دورية أكاديمية
PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.
| العنوان: | PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning. |
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| المؤلفون: | Aswolinskiy W; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Munari E; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy., Horlings HM; The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands., Mulder L; The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands., Bogina G; Pathology Unit, IRCCS Sacro Cuore Don Calabria Hospital, Negrar di Valpolicella, Verona, Italy., Sanders J; The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands., Liu YH; The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands., van den Belt-Dusebout AW; The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands., Tessier L; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.; Center for Integrated Oncology (Institut du cancer de l'Ouest), Angers, France., Balkenhol M; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Stegeman M; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Hoven J; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Wesseling J; The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands.; Leiden University Medical Center, Leiden, The Netherlands., van der Laak J; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Lips EH; The Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands., Ciompi F; Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands. francesco.ciompi@radboudumc.nl. |
| المصدر: | Breast cancer research : BCR [Breast Cancer Res] 2023 Nov 13; Vol. 25 (1), pp. 142. Date of Electronic Publication: 2023 Nov 13. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 100927353 Publication Model: Electronic Cited Medium: Internet ISSN: 1465-542X (Electronic) Linking ISSN: 14655411 NLM ISO Abbreviation: Breast Cancer Res Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: London, UK : BioMed Central Ltd Original Publication: London, UK : Current Science, c1999- |
| مواضيع طبية MeSH: | Breast Neoplasms*/diagnosis , Breast Neoplasms*/drug therapy , Breast Neoplasms*/pathology , Deep Learning*, Humans ; Female ; Neoadjuvant Therapy/methods ; Retrospective Studies ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Lymphocytes, Tumor-Infiltrating/pathology ; Biopsy ; Biomarkers ; Prognosis ; Tumor Microenvironment |
| مستخلص: | Background: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. Methods: In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). Results: We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. Conclusion: The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes. (© 2023. The Author(s).) |
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| فهرسة مساهمة: | Keywords: Computational biomarker; Neoadjuvant chemotherapy; Pathological complete response |
| المشرفين على المادة: | 0 (Biomarkers) |
| تواريخ الأحداث: | Date Created: 20231114 Date Completed: 20231115 Latest Revision: 20231122 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC10644597 |
| DOI: | 10.1186/s13058-023-01726-0 |
| PMID: | 37957667 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1465-542X |
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| DOI: | 10.1186/s13058-023-01726-0 |