Assessing rectal cancer treatment response using coregistered endorectal photoacoustic and us imaging paired with deep learning

Xiandong Leng, K. M.Shihab Uddin, William Chapman, Hongbo Luo, Sitai Kou, Eghbal Amidi, Guang Yang, Deyali Chatterjee, Anup Shetty, Steve Hunt, Matthew Mutch, Quing Zhu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)349-358
Number of pages10
JournalRadiology
Volume299
Issue number2
DOIs
StatePublished - 2021

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