編譯：腫瘤資訊
來源：腫瘤資訊
頭頸部放療靶區勾畫：曆史悠久的傳統與大數據和機器學習代的碰撞
Melvin Lee Kiang Chua教授
新加坡國立癌症中心（NCCS）放療科 臨床科學家、高級顧問醫師
精准放療計劃項目（NCCS）負責人
本次報告，Melvin教授主要從傳統靶區概念、國際指南，以及目前迅速發展的機器學習和人工智能（AI）角度闡述了如何實現加精准的放療靶區勾畫，從而保證患者的生存。他指出頭頸部精准放療需要穩定的質量保證，而精確的靶區勾畫是其中的重要環節。以鼻咽癌爲例，目前傳統的原發腫瘤大體腫瘤靶區（GTV）、臨床靶區（CTV）勾畫中存在一定的問題。
對于原發腫瘤GTV而言，影像手段（CT、MRI、PET-CT）的進步使得勾畫准確性提高。然而，醫生對解剖結構“異常”和“正常”的認定相對主觀；因此，即使在經驗豐富的放療醫生之間，GTV勾畫仍然存在高度異質性，准確性高度依賴醫生的經驗；並且勾畫過程十分耗時耗力。利用深度卷積神經網絡技術實現GTV自動勾畫將有助于改善這些問題。中山大學腫瘤防治中心的研究顯示利用三維卷積神經網絡在多參數MR圖像上進行GTV自動勾畫，其准確性超過參與研究的半數醫生（5/8）。在AI自動勾畫輔助下，勾畫者間差異減少一半，勾畫時間縮短40%。
對于頸部CTV勾畫而言，能否減少預防照射區域或適當降低劑量是目前研究的熱點。最近，依靠大數據的力量，林麗等人在模板CT上標記了959例患者的10651顆淋巴結，建立了鼻咽癌淋巴結分布圖譜並對2013版頭頸腫瘤淋巴引流區勾畫的國際指南指南提出了7點針對鼻咽癌的修改建議。
對于原發竈CTV而言，2017年發表的國際專家共識指南能夠爲減少醫生之間的不確定性提供指導。然而，Melvin教授認爲，在大數據和機器學習的時代，我們應該考慮能否利用大數據優化對CTV範圍的界定呢？因此，探索使用機器學習的方法，利用大量原發腫瘤侵犯範圍的數據，通過計算未受侵犯的CT體素可能侵犯的概率從而生成基于概率的CTV將有可能進一步幫助醫生減少勾畫中的不確定性。
總之，Melvin教授總結人文大量數據的彙集能夠爲靶區勾畫和計劃設計提供以數據爲基礎的方法；在這個不斷創新的時代，靶區勾畫准確性將會提高。
Target Delineation in Head and Neck Radiotherapy: A time-honoured tradition in the era of Big Data and Big Machine
Professor Melvin demonstrated how to achieve precision target contouring in the era of big data and big machine in combination with the time-honoured tradition, so as to ensure patient survival. He pointed out that robust quality assurance is crucial in head and neck radiotherapy planning processes, and accurate target contouring is one of the key steps. Using NPC as a case example, conventional concepts on delineation of primary gross tumor volume (GTV), as well as clinical target volume show several limitations.
As recognition of anatomy and “abnormal” vs “normal” signals can be subjective, NPC primary GTV contouring is highly heterogeneous even between experienced radiation oncologists; and it is extremely labor intensive and highly depends on oncologists’ experience. So, is it possible to automate the process using deep convolutional neural networks (CNN)? A study from Sun Yan-sen University Cancer centre demonstrated that applying three-dimensional CNN (3D CNN) to automate GTV contouring on multi-parametric MR images could performed comparably to experienced radiation oncologists, outperforming 5 of the 8 radiation oncologitst. Additionally, AI-assistance helped to reduce inter-observer variation (by 2-fold) and time-taken (by 40%) substantially.
For neck CTV, current researches are focused on omitting the lower neck or treating neck to lower dose. Harnessing the power of big data, Lin et, al. marked 10651 nodes from 959 patients on a template CT scan to establish a neck nodes distribution probability map for NPC. Based on the distribution of LNs and international guidelines, they suggested 7 moderate modifications of the guidelines defined neck node levels boundaries to make it more specific to NPC.
Although a newly published international guideline on CTV delineation for NPC offers a guide to reduce ambiguity of contours between physicians, Professor Melvin pointed out that are we able to refine the CTV coverage by frequency mapping and computing tumor invasion probability of uninvaded voxels leveraging on big Data? Hence, generate probabilistic CTV by exploring machine learning methods, using large sample of GTV data, and computing tumor invasion probability of uninvaded voxels will help to further reduce uncertainties in CTV contouring
In conclusion, aggregation of large datasets will provide a data-based approach to target contouring and new age of innovation that will improve accuracy and importantly patient outcomes.
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