LITERATURE UPDATE
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.
Fine-needle aspiration of lymph node showing diffuse large B-cell lymphoma (Romanowsky staining).
The findings pointed to an overall positive attitude towards DIPA from the beginning. The clinical staff perceived being rewarded already during implementation with benefits such as improved collaboration both inter- and intra-departmentally promoting better acceptance of DIPA. The clinical staff also experienced some challenges (eg increase in turnaround times) which affected and concerned staff on a personal level. Especially BLS expressed experiencing a demanding and stressful transition due to unexpected increase in workload as well as some barriers for a potentially better implementation process.
The key findings of this study were a need for better preparation of staff through transparent communication of the upcoming challenges of the transition to DIPA, more system- specific training beforehand, more allocation of time and resources in the implementation process, and more focus on BLS work tasks in the requirement specifications.
Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology Lee JH, Song GY, Lee J et al. J Pathol Clin Res. 2024 May; 10 (3): e12370. doi: 10.1002/2056-4538.12370.
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin’s lymphoma that poses diagnostic and prognostic challenges, particularly in predicting
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drug responsiveness. In this study, the authors used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL.
The authors 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, the authors 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
A novel approach correlating pathologic complete response with digital pathology and radiomics in triple- negative breast cancer Hacking SM, Windsor G, Cooper R, Jiao Z, Lourenco A, Wang Y. Breast Cancer. 2024 Feb 13. doi: 10.1007/s12282-024- 01544-y. Online ahead of print.
This rapid communication highlights the correlations between digital pathology- whole slide imaging (WSI) and radiomics- magnetic resonance imaging (MRI) features in triple-negative breast cancer (TNBC) patients. The research collected 12 patients who had both core needle biopsy and MRI performed to evaluate pathologic complete response (pCR). The results showed that higher collagenous values in pathology data were correlated with more homogeneity, whereas higher tumour expression values in pathology data correlated with less homogeneity in the appearance of tumours on MRI by size zone non- uniformity normalised (SZNN). Higher myxoid values in pathology data are correlated with less similarity of grey-level non-uniformity (GLN) in tumour regions on MRIs, while higher immune values in WSIs correlated with the more joint distribution of smaller-size zones by small area low grey-level emphasis (SALGE) in the tumour regions on MRIs. Pathologic complete response (pCR) was associated with collagen, tumour, and myxoid expression in WSI and GLN and SZNN in radiomic features. The correlations of WSI and radiomic features may further our understanding of the TNBC tumoural microenvironment (TME) and could be used in the future to better tailor the use of neoadjuvant chemotherapy (NAC). This communication focused on the post-NAC MRI features correlated with pCR and their association with WSI features from core needle biopsies.
MAY 2024
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