Author correction: quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy

Correction to: _Scientific Reports_ https://doi.org/10.1038/s41598-021-02910-y, published online 07 December 2021 The Acknowledgements section in the original version of this Article was

incomplete. “This work was partially supported by the National Institute of Health (NIH) under Grant No. R01-CA233487. I.E.N. acknowledges support form NIH under Grant Nos. R41CA243722 and

R37CA222215, and support from National Institute of Biomedical Imaging and Bioengineering under Contract No. 75N92020D00018. M.M.M acknowledges support in the form of research grant from

Varian Medical Systems and is the co-director of Michigan Radiation Oncology Quality Consortium (Funded by Blue Cross Blue Shield Michigan). We acknowledge the use of the IBM Q for this

work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Q team.” now reads: “This work was partially supported by the National

Institute of Health (NIH) under Grant No. R01-CA233487. I.E.N. acknowledges support form NIH under Grant Nos. R41CA243722 and R37CA222215, and support from National Institute of Biomedical

Imaging and Bioengineering under Contract No. 75N92020D00018. M.M.M acknowledges support in the form of research grant from Varian Medical Systems and is the co-director of Michigan

Radiation Oncology Quality Consortium (Funded by Blue Cross Blue Shield Michigan). We acknowledge the use of the IBM Q for this work. The views expressed are those of the authors and do not

reflect the official policy or position of IBM or the IBM Q team. The authors also acknowledge that the RTOG0617 dataset was made available by NCT00533949-D1 from the NCTN Data Archive of

the National Cancer Institute’s (NCI’s) National Clinical Trials Network (NCTN). Data were originally collected from clinical trial NCT number NCT00533949 [A Randomized Phase III Comparison

of Standard- Dose (60 Gy) versus High-Dose (74 Gy) Conformal Radiotherapy with Concurrent and Consolidation Carboplatin/Paclitaxel + / − Cetuximab (Ind #103,444) in Patients with Stage

IIIA/IIIB Non-Small Cell Lung Cancer]. All analyses and conclusions in this manuscript are the sole responsibility of the authors and do not necessarily reflect the opinions or views of the

clinical trial investigators, the NCTN, or the NCI." The original Article has been corrected. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Machine Learning, H. Lee

Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA Dipesh Niraula & Issam El Naqa * Department of Nuclear Engineering and Radiological Sciences, University of Michigan,

Ann Arbor, MI, 48109, USA Jamalina Jamaluddin & Martha M. Matuszak * Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48109, USA Martha M. Matuszak & Randall

K. Ten Haken Authors * Dipesh Niraula View author publications You can also search for this author inPubMed Google Scholar * Jamalina Jamaluddin View author publications You can also search

for this author inPubMed Google Scholar * Martha M. Matuszak View author publications You can also search for this author inPubMed Google Scholar * Randall K. Ten Haken View author

publications You can also search for this author inPubMed Google Scholar * Issam El Naqa View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING

AUTHOR Correspondence to Dipesh Niraula. RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,

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and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Niraula, D., Jamaluddin, J., Matuszak, M.M. _et al._ Author Correction: Quantum deep reinforcement learning for clinical decision support

in oncology: application to adaptive radiotherapy. _Sci Rep_ 13, 2318 (2023). https://doi.org/10.1038/s41598-023-28810-x Download citation * Published: 09 February 2023 * DOI:

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