Dataset Information

Evaluation of Ovarian Cancer: Initial Application of Coregistered Photoacoustic Tomography and US.

ABSTRACT: Purpose To assess transvaginal coregistered photoacoustic tomography (PAT) and pulse-echo US for diagnosis of ovarian cancer based on functional parameters provided by PAT. Materials and Methods Between February 2017 and December 2017, 26 ovarian masses from 16 participants were successfully imaged in vivo by multispectral photoacoustic imaging, including nine invasive epithelial ovarian cancers (six serous carcinomas and three endometroid adenocarcinomas), three other tumors (two borderline serous tumors and one sex cord-stromal tumor), and 14 benign and normal (hereafter referred to as benign/normal) ovaries. The relative total hemoglobin concentration (rHbT) and mean oxygen saturation (sO₂) shown at PAT were used to characterize the ovaries identified at US. Results The average rHbT was 1.9 times higher for invasive epithelial cancers than for the benign/normal ovaries (P = .01). Additionally, the rHbT distribution was extensive in invasive epithelial cancers, but was scattered in benign/normal ovaries. However, the rHbT of two borderline serous tumors and one stromal tumor was in the same range as that of benign/normal ovaries. The mean sO₂ of invasive epithelial cancers, and of the borderline and stromal tumors, was 8.2% lower than that of benign/normal ovaries (P = .003). Discussion Invasive epithelial ovarian cancers showed higher and extensive tumor vascularity and lower oxygen saturation than benign and normal ovaries. Two borderline noninvasive serous and one stromal tumor showed low oxygen saturation compared with benign and normal ovaries. ©RSNA, 2018 Online supplemental material is available for this article.

SUBMITTER: Nandy S

PROVIDER: S-EPMC6283286 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description: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 Supplemental material is available for this article. See also the editorial by Klibanov in this issue.

Dataset Information

Evaluation of Ovarian Cancer: Initial Application of Coregistered Photoacoustic Tomography and US.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets