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Radiological tumour classification across imaging modality and histology

Nature Machine Intelligence volume 3, pages 787–798 (2021)Cite this article

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Abstract

Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for the prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumour histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumour morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumour subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumour-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumour segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumour classification may inform prognosis and treatment response for precision medicine.

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Fig. 1: Overview of the study design and quantitative imaging analysis.
Fig. 2: Identification of unifying tumour subtypes based on unsupervised consensus clustering of the extracted image features across three cancer types and across two modalities (CT and MRI).
Fig. 3: Radiological characteristics of the unifying tumour subtypes.
Fig. 4: Evaluation of prognostic value of the four imaging subtypes in three individual cancer types.
Fig. 5: Clinical evaluation of the imaging subtypes in advanced lung cancer treated with immunotherapy.
Fig. 6: Deep learning to automate 3D tumour segmentation.

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Data availability

The data are available within the Article or the Supplementary Information. The imaging data for 9 out of a total of 13 cohorts used in this study are publicly available through the TCIA website (https://www.cancerimagingarchive.net/), as described in the Supplementary Information. The imaging data for the breast cancer cohort from Hokkaido University, Japan are publicly available at https://drive.google.com/drive/folders/1AsI-bvUWwdmwMd7SHXzJttUsKqmImAGz?usp=sharing. The imaging data for the Stanford Lung Cancer, Lung Cancer Immunotherapy and Cambridge GBM cohorts are not publicly available because they contain sensitive information that may compromise patient privacy as well as the ethical restrictions or regulation policy of local institutions. These data will be made available to individuals who contact the corresponding authors with a reasonable request, for example, for non-commercial, research purposes. The gene expression data and mutational data of TCGA samples are publicly available in the Genomic Data Commons (https://gdc.cancer.gov/). The gene expression data for the other cohorts are available from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/, accession nos. GSE22226, GSE103584 and GSE58661).

Code availability

For the spherical harmonic decomposition, we used the SPHARM-MAT software (http://www.iu.edu/~spharm/). For autoencoder, XGboost and consensus clustering, we used R software (version 3.5.3, R Foundation for Statistical Computing, Vienna, Austria), the package autoencoder (version 1.1), XGboost (version 1.1.1.1) and ConsensusClusterPlus (version 1.52.0). The U-Net architecture is available at https://github.com/lyakaap/Kaggle-Carvana-3rd-place-solution. Custom codes46 are available at https://github.com/WuLabMDA/PanCancer.

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Acknowledgements

This research was partially supported by the National Institutes of Health (NIH) grants R01 CA233578, R01 CA222512 and R01 CA193730 (R.L.). J.W. acknowledges the NIH K99/R00 CA218667 and University of Texas MD Anderson Cancer Center Lung Moon Shot Program. S.J.P. is funded by a National Institute for Health Research (NIHR), Career Development Fellowship (CDF-18-11-ST2-003) and NIHR Brain Injury MedTech Co-operative based at Cambridge University Hospitals NHS Foundation Trust and University of Cambridge. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. C.-B.S. acknowledges support from EPSRC Centre grant no. EP/N014588/1. C.L. acknowledges Cancer Research UK grant no. CRUK/A19732 and EPSRC Centre grant no. EP/N014588/1. We thank TCGA and TCIA for sharing the imaging and genomics data for a subset of patients used in this study.

Author information

Author notes

  1. These authors contributed equally: Jia Wu, Chao Li.

Authors and Affiliations

  1. Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA

    Jia Wu, Michael Gensheimer, Billy W. Loo Jr, Maximilian Diehn & Ruijiang Li

  2. Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA

    Jia Wu & David Jaffray

  3. Department of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA

    Jia Wu & John Heymach

  4. The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK

    Chao Li

  5. Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

    Chao Li, Yiran Wei & Stephen John Price

  6. Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA

    Sukhmani Padda, Joel W. Neal & Heather Wakelee

  7. Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan

    Fumi Kato

  8. Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan

    Hiroki Shirato

  9. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK

    Carola-Bibiane Schönlieb

  10. Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX, USA

    David Jaffray

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  1. Jia Wu
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Contributions

J.W. and R.L. contributed to the conception and design of the study. J.W. developed the imaging features and analytics tools. J.W., C.L., M.G., S.P., F.K., H.S., Y.W., C.-B.S., S.J.P., J.W.N., B.W.L., H.W. and M.D. contributed to the acquisition, annotation and curation of the data. J.W., C.L., D.J., B.W.L., M.D. and R.L. contributed to the analysis and interpretation of the data. J.W., C.L. and R.L. wrote the initial manuscript. J.W. and R.L. supervised the work. All authors contributed to the revision of the manuscript.

Corresponding authors

Correspondence to Jia Wu or Ruijiang Li.

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Competing interests

J.H. reports fees for advisory committees from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Catalyst, EMD Serono, Foundation Medicine, Hengrui Therapeutics, Genentech, GSK, Guardant Health, Eli Lilly, Merck, Novartis, Pfizer, Roche, Sanofi, Seattle Genetics, Spectrum and Takeda, research support from AstraZeneca, GlaxoSmithKline, Spectrum, and royalties and licensing fees from Spectrum. M.D. reports research funding from AstraZeneca, Varian Medical Systems and Illumina, ownership interest in CiberMed and Foresight Diagnostics, patent filings related to cancer biomarkers, and paid consultancy from Roche, Genentech, AstraZeneca, Novartis, Boehringer Ingelheim, Gritstone Oncology, RefleXion and BioNTech.

Additional information

Peer review information Nature Machine Intelligence thanks Anum Kazerouni, Yue Wang and Yongjie Zhang for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Morphological characterization of tumours by spherical harmonic decomposition.

Morphological characterization of tumours by spherical harmonic decomposition. a) Overall design of morphological analysis; b) Illustration of 3D spherical harmonic basis functions at different degrees and orders; c) Illustration of 3D tumours reconstructed by coefficients obtained from spherical harmonic decomposition. Each row represents a selected 3D tumour, which is reconstructed using decomposition results at 5 different degree levels. Here, lower degree captures more global patterns and higher degree corresponds to more detailed morphological patterns.

Extended Data Fig. 2 The ridgeline plots present the distribution of 20 regional variation features in three different cancer types.

The ridgeline plots present the distribution of 20 regional variation features in three different cancer types. Here, we investigate 2 tumour regions, tumour core (TC) and tumour invasive margin (TIM), plus 2 peritumour regions, parenchymal margin at 5 mm or 10 mm (PM5 or PM10). In total, 5 pair-wise regions are considered, namely, TC-TIM, TC-PM5, TC-PM10, TIM-PM5, TIM-PM10. Variation for each pair-wise region was quantified with four measures (chi-square, Bhattacharyya distance, correlation, intersection), yielding 5*4 = 20 regional variation features. TC-PM5 and TC-PM10 related features are colored in green, while TIM-PM5 and TIM-PM10 related features are colored in blue.

Extended Data Fig. 3 Details of imaging feature dimension reduction via an autoencoder model.

Details of imaging feature dimension reduction via an autoencoder model. a) The structure of autoencoder used to learn a low-dimensional mapping of the original feature signals with detailed tuning hyperparameters; b) The optimal autoencoder loss curves in training and validation; c) Heatmap of pairwise correlations between 10 autoencoded features.

Extended Data Fig. 4 Distribution of imaging clusters (subtypes) in different clinical groups.

Distribution of imaging clusters (subtypes) in different clinical groups. a) The distribution of all patients in four clusters (subtypes) across three cancer types; b) The distribution of lung cancer patients in four clusters (subtypes) across different clinical stage; The molecular subtype distribution in four imaging subtypes for c) breast cancer with luminal A/B, Her2 + , and triple negative; d) GBM with different MGMT methylation status.

Extended Data Fig. 5 Volcano plot of enrichment scores through single-sample Gene Set Enrichment Analysis (ssGSEA) of 313 proposed imaging features in all three cancer types.

Volcano plot of enrichment scores through single-sample Gene Set Enrichment Analysis (ssGSEA) of 313 proposed imaging features in all three cancer types. a) imaging subtype 1 versus rest, b) subtype 2 versus rest, c) subtype 3 versus rest, and d) subtype 4 versus rest. The data for all enrichment scores are plotted as log2 fold change versus the −log10 of the adjusted p-value. Thresholds are shown as dashed lines. Pathways deemed as significantly different (false discovery rate or FDR < 0.05) are highlighted with different color schemes.

Extended Data Fig. 6 Evaluation of prognostic value of the four imaging subtypes in lung cancer subgroups.

Evaluation of prognostic value of the four imaging subtypes in lung cancer subgroups. Kaplan-Meier curves for a) stage I + II; b) Stage III; c) Patients treated with surgery; d) Patients treated with radiation.

Extended Data Fig. 7 Evaluation of prognostic value of the four imaging subtypes in subgroups within three cancer types.

Evaluation of prognostic value of the four imaging subtypes in subgroups within three cancer types. Kaplan-Meier curves for lung cancer subgroups: a) EGFR Wild Type; b) EGFR Mutant; c) ALK Wild Type; for breast cancer subgroups: d) ER + group; e) HER2 + group; f) Triple Negative (TN) group; for GBM cancer subgroups: g) MGMT Methylated group; h) MGMT Unmethylated group; i) IDH1 Wild group.

Extended Data Fig. 8 Comparison between the proposed imaging subtypes and conventional radiomics analysis for survival prediction in lung cancer cohorts.

Comparison between the proposed imaging subtypes and conventional radiomics analysis for survival prediction in lung cancer cohorts. a) Details of the final radiomic model; b) Distribution of the radiomic risk score in training and validation cohorts; c) Scatterplot shows the correlation between radiomic risk score and tumour size measured in 2D; d) Distribution and comparison of c-index for the radiomic signature and the proposed imaging subtypes in the validation cohort.

Extended Data Fig. 9 Oncogenic processes associated with the imaging subtypes in three cancer types.

Oncogenic processes associated with the imaging subtypes in three cancer types. Limma-modeled enrichment analysis by single-sample Gene Set Enrichment Analysis (ssGSEA) of 50 cancer hallmark pathways is applied. Volcano plot of enrichment scores in lung cancer: a) subtype 1 versus rest, and b) subtype 4 versus rest; in breast cancer: c) subtype 1 versus rest, and d) subtype 4 versus rest; in GBM: e) subtype 1 versus rest, and f) subtype 4 versus rest. The enrichment scores of 50 cancer hallmark pathways are plotted as log2 fold change versus the −log10 of the adjusted p-value. Thresholds are shown as dashed lines. Pathways deemed as significantly different (false discovery rate [FDR] < 0.05) are highlighted with different color schemes.

Extended Data Fig. 10 Evaluation of imaging subtypes in the advanced lung cancer treated with immunotherapy.

Evaluation of imaging subtypes in the advanced lung cancer treated with immunotherapy. Kaplan-Meier curves of overall survival stratified by imaging subtype 1 and 2 versus 4.

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Supplementary methods, Tables 1–13 and Figs. 1–3.

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Wu, J., Li, C., Gensheimer, M. et al. Radiological tumour classification across imaging modality and histology. Nat Mach Intell 3, 787–798 (2021). https://doi.org/10.1038/s42256-021-00377-0

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