Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques

Abstract

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.

Keywords: Artificial intelligence; Deep learning; Diagnostic imaging; Machine learning; Molecular imaging; Optical imaging; Peritoneal neoplasms; Radiomics.

Fig. 1. A 65-year-old male presented with abdominal pain and massive ascites. The cytological examination of the ascites revealed mesothelial cell proliferation. The red dashed lines represent the level of each of the fused axial images. A: Images from fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/CT show diffuse radioactive uptake in the mesentery and greater omentum, but ¹⁸F-FDG uptake is weak in all the lesions (SUVmax2.82). Intrapelvic hypermetabolic foci is a physiological radioactive accumulation of left ureteral urine (black solid arrow) (left image: anterior maximum intensity projection image from ¹⁸F-FDG PET; right images: axial fused PET/CT images). B: Images from 18 (¹⁸F) fibroblast-activation protein inhibitor (FAPI) PET/CT show intense tracer uptake in the primary lesion of the pancreatic tail (red solid arrow, SUVmax9.55), the lesser curvature of the stomach (white solid arrow, SUVmax4.85) as well as the mesentery and greater omentum (dashed arrows, SUVmax6.46) (right image: anterior maximum intensity projection image from ¹⁸F-FAPI PET; left images: axial fused PET/CT images). PET/CT = positron emission tomography/computed tomography, SUV = standard uptake value

Fig. 2. Schematic diagram of the four basic applications of fluorescence-guided debulking or cytoreductive surgery. Green areas indicate fluorescence from tumors or vital structures. The gray area represents tumor tissue. A: Assess surgical margins. B: Identify clinically suspicious lesions. C: Protect important anatomical structures (the green area represents the fluorescence emitted by the ureter). D: Detect deep-tissue micrometastases.

Fig. 3. Active accumulation of targeted fluorescent probes in the tumor tissue. A: Intravenous injection of targeted fluorescent contrast agent. B: Composition of targeted fluorescent tracers and two methods of targeted imaging. Near-infrared fluorescent contrast agent and targeted part (I). Active accumulation of targeted contrast agent in the tumor tissue (II). Targeting folic acid receptor in ovarian cancer and carcinoembryonic antigen in colorectal cancer (III). CEA = carcinoembryonic antigen, FRα = folate receptor alpha, OC = ovarian cancer, CRC = colorectal cancer

Fig. 4. Schematic diagram of the hand-crafted radiomics workflow and deep learning. The workflow of radiomics includes image acquisition and preprocessing, tumor segmentation, feature extraction/selection, model construction, and validation. For deep learning, it is an end-to-end approach with no separate feature extraction, feature selection and modelling steps. The orange area represents the depiction of tumors through manual segmentation. The red dashed square represents the use of deep learning to segment tumors. RS1 reflects the radiomics signature of the primary tumor, while RS2 reflects the radiomics signature of the peritoneum. RS1 and RS2 serve as predictive factors for PM status. ANN = Artificial Neural Network, SVM = support vector machine, ReLU = rectified linear unit, PM = peritoneal metastasis