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Development and validation of a pathomics-driven machine learning model for individualized prediction of neoadjuvant chemotherapy response and early recurrence in HR-positive, HER2-negative breast cancer.

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Title: Development and validation of a pathomics-driven machine learning model for individualized prediction of neoadjuvant chemotherapy response and early recurrence in HR-positive, HER2-negative breast cancer.
Authors: Yue J; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Liu J; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Kang X; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Yuan P; Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Wang W; Thorough Lab, Thorough Future, Beijing, China., Wang Z; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Shang C; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Shang Q; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Li G; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Dong X; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Wang T; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Yang D; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Wang S; Thorough Lab, Thorough Future, Beijing, China., Yang C; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Ying J; Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Wang X; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Source: Frontiers in oncology [Front Oncol] 2026 Feb 23; Vol. 16, pp. 1770037. Date of Electronic Publication: 2026 Feb 23 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101568867 Publication Model: eCollection Cited Medium: Print ISSN: 2234-943X (Print) Linking ISSN: 2234943X NLM ISO Abbreviation: Front Oncol Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: Development and validation of a pathomics-driven machine learning model for individualized prediction of neoadjuvant chemotherapy response and early recurrence in HR-positive, HER2-negative breast cancer.
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  Data: <searchLink fieldCode="AU" term="%22Yue+J%22">Yue J</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Liu+J%22">Liu J</searchLink>; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Kang+X%22">Kang X</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Yuan+P%22">Yuan P</searchLink>; Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+W%22">Wang W</searchLink>; Thorough Lab, Thorough Future, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+Z%22">Wang Z</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Shang+C%22">Shang C</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Shang+Q%22">Shang Q</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Li+G%22">Li G</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Dong+X%22">Dong X</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+T%22">Wang T</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Yang+D%22">Yang D</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+S%22">Wang S</searchLink>; Thorough Lab, Thorough Future, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Yang+C%22">Yang C</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Ying+J%22">Ying J</searchLink>; Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+X%22">Wang X</searchLink>; Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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  Data: <searchLink fieldCode="JN" term="%22101568867%22">Frontiers in oncology</searchLink> [Front Oncol] 2026 Feb 23; Vol. 16, pp. 1770037. <i>Date of Electronic Publication: </i>2026 Feb 23 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Research+Foundation]%22">Frontiers Research Foundation] </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101568867 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>2234-943X (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%222234943X%22">2234943X </searchLink><i>NLM ISO Abbreviation: </i>Front Oncol <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.3389/fonc.2026.1770037
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      – Code: eng
        Text: English
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        StartPage: 1770037
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      – TitleFull: Development and validation of a pathomics-driven machine learning model for individualized prediction of neoadjuvant chemotherapy response and early recurrence in HR-positive, HER2-negative breast cancer.
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