{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:29:40Z","timestamp":1787020180544,"version":"build-2736575974"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100012997","name":"Arab Academy for Science, Technology & Maritime Transport","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100012997","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&amp;E) stained images. We propose a new IHC-GAN that enhances the Pix2PixHD model into a dual generator module, improving its performance and simplifying its structure. Furthermore, to strengthen feature extraction for HE-stained image classification, we integrate MobileNetV3 as the backbone network. The extracted features are then merged with those generated by the generator to improve overall performance. Moreover, the decoder\u2019s performance is enhanced by providing the related features from the classified labels by incorporating the adaptive instance normalization technique. The proposed IHC-GAN was trained and validated on a comprehensive dataset comprising 4,870 registered image pairs, encompassing a spectrum of HER2 expression levels. Our findings demonstrate promising results in translating H&amp;E images to IHC-equivalent representations, offering a potential solution to reduce the costs associated with traditional HER2 assessment methods. We extensively validate our model and the current dataset. We compare it with state-of-the-art techniques, achieving high performance using different evaluation metrics, showing 0.0927 FID, 22.87 PSNR, and 0.3735 SSIM. The proposed approach exhibits significant enhancements over current GAN models, including an 88% reduction in Frechet Inception Distance (FID), a 4% enhancement in Learned Perceptual Image Patch Similarity (LPIPS), a 10% increase in Peak Signal-to-Noise Ratio (PSNR), and a 45% reduction in Mean Squared Error (MSE). This advancement holds significant potential for enhancing efficiency, reducing manpower requirements, and facilitating timely treatment decisions in breast cancer care.<\/jats:p>","DOI":"10.1186\/s12880-024-01522-y","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T03:01:54Z","timestamp":1736132514000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model"],"prefix":"10.1186","volume":"25","author":[{"given":"Afaf","family":"Saad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noha","family":"Ghatwary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Safa M.","family":"Gasser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed S.","family":"ElMahallawy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"issue":"1130","key":"1522_CR1","doi-asserted-by":"publisher","first-page":"20211033","DOI":"10.1259\/bjr.20211033","volume":"95","author":"L Wilkinson","year":"2022","unstructured":"Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. 2022;95(1130):20211033.","journal-title":"Br J Radiol."},{"key":"1522_CR2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","volume":"73","author":"R Siegel","year":"2023","unstructured":"Siegel R, Miller K, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17\u201348. https:\/\/doi.org\/10.3322\/caac.21763.","journal-title":"CA Cancer J Clin."},{"key":"1522_CR3","doi-asserted-by":"publisher","unstructured":"Giaquinto AN, Sung H, Miller K, Kramer J, Newman L, Minihan AK, et\u00a0al. Breast Cancer Statistics, 2022. CA Cancer J Clin. 2022;72. https:\/\/doi.org\/10.3322\/caac.21754.","DOI":"10.3322\/caac.21754"},{"key":"1522_CR4","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1111\/apm.13076","volume":"128","author":"SG Jensen","year":"2020","unstructured":"Jensen SG, Thomas PE, Christensen I, Balslev E, Hansen AB, H\u00f8gdall E. Evaluation of analytical accuracy of HER2 status in patients with breast cancer. APMIS. 2020;128:573\u201382. https:\/\/doi.org\/10.1111\/apm.13076.","journal-title":"APMIS."},{"key":"1522_CR5","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1136\/jcp-2022-208632","volume":"76","author":"E Rakha","year":"2022","unstructured":"Rakha E, Tan P, Quinn C, Provenzano E, Shaaban A, Deb R, et al. UK recommendations for HER2 assessment in breast cancer: an update. J Clin Pathol. 2022;76:217\u201327. https:\/\/doi.org\/10.1136\/jcp-2022-208632.","journal-title":"J Clin Pathol."},{"issue":"6","key":"1522_CR6","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1109\/TRPMS.2021.3071148","volume":"6","author":"Y Akhtar","year":"2022","unstructured":"Akhtar Y, Dakua SP, Abdalla A, Aboumarzouk OM, Ansari MY, Abinahed J, et al. Risk Assessment of Computer-Aided Diagnostic Software for Hepatic Resection. IEEE Trans Radiat Plasma Med Sci. 2022;6(6):667\u201377. https:\/\/doi.org\/10.1109\/TRPMS.2021.3071148.","journal-title":"IEEE Trans Radiat Plasma Med Sci."},{"issue":"3","key":"1522_CR7","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1109\/TETCI.2024.3377676","volume":"8","author":"MY Ansari","year":"2024","unstructured":"Ansari MY, Changaai Mangalote IA, Meher PK, Aboumarzouk O, Al-Ansari A, Halabi O, et al. Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review. IEEE Trans Emerg Top Comput Intell. 2024;8(3):2126\u201349. https:\/\/doi.org\/10.1109\/TETCI.2024.3377676.","journal-title":"IEEE Trans Emerg Top Comput Intell."},{"issue":"13","key":"1522_CR8","doi-asserted-by":"publisher","first-page":"14225","DOI":"10.1002\/cam4.6089","volume":"12","author":"P Rai","year":"2023","unstructured":"Rai P, Ansari MY, Warfa M, Al-Hamar H, Abinahed J, Barah A, et al. Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review. Cancer Med. 2023;12(13):14225\u201351.","journal-title":"Cancer Med."},{"key":"1522_CR9","doi-asserted-by":"publisher","unstructured":"Ansari MY, Mangalote IAC, Masri D, Dakua SP. Neural network-based fast liver ultrasound image segmentation. In: 2023 international joint conference on neural networks (IJCNN).\u00a0Gold Coast, Australia: IEEE; 2023. pp. 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN54540.2023.10191085.","DOI":"10.1109\/IJCNN54540.2023.10191085"},{"key":"1522_CR10","doi-asserted-by":"publisher","first-page":"1282536","DOI":"10.3389\/fonc.2023.1282536","volume":"13","author":"MY Ansari","year":"2023","unstructured":"Ansari MY, Qaraqe M, Righetti R, Serpedin E, Qaraqe K. Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound. Front Oncol. 2023;13:1282536.","journal-title":"Front Oncol."},{"issue":"11","key":"1522_CR11","doi-asserted-by":"publisher","first-page":"153155","DOI":"10.1016\/j.prp.2020.153155","volume":"216","author":"J Zhao","year":"2020","unstructured":"Zhao J, Krishnamurti U, Zhang C, Meisel J, Wei Z, Li Suo A, et al. HER2 immunohistochemistry staining positivity is strongly predictive of tumor response to neoadjuvant chemotherapy in HER2 positive breast cancer. Pathol Res Pract. 2020;216(11):153155. https:\/\/doi.org\/10.1016\/j.prp.2020.153155.","journal-title":"Pathol Res Pract."},{"key":"1522_CR12","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18\u201331.","journal-title":"Med Image Anal."},{"key":"1522_CR13","doi-asserted-by":"crossref","unstructured":"Liu S, Zhu C, Xu F, Jia X, Shi Z, Jin M. Bci: Breast cancer immunohistochemical image generation through pyramid pix2pix. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022. pp. 1815\u201324.","DOI":"10.1109\/CVPRW56347.2022.00198"},{"key":"1522_CR14","doi-asserted-by":"publisher","first-page":"100311","DOI":"10.1016\/j.jpi.2023.100311","volume":"14","author":"M Roy","year":"2023","unstructured":"Roy M, Wang F, Teodoro G, Bhattarai S, Bhargava M, Rekha TS, et al. Deep learning based registration of serial whole-slide histopathology images in different stains. J Pathol Inform. 2023;14:100311. https:\/\/doi.org\/10.1016\/j.jpi.2023.100311.","journal-title":"J Pathol Inform."},{"key":"1522_CR15","doi-asserted-by":"crossref","unstructured":"Zhu C, Liu S, Xu F, Yu Z, Aggarwal A, Corredor G,\u00a0Madabhushi\u00a0A,\u00a0Qu Q,\u00a0Fan H,\u00a0Li F,\u00a0Li Y,\u00a0Guan X,\u00a0Zhang Y,\u00a0Singh VK,\u00a0Akram F,\u00a0Sarker Md. MK, Shi Z,\u00a0Jin M. Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review.\u00a0arXiv\u00a0preprint\u00a0arXiv:2305.03546.\u00a02023.\u00a0https:\/\/arxiv.org\/abs\/2305.03546.","DOI":"10.36227\/techrxiv.22756814"},{"key":"1522_CR16","doi-asserted-by":"publisher","unstructured":"Liu L, Liu Z, Chang J, Qiao H, Sun T, Shang J. MGGAN: A multi-generator generative adversarial network for breast cancer immunohistochemical image generation. Heliyon. 2023;9(10):e20614. ISSN 2405-8440. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e20614.","DOI":"10.1016\/j.heliyon.2023.e20614"},{"key":"1522_CR17","doi-asserted-by":"crossref","unstructured":"Huang S, Wang H, Hao Y, Guo S, Wang Y, Wang T. TC-CycleGAN: Improved CycleGAN with Texture Constraints for Virtual Staining of Pathological Images. In: Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing. New York, Sanya, China: Association for Computing Machinery;\u00a02023. pp. 147\u201352.\u00a0ISBN 9798400700200.","DOI":"10.1145\/3592686.3592713"},{"key":"1522_CR18","doi-asserted-by":"publisher","unstructured":"Li F, Hu Z, Chen W, Kak A. Adaptive Supervised PatchNCE Loss for Learning H &E-to-IHC Stain Translation with Inconsistent Groundtruth Image Pairs. ArXiv.\u00a02023;abs\/2303.06193.\u00a0https:\/\/doi.org\/10.48550\/arXiv.2303.06193.","DOI":"10.48550\/arXiv.2303.06193"},{"key":"1522_CR19","doi-asserted-by":"crossref","unstructured":"Wang TC, Liu MY, Zhu JY, Tao A, Kautz J, Catanzaro B. High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 8798\u201307.","DOI":"10.1109\/CVPR.2018.00917"},{"key":"1522_CR20","doi-asserted-by":"publisher","unstructured":"Koonce B, Koonce B. MobileNetV3. In: Convolutional Neural Networks with Swift for Tensorflow. Berkeley: Apress. https:\/\/doi.org\/10.1007\/978-1-4842-6168-2_11.","DOI":"10.1007\/978-1-4842-6168-2_11"},{"key":"1522_CR21","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M,\u00a0Wang W,\u00a0Zhu Y,\u00a0Pang\u00a0R,\u00a0Vasudevan V,\u00a0Le QV, Adam H.\u00a0Searching for MobileNetV3. In:\u00a0Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 2019. pp. 1314\u201324.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1522_CR22","unstructured":"Liu MY, Tuzel O. Coupled generative adversarial networks. Adv Neural Inf Process Syst.\u00a0Curran Associates, Inc. 2016;29. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2016\/file\/502e4a16930e414107ee22b6198c578f-Paper.pdf."},{"key":"1522_CR23","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. pp. 4510\u201320.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1522_CR24","doi-asserted-by":"crossref","unstructured":"Huang X, Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization. In:\u00a0Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017. pp. 1501\u201310.","DOI":"10.1109\/ICCV.2017.167"},{"key":"1522_CR25","doi-asserted-by":"publisher","unstructured":"Johnson J, Alahi A, Fei-Fei L. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision \u2013 ECCV 2016. ECCV 2016. Springer, Cham: Lecture Notes in Computer Science. vol 9906. https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"1522_CR26","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s12021-018-9377-x","volume":"16","author":"Y Xue","year":"2018","unstructured":"Xue Y, Xu T, Zhang H, Long LR, Huang X. Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation. Neuroinformatics. 2018;16:383\u201392.","journal-title":"Neuroinformatics."},{"issue":"4","key":"1522_CR27","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600\u201312.","journal-title":"IEEE Trans Image Process."},{"key":"1522_CR28","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training gans. Adv Neural Inf Process Syst. 2016;29."},{"key":"1522_CR29","doi-asserted-by":"publisher","unstructured":"Liang C, Zhu M, Wang N, Yang H, Gao X. Pmsgan: Parallel multistage gans for face image translation. IEEE Trans Neural Netw Learn Syst. 2024;35(7):9352\u201365. https:\/\/doi.org\/10.1109\/TNNLS.2022.3233025.","DOI":"10.1109\/TNNLS.2022.3233025"},{"key":"1522_CR30","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. In:\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018. pp. 586\u201395.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"1522_CR31","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $$<$$0.5 MB model size.\u00a0arXiv\u00a0preprint\u00a0arXiv:1602.07360.\u00a02016."},{"issue":"6","key":"1522_CR32","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84\u201390.","journal-title":"Commun ACM."},{"key":"1522_CR33","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.\u00a02014."},{"key":"1522_CR34","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1109\/TIP.2022.3152624","volume":"31","author":"W Quan","year":"2022","unstructured":"Quan W, Zhang R, Zhang Y, Li Z, Wang J, Yan DM. Image inpainting with local and global refinement. IEEE Trans Image Process. 2022;31:2405\u201320.","journal-title":"IEEE Trans Image Process."},{"key":"1522_CR35","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Process Syst.\u00a0Curran Associates, Inc. 2017;30."},{"key":"1522_CR36","doi-asserted-by":"crossref","unstructured":"Park T, Liu MY, Wang TC, Zhu JY. Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2019. pp. 2337\u201346.","DOI":"10.1109\/CVPR.2019.00244"},{"key":"1522_CR37","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ. Densely connected convolutional networks. In:\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. pp. 4700\u20138.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1522_CR38","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.jvcir.2015.10.016","volume":"34","author":"SP Dakua","year":"2016","unstructured":"Dakua SP, Abinahed J, Al-Ansari AA. Pathological liver segmentation using stochastic resonance and cellular automata. J Vis Commun Image Represent. 2016;34:89\u2013102.","journal-title":"J Vis Commun Image Represent."},{"issue":"18","key":"1522_CR39","doi-asserted-by":"publisher","first-page":"5971","DOI":"10.1109\/JSEN.2017.2736641","volume":"17","author":"SP Dakua","year":"2017","unstructured":"Dakua SP. Towards left ventricle segmentation from magnetic resonance images. IEEE Sensors J. 2017;17(18):5971\u201381.","journal-title":"IEEE Sensors J."},{"key":"1522_CR40","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s11045-016-0464-6","volume":"29","author":"SP Dakua","year":"2018","unstructured":"Dakua SP, Abinahed J, Al-Ansari A. A PCA-based approach for brain aneurysm segmentation. Multidim Syst Signal Process. 2018;29:257\u201377.","journal-title":"Multidim Syst Signal Process."},{"key":"1522_CR41","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1007\/s11548-019-02030-z","volume":"14","author":"SP Dakua","year":"2019","unstructured":"Dakua SP, Abinahed J, Zakaria A, Balakrishnan S, Younes G, Navkar N, et al. Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping. Int J CARS. 2019;14:2165\u201376.","journal-title":"Int J CARS."},{"key":"1522_CR42","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jocs.2018.05.002","volume":"27","author":"X Zhai","year":"2018","unstructured":"Zhai X, Eslami M, Hussein ES, Filali MS, Shalaby ST, Amira A, et al. Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip. J Comput Sci. 2018;27:35\u201345.","journal-title":"J Comput Sci."},{"key":"1522_CR43","unstructured":"Zhang R, Isola P, Efros A, Shechtman E, Wang O. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE; 2018."},{"issue":"2","key":"1522_CR44","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1109\/JSYST.2019.2952459","volume":"14","author":"X Zhai","year":"2019","unstructured":"Zhai X, Chen M, Esfahani SS, Amira A, Bensaali F, Abinahed J, et al. Heterogeneous system-on-chip-based Lattice-Boltzmann visual simulation system. IEEE Syst J. 2019;14(2):1592\u2013601.","journal-title":"IEEE Syst J."},{"key":"1522_CR45","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s11548-020-02120-3","volume":"15","author":"SS Esfahani","year":"2020","unstructured":"Esfahani SS, Zhai X, Chen M, Amira A, Bensaali F, AbiNahed J, et al. Lattice-Boltzmann interactive blood flow simulation pipeline. Int J Comput Assist Radiol Surg. 2020;15:629\u201339.","journal-title":"Int J Comput Assist Radiol Surg."}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01522-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01522-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01522-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T15:03:36Z","timestamp":1736175816000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01522-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1522"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01522-y","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"23 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"We confirm that the manuscript represents our own work, is original, and has not been copyrighted, published, submitted, or accepted for publication elsewhere. We further confirm that we all have fully read the manuscript and give consent to be co-authors of the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}