MaPDACEarlyDiagnosis

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Computer-aided Early Diagnosis of Pancreatic Cancer based on Deep Learning

Supervision by: Dr. Kuangyu Shi, Prof. Bjoern Menze
Contact: Dr. Kuangyu Shi, Email: shi@lrz.tum.de

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains as the deadliest cancer worldwide and most of them are diagnosed in the advanced and incurable stage (1). For the year 2020, it is estimated that the number of cancer deaths caused by pancreatic ductal adenocarcinoma (PDAC) will surpass colorectal and breast cancer and will be responsible for the most overall cancer deaths after lung cancer (2). This lethal nature of PDAC has led to the consensus of screening high-risk individuals (HRIs) at early curable stage to improve the survival (3-6). The lethal nature of pancreatic ductal adenocarcinoma (PDAC) has led to the consensus of screening high-risk individuals at early curable stage. However, there is no non-invasive imaging method available for effective screening of PDAC at the moment.
Strong evidence has shown that the pathological progression from normal ductal tissue to PDAC is via paraneoplastic lesions, such as pancreatic intraepithelial neoplasia (PanIN?), intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN) (7). Pancreatic carcinogenesis progresses for years from precursors to invasive cancer, indicating a long window of opportunity for early diagnosis in the curative stage (8).
Deep learning technologies extend the human perception of information from digital data and its implementation has led to record-breaking advancements in many applications. The proposed master thesis will employ deep learning methods on CT or PET imaging for the early diagnosis of the precursor lesion IPMN.
The student is expected to have good knowledge in medical imaging. Advanced skill in python programming is required.

References

1. Ryan DP, Hong TS, & Bardeesy N (2014) Pancreatic adenocarcinoma. The New England journal of medicine 371(11):1039-1049.
2. Rahib L, et al. (2014) Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer research 74(11):2913-2921.
3. Shin EJ & Canto MI (2012) Pancreatic cancer screening. Gastroenterology clinics of North America 41(1):143-157.
4. Vincent A, Herman J, Schulick R, Hruban RH, & Goggins M (2011) Pancreatic cancer. Lancet 378(9791):607-620.
5. Harinck F, et al. (2015) A multicentre comparative prospective blinded analysis of EUS and MRI for screening of pancreatic cancer in high-risk individuals. Gut.
6. Canto MI & Hruban RH (2015) Diagnosis: A step closer to screening for curable pancreatic cancer? Nature reviews. Gastroenterology & hepatology 12(8):431-432.
7. Reichert M & Rustgi AK (2011) Pancreatic ductal cells in development, regeneration, and neoplasia. The Journal of clinical investigation 121(12):4572-4578.
8. Iacobuzio-Donahue CA (2012) Genetic evolution of pancreatic cancer: lessons learnt from the pancreatic cancer genome sequencing project. Gut 61(7):1085-1094.


Students.ProjectForm
Title: Computer-aided Early Diagnosis of Pancreatic Cancer based on Deep Learning
Abstract: Pancreatic ductal adenocarcinoma (PDAC) remains as the deadliest cancer worldwide and most of them are diagnosed in the advanced and incurable stage (1). For the year 2020, it is estimated that the number of cancer deaths caused by pancreatic ductal adenocarcinoma (PDAC) will surpass colorectal and breast cancer and will be responsible for the most overall cancer deaths after lung cancer (2). This lethal nature of PDAC has led to the consensus of screening high-risk individuals (HRIs) at early curable stage to improve the survival (3-6). The lethal nature of pancreatic ductal adenocarcinoma (PDAC) has led to the consensus of screening high-risk individuals at early curable stage. However, there is no non-invasive imaging method available for effective screening of PDAC at the moment. Strong evidence has shown that the pathological progression from normal ductal tissue to PDAC is via paraneoplastic lesions, such as pancreatic intraepithelial neoplasia (PanIN?), intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN) (7). Pancreatic carcinogenesis progresses for years from precursors to invasive cancer, indicating a long window of opportunity for early diagnosis in the curative stage (8). Deep learning technologies extend the human perception of information from digital data and its implementation has led to record-breaking advancements in many applications. The proposed master thesis will employ deep learning methods on CT or PET imaging for the early diagnosis of the precursor lesion IPMN. The student is expected to have good knowledge in medical imaging. Advanced skill in python programming is required.
Student:  
Director: Prof. Dr. Bjoern Menze
Supervisor: Dr. Kuangyu Shi
Type: Master Thesis
Area:  
Status: open
Start:  
Finish:  
Thesis (optional):  
Picture:  


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