Feasibility analysis of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics_Journal of Biomedical Engineering

Authors：

WEI Ran ^1,2 , LIN Kanru ³ ,  GUO Yi ^1,2 , LI Ji ³ , WANG Yuanyuan ^1,2

1. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China;
2. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China;
3. Department of Pancreatic Surgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, P.R.China;

Corresponding?author：

GUO Yi, Email: guoyi@fudan.edu.cn

Keywords：

radiomics; pancreatic cystic neoplasms; multi-detector computed tomography; Ki67 molecular marker

DOI：

10.7507/1001-5515.201805014

Video：

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This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.

Citation： WEI Ran, LIN Kanru, GUO Yi, LI Ji, WANG Yuanyuan. Feasibility analysis of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics. Journal of Biomedical Engineering, 2019, 36(1): 1-6. doi: 10.7507/1001-5515.201805014 Copy

1.	Rahib L, Smith B D, Aizenberg R, et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res, 2014, 74(11): 2913-2921.
2.	Galanis C, Zamani A, Cameron J L, et al. Resected serous cystic neoplasms of the pancreas: a review of 158 patients with recommendations for treatment. J Gastrointest Surg, 2007, 11(7): 820-826.
3.	Jais B, Rebours V, Malleo G, et al. Serous cystic neoplasm of the pancreas: a multinational study of 2622 patients under the auspices of the International Association of Pancreatology and European Pancreatic Club (European Study Group on Cystic Tumors of the Pancreas). Gut, 2015, 65(2): 305-312.
4.	Sawhney M S, Al-Bashir S, Cury M S, et al. International consensus guidelines for surgical resection of mucinous neoplasms cannot be applied to all cystic lesions of the pancreas. Clin Gastroenterol Hepatol, 2009, 7(12): 1373-1376.
5.	陳夢云, 張翠翠, 軒菡, 等. Ki67 在腫瘤中的表達及其臨床指導意義. 現代生物醫學進展, 2015, 15(16): 3193-3196.
6.	何淑蓉, 崔娣, 宮環, 等. 以 Ki-67 陽性指數行胰腺神經內分泌腫瘤細針穿刺細胞學分級及其與組織學分級的比較. 中華病理學雜志, 2017, 46(6): 393-399.
7.	龐旭峰, 王祖森, 吳力群. Ki-67 在肝細胞肝癌患者根治性肝切除術后短期腫瘤復發預測中的價值. 中華肝臟外科手術學電子雜志, 2012, 1(2): 123-128.
8.	Sainani N I, Saokar A, Deshpande V, et al. Comparative performance of MDCT and MRI with MR cholangiopancreatography in characterizing small pancreatic cysts. AJR Am J Roentgenol, 2009, 193(3): 722-731.
9.	Kickingereder P, G?tz M, Muschelli J, et al. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res, 2016, 22(23): 5765-5771.
10.	Guo Yi, Hu Yuzhou, Qiao Mengyun, et al. Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma. Clin Breast Cancer, 2018, 18(3): E335-E344.
11.	劉桐桐, 李佳偉, 胡雨舟, 等. 基于影像組學預測乳腺癌雌激素受體表達情況的可行性分析. 生物醫學工程學雜志, 2017, 34(4): 597-601.
12.	Cameron A, Khalvati F, Haider M A, et al. MAPS: A quantitative radiomics approach for prostate Cancer detection. IEEE Trans Biomed Eng, 2016, 63(6): 1145-1156.
13.	胡玉川, 張欣, 崔光彬. 影像組學在肺癌中的應用研究進展. 放射學實踐, 2017, 32(12): 1239-1241.
14.	Permuth J B, Choi J, Balarunathan Y, et al. Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget, 2016, 7(52): 85785-85797.
15.	Sahani D V, Kambadakone A, Macari M, et al. Diagnosis and management of cystic pancreatic lesions. Am J Roentgenol, 2013, 200(2): 343-354.
16.	Cohen-Scali F, Vilgrain V, Brancatelli G, et al. Discrimination of unilocular macrocystic serous cystadenoma from pancreatic pseudocyst and mucinous cystadenoma with CT: initial observations. Radiology, 2003, 228(3): 727-733.
17.	Li Chao, Lin Xiaozhu, Hui Chun, et al. Computer-aided diagnosis for distinguishing pancreatic mucinous cystic neoplasms from serous oligocystic adenomas in spectral CT images. Technol Cancer Res Treat, 2016, 15(1): 44-54.
18.	Lv Peijie, Mahyoub R, Lin Xiaozhu, et al. Differentiating pancreatic ductal adenocarcinoma from pancreatic serous cystadenoma, mucinous cystadenoma, and a pseudocyst with detailed analysis of cystic features on CT scans: a preliminary study. Korean J Radiol, 2011, 12(2): 187-195.
19.	Zhao Peng, Yu Bin. On model selection consistency of lasso. J Mach Learn Res, 2006, 7(12): 2541-2563.

1. Rahib L, Smith B D, Aizenberg R, et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res, 2014, 74(11): 2913-2921.
2. Galanis C, Zamani A, Cameron J L, et al. Resected serous cystic neoplasms of the pancreas: a review of 158 patients with recommendations for treatment. J Gastrointest Surg, 2007, 11(7): 820-826.
3. Jais B, Rebours V, Malleo G, et al. Serous cystic neoplasm of the pancreas: a multinational study of 2622 patients under the auspices of the International Association of Pancreatology and European Pancreatic Club (European Study Group on Cystic Tumors of the Pancreas). Gut, 2015, 65(2): 305-312.
4. Sawhney M S, Al-Bashir S, Cury M S, et al. International consensus guidelines for surgical resection of mucinous neoplasms cannot be applied to all cystic lesions of the pancreas. Clin Gastroenterol Hepatol, 2009, 7(12): 1373-1376.
5. 陳夢云, 張翠翠, 軒菡, 等. Ki67 在腫瘤中的表達及其臨床指導意義. 現代生物醫學進展, 2015, 15(16): 3193-3196.
6. 何淑蓉, 崔娣, 宮環, 等. 以 Ki-67 陽性指數行胰腺神經內分泌腫瘤細針穿刺細胞學分級及其與組織學分級的比較. 中華病理學雜志, 2017, 46(6): 393-399.
7. 龐旭峰, 王祖森, 吳力群. Ki-67 在肝細胞肝癌患者根治性肝切除術后短期腫瘤復發預測中的價值. 中華肝臟外科手術學電子雜志, 2012, 1(2): 123-128.
8. Sainani N I, Saokar A, Deshpande V, et al. Comparative performance of MDCT and MRI with MR cholangiopancreatography in characterizing small pancreatic cysts. AJR Am J Roentgenol, 2009, 193(3): 722-731.
9. Kickingereder P, G?tz M, Muschelli J, et al. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res, 2016, 22(23): 5765-5771.
10. Guo Yi, Hu Yuzhou, Qiao Mengyun, et al. Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma. Clin Breast Cancer, 2018, 18(3): E335-E344.
11. 劉桐桐, 李佳偉, 胡雨舟, 等. 基于影像組學預測乳腺癌雌激素受體表達情況的可行性分析. 生物醫學工程學雜志, 2017, 34(4): 597-601.
12. Cameron A, Khalvati F, Haider M A, et al. MAPS: A quantitative radiomics approach for prostate Cancer detection. IEEE Trans Biomed Eng, 2016, 63(6): 1145-1156.
13. 胡玉川, 張欣, 崔光彬. 影像組學在肺癌中的應用研究進展. 放射學實踐, 2017, 32(12): 1239-1241.
14. Permuth J B, Choi J, Balarunathan Y, et al. Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget, 2016, 7(52): 85785-85797.
15. Sahani D V, Kambadakone A, Macari M, et al. Diagnosis and management of cystic pancreatic lesions. Am J Roentgenol, 2013, 200(2): 343-354.
16. Cohen-Scali F, Vilgrain V, Brancatelli G, et al. Discrimination of unilocular macrocystic serous cystadenoma from pancreatic pseudocyst and mucinous cystadenoma with CT: initial observations. Radiology, 2003, 228(3): 727-733.
17. Li Chao, Lin Xiaozhu, Hui Chun, et al. Computer-aided diagnosis for distinguishing pancreatic mucinous cystic neoplasms from serous oligocystic adenomas in spectral CT images. Technol Cancer Res Treat, 2016, 15(1): 44-54.
18. Lv Peijie, Mahyoub R, Lin Xiaozhu, et al. Differentiating pancreatic ductal adenocarcinoma from pancreatic serous cystadenoma, mucinous cystadenoma, and a pseudocyst with detailed analysis of cystic features on CT scans: a preliminary study. Korean J Radiol, 2011, 12(2): 187-195.
19. Zhao Peng, Yu Bin. On model selection consistency of lasso. J Mach Learn Res, 2006, 7(12): 2541-2563.

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