Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source
2.2. Study Participants
2.3. Image Preprocessing
2.4. Deep Learning Model
2.5. Statistics and Assessment Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Methods | Training Set | Multiple Endpoints | |||||||
---|---|---|---|---|---|---|---|---|---|
Fold | AUC | TP | TN | FP | FN | Specificity | Sensitivity | Accuracy | |
PET + CT | Fold 1 | 0.817 | 45 | 5 | 2 | 3 | 0.93750 | 0.71429 | 0.90909 |
Fold 2 | 0.707 | 42 | 4 | 4 | 5 | 0.89362 | 0.50000 | 0.83636 | |
Fold 3 | 0.851 | 45 | 4 | 4 | 2 | 0.95745 | 0.50000 | 0.89091 | |
Fold 4 | 0.915 | 42 | 5 | 2 | 5 | 0.89362 | 0.71429 | 0.87037 | |
Fold 5 | 0.657 | 40 | 3 | 4 | 7 | 0.85106 | 0.42857 | 0.79630 | |
mean | 0.789 | 214 | 21 | 16 | 22 | 0.90665 | 0.57143 | 0.86061 | |
CT | Fold 1 | 0.783 | 44 | 4 | 3 | 4 | 0.91667 | 0.57143 | 0.87273 |
Fold 2 | 0.702 | 43 | 3 | 5 | 4 | 0.91489 | 0.37500 | 0.83636 | |
Fold 3 | 0.840 | 46 | 4 | 4 | 1 | 0.97872 | 0.50000 | 0.90909 | |
Fold 4 | 0.830 | 45 | 4 | 3 | 2 | 0.95745 | 0.57143 | 0.90741 | |
Fold 5 | 0.561 | 44 | 1 | 6 | 3 | 0.93617 | 0.14286 | 0.83333 | |
mean | 0.743 | 222 | 16 | 21 | 14 | 0.94078 | 0.43214 | 0.87179 |
References
- El-Serag, H.B. Hepatocellular carcinoma. N. Engl. J. Med. 2011, 365, 1118–1127. [Google Scholar] [CrossRef] [PubMed]
- Parkin, D.M.; Bray, F.; Ferlay, J.; Pisani, P. Estimating the world cancer burden: Globocan 2000. Int. J. Cancer 2001, 94, 153–156. [Google Scholar] [CrossRef] [PubMed]
- Cause of Death Statistics. Available online: http://www.mohw.gov.tw/EN/Ministry/Statistic.aspx?f_list_no=474&fod_list_no=3443 (accessed on 18 June 2016).
- Cherqui, D.; Laurent, A.; Mocellin, N.; Tayar, C.; Luciani, A.; Van Nhieu, J.T.; Decaens, T.; Hurtova, M.; Memeo, R.; Mallat, A.; et al. Liver resection for transplantable hepatocellular carcinoma: Long-term survival and role of secondary liver transplantation. Ann. Surg. 2009, 250, 738–746. [Google Scholar] [CrossRef]
- European Association for the Study of the Liver. EASL clinical practice guidelines: Management of hepatocellular carcinoma. J. Hepatol. 2018, 69, 182–236. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Heimbach, J.K.; Kulik, L.M.; Finn, R.S.; Sirlin, C.B.; Abecassis, M.M.; Roberts, L.R.; Zhu, A.X.; Murad, M.H.; Marrero, J.A. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology 2018, 67, 358–380. [Google Scholar] [CrossRef] [Green Version]
- Vitale, A.; Cucchetti, A.; Qiao, G.L.; Cescon, M.; Li, J.; Ramirez Morales, R.; Frigo, A.C.; ** a novel integrated generalised data envelopment analysis (DEA) to evaluate hospitals providing stroke care services. Bioengineering 2021, 8, 207. [Google Scholar] [CrossRef]
- Mirmozaffari, M.; Yazdani, R.; Shadkam, E.; Khalili, S.M.; Tavassoli, L.S.; Boskabadi, A. A novel hybrid parametric and non-parametric optimisation model for average technical efficiency assessment in public hospitals during and post-COVID-19 pandemic. Bioengineering 2021, 9, 7. [Google Scholar] [CrossRef]
- Lisson, C.S.; Lisson, C.G.; Mezger, M.F.; Wolf, D.; Schmidt, S.A.; Thaiss, W.M.; Tausch, E.; Beer, A.J.; Stilgenbauer, S.; Beer, M.; et al. Deep neural networks and machine learning radiomics modelling for prediction of relapse in mantle cell lymphoma. Cancers 2022, 14, 2008. [Google Scholar] [CrossRef]
- Mirmozaffari, M.; Yazdani, R.; Shadkam, E.; Khalili, S.M.; Mahjoob, M.; Boskabadi, A. An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic. Sustain. Oper. Comput. 2022, 3, 156–167. [Google Scholar] [CrossRef]
- Ripani, D.; Caldarella, C.; Za, T.; Rossi, E.; De Stefano, V.; Giordano, A. Progression to Symptomatic Multiple Myeloma Predicted by Texture Analysis-Derived Parameters in Patients Without Focal Disease at 18F-FDG PET/CT. Clin. Lymphoma Myeloma Leuk. 2021, 21, 536–544. [Google Scholar] [CrossRef]
- Mirmozaffari, M.; Yazdani, R.; Shadkam, E.; Tavassoli, L.S.; Massah, R. VCS and CVS: New combined parametric and non-parametric operation research models. Sustain. Oper. Comput. 2021, 2, 36–56. [Google Scholar] [CrossRef]
- Bowen, S.R.; Chapman, T.R.; Borgman, J.; Miyaoka, R.S.; Kinahan, P.E.; Liou, I.W.; Sandison, G.A.; Vesselle, H.J.; Nyflot, M.J.; Apisarnthanarax, S. Measuring total liver function on sulfur colloid SPECT/CT for improved risk stratification and outcome prediction of hepatocellular carcinoma patients. EJNMMI Res. 2016, 6, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, Y.; Xu, X.; Weng, S.; Yan, C.; Chen, J.; Ye, R. CT image-based texture analysis to predict microvascular invasion in primary hepatocellular carcinoma. J. Digit. Imaging 2020, 33, 1365–1375. [Google Scholar] [CrossRef] [PubMed]
- Hsu, C.C.; Chen, C.L.; Wang, C.C.; Lin, C.C.; Yong, C.C.; Wang, S.H.; Liu, Y.W.; Lin, T.L.; Lee, W.F.; Lin, Y.H.; et al. Combination of FDG-PET and UCSF Criteria for Predicting HCC Recurrence After Living Donor Liver Transplantation. Transplantation 2016, 100, 1925–1932. [Google Scholar] [CrossRef] [PubMed]
- Lee, S.D.; Kim, S.H.; Kim, Y.K.; Kim, C.; Kim, S.K.; Han, S.S.; Park, S.J. (18)F-FDG-PET/CT predicts early tumor recurrence in living donor liver transplantation for hepatocellular carcinoma. Transpl. Int. 2013, 26, 50–60. [Google Scholar] [CrossRef]
- Ludemann, L.; Grieger, W.; Wurm, R.; Wust, P.; Zimmer, C. Glioma assessment using quantitative blood volume maps generated by T1-weighted dynamic contrast-enhanced magnetic resonance imaging: A receiver operating characteristic study. Acta Radiol. 2006, 47, 303–310. [Google Scholar] [CrossRef]
- Obuchowski, N.A. Receiver operating characteristic curves and their use in radiology. Radiology 2003, 229, 3–8. [Google Scholar] [CrossRef]
- Metz, C.E. Basic principles of ROC analysis. Semin Nucl. Med. 1978, 8, 283–298. [Google Scholar] [CrossRef]
- Schraiber, L.D.S.; de Mattos, A.A.; Zanotelli, M.L.; Cantisani, G.P.C.; Brandão, A.B.M.; Marroni, C.A.; Kiss, G.; Ernani, L.; Marcon, P.D.S. Alpha-fetoprotein Level Predicts Recurrence After Transplantation in Hepatocellular Carcinoma. Medicine 2016, 95, e2478. [Google Scholar] [CrossRef]
- Takada, Y.; Kaido, T.; Shirabe, K.; Nagano, H.; Egawa, H.; Sugawara, Y.; Taketomi, A.; Takahara, T.; Wakabayashi, G.; Nakanishi, C.; et al. LTx-PET study group of the Japanese Society of Hepato-Biliary-Pancreatic Surgery and the Japanese Liver Transplantation Society. Significance of preoperative fluorodeoxyglucose-positron emission tomography in prediction of tumor recurrence after liver transplantation for hepatocellular carcinoma patients: A Japanese multicenter study. J. Hepatobiliary Pancreat Sci. 2017, 24, 49–57. [Google Scholar]
- McHugh, P.P.; Gilbert, J.; Vera, S.; Koch, A.; Ranjan, D.; Gedaly, R. Alpha-fetoprotein and tumour size are associated with microvascular invasion in explanted livers of patients undergoing transplantation with hepatocellular carcinoma. HPB 2010, 12, 56–61. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Total | |
---|---|
n = 273 | |
Age (years, mean ± SD) | 55.773 ± 8.138 |
Gender | |
Male | 212 (77.7) |
Female | 61 (22.3) |
BCLC Classification | |
0 | 1 (0.4) |
A | 119 (43.6) |
B | 89 (32.6) |
C | 40 (14.7) |
D | 24 (8.8) |
Milan criteria | |
within | 127 (46.5) |
beyond | 146 (53.5) |
UCSF criteria | |
within | 147 (53.8) |
beyond | 126 (46.2) |
CLIP Score | |
0 | 61 (22.3) |
1 | 105 (38.5) |
2 | 48 (17.6) |
3 | 37 (13.6) |
4 | 17 (6.2) |
>4 | 5 (1.8) |
Child–Pugh Classification | |
Stage A | 167 (61.2) |
Stage B | 81 (29.7) |
Stage C | 25 (9.2) |
Okuda staging system | |
Ⅰ | 162 (59.3) |
Ⅱ | 91 (33.3) |
Ⅲ | 20 (7.3) |
MELD Score | |
<10 | 134 (49.1) |
10–19 | 96 (35.2) |
20–29 | 35 (12.8) |
30–39 | 6 (2.2) |
>39 | 2 (0.7) |
Pretransplant AFP, ng/mL | |
<20 | 139 (50.9) |
20–200 | 74 (27.1) |
>200 | 60 (22.0) |
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Share and Cite
Lai, Y.-C.; Wu, K.-C.; Chang, C.-J.; Chen, Y.-J.; Wang, K.-P.; Jeng, L.-B.; Kao, C.-H. Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation. Diagnostics 2023, 13, 981. https://doi.org/10.3390/diagnostics13050981
Lai Y-C, Wu K-C, Chang C-J, Chen Y-J, Wang K-P, Jeng L-B, Kao C-H. Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation. Diagnostics. 2023; 13(5):981. https://doi.org/10.3390/diagnostics13050981
Chicago/Turabian StyleLai, Yung-Chi, Kuo-Chen Wu, Chao-Jen Chang, Yi-** Chen, Kuan-Pin Wang, Long-Bin Jeng, and Chia-Hung Kao. 2023. "Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation" Diagnostics 13, no. 5: 981. https://doi.org/10.3390/diagnostics13050981