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Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Radiological longitudinal follow-up of liver tumors in CT scans is the standard of care for disease progression assessment and for liver tumor therapy. Finding new tumors in the follow-up scan is essential to determine malignancy, to evaluate the total tumor burden, and to determine treatment efficacy. Since new tumors are typically small, they may be missed by examining radiologists.

Methods

We describe a new method for the automatic detection and segmentation of new tumors in longitudinal liver CT studies and for liver tumors burden quantification. Its inputs are the baseline and follow-up CT scans, the baseline tumors delineation, and a tumor appearance prior model. Its outputs are the new tumors segmentations in the follow-up scan, the tumor burden quantification in both scans, and the tumor burden change. Our method is the first comprehensive method that is explicitly designed to find new liver tumors. It integrates information from the scans, the baseline known tumors delineations, and a tumor appearance prior model in the form of a global convolutional neural network classifier. Unlike other deep learning-based methods, it does not require large tagged training sets.

Results

Our experimental results on 246 tumors, of which 97 were new tumors, from 37 longitudinal liver CT studies with radiologist approved ground-truth segmentations, yields a true positive new tumors detection rate of 86 versus 72% with stand-alone detection, and a tumor burden volume overlap error of 16%.

Conclusions

New tumors detection and tumor burden volumetry are important for diagnosis and treatment. Our new method enables a simplified radiologist-friendly workflow that is potentially more accurate and reliable than the existing one by automatically and accurately following known tumors and detecting new tumors in the follow-up scan.

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Acknowledgements

This work was partially supported by Grant 53681 from the Israel Ministry of Science, Technology and Space entitled: METASEG: a new medical image segmentation paradigm for clinical decision support and big data radiology.

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Authors and Affiliations

  1. The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904, Jerusalem, Israel

    R. Vivanti, A. Szeskin & L. Joskowicz

  2. Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel

    N. Lev-Cohain & J. Sosna

Authors
  1. R. Vivanti
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  2. A. Szeskin
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  3. N. Lev-Cohain
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  4. J. Sosna
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  5. L. Joskowicz
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Corresponding author

Correspondence to R. Vivanti.

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Conflict of interest

None of the authors has any conflict of interest. The authors have no personal financial or institutional interest in any of the materials, software or devices described in this article.

Protection of human and animal rights statement

No animals or humans were involved in this research. All scans were anonymized before delivery to the researchers.

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Vivanti, R., Szeskin, A., Lev-Cohain, N. et al. Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. Int J CARS 12, 1945–1957 (2017). https://doi.org/10.1007/s11548-017-1660-z

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  • DOI: https://doi.org/10.1007/s11548-017-1660-z

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