Rochester Institute of Technology, Rochester, NY, USA.
University of Rochester, Rochester, NY, USA.
Int J Comput Assist Radiol Surg. 2020 Feb;15(2):309-320. doi: 10.1007/s11548-019-02101-1. Epub 2019 Dec 21.
In the case of multispecimen study to locate cancer regions, such as in thyroidectomy and prostatectomy, a significant labor-intensive processing is required at a high cost. Pathology diagnosis is usually done by a pathologist observing tissue-stained glass slide under a microscope.
Multispectral photoacoustic (MPA) specimen imaging has proven successful in differentiating photoacoustic (PA) signal characteristics between a histopathology-defined cancer region and normal tissue. This is mainly due to its ability to efficiently map oxyhemoglobin and deoxyhemoglobin contents from MPA images and key features for cancer detection. A fully automated deep learning algorithm is purposed, which learns to detect the presence of malignant tissue in freshly excised ex vivo human thyroid and prostate tissue specimens using the three-dimensional MPA dataset. The proposed automated deep learning model consisted of the convolutional neural network architecture, which extracts spatially colocated features, and a softmax function, which detects thyroid and prostate cancer tissue at once. This is one of the first deep learning models, to the best of our knowledge, to detect the presence of cancer in excised thyroid and prostate tissue of humans at once based on PA imaging.
The area under the curve (AUC) was used as a metric to evaluate the predictive performance of the classifier. The proposed model detected the cancer tissue with the AUC of 0.96, which is very promising.
This model is an improvement over the previous work using machine learning and deep learning algorithms. This model may have immediate application in cancer screening of the numerous sliced specimens that result from thyroidectomy and prostatectomy. Since the instrument that was used to capture the ex vivo PA images is now being developed for in vivo use, this model may also prove to be a starting point for in vivo PA image analysis for cancer diagnosis.
在多标本研究中定位癌症区域，如甲状腺切除术和前列腺切除术，需要大量的劳动密集型处理，成本高昂。病理诊断通常由病理学家在显微镜下观察组织染色载玻片来完成。
多光谱光声（MPA）标本成像已被证明可成功区分组织学定义的癌症区域和正常组织的光声（PA）信号特征。这主要是由于其能够有效地从 MPA 图像中绘制出氧合血红蛋白和脱氧血红蛋白含量以及癌症检测的关键特征。提出了一种全自动深度学习算法，该算法使用三维 MPA 数据集学习检测新鲜离体人甲状腺和前列腺组织标本中恶性组织的存在。所提出的自动深度学习模型由卷积神经网络架构组成，该架构提取空间共定位特征，以及 softmax 函数，该函数可一次检测甲状腺和前列腺癌组织。据我们所知，这是第一个基于 PA 成像的深度学习模型之一，可以立即检测出切除的甲状腺和前列腺组织中癌症的存在。
曲线下面积（AUC）被用作评估分类器预测性能的指标。所提出的模型以 0.96 的 AUC 检测到癌症组织，这非常有希望。
与以前使用机器学习和深度学习算法的工作相比，该模型是一种改进。该模型可能立即应用于甲状腺切除术和前列腺切除术产生的大量切片标本的癌症筛查。由于用于捕获离体 PA 图像的仪器现在正在开发用于体内使用，因此该模型也可能成为用于癌症诊断的体内 PA 图像分析的起点。
