Computerized diagnosis of liver tumors from CT scans using a deep neural network approach Journal Article
| Authors: | Midya, A.; Chakraborty, J.; Srouji, R.; Narayan, R. R.; Boerner, T.; Zheng, J.; Pak, L. M.; Creasy, J. M.; Escobar, L. A.; Harrington, K. A.; Gonen, M.; D'Angelica, M. I.; Kingham, T. P.; Do, R. K. G.; Jarnagin, W. R.; Simpson, A. L. |
| Article Title: | Computerized diagnosis of liver tumors from CT scans using a deep neural network approach |
| Abstract: | The liver is a frequent site of benign and malignant, primary and metastatic tumors. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver cancers, and colorectal liver metastasis (CRLM) is the most common secondary liver cancer. Although the imaging characteristic of these tumors is central to optimal clinical management, it relies on imaging features that are often non-specific, overlap, and are subject to inter-observer variability. Thus, in this study, we aimed to categorize liver tumors automatically from CT scans using a deep learning approach that objectively extracts discriminating features not visible to the naked eye. Specifically, we used a modified Inception v3 network-based classification model to classify HCC, ICC, CRLM, and benign tumors from pretreatment portal venous phase computed tomography (CT) scans. Using a multi-institutional dataset of 814 patients, this method achieved an overall accuracy rate of 96%, with sensitivity rates of 96%, 94%, 99%, and 86% for HCC, ICC, CRLM, and benign tumors, respectively, using an independent dataset. These results demonstrate the feasibility of the proposed computer-assisted system as a novel non-invasive diagnostic tool to classify the most common liver tumors objectively. © 2013 IEEE. |
| Keywords: | hepatocellular carcinoma; liver cell carcinoma; carcinoma, hepatocellular; liver neoplasms; tomography, x-ray computed; pathology; diagnostic imaging; liver metastasis; computerized tomography; tumors; liver tumor; bile duct carcinoma; bile duct neoplasms; bile ducts, intrahepatic; cholangiocarcinoma; medical computing; diseases; computed tomography; artificial neural network; bile duct tumor; intrahepatic bile duct; computer-aided diagnosis; computer aided diagnosis; liver tumors; noninvasive medical procedures; humans; human; computed tomography scan; x-ray computed tomography; deep learning; computer aided instruction; deep neural networks; neural networks, computer; inception v3; benign and malignant tumors; benign tumour |
| Journal Title: | IEEE Journal of Biomedical and Health Informatics |
| Volume: | 27 |
| Issue: | 5 |
| ISSN: | 2168-2194 |
| Publisher: | IEEE |
| Date Published: | 2023-05-01 |
| Start Page: | 2456 |
| End Page: | 2464 |
| Language: | English |
| DOI: | 10.1109/jbhi.2023.3248489 |
| PUBMED: | 37027632 |
| PROVIDER: | scopus |
| PMCID: | PMC10245221 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- MSK corresponding author is William Jarnagin -- Export Date: 31 May 2023 -- Source: Scopus |
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