Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology

IF 3.4 2区 医学 Q1 PATHOLOGY Journal of Pathology Clinical Research Pub Date : 2024-04-08 DOI:10.1002/2056-4538.12370
Jeong Hoon Lee, Ga-Young Song, Jonghyun Lee, Sae-Ryung Kang, Kyoung Min Moon, Yoo-Duk Choi, Jeanne Shen, Myung-Giun Noh, Deok-Hwan Yang
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Abstract

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. 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-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal 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. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. 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.

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利用人工智能数字病理学预测弥漫大 B 细胞淋巴瘤的免疫化疗反应
弥漫大 B 细胞淋巴瘤(DLBCL)是侵袭性非霍奇金淋巴瘤的一种异质性流行亚型,给诊断和预后带来了挑战,尤其是在预测药物反应性方面。在这项研究中,我们利用数字病理学和深度学习来预测 DLBCL 患者对免疫化疗的反应。我们回顾性地收集了216名接受利妥昔单抗、环磷酰胺、多柔比星、长春新碱和泼尼松(R-CHOP)治疗的DLBCL患者的251张幻灯片图像,以及他们的免疫化疗反应标签。数字病理图像通过对比学习进行特征提取。通过整合临床数据和病理图像特征,建立了一个多模式预测模型。通过知识提炼,减轻了对千兆像素组织病理图像的过度拟合,从而建立了一个仅根据病理图像预测反应的模型。根据模型的注意机制得出的重要性,我们提取了被认为与药物反应性相关的关键纹理的组织学特征。多模态预测模型的 ROC 曲线下面积达到了令人印象深刻的 0.856,显示出与临床变量(如安娜堡分期、国际预后指数和大块疾病)的显著关联。生存期分析表明,该模型能有效预测无复发生存期。使用 TCGA 数据集进行的外部验证支持了该模型预测生存期差异的能力。此外,基于病理学的预测有望成为独立的预后指标。组织病理学分析发现成中心细胞和免疫细胞特征与治疗反应相关,这与之前的形态学分类一致,突出了基于人工智能诊断的客观性和可重复性。本研究介绍了一种结合数字病理学和临床数据预测 DLBCL 患者免疫化疗反应的新方法。该模型有望成为 DLBCL 临床管理的诊断和预后工具。进一步的研究和基因组数据整合有可能增强其对临床实践的影响,最终改善患者的预后。
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来源期刊
Journal of Pathology Clinical Research
Journal of Pathology Clinical Research Medicine-Pathology and Forensic Medicine
CiteScore
7.40
自引率
2.40%
发文量
47
审稿时长
20 weeks
期刊介绍: The Journal of Pathology: Clinical Research and The Journal of Pathology serve as translational bridges between basic biomedical science and clinical medicine with particular emphasis on, but not restricted to, tissue based studies. The focus of The Journal of Pathology: Clinical Research is the publication of studies that illuminate the clinical relevance of research in the broad area of the study of disease. Appropriately powered and validated studies with novel diagnostic, prognostic and predictive significance, and biomarker discover and validation, will be welcomed. Studies with a predominantly mechanistic basis will be more appropriate for the companion Journal of Pathology.
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