Response to letter entitled: Re: Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review.

In their letter referring to our systematic review on combined image analysis and omics classifiers [ [1] Schneider L. Laiouar-Pedari S. Kuntz S. Krieghoff-Henning E. Hekler A. Kather J.N. et al. Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review. Eur J Cancer. 2022; 160: 80-91 Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar ], the authors propose Electrical Impedance Spectroscopy (EIS) as another, unrelated method that might also have the potential to improve current cancer diagnostics [ [2] Sohag M.H. Nicoud O. Amine R. Khalil-Mgharbel A. Alcaraz J.P. Vilgrain et al. Improved micro-impedance spectroscopy to determine cell barrier properties. Eurobiotech J. 2020; 4: 150-116 155https://doi.org/10.2478/ebtj-2020-0017 Crossref Google Scholar ]. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic reviewEuropean Journal of CancerVol. 160PreviewOver the past decade, the development of molecular high-throughput methods (omics) increased rapidly and provided new insights for cancer research. In parallel, deep learning approaches revealed the enormous potential for medical image analysis, especially in digital pathology. Combining image and omics data with deep learning tools may enable the discovery of new cancer biomarkers and a more precise prediction of patient prognosis. This systematic review addresses different multimodal fusion methods of convolutional neural network-based image analyses with omics data, focussing on the impact of data combination on the classification performance. Full-Text PDF Open AccessLetter re: Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review: Label-free diagnostic technique to differentiate cancer cells from healthy cellsEuropean Journal of CancerVol. 172PreviewWe were very interested to read the recent review article by Schneider et al. [1] who emphasised that innovations in cancer diagnostics and therapy are urgently required. Those authors analysed 11 studies aimed to enhance cancer diagnostics by combining molecular omics data with image analysis of haematoxylin and eosin (H&E)-stained slides of tumour tissue. The analysis of H&E-stained slides is a gold standard in cancer diagnosis, and it is a time-consuming process for pathologists and presents a major challenge to develop methods for automated analysis. Full-Text PDF