放射治療是癌症治療當中重要的一環，現代的放射治療大多採用逆向治療計劃設計：放射腫瘤醫師會在電腦斷層影像上描繪出希望照射以及希望避免的區域，再交由醫學物理師設計適合的治療計劃。近年來不少研究致力於發展標記模型用以輔助醫師進行描繪，其針對器官或是腫瘤本體進行標記為主。但實際臨床使用上，臨床靶區的描繪才是放射腫瘤科醫師最終需要解決的目標。
在頭頸部的鱗狀細胞癌中，手術往往為第一線的治療；術後病理若有高風險因子者，則需要接受術後的輔助放射治療。癌症手術因需要額外切除的安全距離，經常會造成解剖構造及外觀上的重大改變。在此類病人身上，照射區域與器官的描繪對於醫師來說相當困難且耗時，還需藉由其他報告輔助判斷。其原因為再制定治療計劃時，不只手術相關區域需要涵蓋，其他潛在高風險區域也將會是治療的目標。本研究將整合醫師藉由報告所得知的資訊，基於電腦斷層影像中所提供的特徵，自動化描繪出臨床靶區。
本研究中利用深度學習的方式來實現臨床靶區自動化標記。於電腦斷層影像中採用CNN模型將提取高維度之特徵，模擬醫師先備知識及其關注重點，並加上額外的目標向量，對模型進行決策上的指引，模擬醫師經由報告輔助後所下的判斷。藉上述兩者最終達到目標臨床靶區之描繪。
在實驗中，本研究利用成大醫院放射腫瘤部中204位術後頭頸癌病人，當中使用193位病人資料進行模型訓練，11位病人作獨立測試資料使用。實驗結果說明本研究所提出的方法能克服現今模型受限於目標臨床靶區進行標記的短處，並證明所提出之目標向量有其重要性。實驗最後於臨床上進行評估，比對有無使用自動化標記輔助其時間與不同醫師在標記上風格的差異。實際輔助醫師進行標記能夠減少強烈的個人特色於標記上，更貼近醫師間共識性的標記，其所帶來的成效相當不錯。

Radiation therapy is an important part of cancer treatment. Modern radiation therapy often adopted inverse treatment planning design, radiation oncologist contouring the area to treated and the area to be avoided. Subsequently, medical physicists planning the appropriate treatment planning. However, a lot of research considering to develop the automatic segmentation model recently to assist physician contouring. To our knowledge, these models usually focus on organ or tumor itself, but clinical target volume will be the final goal in clinical practice.
In squamous cell carcinoma of head and neck, surgical resection often is the essential one. Along with adjuvant radiotherapy for patients with high-risk pathological features. Wide excision of the primary tumor results in anatomical changes that caused the delineation of the clinical target volume is difficult for physicians, and have to depend on the report and nearly impossible for traditional automatic segmentation models.
In our study, we aimed to adopt deep learning method to perform automatic clinical target volume segmentation. Firstly, we extracted the high dimension feature via the CNN model to simulate physician domain knowledge and the main interesting area of it. Secondly, we imported additional information called "Target Vector" acting the roles of reports that physicians depend on. Finally, combining two types of information in deep learning architecture then got the final segmentation results.
We conducted our experiments on postoperative head and neck cancer patients’ data from the Department of Radiation Oncology at National Cheng Kung University Hospital (NCKUH). Totally 204 cancer patients, 193 patients for model training, and 11 patients for independent testing. The experimental results demonstrated that our study proposed a model to outperform the traditional model limitation segment on the clinical target volume and showed the importance of the Target Vector. Finally, we compared the cost of time and delineation style's difference with and without our model in clinical practice. We used it able to eliminate the strong personal style in contouring via our model and brought better results.

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