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AI-based pathology predicts origins for cancers of unknown primary

Author

Listed:
  • Ming Y. Lu

    (Harvard Medical School
    Broad Institute of Harvard and MIT
    Dana-Farber Cancer Institute)

  • Tiffany Y. Chen

    (Harvard Medical School
    Broad Institute of Harvard and MIT)

  • Drew F. K. Williamson

    (Harvard Medical School
    Broad Institute of Harvard and MIT)

  • Melissa Zhao

    (Harvard Medical School)

  • Maha Shady

    (Harvard Medical School
    Broad Institute of Harvard and MIT
    Dana-Farber Cancer Institute
    Harvard Medical School)

  • Jana Lipkova

    (Harvard Medical School
    Broad Institute of Harvard and MIT
    Dana-Farber Cancer Institute)

  • Faisal Mahmood

    (Harvard Medical School
    Broad Institute of Harvard and MIT
    Dana-Farber Cancer Institute)

Abstract

Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined1,2. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour3. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour4–9. However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm—Tumour Origin Assessment via Deep Learning (TOAD)—that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.

Suggested Citation

  • Ming Y. Lu & Tiffany Y. Chen & Drew F. K. Williamson & Melissa Zhao & Maha Shady & Jana Lipkova & Faisal Mahmood, 2021. "AI-based pathology predicts origins for cancers of unknown primary," Nature, Nature, vol. 594(7861), pages 106-110, June.
  • Handle: RePEc:nat:nature:v:594:y:2021:i:7861:d:10.1038_s41586-021-03512-4
    DOI: 10.1038/s41586-021-03512-4
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    Citations

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    Cited by:

    1. Weiwei Wang & Yuanshen Zhao & Lianghong Teng & Jing Yan & Yang Guo & Yuning Qiu & Yuchen Ji & Bin Yu & Dongling Pei & Wenchao Duan & Minkai Wang & Li Wang & Jingxian Duan & Qiuchang Sun & Shengnan Wan, 2023. "Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Alicia-Marie Conway & Simon P. Pearce & Alexandra Clipson & Steven M. Hill & Francesca Chemi & Dan Slane-Tan & Saba Ferdous & A. S. Md Mukarram Hossain & Katarzyna Kamieniecka & Daniel J. White & Clai, 2024. "A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Luan Nguyen & Arne Hoeck & Edwin Cuppen, 2022. "Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Md Tauhidul Islam & Zixia Zhou & Hongyi Ren & Masoud Badiei Khuzani & Daniel Kapp & James Zou & Lu Tian & Joseph C. Liao & Lei Xing, 2023. "Revealing hidden patterns in deep neural network feature space continuum via manifold learning," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    5. Kang-Bo Huang & Cheng-Peng Gui & Yun-Ze Xu & Xue-Song Li & Hong-Wei Zhao & Jia-Zheng Cao & Yu-Hang Chen & Yi-Hui Pan & Bing Liao & Yun Cao & Xin-Ke Zhang & Hui Han & Fang-Jian Zhou & Ran-Yi Liu & Wen-, 2024. "A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Shirong Zhang & Shutao He & Xin Zhu & Yunfei Wang & Qionghuan Xie & Xianrang Song & Chunwei Xu & Wenxian Wang & Ligang Xing & Chengqing Xia & Qian Wang & Wenfeng Li & Xiaochen Zhang & Jinming Yu & She, 2023. "DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    7. David L. Hölscher & Nassim Bouteldja & Mehdi Joodaki & Maria L. Russo & Yu-Chia Lan & Alireza Vafaei Sadr & Mingbo Cheng & Vladimir Tesar & Saskia V. Stillfried & Barbara M. Klinkhammer & Jonathan Bar, 2023. "Next-Generation Morphometry for pathomics-data mining in histopathology," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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