Publications

2023

  1. Frei AL, Oberson R, Baumann E, Perren A, Grobholz R, Lugli A, Dawson H, Abbet C, Lertxundi I, Reinhard S, Mookhoek A, Feichtinger J, Sarro R, Gadient G, Dommann-Scherrer C, Barizzi J, Berezowska S, Glatz K, Dertinger S, Banz Y, Schoenegg R, Rubbia-Brandt L, Fleischmann A, Saile G, Mainil-Varlet P, Biral R, Giudici L, Soltermann A, Chaubert AB, Stadlmann S, Diebold J, Egervari K, Bénière C, Saro F, Janowczyk A, Zlobec I. Pathologist computer-aided diagnostic scoring of tumor cell fraction: A Swiss national study. Mod Pathol. 2023 Sep 22:100335. doi: 10.1016/j.modpat.2023.100335. Epub ahead of print. PMID:37742926.

  2. Lundström OS, Adriaan Verbiest M, Xia F, Jam HZ, Zlobec I, Anisimova M, Gymrek M. WebSTR: A Population-wide Database of Short Tandem Repeat Variation in Humans. J Mol Biol. 2023 Oct 15;435(20):168260. doi: 10.1016/j.jmb.2023.168260. Epub 2023 Sep 7. PMID: 37678708.

  3. Willis J, Anders RA, Torigoe T, Hirohashi Y, Bifulco C, Zlobec I, Mlecnik B, Demaria S, Choi WT, Dundr P, Tatangelo F, Di Mauro A, Baldin P, Bindea G, Marliot F, Haicheur N, Fredriksen T, Kirilovsky A, Buttard B, Vasaturo A, Lafontaine L, Maby P, El Sissy C, Hijazi A, Majdi A, Lagorce C, Berger A, Van den Eynde M, Pagès F, Lugli A, Galon J. Multi-Institutional Evaluation of Pathologists' Assessment Compared to Immunoscore. Cancers (Basel). 2023 Aug 10;15(16):4045. doi: 10.3390/cancers15164045. PMID: 37627073; PMCID: PMC10452341.

  4. Haddad TS, van den Dobbelsteen L, Öztürk SK, Geene R, Nijman IJ, Verrijp K, Jamieson NB, Wood C, van Vliet S, Reuvers L, Achouiti S, Rutgers N, Brouwer N, Simmer F, Zlobec I, Lugli A, Nagtegaal ID. Pseudobudding: ruptured glands do not represent true tumor buds. J Pathol. 2023 Sep;261(1):19-27. doi: 10.1002/path.6146. Epub 2023 Jul 4. PMID: 37403270.

  5. Bokhorst JM, Ciompi F, Öztürk SK, Oguz Erdogan AS, Vieth M, Dawson H, Kirsch R, Simmer F, Sheahan K, Lugli A, Zlobec I, van der Laak J, Nagtegaal ID. Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer. Mod Pathol. 2023 Sep;36(9):100233. doi: 10.1016/j.modpat.2023.100233. Epub 2023 May 30. PMID: 37257824.

  6. Khan A, Brouwer N, Blank A, et al. Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model. Mod Pathol. 2023;36(5):100118. doi:10.1016/J.MODPAT.2023.100118

  7. Bokhorst J-M, Nagtegaal ID, Zlobec I, et al. Semi-supervised learning to automate tumor bud detection in cytokeratin-stained whole-slide images of colorectal cancer. Cancers (Basel). 2023;15(7):2079.

  8. Neto PC, Montezuma D, Oliveira SP, et al. A CAD System for Colorectal Cancer from WSI: A Clinically Validated Interpretable ML-based Prototype. arXiv Prepr arXiv230102608. 2023.

  9. Mlecnik B, Lugli A, Bindea G, et al. Multicenter International Study of the Consensus Immunoscore for the Prediction of Relapse and Survival in Early-Stage Colon Cancer. Cancers (Basel). 2023;15(2):418.

  10. Karamitopoulou E, Wenning AS, Acharjee A, et al. Spatially restricted tumour-associated and host-associated immune drivers correlate with the recurrence sites of pancreatic cancer. Gut. 2023.

  11. Jungen SH, Noti L, Christe L, et al. Spatial distribution of CD3-and CD8-positive lymphocytes as pretest for POLE wild-type in molecular subgroups of endometrial carcinoma. Front Med. 2023;10.

  12. Rodrigues D, Neppl C, Dorward D, et al. Development and validation of an AI-based PD-L1 scoring algorithm in NSCLC samples. Cancer Res. 2023;3294.

  13. Rodrigues D, Reinhard S, Waldburger T, et al. SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images. Cancer Res. 2023; 5442.

  14. Studer L, Bokhorst J-M, Nagtegaal I, Zlobec I, Dawson H, Fischer A. Tumor Budding T-cell Graphs: Assessing the Need for Resection in pT1 Colorectal Cancer Patients. In: Medical Imaging with Deep Learning. ; 2023.

  15. Frei AL, Khan A, Zens P, Lugli A, Zlobec I, Fischer A. GammaFocus: An image augmentation method to focus model attention for classification. In: Medical Imaging with Deep Learning, Short Paper Track. ; 2023.

  16. Frei AL, Khan A, Studer L, et al. Local and global feature aggregation for accurate epithelial cell classification using graph attention mechanisms in histopathology images. In: Medical Imaging with Deep Learning, Short Paper Track. ; 2023.

2022

  1. Williams, H. L., Dias Costa, A., Zhang, J., Raghavan, S., Winter, P. S., Kapner, K. S., Ginebaugh, S. P., Väyrynen, S. A., Väyrynen, J. P., & Yuan, C. Spatially-resolved single-cell assessment of pancreatic cancer expression subtypes reveals co-expressor phenotypes and extensive intra-tumoral heterogeneity. Cancer Research, 2022.

  2. Gwerder M, Khan A, Neppl C, Zlobec I. Detection of lung cancer metastases in lymph nodes using a multiple instance learning approach. https://doi.org/101117/122612806. 2022;12039:332-337. doi:10.1117/12.2612806

  3. Khan A, Janowczyk A, Müller F, et al. Impact of scanner variability on lymph node segmentation in computational pathology. J Pathol Inform. 2022;13:100127. doi:10.1016/j.jpi.2022.100127

  4. Abbet C, Studer L, Fischer A, et al. Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection. Med Image Anal. 2022;79:102473.

  5. Nguyen H-G, Lundström O, Blank A, et al. Image-based assessment of extracellular mucin-to-tumor area predicts consensus molecular subtypes (CMS) in colorectal cancer. Mod Pathol. 2022;35(2):240-248.

  6. Koelzer VH, Grobholz R, Zlobec I, Janowczyk A. Update on the current opinion, status and future development of digital pathology in Switzerland in light of COVID-19. J Clin Pathol. 2022;75(10):687-689.

  7. Wu J, Zlobec I, Lafarge M, He Y, Koelzer VH. Towards IID representation learning and its application on biomedical data. arXiv Prepr arXiv220300332. 2022.

  8. Nguyen HG, Khan A, Dawson H, Lugli A, Zlobec I. Group affinity weakly supervised segmentation from prior selected tissue in colorectal histopathology images. In: Medical Imaging 2022: Digital and Computational Pathology. Vol 12039. ; 2022:234-241.

  9. Noti L, Galván JA, Dawson H, et al. A combined spatial score of granzyme B and CD68 surpasses CD8 as an independent prognostic factor in TNM stage II colorectal cancer. BMC Cancer. 2022;22(1):1-13.

  10. Bisson T, Kiehl R, Carvalho R, et al. Best Practices of Dataset Generation for Clinical-Grade Deep Learning Image Analysis with Application to Mitosis Detection. In: LABORATORY INVESTIGATION. Vol 102. ; 2022:1063-1064.

  11. Abbet C, Studer L, Zlobec I, Thiran J-P. Toward Automatic Tumor-Stroma Ratio Assessment for Survival Analysis in Colorectal Cancer. In: Medical Imaging with Deep Learning. ; 2022.

  12. Janowczyk A, Baumhoer D, Dirnhofer S, et al. Towards a national strategy for digital pathology in Switzerland. Virchows Arch. 2022;481(4):647-652.

  13. Rumberger JL, Baumann E, Hirsch P, Janowczyk A, Zlobec I, Kainmueller D. Panoptic segmentation with highly imbalanced semantic labels.

  14. 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC). ; 2022:1-4.

  15. Khan A, Brouwer N, Muller F, et al. An assisting deep learning tool for accurate detection of colorectal cancer lymph node metastasis. In: VIRCHOWS ARCHIV. Vol 481. ; 2022:S83--S84.

  16. Demir CS, Gwerder M, Eroglu D, et al. 1459 Characterization of tumor budding and the tumor microenvironment in colorectal cancer using hyperplex immunofluorescence. 2022.

  17. Abbet C, Studer L, Thiran J-P, Zlobec I. Self-rule to multi adapt automates the tumor-stroma assessment in colorectal cancer. In: Proceedings of the ECDP 2022 18th European Congress on Digital Pathology, 15-18 June 2022, Berlin, Germany. ; 2022.

  18. Abbet C, Studer L, Fischer A, et al. Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection. Med Image Anal. 2022;79(CONFERENCE):102473. doi:10.1016/j.media.2022.102473

2021

  1. Georges NDF, Oberli B, Rau TT, et al. Tumour budding and CD8+ T cells:‘attackers’ and ‘defenders’ in rectal cancer with and without neoadjuvant chemoradiotherapy. Histopathology. 2021;78(7):1009-1018.

  2. Nguyen H-G, Blank A, Dawson HE, Lugli A, Zlobec I. Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods. Sci Rep. 2021;11(1):1-11.

  3. Abbet C, Studer L, Fischer A, et al. Self-rule to adapt: Learning generalized features from sparsely-labeled data using unsupervised domain adaptation for colorectal cancer tissue phenotyping. In: Medical Imaging with Deep Learning. ; 2021.

  4. Studer L, Wallau J, Dawson H, Zlobec I, Fischer A. Classification of intestinal gland cell-graphs using graph neural networks. In: 2020 25th International Conference on Pattern Recognition (ICPR). ; 2021:3636-3643.

  5. Jiang S, Mukherjee N, Bennett RS, et al. Rhesus Macaque CODEX multiplexed immunohistochemistry panel for studying immune responses during Ebola infection. Front Immunol. 2021:5164.

  6. Raghavan, S., Winter, P. S., Navia, A. W., Williams, H. L., DenAdel, A., Lowder, K. E., Galvez-Reyes, J., Kalekar, R. L., Mulugeta, N., & Kapner, K. S. (2021).

    Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer. Cell, 184(25), 6119–6137.

2020

  1. Dawson H, Christe L, Eichmann M, et al. Tumour budding/T cell infiltrates in colorectal cancer: proposal of a novel combined score. Histopathology. 2020;76(4):572-580.

  2. Unternaehrer J, Grobholz R, Janowczyk A, Zlobec I. Current opinion, status and future development of digital pathology in Switzerland. J Clin Pathol. 2020;73(6):341-346.

  3. Abbet C, Zlobec I, Bozorgtabar B, Thiran J-P. Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer. arXiv Prepr arXiv200703292. 2020.

  4. Bokhorst JM, Blank A, Lugli A, et al. Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning. Mod Pathol. 2020;33(5):825-833.

  5. Bozorgtabar B, Mahapatra D, Zlobec I, Rau TT, Thiran J-P. Editorial: Computational Pathology. Front Med. 2020;7. doi:10.3389/fmed.2020.00245

  6. Nguyen HG, Blank A, Lugli A, Zlobec I. An Effective Deep Learning Architecture Combination for Tissue Microarray Spots Classification of HE Stained Colorectal Images. In: Proceedings - International Symposium on Biomedical Imaging. Vol 2020-April. IEEE Computer Society; 2020:1271-1274. doi:10.1109/ISBI45749.2020.9098636

  7. Schürch CM, Bhate SS, Barlow GL, et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell. 2020;182(5):1341-1359.

  8. Mlecnik B, Bifulco C, Bindea G, et al. Multicenter international society for immunotherapy of cancer study of the consensus immunoscore for the prediction of survival and response to chemotherapy in stage III colon cancer. J Clin Oncol. 2020;38(31):3638.

  9. Bokhorst J, Nagtegaal I, Zlobec I, et al. Deep learning-based tumour bud detection in pan-cytokeratin stained colorectal cancer whole-slide images. In: VIRCHOWS ARCHIV. Vol 477. ; 2020:S36--S36.

  10. Studer L, Bokhorst J, Zlobec I, et al. Validation of computer-assisted tumour-bud and T-cell detection in pT1 colorectal cancer. In: VIRCHOWS ARCHIV. Vol 477. ; 2020:S37--S38.

  11. Bokhorst J, Ciompi F, Zlobec I, et al. Computer-assisted hot-spot selection for tumour budding assessment in colorectal cancer. In: VIRCHOWS ARCHIV. Vol 477. ; 2020:S14--S14.

2019

  1. Eichmann MD, Reinhard S, Zlobec I. Scorenado: a customisable, user-friendly and open-source visual assessment tool for histological slides. In: VIRCHOWS ARCHIV. Vol 475. ; 2019:S36--S36.

  2. Studer L, Toneyan S, Zlobec I, Dawson H, Fischer A. Graph-based classification of intestinal glands in colorectal cancer tissue images. 2019.

  3. Studer L, Toneyan S, Zlobec I, Lugli A, Fischer A, Dawson H. Intestinal gland classification from colorectal cancer tissue images using graph-based methods. In: Proceedings of the 4th Joint Annual Meeting of the Swiss and Austrian Societies of Pathology, 7-9 November 2019, Luzern, Switzerland; Der Pathologe. ; 2019.

  4. Zahnd S, Braga-Lagache S, Buchs N, et al. A digital pathology-based shotgun-proteomics approach to biomarker discovery in colorectal cancer. J Pathol Inform. 2019;10.

  5. Koelzer VH, Zlobec I, Willi N, et al. Pathologie: Digitale Pathologie--vom Objektträger zum Datenträger. In: Swiss Medical Forum. Vol 19. ; 2019:49-51.

2018

  1. Pagès F, Mlecnik B, Marliot F, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391(10135):2128-2139.

  2. Unternaehrer J, Reinhard S, Waldburger T, Hewer E, Zlobec I. Digitalisation of diagnostic histopathology slides: a comparative study and internal survey on perception toward the digital future. In: VIRCHOWS ARCHIV. Vol 473. ; 2018:S19--S19.

2017

  1. Zahnd S, Heller M, Zlobec I. Shotgun proteomics-based mass spectrometry analysis of purified cell populations isolated from fresh colorectal cancer tissue. In: VIRCHOWS ARCHIV. Vol 471. ; 2017:S193--S193.

  2. Koelzer VH, Sokol L, Zahnd S, et al. Digital analysis and epigenetic regulation of the signature of rejection in colorectal cancer. Oncoimmunology. 2017;6(4):e1288330.

  3. Zahnd S, Heller M, Zlobec I. A digital pathology-based mass spectrometry approach to biomarker discovery in formalin-fixed paraffin-embedded colorectal cancer tissue. In: VIRCHOWS ARCHIV. Vol 471. ; 2017:S192--S193.