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Your search keyword '"Cifci, Didem"' showing total 14 results
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14 results on '"Cifci, Didem"'

4. Swarm learning for decentralized artificial intelligence in cancer histopathology

Author

Saldanha, Oliver Lester, Quirke, Philip, West, Nicholas P., James, Jacqueline A., Loughrey, Maurice B., Grabsch, Heike I., Salto-Tellez, Manuel, Alwers, Elizabeth, Cifci, Didem, Ghaffari Laleh, Narmin, Seibel, Tobias, Gray, Richard, Hutchins, Gordon G. A., Brenner, Hermann, van Treeck, Marko, Yuan, Tanwei, Brinker, Titus J., Chang-Claude, Jenny, Khader, Firas, Schuppert, Andreas, Luedde, Tom, Trautwein, Christian, Muti, Hannah Sophie, Foersch, Sebastian, Hoffmeister, Michael, Truhn, Daniel, and Kather, Jakob Nikolas

Published
2022
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6. Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types.

Author

Loeffler, Chiara Maria Lavinia, El Nahhas, Omar S. M., Muti, Hannah Sophie, Carrero, Zunamys I., Seibel, Tobias, van Treeck, Marko, Cifci, Didem, Gustav, Marco, Bretz, Kevin, Gaisa, Nadine T., Lehmann, Kjong-Van, Leary, Alexandra, Selenica, Pier, Reis-Filho, Jorge S., Ortiz-Bruechle, Nadina, and Kather, Jakob Nikolas

Subjects
HOMOLOGOUS recombination, RECEIVER operating characteristic curves, WHOLE genome sequencing, DNA repair, DEEP learning
Abstract

Background: Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study whether Deep Learning (DL) can predict HRD status solely based on routine hematoxylin & eosin (H&E) histology images across nine different cancer types. Methods: We developed a deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. As part of our approach, we calculated a genomic scar HRD score by combining loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST) from whole genome sequencing (WGS) data of n = 5209 patients across two independent cohorts. The model's effectiveness was evaluated using the area under the receiver operating characteristic curve (AUROC), focusing on its accuracy in predicting genomic HRD against a clinically recognized cutoff value. Results: Our study demonstrated the predictability of genomic HRD status in endometrial, pancreatic, and lung cancers reaching cross-validated AUROCs of 0.79, 0.58, and 0.66, respectively. These predictions generalized well to an external cohort, with AUROCs of 0.93, 0.81, and 0.73. Moreover, a breast cancer-trained image-based HRD classifier yielded an AUROC of 0.78 in the internal validation cohort and was able to predict HRD in endometrial, prostate, and pancreatic cancer with AUROCs of 0.87, 0.84, and 0.67, indicating that a shared HRD-like phenotype occurs across these tumor entities. Conclusions: This study establishes that HRD can be directly predicted from H&E slides using attMIL, demonstrating its applicability across nine different tumor types. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Deep learning models predicting hormone receptor status in breast cancer trained on females do not generalize to males: further evidence of sex-based disparity in breast cancer

Author

Chatterji, Subarnarekha, primary, Niehues, Jan, additional, Treeck, Marko van, additional, Loeffler, Chiara, additional, Saldanha, Oliver, additional, Veldhuizen, Gregory, additional, Cifci, Didem, additional, Carrero, Zunamys, additional, Abu-Eid, Rasha, additional, Kather, Jakob, additional, and Speirs, Valerie, additional

Published
2023
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8. Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study

Author

Loeffler, Chiara Maria Lavinia, primary, El Nahhas, Omar S.M., additional, Muti, Hannah Sophie, additional, Seibel, Tobias, additional, Cifci, Didem, additional, Treeck, Marko van, additional, Gustav, Marco, additional, Carrero, Zunamys I., additional, Gaisa, Nadine, additional, Lehmann, Kjong Van, additional, Leary, Alexandra, additional, Selenica, Pier, additional, Reis-Filho, Jorge S., additional, Ortiz-Bruechle, Nadina, additional, and Kather, Jakob Nikolas, additional

Published
2023
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10. Self-supervised deep learning for pan-cancer mutation prediction from histopathology

Author

Saldanha, Oliver Lester, primary, Loeffler, Chiara M. L., additional, Niehues, Jan Moritz, additional, van Treeck, Marko, additional, Seraphin, Tobias P., additional, Hewitt, Katherine Jane, additional, Cifci, Didem, additional, Veldhuizen, Gregory Patrick, additional, Ramesh, Siddhi, additional, Pearson, Alexander T., additional, and Kather, Jakob Nikolas, additional

Published
2022
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12. DeepMed: A unified, modular pipeline for end-to-end deep learning in computational pathology

Author

van Treeck, Marko, primary, Cifci, Didem, additional, Laleh, Narmin Ghaffari, additional, Saldanha, Oliver Lester, additional, Loeffler, Chiara M. L., additional, Hewitt, Katherine J., additional, Muti, Hannah Sophie, additional, Echle, Amelie, additional, Seibel, Tobias, additional, Seraphin, Tobias Paul, additional, Trautwein, Christian, additional, Foersch, Sebastian, additional, Luedde, Tom, additional, Truhn, Daniel, additional, and Kather, Jakob Nikolas, additional

Published
2021
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13. Swarm learning for decentralized artificial intelligence in cancer histopathology

Author

Saldanha, Oliver Lester, primary, Quirke, Philip, additional, West, Nicholas P., additional, James, Jacqueline A., additional, Loughrey, Maurice B., additional, Grabsch, Heike I., additional, Salto-Tellez, Manuel, additional, Alwers, Elizabeth, additional, Cifci, Didem, additional, Laleh, Narmin Ghaffari, additional, Seibel, Tobias, additional, Gray, Richard, additional, Hutchins, Gordon G. A., additional, Brenner, Hermann, additional, Yuan, Tanwei, additional, Brinker, Titus J., additional, Chang-Claude, Jenny, additional, Khader, Firas, additional, Schuppert, Andreas, additional, Luedde, Tom, additional, Foersch, Sebastian, additional, Muti, Hannah Sophie, additional, Trautwein, Christian, additional, Hoffmeister, Michael, additional, Truhn, Daniel, additional, and Kather, Jakob Nikolas, additional

Published
2021
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14. Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study.

Author

Loeffler CML, El Nahhas OSM, Muti HS, Seibel T, Cifci D, van Treeck M, Gustav M, Carrero ZI, Gaisa NT, Lehmann KV, Leary A, Selenica P, Reis-Filho JS, Bruechle NO, and Kather JN

Abstract

Background: Homologous Recombination Deficiency (HRD) is a pan-cancer predictive biomarker that identifies patients who benefit from therapy with PARP inhibitors (PARPi). However, testing for HRD is highly complex. Here, we investigated whether Deep Learning can predict HRD status solely based on routine Hematoxylin & Eosin (H&E) histology images in ten cancer types., Methods: We developed a fully automated deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. A combined genomic scar HRD score, which integrated loss of heterozygosity (LOH), telomeric allelic imbalance (TAI) and large-scale state transitions (LST) was calculated from whole genome sequencing data for n=4,565 patients from two independent cohorts. The primary statistical endpoint was the Area Under the Receiver Operating Characteristic curve (AUROC) for the prediction of genomic scar HRD with a clinically used cutoff value., Results: We found that HRD status is predictable in tumors of the endometrium, pancreas and lung, reaching cross-validated AUROCs of 0.79, 0.58 and 0.66. Predictions generalized well to an external cohort with AUROCs of 0.93, 0.81 and 0.73 respectively. Additionally, an HRD classifier trained on breast cancer yielded an AUROC of 0.78 in internal validation and was able to predict HRD in endometrial, prostate and pancreatic cancer with AUROCs of 0.87, 0.84 and 0.67 indicating a shared HRD-like phenotype is across tumor entities., Conclusion: In this study, we show that HRD is directly predictable from H&E slides using attMIL within and across ten different tumor types., Competing Interests: Competing Interests JNK reports consulting services for Owkin, France, Panakeia, UK and DoMore Diagnostics, Norway and has received honoraria for lectures by MSD, Eisai and Fresenius. JSRF reports a leadership (board of directors) role at Grupo Oncoclinicas, stock or other ownership interests at Repare Therapeutics and Paige.AI, and a consulting or Advisory Role at Genentech/Roche, Invicro, Ventana Medical Systems, Volition RX, Paige.AI, Goldman Sachs, Bain Capital, Novartis, Repare Therapeutics, Lilly, Saga Diagnostics, Swarm and Personalis. No other potential conflicts of interest are reported by any of the authors.

Published
2023
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