Multimodality Annotated Hepatocellular Carcinoma Data Set Including Pre- and Post-TACE with Imaging Segmentation

Overview

Journal Sci Data

Specialty Science

Date 2023 Jan 18

PMID 36653372

Authors

Ahmed W Moawad

Ali Morshid

Ahmed M Khalaf

Mohab M Elmohr

John D Hazle

David Fuentes

Mohamed Badawy

Ahmed O Kaseb

Manal Hassan

Armeen Mahvash

Janio Szklaruk

Aliyya Qayyum

Abdelrahman Abusaif

William C Bennett

Tracy S Nolan

Brittney Camp

Khaled M Elsayes

Affiliations

Soon will be listed here.

Abstract

Hepatocellular carcinoma (HCC) is the most common primary liver neoplasm, and its incidence has doubled over the past two decades owing to increasing risk factors. Despite surveillance, most HCC cases are diagnosed at advanced stages and can only be treated using transarterial chemo-embolization (TACE) or systemic therapy. TACE failure may occur with incidence reaching up to 60% of cases, leaving patients with a financial and emotional burden. Radiomics has emerged as a new tool capable of predicting tumor response to TACE from pre-procedural computed tomography (CT) studies. This data report defines the HCC-TACE data collection of confirmed HCC patients who underwent TACE and have pre- and post-procedure CT imaging studies and available treatment outcomes (time-to-progression and overall survival). Clinically curated segmentation of pre-procedural CT studies was done for the purpose of algorithm training for prediction and automatic liver tumor segmentation.

Citing Articles

Application of artificial intelligence in the diagnosis of hepatocellular carcinoma.

Koh B, Danpanichkul P, Wang M, Tan D, Ng C eGastroenterology. 2025; 1(2):e100002.

PMID: 39944000 PMC: 11770452. DOI: 10.1136/egastro-2023-100002.

Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma.

Ren L, Chen D, Yan X, She S, Yang Y, Zhang X J Hepatocell Carcinoma. 2024; 11:2359-2372.

PMID: 39619602 PMC: 11608547. DOI: 10.2147/JHC.S423549.

Multisequence MRI-Based Radiomic Features Combined with Inflammatory Indices for Predicting the Overall Survival of HCC Patients After TACE.

Zhou M, Zhang P, Mao Q, Shi Y, Yang L, Zhang X J Hepatocell Carcinoma. 2024; 11:2049-2061.

PMID: 39469284 PMC: 11514804. DOI: 10.2147/JHC.S481301.

WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data.

Bartnik K, Bartczak T, Krzyzinski M, Korzeniowski K, Lamparski K, Wegrzyn P Radiol Artif Intell. 2024; 6(6):e240296.

PMID: 39441110 PMC: 11605144. DOI: 10.1148/ryai.240296.

Single-cell and spatial transcriptomics reveal apelin/APJ pathway's role in microvessel formation and tumour progression in hepatocellular carcinoma.

Zhu Y, Zhang P, Huo X, Ling Y, Lv X, Lin S J Cell Mol Med. 2024; 28(20):e70152.

PMID: 39434201 PMC: 11493554. DOI: 10.1111/jcmm.70152.

References

Marrero J, Kulik L, Sirlin C, Zhu A, Finn R, Abecassis M . Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018; 68(2):723-750. DOI: 10.1002/hep.29913. View

Kim B, Shim J, Kim S, Park J, Kim D, Ahn S . Risk prediction for patients with hepatocellular carcinoma undergoing chemoembolization: development of a prediction model. Liver Int. 2015; 36(1):92-9. DOI: 10.1111/liv.12865. View

Jin J, Yao Z, Zhang T, Zeng J, Wu L, Wu M . Deep learning radiomics model accurately predicts hepatocellular carcinoma occurrence in chronic hepatitis B patients: a five-year follow-up. Am J Cancer Res. 2021; 11(2):576-589. PMC: 7868753. View

Minagawa M, Ikai I, Matsuyama Y, Yamaoka Y, Makuuchi M . Staging of hepatocellular carcinoma: assessment of the Japanese TNM and AJCC/UICC TNM systems in a cohort of 13,772 patients in Japan. Ann Surg. 2007; 245(6):909-22. PMC: 1876960. DOI: 10.1097/01.sla.0000254368.65878.da. View

Kim H, El-Serag H . The Epidemiology of Hepatocellular Carcinoma in the USA. Curr Gastroenterol Rep. 2019; 21(4):17. DOI: 10.1007/s11894-019-0681-x. View

. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol. 2018; 69(1):182-236. DOI: 10.1016/j.jhep.2018.03.019. View

Shaish H, Aukerman A, Vanguri R, Spinelli A, Armenta P, Jambawalikar S . Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter.... Eur Radiol. 2020; 30(11):6263-6273. DOI: 10.1007/s00330-020-06968-6. View

Moawad A, Szklaruk J, Lall C, Blair K, Kaseb A, Kamath A . Angiogenesis in Hepatocellular Carcinoma; Pathophysiology, Targeted Therapy, and Role of Imaging. J Hepatocell Carcinoma. 2020; 7:77-89. PMC: 7188073. DOI: 10.2147/JHC.S224471. View

Morshid A, Elsayes K, Khalaf A, Elmohr M, Yu J, Kaseb A . A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell. 2019; 1(5). PMC: 6920060. DOI: 10.1148/ryai.2019180021. View

10.

Shui L, Ren H, Yang X, Li J, Chen Z, Yi C . The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front Oncol. 2021; 10:570465. PMC: 7870863. DOI: 10.3389/fonc.2020.570465. View

11.

. A new prognostic system for hepatocellular carcinoma: a retrospective study of 435 patients: the Cancer of the Liver Italian Program (CLIP) investigators. Hepatology. 1998; 28(3):751-5. DOI: 10.1002/hep.510280322. View

12.

Bi W, Hosny A, Schabath M, Giger M, Birkbak N, Mehrtash A . Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019; 69(2):127-157. PMC: 6403009. DOI: 10.3322/caac.21552. View

13.

Sciarra A, Ronot M, Di Tommaso L, Raschioni C, Castera L, Belghiti J . TRIP: a pathological score for transarterial chemoembolization resistance individualized prediction in hepatocellular carcinoma. Liver Int. 2015; 35(11):2466-73. DOI: 10.1111/liv.12844. View

14.

Huang G, Yang L, Lu W . Expression of hypoxia-inducible factor 1alpha and vascular endothelial growth factor in hepatocellular carcinoma: Impact on neovascularization and survival. World J Gastroenterol. 2005; 11(11):1705-8. PMC: 4305959. DOI: 10.3748/wjg.v11.i11.1705. View

15.

Warfield S, Zou K, Wells W . Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004; 23(7):903-21. PMC: 1283110. DOI: 10.1109/TMI.2004.828354. View

16.

Aly A, Ronnebaum S, Patel D, Doleh Y, Benavente F . Epidemiologic, humanistic and economic burden of hepatocellular carcinoma in the USA: a systematic literature review. Hepat Oncol. 2020; 7(3):HEP27. PMC: 7399607. DOI: 10.2217/hep-2020-0024. View

17.

Singal A, Pillai A, Tiro J . Early detection, curative treatment, and survival rates for hepatocellular carcinoma surveillance in patients with cirrhosis: a meta-analysis. PLoS Med. 2014; 11(4):e1001624. PMC: 3972088. DOI: 10.1371/journal.pmed.1001624. View

18.

Guo J, Zhu X, Li X, Yang R . Impact of serum vascular endothelial growth factor on prognosis in patients with unresectable hepatocellular carcinoma after transarterial chemoembolization. Chin J Cancer Res. 2013; 24(1):36-43. PMC: 3555251. DOI: 10.1007/s11670-012-0036-8. View

19.

Garwood E, Fidelman N, Hoch S, Kerlan Jr R, Yao F . Morbidity and mortality following transarterial liver chemoembolization in patients with hepatocellular carcinoma and synthetic hepatic dysfunction. Liver Transpl. 2012; 19(2):164-73. DOI: 10.1002/lt.23552. View

20.

Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B . DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ. 2016; 4:e2057. PMC: 4888317. DOI: 10.7717/peerj.2057. View