Imaging bridges pathology and radiology

Hansmann, Martin‐Leo; Klauschen, Frederick; Samek, Wojciech; Müller, Klaus‐Robert; Donnadieu, Emmanuel; Scharf, Sonja; Hartmann, Sven; Koch, Ina; Ackermann, Jörg; Pantanowitz, Liron; Schäfer, Hendrik; Wurzel, Patrick

doi:10.1016/j.jpi.2023.100298

Cited by 6 publications

(1 citation statement)

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“…Do they provide novel insights into the clinical and biological features that are complementary in nature? [4,5,27]. There are several works in the literature that show the ability of radiomics and pathomics to predict disease outcomes among NSCLC patients treated with immunotherapy; however, there is a paucity of studies on the combined role of radiomics and pathomics in inferring a patient's response and survival.…”

Section: Introductionmentioning

confidence: 99%

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

Dia,

Ebrahimpour,

Yolchuyeva

et al. 2024

Cancers

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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.

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Section: Introductionmentioning

confidence: 99%