Clinicopathological Characteristics and Prediction of Overall Survival and Death Within 2 Years in Diffuse Large...

Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. To date, it is not possible to identify which DLBCL patients will have an aggressive clinical evolution only by using hematoxylin and eosin (H&E) histological images. Methods: This study predicted the prognosis of DLBCL using H&E images, computer vision and deep learning. The series included 114 DLBCL cases, split into 2 prognostic groups according to overall survival, and 44 cases of reactive lymphoid tissue. Results: The curve fitting and slope analysis showed a point of inflection at 2 years (24 months), which differentiated patients with aggressive clinical evolution ("Dead < 2 years", b1 = -0.024) from the rest with moderate clinical evolution ("Others", b1 = -0.003). Twenty different convolutional neural networks (CNNs) were used, and explainable artificial intelligence (XAI) was also applied. The final model based on DarkNet-19 predicted prognosis groups with high performance (test set accuracy = 96.3%). The other performance parameters were precision (94.5%), recall (95.0%), false positive rate (3.1%), specificity (96.9%), and F1 score (94.7%). XAI, including grad-CAM, occlusion sensitivity, and image-LIME, confirmed that the CNN focused on the correct areas. Hybrid partitioning to prevent information leakage with patient-based analysis, image classification between DLBCL and 44 cases of reactive lymphoid tissue, and hyperparameter tuning were also successfully performed. Correlation with the clinicopathological characteristics found that the Dead < 2 years group was correlated with stage III-IV, International Prognostic Index (IPI) High + High/intermediate, progressive disease, non-GCB cell-of-origin, CD10-, BCL2+, and Epstein-Barr virus (EBER)+. Analysis of the microenvironment, immune checkpoint, cell cycle, and germinal center markers showed that Dead < 2 years had higher IL10, PD-L1, and CD163 levels and lower E2F1 protein expression. No differences were found for Ki67, CSF1R, CASP8, TNFAIP8, LMO2, MYC, MDM2, CDK6, and TP53 markers at a quantitative level. Conclusions: The DLBCL overall survival can be predicted using H&E histological images and deep learning using the 2-year (24 months) point (similar to POD24). This trained CNN can be used as a pretrained model for transfer learning in the future.