Convolutional neural network for the detection of pancreatic cancer on CT scans – Authors' reply
Authors: Wei-Chih Liao, Amber L Simpson, Weichung Wang
Journal: The Lancet Digital Health (2020)

We thank Garima Suman and colleagues for comments on our Article.1Liu K-L Wu T Chen P-T et al.Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.Lancet Digital Health. 2020; 2: e303-e313Summary Full Text Full Text PDF Scopus (61) Google Scholar Because segmentation was not the focus of our study, we did not store the initial segmentation and thus cannot assess variabilities between the initial and final segmentation. We agree that such information is useful and should be stored in future studies. Because a study2Attiyeh MA Chakraborty J Doussot A et al.Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis.Ann Surg Oncol. 2018; 25: 1034-1042Crossref PubMed Scopus (76) Google Scholar from the centre that provided the external dataset in our study (Medical Segmentation Decathlon Dataset [MSDD]) included 161 patients with pancreatic adenocarcinoma, Suman and colleagues inferred that MSDD included only 161 pancreatic adenocarcinomas. However, those 161 patients were selected from 391 patients with pancreatic adenocarcinoma undergoing resection between 2009 and 2012,2Attiyeh MA Chakraborty J Doussot A et al.Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis.Ann Surg Oncol. 2018; 25: 1034-1042Crossref PubMed Scopus (76) Google Scholar whereas MSDD included 420 patients without information on inclusion period and treatment, and 281 patients with tumour labelling were used in our study. Given incomplete information and inconsistent numbers, we cannot exclude the possibility that some of those 281 external patients had non-pancreatic adenocarcinoma tumours, but we cannot verify this proposition. Therefore, our results of testing in the external dataset should be interpreted with caution. We appreciate the providers of MSDD, the only public pancreatic tumour CT dataset of sufficient volume, for their tremendous efforts and generosity. On the other hand, our experience highlights the challenges posed by the paucity of public data and difficulties in verifying and using external datasets. Because MSDD was intended for a segmentation challenge, information such as outcomes and histology was not provided. When accessing MSDD we sought to request further information, and a subsequently added document3Simpson AL Antonelli M Bakas S et al.A large annotated medical image dataset for the development and evaluation of segmentation algorithms.arXiv. 2019; (published online Feb 25.) (preprint)http://arxiv.org/abs/1902.09063Google Scholar clarified that the dataset included pancreatic adenocarcinomas, neuroendocrine tumours, and intraductal mucinous neoplasms. However, the diagnosis of each image and method of diagnosis remain unclear. Notably, imaging findings might overlap between various pancreatic tumours and even benign conditions such as chronic pancreatitis;4To'o KJ Raman SS Yu NC et al.Pancreatic and peripancreatic diseases mimicking primary pancreatic neoplasia.Radiographics. 2005; 25: 949-965Crossref PubMed Scopus (50) Google Scholar therefore, in the local datasets we only included histologically or cytologically confirmed pancreatic adenocarcinomas. We understand that making such information publicly available might not be feasible given regulations on patient privacy and health data protection, which vary across regions and institutions. We agree that transparent, carefully curated public datasets with detailed clinical information are needed to facilitate future research. Data sharing efforts are undertaken by individual investigators based on goodwill. Mitigating data paucity requires incentives for dataset providers and validated tools to facilitate data collection, processing, and de-identification. Standardising the process of dataset preparation and sharing is needed to enable precise dataset interpretation and use by external users. W-CL and WW report grants from Taiwan Ministry of Science and Technology, during the conduct of the study. W-CL and WW have a patent pending—differentiation between pancreatic cancer and non-cancerous pancreas on contrast-enhanced CT by deep learning. AS declares no competing interests. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validationCNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation. Full-Text PDF Open AccessConvolutional neural network for the detection of pancreatic cancer on CT scansWe applaud Kao-Lang Liu and colleagues1 for the development of a convolutional neural network (CNN) to classify CT image patches into cancerous and non-cancerous pancreatic tissue groups. Specifically, the patients with abnormal images were those who had histologically confirmed or cytologically confirmed pancreatic adenocarcinoma. In this study, the pancreas and tumours were segmented by two experienced abdominal radiologists followed by joint review because pancreatic cancer on CT scans tends to be infiltrative and can be subtle. Full-Text PDF Open Access