Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning
Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning
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DOI：
10.1148/radiol.2021202208
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发表时间：
2021-05-01
期刊：
影响因子：
19.7
通讯作者：
Zhu, Quing
中科院分区：
文献类型：
--
作者：
Leng, Xiandong;Uddin, K. M. Shihab;Zhu, Quing
背景：传统的放射方式在放射直肠中表现不佳，并且常常无法区分残余癌和治疗瘢痕。目的：报告一种成像系统的开发和初步患者研究，该成像系统包括直肠内共登记光声显微镜（PAM）和US系统，并与卷积神经网络（CNN）配对，以评估直肠癌治疗反应。材料和方法：在这项前瞻性研究（ClinicalTrials.gov识别符NCT04339374）中，参与者于2019年9月至2020年9月完成放疗和化疗，在手术前使用PAM/US系统获取图像。另一组结肠标本进行离体研究。PAM/US系统由一个直肠内成像探头、一个1064 nm激光器和一个US环换能器组成。PAM CNN和US CNN模型通过离体和体内患者数据进行训练和验证，以区分正常和恶性结直肠组织。PAM CNN和US CNN随后使用CNN在训练和验证期间未看到的其他体内患者数据进行测试。结果：使用22例患者离体标本和5例患者在体图像（共2693个US感兴趣区域[roi]和2208个PA roi）进行CNN训练和验证。另外5名患者的数据被用于测试。共有32名参与者（平均年龄60岁，范围35-89岁）被评估。发现了完全肿瘤反应的独特PAM成像标记，特别是包括治疗后肿瘤床内正常粘膜下血管结构的恢复。PAM CNN模型捕获了这一恢复过程，并正确地将这些变化与残留肿瘤区分开来。该成像系统仍然能够高度区分肿瘤和正常组织，在5名参与者的数据中，受试者工作特征曲线下的面积为0.98 （95% CI: 0.98, 0.99）。相比之下，美国CNN的受者工作特征曲线下面积为0.71 （95% CI: 0.70, 0.73）。结论：结合卷积神经网络模型的直肠内共登记光声显微镜/US系统在评估直肠癌治疗反应方面表现出较高的诊断性能，并显示出优化治疗后管理的潜力。(c) rsna, 2021
Background: Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatment scarring.Purpose: To report the development and initial patient study of an imaging system comprising an endorectal coregistered photoacoustic (PA) microscopy (PAM) and US system paired with a convolution neural network (CNN) to assess the rectal cancer treatment response.Materials and Methods: In this prospective study (ClinicalTrials.gov identifier NCT04339374), participants completed radiation and chemotherapy from September 2019 to September 2020 and images were obtained with the PAM/US system prior to surgery. Another group's colorectal specimens were studied ex vivo. The PAM/US system consisted of an endorectal imaging probe, a 1064-nm laser, and one US ring transducer. The PAM CNN and US CNN models were trained and validated to distinguish normal from malignant colorectal tissue using ex vivo and in vivo patient data. The PAM CNN and US CNN were then tested using additional in vivo patient data that had not been seen by the CNNs during training and validation.Results: Twenty-two patients' ex vivo specimens and five patients' in vivo images (a total of 2693 US regions of interest [ROIs] and 2208 PA ROIs) were used for CNN training and validation. Data from five additional patients were used for testing. A total of 32 participants (mean age, 60 years; range, 35-89 years) were evaluated. Unique PAM imaging markers of the complete tumor response were found, specifically including recovery of normal submucosal vascular architecture within the treated tumor bed. The PAM CNN model captured this recovery process and correctly differentiated these changes from the residual tumor. The imaging system remained highly capable of differentiating tumor from normal tissue, achieving an area under the receiver operating characteristic curve of 0.98 (95% CI: 0.98, 0.99) for data from five participants. By comparison, the US CNN had an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.70, 0.73).Conclusion: An endorectal coregistered photoacoustic microscopy/US system paired with a convolutional neural network model showed high diagnostic performance in assessing the rectal cancer treatment response and demonstrated potential for optimizing posttreatment management. (C) RSNA, 2021