用多个值框计数算法对组织病理学图像上的前列腺癌瘤相关细胞类型的碎形维度
Anton Schwarz1,2, Hidetaka Arimura3,4, Yunhao Cui1
1Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 813-8582, Japan.
Biophysics and physicobiology
|February 9, 2026
概括
分形尺寸 (FD) 描述前列腺癌细胞特性,与等级组 (GGs) 相对应. 这种方法有助于人工智能将患者分为低GG和高GG类别.
科学领域:
- 数字病理学数字病理学
- 计算生物学是一种计算生物学.
- 癌症成像分析分析 癌症成像分析
背景情况:
- 前列腺癌分级依赖于组织病理学图像,通过1至5的等级组 (GGs) 评估瘤细胞分化.
- 与瘤相关的细胞 (新生细胞,炎症细胞,结合细胞,死细胞,非新生细胞) 呈现出与GG相关的不同自我相似性.
- 分形维度 (FDs) 提供了一种定量方法来分析复杂的结构,如细胞形态学.
研究的目的:
- 研究各种前列腺瘤相关细胞类型的FD与相应的GG之间的关联.
- 评估在前列腺癌GG分类人工智能 (AI) 模型中使用FD衍生功能的可行性.
- 通过使用FD来改善前列腺瘤细胞的物理性质,以改善GG评估.
主要方法:
- 采用多值盒计数 (MTBC) 算法,在9个图像通道中计算5种瘤相关细胞类型的FD.
- 从组织病理学补丁中构建FD值图像以表示细胞特征.
- 训练了一种浅卷积神经网络 (sCNN) 模型,使用FD值图像进行低 (GG≤3) 与高 (GG≥4) 度前列腺癌的二元分类.
主要成果:
- 在非新发性上皮细胞和GG的FD之间发现了统计学上显著的负相关性 (Pearson r=-0.849,p=0.001).
- 连接组织FD和原始图像特征也观察到显著的相关性.
- 该sCNN模型实现了收音机运行特征曲线 (AUC) 下的面积为0.811,用于分类低GG与高GG.
结论:
- FD有效地描述前列腺瘤相关细胞的物理性质,区分低GG和高GG.
- 从MTBC算法获得的FD值图像显示了人工智能驱动的前列腺癌分级的前景.
- 这种方法提供了一种新的定量方法,以补充前列腺恶性瘤的传统体系病理学评估.
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