A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy
Autoři:
Georgios Kaissis aff001; Sebastian Ziegelmayer aff001; Fabian Lohöfer aff001; Katja Steiger aff002; Hana Algül aff003; Alexander Muckenhuber aff002; Hsi-Yu Yen aff002; Ernst Rummeny aff001; Helmut Friess aff004; Roland Schmid aff003; Wilko Weichert aff002; Jens T. Siveke aff005; Rickmer Braren aff001
Působiště autorů:
Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany
aff001; Department of Pathology, School of Medicine, Technical University of Munich, Munich, Germany
aff002; Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany
aff003; Department of Surgery, School of Medicine, Technical University of Munich, Munich, Germany
aff004; Division of Solid Tumor Translational Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany
aff005; German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
aff006
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0218642
Souhrn
Purpose
Development of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.
Methods
The retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted with PyRadiomics. A gradient-boosted-tree algorithm was trained on 70% of the patients (N = 28) and tested on 30% (N = 17) to predict KRT81+ vs. KRT81- tumor subtypes. A gradient-boosted survival regression model was fit to the disease-free and overall survival data. Chemotherapy response and survival were assessed stratified by subtype and radiomic signature. Radiomic feature importance was ranked.
Results
The mean±STDEV sensitivity, specificity and ROC-AUC were 0.90±0.07, 0.92±0.11, and 0.93±0.07, respectively. The mean±STDEV concordance indices between the disease-free and overall survival predicted by the model based on the radiomic parameters and actual patient survival were 0.76±0.05 and 0.71±0.06, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81- patients (7.0 vs. 22.6 months, HR 4.03, log-rank-test P = <0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 2.33, P = 0.037) compared to KRT81- patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 2.41, P = 0.027). Entropy was ranked as the most important radiomic feature.
Conclusions
The machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for disease-free and overall patient survival and response to chemotherapy.
Klíčová slova:
Cancer treatment – Algorithms – Magnetic resonance imaging – Machine learning algorithms – Cancer chemotherapy – Chemotherapy – Adenocarcinomas – Entropy
Zdroje
1. Von Hoff DD, Ervin TJ, Arena FP, Chiorean EG, Infante JR, Moore MJ, et al. Results of a randomized phase III trial (MPACT) of weekly nab-paclitaxel plus gemcitabine versus gemcitabine alone for patients with metastatic adenocarcinoma of the pancreas with PET and CA19-9 correlates. J Clin Oncol. 2013;31(15_suppl):4005.
2. Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, et al. Increased Survival in Pancreatic Cancer with nab-Paclitaxel plus Gemcitabine. N Engl J Med. 2013 Oct;369(18):1691–703. doi: 10.1056/NEJMoa1304369 24131140
3. Conroy T, Desseigne F, Ychou M, Bouche O, Guimbaud R, Becouarn Y, et al. FOLFIRINOX versus Gemcitabine for Metastatic Pancreatic Cancer. N Engl J Med [Internet]. 2011 May 12;364(19):1817–25. Available from: https://www.ncbi.nlm.nih.gov/pubmed/21561347 doi: 10.1056/NEJMoa1011923 21561347
4. Collisson EA, Bailey P, Chang DK, Biankin A V. Molecular subtypes of pancreatic cancer. Nat Rev Gastroenterol Hepatol [Internet]. 2019 Apr 4;16(4):207–20. Available from: doi: 10.1038/s41575-019-0109-y 30718832
5. Muckenhuber A, Berger AK, Schlitter AM, Steiger K, Konukiewitz B, Trumpp A, et al. Pancreatic Ductal Adenocarcinoma Subtyping Using the Biomarkers Hepatocyte Nuclear Factor-1A and Cytokeratin-81 Correlates with Outcome and Treatment Response. Clin Cancer Res [Internet]. 2018;24(2):351–9. Available from: https://www.ncbi.nlm.nih.gov/pubmed/29101303 doi: 10.1158/1078-0432.CCR-17-2180 29101303
6. Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med [Internet]. 2011;17(4):500–3. Available from: https://www.ncbi.nlm.nih.gov/pubmed/21460848 doi: 10.1038/nm.2344 21460848
7. Aung KL, Fischer SE, Denroche RE, Jang G-H, Dodd A, Creighton S, et al. Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer: Early Results from the COMPASS Trial. Clin Cancer Res [Internet]. 2018 Mar 15;24(6):1344–54. Available from: doi: 10.1158/1078-0432.CCR-17-2994 29288237
8. Heid I, Steiger K, Trajkovic-Arsic M, Settles M, Esswein MR, Erkan M, et al. Co-clinical Assessment of Tumor Cellularity in Pancreatic Cancer. Clin Cancer Res [Internet]. 2017;23(6):1461–70. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27663591 doi: 10.1158/1078-0432.CCR-15-2432 27663591
9. Trajkovic-Arsic M, Heid I, Steiger K, Gupta A, Fingerle A, Wörner C, et al. Apparent Diffusion Coefficient (ADC) predicts therapy response in pancreatic ductal adenocarcinoma. Sci Rep [Internet]. 2017 Dec 6;7(1):17038. Available from: http://www.nature.com/articles/s41598-017-16826-z doi: 10.1038/s41598-017-16826-z 29213099
10. Kaissis G, Ziegelmayer S, Lohöfer F, Algül H, Eiber M, Weichert W, et al. A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma. bioRxiv. 2019 Jan;643809.
11. Pölsterl S, Gupta P, Wang L, Conjeti S, Katouzian A, Navab N. Heterogeneous ensembles for predicting survival of metastatic, castrate-resistant prostate cancer patients. F1000Research [Internet]. 2017 Jul 6;5:2676. Available from: https://f1000research.com/articles/5-2676/v3
12. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res [Internet]. 2017 Nov 1;77(21):e104–7. Available from: doi: 10.1158/0008-5472.CAN-17-0339 29092951
13. Chen T, Guestrin C. XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD ‘16. New York, New York, USA: ACM Press; 2016. p. 785–94.
14. Ojala M, Garriga GC. Permutation Tests for Studying Classifier Performance. In: 2009 Ninth IEEE International Conference on Data Mining. IEEE; 2009. p. 908–13.
15. Puleo F, Nicolle R, Blum Y, Cros J, Marisa L, Demetter P, et al. Stratification of Pancreatic Ductal Adenocarcinomas Based on Tumor and Microenvironment Features. Gastroenterology [Internet]. 2018 Dec;155(6):1999–2013.e3. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0016508518349199 doi: 10.1053/j.gastro.2018.08.033 30165049
16. PyRadiomics. Feature Documentation. Available from: https://pyradiomics.readthedocs.io/en/latest/features.html
17. Hanania AN, Bantis LE, Feng Z, Wang H, Tamm EP, Katz MH, et al. Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget [Internet]. 2016 Dec 27;7(52):85776–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27588410 doi: 10.18632/oncotarget.11769 27588410
18. Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RTH, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol [Internet]. 2015;114(3):345–50. Available from: doi: 10.1016/j.radonc.2015.02.015 25746350
19. Lao J, Chen Y, Li ZC, Li Q, Zhang J, Liu J, et al. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. Sci Rep [Internet]. 2017;7(1):1–8. Available from: doi: 10.1038/s41598-016-0028-x
20. Caramella C, Allorant A, Orlhac F, Bidault F, Asselain B, Ammari S, et al. Can we trust the calculation of texture indices of CT images? A phantom study. Med Phys [Internet]. 2018 Feb 14; Available from: http://doi.wiley.com/10.1002/mp.12809
21. Verma V, Simone CB, Krishnan S, Lin SH, Yang J, Hahn SM. The Rise of Radiomics and Implications for Oncologic Management. J Natl Cancer Inst. 2017;
22. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: The process and the challenges. Magn Reson Imaging [Internet]. 2012;30(9):1234–48. Available from: doi: 10.1016/j.mri.2012.06.010 22898692
23. Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep [Internet]. 2016;6:1–7. Available from: doi: 10.1038/s41598-016-0001-8
24. Traverso A, Wee L, Dekker A, Gillies R. Repeatability and Reproducibility of Radiomic Features: A Systematic Review. Int J Radiat Oncol [Internet]. 2018 Jun; Available from: https://linkinghub.elsevier.com/retrieve/pii/S0360301618309052
25. Khalvati F, Zhang Y, Baig S, Lobo-Mueller EM, Karanicolas P, Gallinger S, et al. Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma. Sci Rep [Internet]. 2019 Dec 1;9(1):5449. Available from: http://www.nature.com/articles/s41598-019-41728-7 doi: 10.1038/s41598-019-41728-7 30931954
26. Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma—a quantitative analysis. BMC Med Imaging. 2017;17(1):1–7. doi: 10.1186/s12880-016-0171-7
27. Mueller S, Engleitner T, Maresch R, Zukowska M, Lange S, Kaltenbacher T, et al. Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes. Nature [Internet]. 2018 Feb 24;554(7690):62–8. Available from: http://www.nature.com/articles/nature25459 doi: 10.1038/nature25459 29364867
28. Bailey DL, Pichler BJ, Gückel B, Antoch G, Barthel H, Bhujwalla ZM, et al. Combined PET/MRI: Global Warming—Summary Report of the 6th International Workshop on PET/MRI, March 27–29, 2017, Tübingen, Germany. Mol Imaging Biol [Internet]. 2018 Feb 2;20(1):4–20. Available from: doi: 10.1007/s11307-017-1123-5 28971346
29. Kaufman B, Shapira-Frommer R, Schmutzler RK, Audeh MW, Friedlander M, Balmaña J, et al. Olaparib monotherapy in patients with advanced cancer and a germline BRCA1/2 mutation. J Clin Oncol. 2015 Jan;33(3):244–50. doi: 10.1200/JCO.2014.56.2728 25366685
30. Bach M, Röthke M, Henzler T, Kreft M, Amler B SH. Standardized and quality assured prostate diffusion MRI. Eur Congr Radiol [Internet]. 2019; Available from: https://www.radiagnostix.de/fileadmin/radiagnostix/PDF/Artikel/Poster_ECR2019_C-2163__002_.pdf
31. Zwanenburg A, Leger S, Vallières M, Löck S, Initiative for the IBS. Image biomarker standardisation initiative. CoRR [Internet]. 2016;abs/1612.0. Available from: http://arxiv.org/abs/1612.07003
Článok vyšiel v časopise
PLOS One
2019 Číslo 10
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Experimentally validated simulation of coronary stents considering different dogboning ratios and asymmetric stent positioning
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis