Photoacoustic imaging of colorectal cancer and ovarian cancer

Photoacoustic (PA) imaging is an emerging hybrid imaging technology that uses a short-pulsed laser to excite tissue. The resulting photoacoustic waves are used to image the optical absorption distribution of the tissue, which is directly related to micro-vessel networks and thus to tumor angiogenesis, a key process in tumor growth and metastasis. In this thesis, the acoustic-resolution photoacoustic microscopy (AR-PAM) was first investigated on its role in human colorectal tissue imaging, and the optical-resolution photoacoustic microscopy (OR-PAM) was investigated on its role in human ovarian tissue imaging.Colorectal cancer is the second leading cause of cancer death in the United States. Significant limitations in screening and surveillance modalities continue to hamper early detection of primary cancers or recurrences after therapy. In the first phase of the study, benchtop co-registered ultrasound (US) and AR-PAM systems were constructed and tested in ex vivo human colorectal tissue. In the second phase of the study, a co-registered endorectal AR-PAM imaging system was constructed, and a pilot patient study was conducted on patients with rectal cancer treated with radiation and chemotherapy. To automate the data analysis, we designed and trained convolutional neural networks (PAM-CNN and US-CNN) using mixed ex vivo and in vivo patient data. 22 patients’ ex vivo specimens and five patients’ in vivo images (a total of 2693 US 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 complete tumor response were found, specifically 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 a residual tumor tissue. The imaging system remained highly capable of differentiating tumor from normal tissue, achieving an area under receiver operating characteristic curve (AUC) of 0.98 from the five patients tested. By comparison, US-CNN had an AUC of 0.71. As an alternative to CNN, a generalized linear model (GLM) was investigated for classification and results showed that CNN outperformed GLM in classification of both US and PAM images. Ovarian cancer is the leading cause of death among gynecological cancers but is poorly amenable to preoperative diagnosis. In the second project of this thesis, we have investigated the feasibility of “optical biopsy,” using OR-PAM to quantify the microvasculature of ovarian tissue and fallopian tube tissue. The technique was demonstrated using excised human ovary and fallopian tube specimens imaged immediately after surgery. Initially, a commercial software Amira was used to characterize tissue vasculature patterns, and later, an effective and easy-access algorithm was developed to quantify the mean diameter, total length, total volume, and fulfillment rate of tissue vasculature. Our initial results demonstrate the potential of OR-PAM as an imaging tool for quick assessment of ovarian tissue and fallopian tube tissue

Rectal cancer treatment management: Deep-learning neural network based on photoacoustic microscopy image outperforms histogram-feature-based classification

We have developed a novel photoacoustic microscopy/ultrasound (PAM/US) endoscope to image post-treatment rectal cancer for surgical management of residual tumor after radiation and chemotherapy. Paired with a deep-learning convolutional neural network (CNN), the PAM images accurately differentiated pathological complete responders (pCR) from incomplete responders. However, the role of CNNs compared with traditional histogram-feature based classifiers needs further exploration. In this work, we compare the performance of the CNN models to generalized linear models (GLM) across 2

Quantification of ovarian lesion and fallopian tube vasculature using optical-resolution photoacoustic microscopy

The heterogeneity in the pathological and clinical manifestations of ovarian cancer is a major hurdle impeding early and accurate diagnosis. A host of imaging modalities, including Doppler ultrasound, MRI, and CT, have been investigated to improve the assessment of ovarian lesions. We hypothesized that pathologic conditions might affect the ovarian vasculature and that these changes might be detectable by optical-resolution photoacoustic microscopy (OR-PAM). In our previous work, we developed a benchtop OR-PAM and demonstrated it on a limited set of ovarian and fallopian tube specimens. In this study, we collected data from over 50 patients, supporting a more robust statistical analysis. We then developed an efficient custom analysis pipeline for characterizing the vascular features of the samples, including the mean vessel diameter, vascular density, global vascular directionality, local vascular definition, and local vascular tortuosity/branchedness. Phantom studies using carbon fibers showed that our algorithm was accurate within an acceptable error range. Between normal ovaries and normal fallopian tubes, we observed significant differences in five of six extracted vascular features. Further, we showed that distinct subsets of vascular features could distinguish normal ovaries from cystic, fibrous, and malignant ovarian lesions. In addition, a statistically significant difference was found in the mean vascular tortuosity/branchedness values of normal and abnormal tubes. The findings support the proposition that OR-PAM can help distinguish the severity of tubal and ovarian pathologies