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Global Translational Medicine Computational advances in cancer liquid biopsy

2023;616(7955):113-122. doi: 10.1038/s41597-022-01712-9
doi: 10.1038/s41586-023-05795-1 114. Mignan A, Broccardo M. One neuron versus deep learning
in aftershock prediction. Nature. 2019;574(7776):E1-E3.
103. Van TM, Blank CU. A user’s perspective on GeoMxTM
digital spatial profiling. Immunooncol Technol. 2019;1:11-18. doi: 10.1038/s41586-019-1582-8
doi: 10.1016/j.iotech.2019.05.001 115. Laubenbacher R, Niarakis A, Helikar T, et al. Building digital
twins of the human immune system: Toward a roadmap.
104. Zeune LL, De Wit S, Berghuis AMS, IJzerman MJ, Terstappen NPJ Digit Med. 2022;5(1):64.
LWMM, Brune C. How to agree on a CTC: Evaluating the
consensus in circulating tumor cell scoring. Cytometry A. doi: 10.1038/s41746-022-00610-z
2018;93(12):1202-1206. 116. Popa EO, Van Hilten M, Oosterkamp E, Bogaardt MJ. The
doi: 10.1002/cyto.a.23576 use of digital twins in healthcare: Socio-ethical benefits and
socio-ethical risks. Life Sci Soc Policy. 2021;17(1):6.
105. Nanou A, Stoecklein NH, Doerr D, Driemel C, Terstappen
LWMM, Coumans FAW. Training an automated circulating doi: 10.1186/s40504-021-00113-x
tumor cell classifier when the true classification is uncertain. 117. Goodwin S, McPherson JD, McCombie WR. Coming of age:
PNAS Nexus. 2024;3(2):pgae048. Ten years of next-generation sequencing technologies. Nat
doi: 10.1093/pnasnexus/pgae048 Rev Genet. 2016;17(6):333-351.
106. Svensson CM, Hübler R, Figge MT. Automated classification doi: 10.1038/nrg.2016.49
of circulating tumor cells and the impact of interobsever 118. Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating
variability on classifier training and performance. J Immunol liquid biopsies into the management of cancer. Nat Rev Clin
Res. 2015;2015:573165. Oncol. 2017;14(9):531-548.
doi: 10.1155/2015/573165 doi: 10.1038/nrclinonc.2017.14
107. Lannin TB, Thege FI, Kirby BJ. Comparison and 119. Moser T, Kühberger S, Lazzeri I, Vlachos G, Heitzer E.
optimization of machine learning methods for automated Bridging biological cfDNA features and machine learning
classification of circulating tumor cells. Cytometry A. approaches. Trends Genet. 2023;39(4):285-307.
2016;89(10):922-931. doi: 10.1016/j.tig.2023.01.004
doi: 10.1002/cyto.a.22993 120. Han Y, Li X, Zhang M, et al. Enhanced detection of
108. Raub CB, Nehmetallah G. Holography, machine learning, genitourinary cancers using fragmentation and copy
and cancer cells. Cytometry A. 2017;91(8):754-756. number profiles obtained from urinary cell-free DNA. Clin
Chem. 2021;67(2):394-403.
doi: 10.1002/cyto.a.23112
doi: 10.1093/clinchem/hvaa283
109. Ko J, Baldassano SN, Loh PL, Kording K, Litt B, Issadore D.
Machine learning to detect signatures of disease in liquid 121. Ge G, Peng D, Guan B, et al. Urothelial carcinoma detection
biopsies - a user’s guide. Lab Chip. 2018;18(3):395-405. based on copy number profiles of urinary cell-free DNA
by shallow whole-genome sequencing. Clin Chem.
doi: 10.1039/C7LC00955K 2020;66(1):188-198.
110. Pirone D, Montella A, Sirico DG, et al. Label-free liquid doi: 10.1373/clinchem.2019.309633
biopsy through the identification of tumor cells by machine
learning-powered tomographic phase imaging flow 122. Zeune LL, Boink YE, Van Dalum G, et al. Deep learning of
cytometry. Sci Rep. 2023;13(1):6042. circulating tumour cells. Nat Mach Intell. 2020;2(2):124-133.
doi: 10.1038/s41598-023-32110-9 doi: 10.1038/s42256-020-0153-x
111. Rieke N, Hancox J, Li W, et al. The future of digital health 123. Fernandez-Garcia D, Nteliopoulos G, Hastings RK, et al.
with federated learning. NPJ Digit Med. 2020;3(1):119. Shallow WGS of individual CTCs identifies actionable
targets for informing treatment decisions in metastatic
doi: 10.1038/s41746-020-00323-1 breast cancer. Br J Cancer. 2022;127(10):1858-1864.
112. Sohn E. The reproducibility issues that haunt health-care AI. doi: 10.1038/s41416-022-01962-9
Nature. 2023;613(7943):402-403.
124. Chen JY, Chou HH, Lim SC, et al. Multiomic
doi: 10.1038/d41586-023-00023-2 characterization and drug testing establish circulating
113. Ravi N, Chaturvedi P, Huerta EA, et al. FAIR principles for tumor cells as an ex vivo tool for personalized medicine.
AI models with a practical application for accelerated high iScience. 2022;25(10):105081.
energy diffraction microscopy. Sci Data. 2022;9(1):657. doi: 10.1016/j.isci.2022.105081

Volume 3 Issue 3 (2024) 15 doi: 10.36922/gtm.3063