Immune checkpoint inhibitors have revolutionized the treatment of urothelial and renal cell cancers. Image-guided biopsy provides in-depth information to interrogate tumor and immune microenvironment, therefore facilitates biomarker development and improves treatment decision making to identify patients who will benefit.
In clinical practice, target lesions are approached by multiple disciplines for various purposes, and lesion selection is crucial for tumor/response measurement,5 radiation treatment as well as biopsy acquisition. In many clinical trials, pre-and post-treatment (paired) biopsy specimens are required to measure dynamic changes of tumor and immune micro-environment and drug pharmacologic modulation. Maintaining the consistency of designated lesion sites enables accurate tumor/response evaluation; precise targeting of radiation sites and minimizes inter-tumoral heterogeneity of biopsy obtained during longitudinal sampling. Nevertheless, currently lack in standardized guidelines and algorithms, lesion selection for image-guided biopsies is often based on the operating feasibility and interventional radiologist’s preference. Lesion selection for evaluating the response to cancer treatment is usually made by diagnostic radiology, while biopsy collection is typically performed by interventional radiology. However, the final lesion assignments for various purposes and determination of biopsy acquisition would involve multiple clinical professions, including oncology/clinical study team, surgical oncology, diagnostic radiology, radiation oncology, interventional radiology, histopathology, etc. Given the complications of the biopsy acquisition process and lacking standardized guidelines how to efficiently coordinate multiple collaborators to ensure the designated sampling accuracy becomes very challenging and urgent. Therefore, our group at The University of Texas MD Anderson Cancer Center spearheaded the effort to develop this specialized lesion selection workflow, namely the Naing tool to facilitate efficient and clear communication, provide guidance and track for lesion selection, and eventually streamline the entire biopsy acquisition process.
In this article,6 Xu et al. describe the Naing tool, a six-step workflow featured with automatic notifications and integration in the department’s online database. Moreover, the oncology study team plays a central role in overseeing the entire process and make the final assignments for lesion selection and sequential biopsy acquisition. In the retrospective study, by following these Naing tool steps throughout the entire biopsy acquisition process, the biopsy sampling consistency was significantly improved compared to the control group (p=0.007), and the number of biopsy cores obtained per timepoint was also substantially increased (baseline and on-treatment-1, p< 0.001; on-treatment-2, P=0.055) compared to control group. This tool is simple, straightforward, efficient, and easily adaptable in any hospital setting. These findings are of significance, considering its’ demonstrated capability of improving biopsy acquisition. Even so, the Naing tool has not been extensively applied in clinical setting yet; some limitations delay the application of the Naing tool: in the analysis with a limited sample size, the completion rate was low, only 34% of patients enrolled in the lesion selection group completed the Naing tool workflow, even though the flow is simple. Moreover, the Naing tool has limited accessibility since it was not integrated into the Electronic 1) Validate it in larger studies. 2) Increase funding to support the Naing tool operations and compensate the personnel efforts as needed. 3) Integration of Naing tool in a larger platform such as EHR to expand its uses. Given its potential to provide us with more precise insights into the tumor microenvironment and thus better understanding the underlying drug action and treatment resistance mechanism, further investing and efforts towards the Naing tool are worthwhile further to advance clinical studies and next line precision medicine.
Written by: Mingxuan Xu1 & Christian Rolfo2
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Center for Thoracic Oncology, The Tisch Cancer Institute, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Amalou H, Wood BJ. Biopsy and personalized medicine. Nat Rev Gastroenterol Hepatol 2012;9:683; author reply
- O'Shea A, Tam AL, Kilcoyne A, Flaherty KT, Lee SI. Image-guided biopsy in the age of personalised medicine: strategies for success and safety. Clin Radiol 2021;76.
- Tam AL, Lim HJ, Wistuba, II, et al. Image-Guided Biopsy in the Era of Personalized Cancer Care: Proceedings from the Society of Interventional Radiology Research Consensus Panel. J Vasc Interv Radiol 2016;27:8-19.
- Pritzker KPH, Nieminen HJ. Needle Biopsy Adequacy in the Era of Precision Medicine and Value-Based Health Care. Archives of Pathology & Laboratory Medicine 2019;143:1399-415.
- Keil S, Barabasch A, Dirrichs T, et al. Target lesion selection: an important factor causing variability of response classification in the Response Evaluation Criteria for Solid Tumors 1.1. Invest Radiol 2014;49:509-17.
- Xu M, Tapia C, Hajjar J, al. e. Implementation of a Novel Web-Based Lesion Selection Tool to Improve Acquisition of Tumor Biopsy Specimens. Journal of Immunotherapy and Precision Oncology 2021;4:45-52.
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