Geisler J P, Wiemann M C, Zhou Z, Miller G A, Geisler H E
Department of Obstetrics and Gynecology, St. Vincent Hospital and Health Care Center, Indianapolis, Indiana 46260, USA.
Gynecol Oncol. 1996 Aug;62(2):174-80. doi: 10.1006/gyno.1996.0212.
Texture is a descriptive property of a surface distinct from color and shape. Image analysis allows gray-scale images to have their optical texture measured and analyzed. The authors, utilizing image analysis, prospectively studied Markov texture parameters to determine their relevance as prognostic indicators of disease recurrence in endometrial cancer.
Seventy-four consecutive patients, surgically treated, with endometrial cancer, were evaluated for their DNA index (DI), time to recurrence, peritoneal cytology, depth of invasion, lymphovascular space invasion, FIGO stage, grade, histology, as well as 21 Markov parameters. DI and the Markov parameters were quantified using image analysis.
Median follow-up for the study population was 31 months with a range from 1 to 44 months. Fifteen patients had recurrence of their cancer and 12 patients died from disease during the observation period of the study. Eleven Markov parameters showed significant correlation with increasing FIGO stage (P < 0.05), while 14 Markov parameters showed significant correlation with survival (P < 0.05). Three Markov parameters, difference entropy (P = 0.025), information measure B (P = 0.01), and diagonal moment (P = 0.046), were demonstrated to be independent prognostic indicators along with the more traditional prognostic indicators, stage (P = 0.006), grade (P = 0.029), and depth of myometrial invasion (P = 0.03).
Image analysis is able to quantify optical texture. Utilizing bivariate correlations and multivariate analysis, three of these parameters were demonstrated to be independent prognostic indicators in endometrial cancer, specifically difference entropy, information measure B, and diagonal moment.
质地是一种与颜色和形状不同的表面描述特性。图像分析可对灰度图像的光学质地进行测量和分析。作者利用图像分析前瞻性地研究马尔可夫质地参数，以确定其作为子宫内膜癌疾病复发预后指标的相关性。
对74例接受手术治疗的子宫内膜癌患者进行评估，分析其DNA指数（DI）、复发时间、腹腔细胞学检查、浸润深度、淋巴管间隙浸润、国际妇产科联盟（FIGO）分期、分级、组织学类型以及21个马尔可夫参数。DI和马尔可夫参数通过图像分析进行量化。
研究人群的中位随访时间为31个月，范围为1至44个月。在研究观察期内，15例患者癌症复发，12例患者死于该疾病。11个马尔可夫参数与FIGO分期增加呈显著相关（P < 0.05），而14个马尔可夫参数与生存率呈显著相关（P < 0.05）。三个马尔可夫参数，即差分熵（P = 0.025）、信息测度B（P = 0.01）和对角矩（P = 0.046），与更传统的预后指标，即分期（P = 0.006）、分级（P = 0.029）和肌层浸润深度（P = 0.03）一起，被证明是独立的预后指标。
图像分析能够量化光学质地。通过双变量相关性和多变量分析，其中三个参数被证明是子宫内膜癌的独立预后指标，特别是差分熵、信息测度B和对角矩。
