Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT

Elmohr, Mohab M.; Fuentes, David; Habra, Mouhammed Amir; Bhosale, Priya; Qayyum, Aliya; Gates, Evan; Morshid, Ali; Hazle, John D.; Elsayes, Khaled M.

doi:10.1016/j.crad.2019.06.021

Cited by 43 publications

(38 citation statements)

References 27 publications

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“…One small series 12 found that the mean values for 18 textural features on contrast-enhanced CT and nine on unenhanced CT differed significantly between lipid-poor adenomas and adrenal malignancy, whereas the other 13 , which included 29 adrenocortical carcinomas and 25 adenomas larger than 4 cm, reported an accuracy of CT texture analysis in predicting malignancies of 61–76 per cent. The latter series, however, also included hypersecreting adrenal masses and the predictive model using CT texture analysis combined with CT attenuation values achieved a diagnostic accuracy of only 82 per cent.…”

Section: Discussionmentioning

confidence: 99%

“…Texture analysis has been applied to many organs, including brain, lung, liver, stomach, pancreas, kidney, and soft tissue to differentiate benign from malignant lesions 24 . Only two studies 12 , 13 have focused on primary adrenocortical lesions.…”

Section: Discussionmentioning

confidence: 99%

“…Few studies have evaluated radiomic approaches to differentiate between benign and malignant adrenal masses 12 , 13 . The aim of this pilot study was to investigate whether CT texture analysis could be a suitable method for distinguishing benign from malignant adrenocortical non-secreting lesions in order to strengthen the indication for surgery, even with an unsupervised machine learning approach.…”

Section: Introductionmentioning

confidence: 99%

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Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

et al. 2021

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Background The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. Methods CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Results Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Conclusion Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

et al. 2021

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“…A total of 29 relevant articles from the literature were reviewed (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(14)(15)(16)19,20,(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34). Sixteen studies provided data on both adenomas (n = 964) and nonadenomas mostly comprising metastases (n = 623), all of which showed higher absolute enhancement in adenomas than in nonadenomas (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)14,16,22,29,30) (Table E4 [online]).…”

Section: Review Of the Literaturementioning

confidence: 99%

Relative Enhancement Ratio of Portal Venous Phase to Unenhanced CT in the Diagnosis of Lipid-poor Adrenal Adenomas

et al. 2021

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Background:The development of an accurate, practical, noninvasive, and widely available diagnostic approach to characterize lipidpoor adrenal lesions (greater than 10 HU at unenhanced CT) remains an ongoing demand.Purpose: To investigate whether combined assessment of unenhanced and portal venous phase CT allows for the differentiation of lipid-poor adrenal adenomas from nonadenomas. Materials and Methods:Patients with lipid-poor adrenal lesions who underwent unenhanced and portal venous phase CT with a single-energy scanner between January 2016 and March 2020 were identified retrospectively. For each lesion, the unenhanced and contrast-enhanced attenuation were measured; the absolute enhancement (contrast-enhanced minus unenhanced attenuation [HU]) and relative enhancement ratio ([absolute enhancement divided by unenhanced attenuation] 3 100%) were calculated. The sensitivity achieved at 95% specificity to distinguish adenomas from nonadenomas was determined with receiver operating characteristic curve analysis and compared among parameters with use of the McNemar test.Results: A total of 220 patients (mean age 6 standard deviation, 66 years 6 12; 134 men) with 131 lipid-poor adenomas and 89 nonadenomas were analyzed. The sensitivity (achieved at 95% specificity) of the relative enhancement ratio (86% [113 of 131 adenomas; 95% CI: 79, 92] at a threshold of 210%) was higher than that of unenhanced attenuation (50% [66 of 131 adenomas; 95% CI: 42, 59] at a threshold of 21 HU), contrast-enhanced attenuation (3% [

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“…В свою очередь, были опубликованы данные, где сравнивалась эффективность текстурного анализа и данных рентгенологов при оценке образований надпочечников больших размеров (4-10 см) по данным КТ [32]. При этом показатель точности текстурного анализа оказался выше точности показателей рентгенологов (82% против 68,5%).…”

Section: радиомикаunclassified

Adrenal imaging: anatomy and pathology (literature review)

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Blokhin

et al. 2021

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This literature review focuses on the normal adrenal gland anatomy and typical imaging features necessary to evaluate benign and malignant lesions. In particular, adenoma, pheochromocytoma, metastases and adrenocortical carcinoma were discussed as some of the most common lesions. For this purpose, a review of relevant local and international literature sources up to January 2021 was conducted.In many cases, adrenal incidentalomas have distinctive features allowing characterization using noninvasive methods. It is possible to suspect a malignant nature and promptly refer the patient for the necessary invasive examinations in some cases. Computed tomography, especially with intravenous contrast enhancement, is the primary imaging modality because it enables differential diagnosis. Magnetic resonance tomography remains a sensitive method in lesion detection and follow-up but is not very specific for determining the malignant potential. Positron emission computed tomography also remains an additional method and is used mainly for differential diagnosis of malignant tumors, detecting metastases and recurrences after surgical treatment. Ultrasound has a limited role but is nevertheless of great importance in the pediatric population, especially newborns. Promising techniques such as radiomics and dual-energy CT can expand imaging capabilities and improve diagnostic accuracy.Because adrenal lesions are often incidentally detected by imaging performed for other reasons, it is vital to interpret such findings correctly. This review should give the reader a broad overview of how different imaging modalities can evaluate adrenal pathology and guide radiologists and clinicians.

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Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT

Cited by 43 publications

References 27 publications

Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

Relative Enhancement Ratio of Portal Venous Phase to Unenhanced CT in the Diagnosis of Lipid-poor Adrenal Adenomas

Adrenal imaging: anatomy and pathology (literature review)

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