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研究生:柯宏穎
研究生(外文):Hong-Ying Ke
論文名稱:基於手術與電燒治療之肝癌復發模型預測系統
論文名稱(外文):Prediction System for HCC Recurrence on Surgical and Radiofrequency Ablation Treatments
指導教授:周承復
指導教授(外文):Cheng-Fu Chou
口試委員:梁嘉德吳曉光蔡子傑呂政修
口試委員(外文):Jia-De LiangHsiao-Kuang WuTzu-Chieh TsaiJenq-Shiou Leu
口試日期:2023-07-07
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
論文頁數:63
中文關鍵詞:肝細胞癌文本分析深度學習時序資料射頻灼燒術
外文關鍵詞:HCCFree-Text AnalysisDeep LearningTime Series DataRadiofrequency Ablation
DOI:10.6342/NTU202300885
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肝癌的死亡率多年來位居各癌症前幾名,有一原因就是肝臟沒有神經,早期肝癌大多數都沒有症狀,在手術後復發亦然,除非透過檢驗才會知道。在肝癌復發機會高的情況下,若能從一些影像報告,或其它檢驗與檢體報告中做輔助分析並及早發現復發,早日進行治療,能大幅地降低死亡率。
在肝癌病患中,肝細胞癌佔了七成以上,本篇論文使用的資料是於 20072017 年間,以電燒術與手術作為第一次肝細胞癌治療的病患,兩者會進行比較觀察差異。這份資料集來自臺大醫學院醫研部資料庫,先前有團隊研究了較早期的資料,並且發表數篇研究成果,其涵蓋資料庫建立、提取特徵等領域,但其中有許多資料並沒有良好地與醫生團隊討論與檢查過,在資料不乾淨的情況下所得到的結果也會有比較大的不穩定性。
這篇論文將會著重在資料的前處理,如何從醫研部拿到最原始的資料開始建立新的資料庫,這幾年的研究其間都有定期與醫生做詢問與檢查。在得到資料後再進行模型預測,最後也會著重在我們的模型如何優於先前所使用的,與預測的結果的比較。
The mortality rate of liver cancer has consistently ranked high for many years, one reason being that the liver has no nerves, and early-stage liver cancer often has no symptoms. Even after surgery, there is still a high chance of recurrence, which can only be detected through testing. In situations where the chance of liver cancer recurrence is high, early detection through analysis of imaging reports or other test and specimen reports can significantly reduce the mortality rate by allowing for prompt treatment.
In liver cancer patients, hepatocellular carcinoma accounts for more than 70%. The data used in this paper were collected between 2007 and 2018 from patients who received either radio frequency ablation or surgery as their first treatment for HCC, and the two methods were compared for differences in outcomes. The dataset was obtained from the research database of the National Taiwan University Hospital. Previous research by a team in this field covered the establishment of the database, feature extraction, and other areas. However, many of the data points were not properly discussed or examined with medical teams, and the results obtained under such conditions may be highly unstable.
This paper will focus on data preprocessing, starting from obtaining the most raw data from the research department to building a new database, and regularly consulting and examining with medical teams during the past few years of research. After obtaining the data, the paper will also focus on how our model outperforms previous models and compare the predictive results.
Verification Letter from the Oral Examination Committee i
Acknowledgements iii
摘要 v
Abstract vii
Contents ix
List of Figures xiii
List of Tables xv
Chapter 1 Introduction 1
Chapter 2 Related Work 5
2.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Language Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.3 Imbalance Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Recurrence Predict Model . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Basic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . 11
Chapter 3 Dataset 15
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Free Text Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2.1 Radiology Report . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.2 Ultrasound Report . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.3 Surgery Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2.4 Admission & Discharge Report . . . . . . . . . . . . . . . . . . . . 20
3.3 Laboratory Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.1 Basic Information . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.2 Laboratory Inspection . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.3 Drug History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Chapter 4 Method 27
4.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1.1 Feature Extracting . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.1.1 Free Text Report . . . . . . . . . . . . . . . . . . . . . 28
4.1.1.2 Laboratory Data . . . . . . . . . . . . . . . . . . . . . 31
4.1.2 Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1.3 Data Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2.1 Laboratory Inspection Encoder . . . . . . . . . . . . . . . . . . . . 40
4.2.2 Tumor Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.2.3 Basic Information Encoder . . . . . . . . . . . . . . . . . . . . . . 43
4.2.4 Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.5 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Chapter 5 Experiments 45
5.1 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.2 Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.3.1 One Year Recurrence . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.3.2 Three Years Recurrence . . . . . . . . . . . . . . . . . . . . . . . . 49
5.3.3 Hepatitis B and Hepatitis C . . . . . . . . . . . . . . . . . . . . . . 51
5.3.4 Model Explain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Chapter 6 Conclusion 59
References 61
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