利用可解释的规则学习人工智能与CT放射组学最佳区分肾上腺嗜铬细胞瘤和腺瘤。

IF 2.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Daniel I. Glazer, Melissa Viator, Andrew Sharp, Jay B. Patel, Borna E. Dabiri, Christopher P. Bridge, Justine A. Barletta, Oleg S. Pianykh, William W. Mayo-Smith
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引用次数: 0

摘要

目的:鉴别可解释的基于ct的放射组学特征,以区分肾上腺嗜铬细胞瘤和腺瘤。方法:采用机构数据库对5/1/05-5/1/23经病理证实的肾上腺嗜铬细胞瘤患者进行鉴定。纳入的患者需要在病理后12个月内进行腹部增强CT检查,并发现肾上腺肿块(n = 95)。作为比较,我们从一组连续的CT检查中发现了57个腺瘤。最终数据集包括152个肾上腺肿块(嗜铬细胞瘤95个;57个腺瘤),其中121个用于开发组,31个用于测试组。在确认准确的自动分割后,对463个放射学特征进行评估,并用于创建可解释的人工智能(AI)规则学习模型。采用F1评分报告模型性能。结果:研究纳入146例患者(年龄59岁+/- 21岁;89女性)。一个三特征规则,高灰度区域强调> 184,圆度> 0.35,边界低灰度强调125 HU,导致F1得分在训练集中为0.89 (95% CI: 0.83, 0.94),在测试集中为0.93 (95% CI: 0.83, 0.99)。结论:规则学习AI模型确定了最小的可解释的CT放射组学特征集,足以在增强CT上鉴别肾上腺嗜铬细胞瘤和腺瘤的准确率达到96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Using interpretable rule-learning artificial intelligence to optimally differentiate adrenal pheochromocytomas from adenomas with CT radiomics

Using interpretable rule-learning artificial intelligence to optimally differentiate adrenal pheochromocytomas from adenomas with CT radiomics

Using interpretable rule-learning artificial intelligence to optimally differentiate adrenal pheochromocytomas from adenomas with CT radiomics

Purpose

To identify interpretable CT-based radiomics features that can differentiate adrenal pheochromocytomas from adenomas.

Methods

An institutional database was used to identify patients with pathologically proven adrenal pheochromocytomas 5/1/05–5/1/23. To be included, patients needed to have a contrast-enhanced abdominal CT with an adrenal mass within 12 months of pathology (n = 95). For comparison, 57 adenomas were identified from a set of consecutive CT examinations. The final dataset included 152 adrenal masses (95 pheochromocytomas; 57 adenomas) with 121 used in the development set and 31 in the test set. Following confirmation of accurate automated segmentation, 463 radiomic features were evaluated and used to create an interpretable artificial intelligence (AI) rule-learning model. Model performance was reported using F1 score.

Results

The study included 146 patients (age 59 years +/- 21; 89 females). A three-feature rule, High Gray Level Zone Emphasis > 184, Roundness > 0.35, and Boundary Low Gray Level Emphasis < 0.021 produced an F1 score of 0.97 on the train set (95% confidence interval [CI]: 0.94, 0.99) and 0.96 on the test set (95% CI: 0.89, 1.00). The rule-learning model determined that the rule most predictive of pheochromocytoma was Maximum Pixel Attenuation > 125 HU resulting in an F1 score of 0.89 (95% CI: 0.83, 0.94) on the training set and 0.93 (95% CI: 0.83, 0.99) on the test set.

Conclusion

A rule-learning AI model identified the smallest optimal set of interpretable CT radiomics features, sufficient to achieve 96% accuracy in differentiating adrenal pheochromocytomas from adenomas on contrast enhanced CT.

Graphical abstract

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来源期刊
Abdominal Radiology
Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.20
自引率
8.30%
发文量
334
期刊介绍: Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits
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