Retrospective application of a validated algorithm for estimation of adrenal gland volume after computed tomography on 46 dogs undergoing adrenalectomy

IF 1.3 4区 农林科学 Q2 VETERINARY SCIENCES Australian Veterinary Journal Pub Date : 2024-04-25 DOI:10.1111/avj.13335
R Swepson, G Hosgood, N Stander, M Thompson
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Abstract

Canine adrenal gland volume can be predicted based on body weight and computed tomography (CT) measurements using a validated algorithm. Use of this algorithm to detect adrenal pathology, including hyperplasia, hypoplasia and neoplasia, in clinical cases has not been described. The objective of this study was to illustrate application of the algorithm by estimating subject-specific adrenal gland volume in a historical cohort of dogs with known adrenal disease. Forty-six dogs that underwent CT and subsequent adrenalectomy were included. Clinical records and CT images from dogs that underwent adrenalectomy and histologic examination of the excised adrenal gland(s) were reviewed. Normal adrenal gland volumes for each dog were estimated using the algorithm, and compared with measured volumes of the affected glands. Linear measurement of the largest lesion diameter was also recorded. Fifty-eight adrenal glands were removed from 46 dogs, with pathology confirmed in all glands. Pathology included 28 adenomas, 13 carcinomas, 11 pheochromocytomas and 6 other benign pathologies. The volume of all removed adrenal glands was measured to be larger than the expected normal volume estimated by the algorithm, ranging from 1.1 to 212.9 times larger than estimated. Adrenal glands with malignant and benign pathology showed variable volumes with overlapping ranges recorded. Assessment of the dimensions of any focal lesion against a cut-off of 20 mm failed to discriminate malignancy. This study illustrates and supports the application of a validated volumetric algorithm for estimation of subject-specific adrenal gland volume to identify the presence of pathology and as a tool to assist clinical decision-making.

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在对 46 只接受肾上腺切除术的狗进行计算机断层扫描后,对估算肾上腺体积的有效算法进行了回顾性应用。
犬肾上腺的体积可根据体重和计算机断层扫描(CT)测量结果,采用一种经过验证的算法进行预测。在临床病例中使用该算法检测肾上腺病变(包括增生、发育不良和肿瘤)的情况尚未见报道。本研究的目的是通过估算已知患有肾上腺疾病的狗的历史群组中特定对象的肾上腺体积来说明该算法的应用。研究纳入了 46 只接受 CT 和后续肾上腺切除术的狗。回顾了接受肾上腺切除术和切除肾上腺组织学检查的犬的临床记录和 CT 图像。使用算法估算每只狗的正常肾上腺体积,并将其与受影响腺体的测量体积进行比较。此外,还记录了最大病变直径的线性测量值。从 46 只狗身上切除了 58 个肾上腺,并对所有腺体进行了病理确诊。病理结果包括 28 个腺瘤、13 个癌、11 个嗜铬细胞瘤和 6 个其他良性病变。经测量,所有切除肾上腺的体积均大于算法估计的预期正常体积,从大于估计值的1.1倍到212.9倍不等。恶性和良性病变的肾上腺体积各不相同,记录的范围也有重叠。以 20 毫米为临界值评估任何病灶的尺寸都无法区分恶性程度。这项研究说明并支持应用经过验证的体积算法来估算特定受试者的肾上腺体积,以确定是否存在病变,并作为辅助临床决策的工具。
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来源期刊
Australian Veterinary Journal
Australian Veterinary Journal 农林科学-兽医学
CiteScore
2.40
自引率
0.00%
发文量
85
审稿时长
18-36 weeks
期刊介绍: Over the past 80 years, the Australian Veterinary Journal (AVJ) has been providing the veterinary profession with leading edge clinical and scientific research, case reports, reviews. news and timely coverage of industry issues. AJV is Australia''s premier veterinary science text and is distributed monthly to over 5,500 Australian Veterinary Association members and subscribers.
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