workup not related to the adrenal glands; they have an incidence as high as 5%. Small adrenal incidentalomas (< 4 cm) with high attenuation values on pre-contrast CT(> 10 HU) need further evaluation to calculate the absolute percentage of washout (APW). If the APW is < 60%, these lesions are considered non-adenomas and commonly classified as indeterminate adrenal lesions. Further workup for indeterminate lesions includes more complicated and expensive radiological studies or invasive procedures like biopsy or surgical resection.”
Machine learning‐based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans
Ahmed W. Moawad et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03136-2 
Machine learning‐based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans
Ahmed W. Moawad et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03136-2- "We did not include pheochromocytomas in the current study, as they have variable imaging features with the adrenal protocol. Pheochromocytoma usually demon- strates attenuation > 10 HU owing to its low fat content, but some cases contain sufficient fat to drop the attenuation to < 10 HU. Also, there are no typical washout features of pheochromocytoma during the adrenal protocol and a wide range of washout criteria. In addition, iodinated contrast agent is not recommended for patients with pheochromocytoma.”
Machine learning‐based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans
Ahmed W. Moawad et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03136-2 - “In the current study, texture analysis of contrast-enhanced CT can differentiate between benign and malignant adrenal lesions smaller than 4 cm with pre-contrast attenuation values > 10 HU. Using features extracted from contrast- enhanced studies, we have shown that machine learning- based texture analysis can be used as a non-invasive tool for differentiation prior to invasive procedures. Our study differs from other that we specifically examined use of radiomics in radiologically indeterminant adrenal lesions, which cause additional financial, and emotional burden for the healthcare system. A more uniform prospective study is needed to accurately determine the clinical suitability of our model and to incorporate it into a decision support system.”
Machine learning‐based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans
Ahmed W. Moawad et al.
Abdominal Radiology https://doi.org/10.1007/s00261-021-03136-2 - Objective: The aim of this study was to evaluate the use of texture analysis for differentiation between benign from malignant adrenal lesions on contrast-enhanced abdominal computed tomography (CT).
Conclusion: Texture analysis offers a noninvasive tool for differentiating benign from malignant adrenal tumors on contrast-enhanced CT images. These results support the further development of texture analysis as a quantitative biomarker for characterizing adrenal tumors.
Texture Analysis as a Radiomic Marker for Differentiating Benign From Malignant Adrenal Tumors.
Yu H et al.
Journal of Computer Assisted Tomography 2020, 44 (5), 766-771 - “One hundred twenty-five patients were included in the analysis. Excellent discriminators of benign from malignant lesions were identified, including entropy and standard deviation. These texture features demonstrated lower values for benign lesions compared with malignant lesions. Entropy values of benign lesions averaged 3.95 using a spatial scaling factor of 4 compared with an average of 5.08 for malignant lesions (P < .0001). Standard deviation values of benign lesions averaged 19.94 on the unfiltered image compared with an average of 34.32 for malignant lesions (P < .0001). Entropy demonstrated AUCs ranging from 0.95 to 0.97 for discriminating tumors, with sensitivities and specificities ranging from 81% to 95% and 88% to 100%, respectively. Standard deviation demonstrated AUCs ranging from 0.91 to 0.94 for discriminating tumors, with sensitivities and specificities ranging from 73% to 93% and 86% to 95%, respectively.”
Texture Analysis as a Radiomic Marker for Differentiating Benign From Malignant Adrenal Tumors.
Yu H et al.
Journal of Computer Assisted Tomography 2020, 44 (5), 766-771 - “Most patients with an IAM do not have an initial evaluation. The radiology report has been identified as a key component in determining whether IAMs are evaluated ap- propriately. Care teams dedicated to management of incidental radiographic findings also improve IAM follow-up. Although the evidence base is sparse, these interventions may be a starting point for further inquiry into optimizing care in this common clinical scenario.”
Incidental Adrenal Masses: Adherence to Guidelines and Methods to Improve Initial Follow-Up, A Systematic Review
Timothy Feeney et al.
Journal of Surgical Research 2022 (269) 18–27 - "Although IAM guidelines have evolved from 2002 until today, most IAMs are recommended to undergo an initial evaluation. Common objectives among guidelines are to determine whether an IAM is malignant or benign, whether it is hormonally active, and whether specialized clinicians are needed. There is evidence that these es- tablished guidelines are effective at identifying patients with clinically meaningful lesions, that they are cost effective, and that adherence reduces missed opportunities for timely diagnosis.”
Incidental Adrenal Masses: Adherence to Guidelines and Methods to Improve Initial Follow-Up, A Systematic Review
Timothy Feeney et al.
Journal of Surgical Research 2022 (269) 18–27 - "The findings reported in this systematic review indicate that guidelines are not, in and of themselves, adequate to improve appropriate evaluation of IAMs. Strategies to promote adherence are necessary, and understanding the barriers faced by healthcare systems and individual clinicians is part of meeting the challenge. The field of implementation science, in which effective strategies are studied as interventions in a “real world” context, may offer many advantages for future work in this space. For instance, basic implementation metrics, such as adoption and uptake measurements, costs and cost-effectiveness, feasibility, and sustainability need to be considered alongside metrics such as rates of biochemical and radiographic evaluation.”
Incidental Adrenal Masses: Adherence to Guidelines and Methods to Improve Initial Follow-Up, A Systematic Review
Timothy Feeney et al.
Journal of Surgical Research 2022 (269) 18–27 - “In conclusion, the majority of patients with an IAM are not managed appropriately. Under “usual care,” approximately 1- third of patients (median 34%, IQR 20%-50%) can be expected to undergo necessary radiographic follow-up and only 1-fifth of patients (median 18%, IQR 15%-28%) will undergo any component of a biochemical evaluation. The radiology report has been identified as a key factor in determining whether IAMs are evaluated. When patients are referred to an endocrinologist, they nearly always have an appropriate evaluation, but few are referred. MDTs and guidelines embedded within the “impression” section of the radiology report are two interventions supported by the literature that lead to improved rates of IAM evaluation. Other interventions may also be effective, but studies with relevant endpoints are rare. Millions of CT scans are performed annually, and research dedicated to optimizing management of IAMs is necessary; indeed, we found only 14 studies in our systematic review of the last 20 years. This is an area rich in opportunities for further research and quality improvement.”
Incidental Adrenal Masses: Adherence to Guidelines and Methods to Improve Initial Follow-Up, A Systematic Review
Timothy Feeney et al.
Journal of Surgical Research 2022 (269) 18–27
- “Texture analysis has a potential role in distinguishing benign from malignant adrenal nodules on CECT and may decrease the need for additional imaging studies in the workup of incidentally discovered adrenal nodules.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Current imaging methods can diagnose lipid-rich adenomas with the use of either unenhanced CT or chemical-shift MRI and can diagnose lipid-poor adenomas on the basis of calculation of the percentage washout on contrast-enhanced CT (CECT).”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Image-based texture analysis is a quantitative technique that provides a measure of lesion heterogeneity on the basis of local variations in image brightness. First-order statistics- based texture analysis evaluates the number of pixels that have a particular gray-level value within a defined ROI. First-order texture analysis does not account for the location of the pixels within the ROI. Second-order statistics- based texture analysis evaluates the location and spatial interrelationship s between pixels of variable gray-level values.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - For example, first-order texture analysis can determine how many pixels have attenuation of 0 HU within an adrenal nodule. Second-order texture analysis can determine whether those pixels with an attenuation of 0 HU within an adrenal nodule are distributed evenly or are clustered in groups.
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Texture analysis of CECT images showed higher diagnostic performance for the diagnosis of malignancy, compared with CECT attenuation. The performance of select individual CECT texture features (long-run high gray-level emphasis, entropy, and short-run low gray-level emphasis) were comparable to unenhanced attenuation on CT and the SII on MRI, which are the standard diagnostic imaging tests used to distinguish adrenal adenomas from metastases in clinical practice.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - Increased tumor heterogeneity is the most likely reason for the ability of texture analysis to predict adrenal malignancy on CECT. As is seen in Figure 2, lipid-poor adenomas appeared homogeneous on CECT, compared with malignant lesions, which appeared heterogeneous. We speculate that the administration of contrast material may make lipid- poor adenomas appear more homogeneous because both lipid-rich and lipid-poor areas will have uptake of contrast medium.
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “Malignant adrenal lesions become more heterogeneous after contrast material administration because of tumor angiogenesis and increased conspicuity of tumor necrosis. In support of our theory, a recent study by Sasaguri et al. showed that adrenal metastases from renal carcinoma showed visibly higher internal heterogeneity, compared with benign adrenal masses on CECT.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - Another limitation of the present study is the retrospective nature of the data acquisition. Because this is an observational study, the type of scanner used for each patient was not controlled. One cannot underestimate the potential impact of variation in CT and MR image quality on the results of texture analysis. This factor alone represents a major challenge when one considers the robustness of applying texture analysis in the clinical setting.
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561 - “In summary, the results of the present study indicate that the use of texture analysis for evaluation of adrenal nodules works best with CECT. This finding suggests that CT texture analysis may have a potential role in distinguishing benign lipid-poor ad- enomas from adrenal malignancy on single- phase CECT. Furthermore, the application of texture analysis may potentially decrease the need for additional imaging studies to workup incidentally discovered adrenal nodules.”
Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In- Phase and Opposed-Phase MRI?
Ho LM et al.
AJR 2019; 212:554–561