Correction: Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy

Yue Yan^1,2*Daniel A Alexander^1,2Bryan P Bednarz³Lawrence F Bronk⁴Huixiao Chen⁵David J Gladstone^1,2,6Bin Han⁷Christopher M Iannuzzi⁸Yuting Li⁴Ngoc Nguyen⁵Natasha Mulenga⁶Natalie N Viscariello⁹Yuenan Wang⁸Joseph Weygand^1,2Yana Zlateva⁴Fada Guan⁴

• ¹Dartmouth Hitchcock Medical Center, Lebanon, United States
• ²Dartmouth College Geisel School of Medicine, Hanover, United States
• ³University of Wisconsin-Madison, Madison, United States
• ⁴The University of Texas MD Anderson Cancer Center Division of Radiation Oncology, Houston, United States
• ⁵Yale University Yale Cancer Center, New Haven, United States
• ⁶Dartmouth College Thayer School of Engineering, Hanover, United States
• ⁷Stanford University Cancer Institute, Stanford, United States
• ⁸St Vincent's Medical Center, Bridgeport, United States
• ⁹The University of Alabama at Birmingham, Birmingham, United States

Correction on: Yan, Y., Alexander, D. A., Bednarz, B. P., Bronk, L. F., Chen, H., Gladstone, D. J., et al. Innovative Approaches in Precision Radiation Oncology: Advanced Imaging Technologies and Challenges Which Shape the Future of Radiation Therapy. Frontiers in Medicine, 12, 1686593. The original version of this article has been updated. The reference for Architecture of the U-Net in caption of Figure 6 was erroneously written as "Maas AL, Hannun AY, Ng AY. June. Rectifier nonlinearities improve neural network acoustic models. In Proc ICML (Vol. 30,No. 1,p. 3) The reference for Visualization of segmented bio-morphological structures derived from Cherenkov imaging in ten representative breast cancer patients in caption of Figure 9 was erroneously written as "Alexander DA, Decker SM, Jermyn M, Bruza P, Zhang R, Chen E, et al. One year of clinic-wide cherenkov imaging for discovery of quality improvement opportunities in radiation therapy. Pract Radiat Oncol. ( 2023 (96)(97)(98). Purpose-built for grid-like data, they have become ubiquitous in medical imaging applications (99-102). Each convolutional layer deploys a bank of learnable filters that scan the input, capturing local patterns-such as edges and textures-while sharing parameters across the field of view to curb model complexity and ensure translation invariance. Stacking multiple convolutional layers with non-linear activation yields progressively abstract, hierarchical feature representations (103-105). Pooling ( 106) and other down-sampling operations (107) further condense contextual information, whereas random dropout (108) regularizes the network and mitigates overfitting. By learning features directly from data rather than relying on hand-crafted descriptors, CNNs have become the backbone of image analysis and synthesis tasks in RT. One of the most well-known CNN models is the U-shaped net (U-Net) proposed by Ronneberger et al. (59) (Figure 6). One important modification of the U-Net is direct skip connections between the encoder and the decoder. The U-Net does not have any fully connected layers. Instead, it only uses the valid part of each convolution, which allows the network to propagate context information to the up-sampling layers."The original version of this article has been updated.End of template. If you would like to request a correction for a reason not seen here, please contact the journal's editorial office.