• Linda C. Chu
• Seyoun Park
• Satomi Kawamoto
• Alejandra Blanco
• Shahab Shayesteh
• Saeed Ghandili
• Daniel F. Fouladi
• Alan L. Yuille
• Kenneth Kinzler
• Bert Vogelstein
• Elliot K. Fishman

• Overview of current state-of-the art pancreatic cancer imaging
• Current areas of need and potential solutions
• Potential applications of emerging techniques in pancreatic cancer imaging:
  • Radiomics
  • Deep learning
  • Cinematic rendering

• Pancreatic ductal adenocarcinoma (PDAC) is the 3rd most common cause of cancer death in the US, with dismal five-year survival of 8.2%
• CT is the first-line imaging modality for the initial diagnosis and staging of PDAC
• Accuracy of PDAC detection and determination of resectability critically depend on imaging technique and experience of the radiologists
• Society of Abdominal Radiology and the American Pancreatic Association have consensus recommended CT protocol and reporting template

• We have achieved high accuracy for detection and assessment of resectability with state-of-the-art CT imaging
  • CT accuracy for detection: 76-96%
  • CT accuracy for resectability: 73-89%
• However, there are several frontiers we can improve upon:
  1. Patient prognostication to develop personalized risk assessment
  2. Computer aided diagnosis to reduce misdiagnosis
  3. Advanced visualization techniques to facilitate pretreatment planning

• Surgical resection is the only curative treatment for patients with PDAC, but it is a major surgery with its associated morbidities
• Some of the currently known prognostic features are based on pathologic features that are available AFTER surgery (TNM stage, resection margin)
• Improving patient selection using imaging biomarkers available BEFORE surgery is critical in identifying patients most likely to benefit from surgical resection

• Radiomics converts imaging data into high dimensional mineable feature space
• Potential to provide imaging biomarkers to quantify tumor heterogeneity and predict biologic behavior
• Radiomics features can be classified into first-order, shape, texture, and higher-order statistical outputs

• Patient age
• Gender
• Baseline CA 19-9
• Tumor size
• 489 radiomics features

• Patients were stratified based on low-risk (survival >900 days) and high-risk groups (survival <350 days)
• Feature reduction by minimum-redundancy maximum-relevance selection and random forest method
• 10 most relevant radiomics features were selected
• Survival analysis was performed based on clinical parameters with or without the 10 radiomics features

• Pancreatic cysts are identified incidentally in approximately 2% of abdominal CTs
• Malignant potential of pancreatic cystic lesions vary based on underlying pathologic diagnosis
• These cystic lesions can have overlapping imaging features and can be difficult to differentiate clinically

• Retrospective study of 214 pancreatic cysts:
  • 64 IPMNs
  • 33 Mucinous cystic neoplasms
  • 60 Serous cystadenomas
  • 24 Solid pseudopapillary neoplasms
  • 33 Cystic pancreatic neuroendocrine tumors
• Compared diagnostic performance of radiomics and random-forest classifier vs. a radiologist

• Academic radiologist with >20 years experience:
  • Accuracy = 77.1%
• Radiomics features with random-forest:
  • Accuracy = 82%

• Although the reported accuracy of detection of PDAC from CT scans is >95%, the diagnosis remains challenging and many cases are missed
• 18.7% of patients referred to a pancreatic cancer center of excellence had a change in the diagnosis and/or clinical stage
• In a retrospective study, early findings of PDAC can be detected up to 34 months prior to clinical diagnosis of PDAC

• Opportunity to use computer aided diagnosis as a “second-reader” to reduce the number of misses
• This can detect disease at an earlier stage, which will significantly improve patient outcomes
• Traditional computer assisted diagnosis (CAD) systems depend on human-designed features
• Newer CAD systems based on deep learning and artificial intelligence have achieved superior results compared to traditional CAD

• Currently, much of the deep learning research in radiology have focused on mammograms, chest radiographs, and chest CTs due the availability of publicly available datasets
• The goal of our FELIX Project is to use deep learning for automatic detection of PDAC from abdominal CTs
• Automatic detection of PDAC is a difficult task, which can divided into more manageable tasks:
  • Automatic segmentation of abdominal organs
  • Classification of normal vs. abnormal pancreas

• Deep learning requires large amount of high quality input data
• We have manually segmented abdominal organs and vasculature on dual-phase abdominal CTs from:
  • >750 PDAC patients
  • >700 normal controls without known pancreatic disease

• Enrich training dataset with small and challenging tumors to improve the performance of the deep network
• Include other tumors types (e.g. PNETs, cystic neoplasms) and pancreatitis for detection and classification
• Test our algorithm on datasets from other institutions to ensure its performance with other scanner types and scan protocols

• CT imaging plays a critical role in triaging patients with PDAC into resectable, borderline resectable, and unresectable categories and guide management decisions
• Multiplanar and 3D reconstructions are helpful in defining extent of vascular involvement and in determining vascular reconstruction options
• Advanced visualization becomes even more important in cases of planned minimally invasive surgery, since the field-of-view can be limited

• Cinematic rendering (CR) is a recently described rendering technique inspired by quality of computer animation programs
• It uses a global illumination model that take direct and indirect illumination into account to generate photorealistic images

• State-of-the-art CT imaging has excellent accuracy in detection and staging of pancreatic ductal adenocarcinoma
• Several emerging techniques have the potential to improve upon pancreatic cancer imaging and provide additional value
• Artificial intelligence (AI) may function as a second-reader for automatic detection of pancreatic cancer, which can lead to earlier detection and improve patient outcomes

• AI augmented workflow can automatically extract quantifiable imaging biomarkers for tumor classification and patient prognostication
• These additional information will be available to the radiologists and clinicians to guide management decisions
• Advanced visualization techniques can improve appreciation of tumor extension and assist in preoperative planning

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Acknowledgements

• Linda C. Chu
• Seyoun Park
• Satomi Kawamoto
• Alejandra Blanco
• Shahab Shayesteh
• Saeed Ghandili
• Daniel F. Fouladi
• Alan L. Yuille
• Ralph H. Hruban
• Kenneth W. Kinzler
• Bert Vogelstein
• Elliot K. Fishman