Rayn Kareem, Gokhroo Garima, Jeffers Brian, Gupta Vibhor, Chaudhari Suresh, Clark Ryan, Magliari Anthony, Beriwal Sushil
Department of Radiation Oncology, Columbia University Irving Medical Center, New York, New York.
Varian Medical Systems Inc, Palo Alto, California.
Adv Radiat Oncol. 2023 Jul 28;9(2):101326. doi: 10.1016/j.adro.2023.101326. eCollection 2024 Feb.
The autosegmentation algorithm of Siemens Healthineers version VA 30 (AASH) (Siemens Healthineers, Erlangen, Germany) was trained and developed in the male pelvis, with no published data on its usability in the female pelvis. This is the first multi-institutional study to describe and evaluate an artificial intelligence algorithm for autosegmentation of the pelvic nodal region by gender.
We retrospectively evaluated AASH pelvic nodal autosegmentation in both male and female patients treated at our network of institutions. The automated pelvic nodal contours generated by AASH were evaluated by 1 board-certified radiation oncologist. A 4-point scale was used for each nodal region contour: a score of 4 is clinically usable with minimal edits; a score of 3 requires minor edits (missing nodal contour region, cutting through vessels, or including bowel loops) in 3 or fewer computed tomography slices; a score of 2 requires major edits, as previously defined but in 4 or more computed tomography slices; and a score of 1 requires complete recontouring of the region. Pelvic nodal regions included the right and left side of the common iliac, external iliac, internal iliac, obturator, and midline presacral nodes. In addition, patients were graded based on their lowest nodal contour score. Statistical analysis was performed using Fisher exact tests and Yates-corrected χ tests.
Fifty-two female and 51 male patients were included in the study, representing a total of 468 and 447 pelvic nodal regions, respectively. Ninety-six percent and 99% of contours required minor edits at most (score of 3 or 4) for female and male patients, respectively ( = .004 using Fisher exact test; = .007 using Yates correction). No nodal regions had a statistically significant difference in scores between female and male patients. The percentage of patients requiring no more than minor edits was 87% (45 patients) and 92% (47 patients) for female and male patients, respectively ( = .53 using Fisher exact test; = .55 using Yates correction).
AASH pelvic nodal autosegmentation performed very well in both male and female pelvic nodal regions, although with better male pelvic nodal autosegmentation. As autosegmentation becomes more widespread, it may be important to have equal representation from all sexes in training and validation of autosegmentation algorithms.
西门子医疗系统公司版本VA 30(AASH)(德国埃尔朗根西门子医疗系统公司)的自动分割算法是在男性骨盆中训练和开发的,尚无关于其在女性骨盆中可用性的公开数据。这是第一项描述和评估按性别自动分割盆腔淋巴结区域的人工智能算法的多机构研究。
我们回顾性评估了在我们机构网络中接受治疗的男性和女性患者的AASH盆腔淋巴结自动分割情况。由一名获得委员会认证的放射肿瘤学家评估AASH生成的自动盆腔淋巴结轮廓。每个淋巴结区域轮廓使用4分制:4分表示临床上可用,只需进行最少编辑;3分表示在3个或更少的计算机断层扫描切片中需要进行小的编辑(淋巴结轮廓区域缺失、穿过血管或包含肠袢);2分表示需要进行重大编辑,如先前定义,但在4个或更多计算机断层扫描切片中;1分表示需要对该区域进行完全重新勾勒轮廓。盆腔淋巴结区域包括双侧髂总、髂外、髂内、闭孔和骶前中线淋巴结。此外,根据患者最低的淋巴结轮廓评分对患者进行分级。使用Fisher精确检验和Yates校正χ检验进行统计分析。
该研究纳入了52名女性患者和51名男性患者,分别代表468个和447个盆腔淋巴结区域。女性和男性患者分别有96%和99%的轮廓最多需要进行小的编辑(3分或4分)(Fisher精确检验P = 0.004;Yates校正P = 0.007)。女性和男性患者之间的淋巴结区域评分无统计学显著差异。女性和男性患者中分别有87%(45名患者)和92%(47名患者)的患者只需进行小的编辑(Fisher精确检验P = 0.53;Yates校正P = 0.55)。
AASH盆腔淋巴结自动分割在男性和女性盆腔淋巴结区域的表现都非常好,尽管男性盆腔淋巴结自动分割更好。随着自动分割变得更加广泛,在自动分割算法的训练和验证中确保所有性别的平等代表性可能很重要。