{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T10:03:14Z","timestamp":1789639394205,"version":"4.0.0"},"reference-count":46,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA231513"],"award-info":[{"award-number":["R01 CA231513"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Quantitative mapping of extracellular tumor pH (pHe) using acidoCEST MRI offers a method to characterize the tumor microenvironment. Conventional analysis of acidoCEST MRI relies on fitting the Bloch\u2013McConnell equations to experimental CEST spectra. However, this \u201cBloch fitting\u201d method is computationally intensive. Recent work has demonstrated that machine learning can accurately predict pH from CEST spectra of phantoms containing iopamidol, providing an alternative to Bloch fitting for acidoCEST MRI analysis. In this study, we evaluated the ability of machine learning models to estimate extracellular tumor pHe using acidoCEST MRI in a 4T1 murine breast cancer model. Thirty\u2010eight tumor\u2010bearing mice were imaged using a CEST\u2010FISP acquisition, and CEST spectra were extracted along with T\n                    <jats:sub>1<\/jats:sub>\n                    , T\n                    <jats:sub>2<\/jats:sub>\n                    , B\n                    <jats:sub>1<\/jats:sub>\n                    , and B\n                    <jats:sub>0<\/jats:sub>\n                    maps. Pixel\u2010wise pH values derived from Bloch fitting served as reference values. We trained and tested three machine learning models: (1) random forest regression (RFR) using CEST spectra and T\n                    <jats:sub>1<\/jats:sub>\n                    , T\n                    <jats:sub>2<\/jats:sub>\n                    , B\n                    <jats:sub>1<\/jats:sub>\n                    , and B\n                    <jats:sub>0<\/jats:sub>\n                    maps; (2) RFR using only CEST spectra; and (3) a convolutional neural network (CNN) using only CEST spectra. The RFR model using only CEST spectra achieved the best performance, with mean absolute percentage errors of 0.29% (training) and 0.79% (testing), corresponding to 0.020 and 0.054 pH units, respectively. Spatial pHe maps generated by the RFR methods closely matched those from Bloch\u2013McConnell fitting, whereas CNN\u2010derived maps showed compressed pH ranges and reduced correlation with reference pH values. These findings demonstrate that RFR provides a computationally efficient alternative to Bloch fitting for in\u00a0vivo acidoCEST MRI.\n                  <\/jats:p>","DOI":"10.1002\/nbm.70379","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T10:24:28Z","timestamp":1787739868000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning Approach That Measures Extracellular pH Within an In\u00a0Vivo Tumor Model Using acidoCEST MRI"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5539-9694","authenticated-orcid":false,"given":"Alia","family":"Khaled","sequence":"first","affiliation":[{"name":"Department of Cancer Systems Imaging University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1218-0320","authenticated-orcid":false,"given":"Chetan B.","family":"Dhakan","sequence":"additional","affiliation":[{"name":"Department of Cancer Systems Imaging University of Texas MD Anderson Cancer Center  Houston Texas USA"},{"name":"Department of Medical Physics University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9788-7987","authenticated-orcid":false,"given":"Lucas B.","family":"McCullum","sequence":"additional","affiliation":[{"name":"Department of Cancer Systems Imaging University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7344-4249","authenticated-orcid":false,"given":"Jorge","family":"de la Cerda","sequence":"additional","affiliation":[{"name":"Department of Cancer Systems Imaging University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F. William","family":"Schuler","sequence":"additional","affiliation":[{"name":"Department of Cancer Systems Imaging University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5671-3163","authenticated-orcid":false,"given":"Tianzhe","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Nebraska  Omaha Nebraska USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8109-3995","authenticated-orcid":false,"given":"Mark D.","family":"Pagel","sequence":"additional","affiliation":[{"name":"Department of Cancer Systems Imaging University of Texas MD Anderson Cancer Center  Houston Texas USA"},{"name":"Department of Medical Physics University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrc3298"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.123.3191.309"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrc.2017.77"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2013.0099"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrc3110"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/1475\u20102867\u201013\u201089"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1608247113"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.1644"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22761"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1006\/jmre.1999.1956"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11307\u2010014\u20100816\u20102"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25053"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22608"},{"key":"e_1_2_12_15_1","first-page":"978815","volume-title":"Respiration Gating and Bloch Fitting Improve pH Measurements With acidoCEST MRI in an Ovarian Orthotopic Tumor Model","author":"Jones K. 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