<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>cirpublications.com-QBWo-1790880671-e516865639</doi_batch_id>
		<timestamp>1790880671</timestamp>
		<depositor>
			<depositor_name>Clinical Intelligence Research Press</depositor_name>
			<email_address>info@cirpublications.com</email_address>
		</depositor>
		<registrant>Clinical Intelligence Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2024</year>
				</publication_date>
				<journal_volume>
					<volume>3</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Neural ODE-Based Tumor Dynamics Modeling for Pathologic Complete Response Prediction in Breast Cancer from Serial MRI</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Alejandro</given_name>
            <surname>Torres</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Miguel</given_name>
            <surname>Fernandez</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/e516865639</doi>
					<resource>https://cirpublications.com/pub/journal/1/article/e516865639</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/e516865639-12e5fd21-62aa-4f56-85cb-8d0299289f59">
					  <unstructured_citation>Chen RT, Rubanova Y, Bettencourt J, Duvenaud DK. Neural ordinary differential equations. Adv Neural Inf Process Syst. 2018;31:6571-83.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-af006e2d-f182-47fc-98f3-2a988ac4ddea">
					  <unstructured_citation>Rubanova Y, Chen RT, Duvenaud DK. Latent ordinary differential equations for irregularly-sampled time series. Adv Neural Inf Process Syst. 2019;32:5321-31.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-c012d608-e36a-4130-bdb9-e4a4a555a01c">
					  <unstructured_citation>Rackauckas C, Ma Y, Martensen J, Warner C, Zubov K, Supekar R, et al. Universal differential equations for scientific machine learning. arXiv [Preprint]. 2020:arXiv:2001.04385.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-019e414e-7d6c-4afa-9043-1be5df8b075e">
					  <unstructured_citation>Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys. 2019;378:686-707.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-204d04d4-308e-40a9-8c88-4b216cf69466">
					  <unstructured_citation>Zhu Z, Albadawy E, Saha A, Zhang J, Harowicz MR, Mazurowski MA. Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med. 2019;109:85-90.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-dd75435e-4539-45fd-9144-26c6a19c42a1">
					  <unstructured_citation>Braman N, Adoui ME, Vulchi M, Turk P, Etesami M, Fu P, et al. Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: a multi-institutional validation study. arXiv [Preprint]. 2020:arXiv:2001.08570.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-ce08ebef-f30c-49a7-ac6a-085a45f09f2c">
					  <unstructured_citation>Ha R, Chin C, Karcich J, Liu MZ, Chang P, Mutasa S, et al. Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset. J Digit Imaging. 2019;32(5):693-701.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-fa519ee3-fd2b-4531-967c-3a4fa1933310">
					  <unstructured_citation>Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast Cancer Res Treat. 2019;173(2):455-63.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-13f14c68-86a6-4b14-ace0-267847bc5e85">
					  <unstructured_citation>Peng Y, Cheng Z, Gong C, Zheng C, Zhang X, Wu Z, et al. Pretreatment DCE-MRI-based deep learning outperforms radiomics analysis in predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. Front Oncol. 2022;12:846775.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-0290d393-ce06-43f4-a22a-5a48ad19236b">
					  <unstructured_citation>Joo S, Ko ES, Kwon S, Jeon E, Jung H, Kim JY, et al. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Sci Rep. 2021;11(1):18800.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-0fbee232-6949-45aa-a964-dacd7e325943">
					  <unstructured_citation>Qu YH, Zhu HT, Cao K, Li XT, Ye M, Sun YS. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning method. Thorac Cancer. 2020;11(3):651-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-b17464fb-ae93-4897-a907-5a807881fce8">
					  <unstructured_citation>Jin C, Yu H, Ke J, Ding P, Yi Y, Jiang X, et al. Predicting treatment response from longitudinal images using multi-task deep learning. Nat Commun. 2021;12(1):1851.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-d51b5e99-c553-4d9d-b9bc-7a55e7ed1096">
					  <unstructured_citation>Li F, Yang Y, Wei Y, He P, Chen J, Zheng Z, et al. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl Med. 2021;19(1):348.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-7c9532f9-1a10-46c9-b36b-57ba72c81531">
					  <unstructured_citation>Dammu H, Ren T, Duong TQ. Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. PLoS One. 2023;18(1):e0280148.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-7a3fbd86-c5df-433c-a664-f3445fde0d67">
					  <unstructured_citation>Panthi B, Mohamed RM, Adrada BE, Boge M, Candelaria RP, Chen H, et al. Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Front Oncol. 2023;13:1264259.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-e8fac257-9d63-4fa3-9e0f-2b9ec4fafa5f">
					  <unstructured_citation>Mohamed RM, Panthi B, Adrada BE, Boge M, Candelaria RP, Chen H, et al. Multiparametric MRI-based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Sci Rep. 2024;14(1):16073.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-02cc54ee-9541-4058-b3f1-3c860aaf57e6">
					  <unstructured_citation>Zhou Z, Adrada BE, Candelaria RP, Elshafeey NA, Boge M, Mohamed RM, et al. Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI. Sci Rep. 2023;13(1):1171.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-62f8f339-5386-4939-82ad-81edb4af4f89">
					  <unstructured_citation>Zeng H, Qiu S, Zhuang S, Wei X, Wu J, Zhang R, et al. Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study. Front Physiol. 2024;15:1279982.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-6797b4eb-6de6-494b-9683-938f60f1b9bd">
					  <unstructured_citation>Guo J, Chen B, Cao H, Dai Q, Qin L, Zhang J, et al. Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. NPJ Precis Oncol. 2024;8(1):189.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-a72a4056-1164-4ec9-9336-d897d67f18cc">
					  <unstructured_citation>Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep learning in breast cancer imaging: state of the art and recent advancements in early 2024. Diagnostics (Basel). 2024;14(8):848.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-b808779c-912a-440a-bc1d-564aa9aef2bf">
					  <unstructured_citation>Wong C, Fu Y, Li M, Mu S, Chu X, Fu J, et al. MRI-based artificial intelligence in rectal cancer. J Magn Reson Imaging. 2023;57(1):45-56.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-865bb59a-42f1-4829-95af-f39c3e640ff7">
					  <unstructured_citation>Ravichandran K, Braman N, Janowczyk A, Madabhushi A. A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI. In: Medical Imaging 2018: Computer-Aided Diagnosis. Bellingham (WA): SPIE; 2018. p. 79-88.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-b7ef4138-0030-40ed-b673-42890f54f376">
					  <unstructured_citation>Raissi M, Karniadakis GE. Hidden physics models: machine learning of nonlinear partial differential equations. J Comput Phys. 2018;357:125-41.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-a04dd069-3861-4eb2-a0de-d7a341b2a8ca">
					  <unstructured_citation>Ansari AF, Heng A, Lim A, Soh H. Neural continuous-discrete state space models for irregularly-sampled time series. In: International Conference on Machine Learning. PMLR; 2023. p. 926-51.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-7eb7b0cb-529a-4e37-90d2-65a4a9686cd9">
					  <unstructured_citation>Bilic A, Chen C. BC-MRI-SEG: a breast cancer MRI tumor segmentation benchmark. In: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). IEEE; 2024. p. 674-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-b1934cca-0722-4a72-989a-b7f1b570f885">
					  <unstructured_citation>Kim J, Park H. Radiomics-guided multimodal self-attention network for predicting pathological complete response in breast MRI. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2024. p. 1-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-4a423525-4fc2-4c5b-a5db-9ccd61980809">
					  <unstructured_citation>Sharma D, Purushotham S, Reddy CK. MedFuseNet: an attention-based multimodal deep learning model for visual question answering in the medical domain. Sci Rep. 2021;11(1):19826.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-c7cc09c6-839a-4989-aea2-aa4bc46cd4dc">
					  <unstructured_citation>Zhao Q, Liu Z, Adeli E, Pohl KM. Longitudinal self-supervised learning. Med Image Anal. 2021;71:102051.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/e516865639-ccedba53-e27a-4481-bda2-1fdd20d0d83e">
					  <unstructured_citation>Vieira BH, Liem F, Dadi K, Engemann DA, Gramfort A, Bellec P, et al. Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging. Neurobiol Aging. 2022;118:55-65.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
