Imaging bridges pathology and radiology

Lymphomas in 3D and 4D spaces

The cellular compartments in the lymph node form dynamic networks, enabling coordinated innate and adaptive immunological responses. This compartmentalization of the lymph node into subcompartments, such as the T and B zones, has been proven to be beneficial. The study of lymph node microarchitecture has yielded new insights into a range of fields, including anatomy, pathology and biological processes. This review focuses on three-dimensional (3D) and four-dimensional (4D) investigations of human lymph nodes, with a particular emphasis on comparisons with data obtained from mice. It will discuss the findings of 3D/4D investigations of human lymph nodes. The investigation of the immune system in 3D space and time offers numerous advantages over the analysis of thin tissue sections. It provides data that is not visible in two-dimensional (2D) representations. A comparison of volumes, surfaces, cell speeds, cell contact numbers, contact duration times, morphologies and other variables can be made in the context of immune responses and lymphomas. The evaluation of data, the application of statistics and the use of machine learning have all been demonstrated to be valuable. In conditions of reactivity and neoplasia, T cells are the fastest-moving cells. In contrast, B cells show slower movement and higher turning angles in reactive lymphoid tissue and lymphomas. Even slower than B cells are reticulum cells, like follicular dendritic reticulum cells (FDC) of the B zones and macrophages. Fast T cells are especially found in Hodgkin lymphomas and mantle cell lymphomas. Contact times between T and B cells differ between different lymphoma types and may prove useful in defining lymphomas. 4D technologies, which evaluate living tissue slices, are suitable for use in testing checkpoint blockers (such as nivolumab) and other therapeutic drugs or cells. Following incubation with nivolumab, the duration of contacts between CD4-positive T cells and CD30-positive Hodgkin-Reed-Sternberg cells was documented. The preliminary data indicate that 3D and 4D experiments in hematopathology may facilitate new insights into diagnostics, biology, and clinical applications, including the development of new lymphoma classifications.

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration

PURPOSE

Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolutionize medical practice by enabling personalized, more effective, targeted treatments, ultimately improving patient outcomes.

METHODS

The search strategy was developed following PRISMA guidelines. A comprehensive search was performed across four electronic databases: PubMed, Scopus, EMBASE, and Web of Science, covering all English publications up to September 2022. The Newcastle-Ottawa Scale (NOS) was utilized to assess bias, categorizing studies as "good," "fair," or "poor" quality based on their NOS scores. Descriptive statistics for all included studies were compiled and reviewed, along with the NOS scores for each study to indicate their quality assessment.

RESULTS

Our results showed that SVM and RF are the most widely used algorithms in machine learning and data analysis, particularly for biomarker identification. SVM, a supervised learning algorithm, is employed for both classification and regression by mapping data points in high-dimensional space to identify the optimal separating hyperplane between classes.

CONCLUSIONS

The application of machine-learning algorithms in the search for novel biomarkers in pancreatic cancer represents a significant advancement in the field. By harnessing the power of artificial intelligence, researchers are poised to make strides towards earlier detection and more effective treatment, ultimately improving patient outcomes in this challenging disease.

The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study

BACKGROUND

Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients.

METHODS

This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients.

RESULTS

Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts.

CONCLUSION

The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.