From the Department of Biomedical Engineering (X.L., K.M.S.U., S.K., E.A., G.Y., Q.Z.), Division of Surgery, Barnes-Jewish Hospital (W.C., S.H., M.M.), and Department of Electrical and System Engineering (H.L.), Washington University in St. Louis, 1 Brookings Dr, Mail Box 1097, St Louis, MO 63130; Department of Pathology (D.C.) and Mallinckrodt Institute of Radiology (A.S., Q.Z.), Washington University School of Medicine, St Louis, Mo.
Radiology. 2021 May;299(2):349-358. doi: 10.1148/radiol.2021202208. Epub 2021 Mar 23.
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. © RSNA, 2021 See also the editorial by Klibanov in this issue.
背景 传统的放射学模式在放射直肠中表现不佳，通常无法区分残留癌症与治疗瘢痕。 目的 报告一种成像系统的开发和初步患者研究，该系统包括直肠内配准的光声（PA）显微镜（PAM）和超声（US）系统，并与卷积神经网络（CNN）配对，以评估直肠癌的治疗反应。 材料与方法 本前瞻性研究（ClinicalTrials.gov 标识符：NCT04339374）纳入 2019 年 9 月至 2020 年 9 月期间接受放化疗的参与者，并在手术前使用 PAM/US 系统获得图像。另一组的结直肠标本进行了离体研究。PAM/US 系统由直肠内成像探头、1064nm 激光和一个 US 环形换能器组成。使用离体和体内患者数据对 PAM CNN 和 US CNN 模型进行训练和验证，以区分正常和恶性结直肠组织。然后，使用在训练和验证过程中未被 CNN 看到的额外体内患者数据对 PAM CNN 和 US CNN 进行测试。 结果 22 名患者的离体标本和 5 名患者的体内图像（共 2693 个 US 感兴趣区[ROI]和 2208 个 PA ROI）用于 CNN 训练和验证。另外 5 名患者的数据用于测试。共评估了 32 名参与者（平均年龄 60 岁；范围，35-89 岁）。发现了完全肿瘤反应的独特 PAM 成像标志物，特别是在治疗后的肿瘤床内恢复正常黏膜下血管结构。PAM CNN 模型捕获了这一恢复过程，并正确地区分了这些变化与残留肿瘤。该成像系统仍然能够非常有效地区分肿瘤与正常组织，对于来自 5 名参与者的数据，其受试者工作特征曲线下面积为 0.98（95%CI：0.98，0.99）。相比之下，US CNN 的受试者工作特征曲线下面积为 0.71（95%CI：0.70，0.73）。 结论 直肠内配准的光声显微镜/US 系统与卷积神经网络模型配对，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。
