Suboptimal Sensitivity and Specificity of PET and Other Gross Imaging Techniques in Assessing Lymph Node Metastasis

Role of the tumor microenvironment in the lymphatic metastasis of cervical cancer (Review)

Lymphatic metastasis is the primary type of cervical cancer metastasis and is associated with an extremely poor prognosis in patients. The tumor microenvironment primarily includes cancer-associated fibroblasts, tumor-associated macrophages, myeloid-derived suppressor cells, immune and inflammatory cells, and blood and lymphatic vascular networks, which can promote the establishment of lymphatic metastatic sites within immunosuppressive microenvironments or promote lymphatic metastasis by stimulating lymphangiogenesis and epithelial-mesenchymal transformation. As the most important feature of the tumor microenvironment, hypoxia plays an essential role in lymph node metastasis. In this review, the known mechanisms of hypoxia, and the involvement of stromal components and immune inflammatory cells in the tumor microenvironment of lymphatic metastasis of cervical cancer are discussed. Additionally, a summary of the clinical trials targeting the tumor microenvironment for the treatment of cervical cancer is provided, emphasizing the potential and challenges of immunotherapy.

Carbonic anhydrase IX stratifies patient prognosis and identifies nodal status in animal models of nasopharyngeal carcinoma using a targeted imaging strategy

PURPOSE

Accurate identification of nodal status enables adequate neck irradiation for nasopharyngeal carcinoma (NPC). However, most conventional techniques are unable to pick up occult metastases, leading to underestimation of tumor extensions. Here we investigate the clinical significance of carbonic anhydrase IX (CAIX) in human NPC samples, and develop a CAIX-targeted imaging strategy to identify occult lymph node metastases (LNMs) and extranodal extension (ENE) in animal studies.

METHODS

A total of 211 NPC samples are performed CAIX staining, and clinical outcomes are analyzed. The metastatic murine models are generated by foot pad injection of NPC cells, and a CAIX-targeted imaging agent (CAIX-800) is intravenously administered. We adopt fluorescence molecular tomography and ultrasonography (US)-guided spectroscopic photoacoustic (sPA) imaging to perform in vivo studies. Histological and immunohistochemical characterization are carried out via node-by-node analysis.

RESULTS

For clinical samples, 90.1% (91/101) primary tumors, 73.3% (66/90) metastases, and 100% (20/20) local recurrences are CAIX positive. In metastases group, 84.7% (61/72) nodal metastases and 22.2% (4/18) organ metastases are CAIX positive. CAIX expression in primary tumors is significantly associated with NPC stage and prognosis. For animal studies, CAIX-800-based fluorescence imaging achieves 81.3% sensitivity and 93.8% specificity in detecting occult LNMs in vivo, with a minimum detectable diameter of 1.7 mm. Coupled with CAIX-800, US-guided sPA imaging could not only detect subcapsular deposits of metastatic cancer cells 2 weeks earlier than conventional techniques, but also successfully track pathological ENE.

CONCLUSION

CAIX remarkably expresses in human NPCs and stratifies patient prognosis. In preclinical studies, CAIX-800-based imaging successfully identifies occult LNMs and tracks early stage of pathological ENE. This attractive method shows potential in clinic, allowing medical workers to longitudinally monitor nodal status and helping to reduce unnecessary nodal biopsy for patients with NPC. The schematic diagram for the study. CAIX, carbonic anhydrase IX; NPC, nasopharyngeal carcinoma; US, ultrasonography; sPA, spectroscopic photoacoustic.

Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival

INTRODUCTION

Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions.

METHODS

A group of 399 patients with biopsy-proven PCa who had undergone ¹⁸ F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated.

RESULTS

The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p = .045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111; p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival.

CONCLUSION

This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.