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In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R\u2010CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi\u2010modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. 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This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.<\/jats:p>","DOI":"10.1002\/2056-4538.12370","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T04:05:29Z","timestamp":1712549129000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Prediction of immunochemotherapy response for diffuse large\n                    <scp>B<\/scp>\n                    \u2010cell lymphoma using artificial intelligence digital pathology"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1789-8270","authenticated-orcid":false,"given":"Jeong Hoon","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Radiology Stanford University School of Medicine  Stanford CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ga\u2010Young","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Hematology\u2010Oncology Chonnam National University Hwasun Hospital  Hwasun Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4528-5965","authenticated-orcid":false,"given":"Jonghyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Medical and Digital Engineering Hanyang University College of Engineering  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sae\u2010Ryung","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine Chonnam National University Hwasun Hospital and Medical School  Hwasun\u2010gun Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0830-0572","authenticated-orcid":false,"given":"Kyoung Min","family":"Moon","sequence":"additional","affiliation":[{"name":"Division of Pulmonary and Allergy Medicine, Department of Internal Medicine Chung\u2010Ang University Hospital, Chung\u2010Ang University College of Medicine  Seoul Republic of Korea"},{"name":"Artificial Intelligence, Ziovision Co., Ltd.  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