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A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT

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Skeletal Radiology Aims and scope Submit manuscript

Abstract

Background

Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma.

Methods

Axial slices (2-mm section thickness) from whole-body low-dose CT scans of subjects with biochemically confirmed plasma cell dyscrasias were included in the study. Data were split into train and test sets at the patient level targeting a 90%/10% split. Two musculoskeletal radiologists annotated lytic lesions on the images with bounding boxes. Subsequently, we developed a two-step deep learning model comprising bone segmentation followed by lesion detection. Unet and “You Look Only Once” (YOLO) models were used as bone segmentation and lesion detection algorithms, respectively. Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUROC).

Results

Forty whole-body low-dose CTs from 40 subjects yielded 2193 image slices. A total of 5640 lytic lesions were annotated. The two-step model achieved a sensitivity of 91.6% and a specificity of 84.6%. Lesion detection AUROC was 90.4%.

Conclusion

We developed a deep learning model that detects lytic bone lesions of multiple myeloma on whole-body low-dose CTs with high performance. External validation is required prior to widespread adoption in clinical practice.

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Abbreviations

IMWG:

International Myeloma Working Group

CNN:

Convolutional neural network

DL:

Deep learning

DICOM:

Digital Imaging and Communications in Medicine

PNG:

Portable Network Graphics

YOLO:

You Only Look Once

AUROC:

Area under the receiver operating characteristic curve

DSC:

Dice similarity coefficient

mAP:

Mean average precision

SD:

Standard deviation

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st St. SW, Rochester, MN, 55905, USA

    Shahriar Faghani, Pouria Rouzrokh, Mana Moassefi, Bardia Khosravi & Bradley J. Erickson

  2. Division of Musculoskeletal Radiology, Department of Radiology, Mayo Clinic, Rochester, MN, USA

    Francis I. Baffour & Michael D. Ringler

  3. Mayo Clinic Alix School of Medicine, Rochester, MN, USA

    Matthew Hamilton-Cave

Authors
  1. Shahriar Faghani
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Correspondence to Francis I. Baffour.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Bradley Erickson: officer: FlowSIGMA, Inc. and Yunu, Inc. The other others declare no competing interests.

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Faghani, S., Baffour, F.I., Ringler, M.D. et al. A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT. Skeletal Radiol 52, 91–98 (2023). https://doi.org/10.1007/s00256-022-04160-z

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