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SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

Histopathological examination (HPE) is the current gold standard for assessing chemotherapy response to tumor, but it is possible only after surgery. The purpose of the study was to develop a noninvasive, imaging-based robust method to delineate, visualize, and quantify the proportions of necrosis and viable tissue present within the tumor along with peritumoral edema before and after neoadjuvant chemotherapy (NACT) and to evaluate treatment response with correlation to HPE necrosis after surgery.

Methods

The MRI dataset of 30 patients (N = 30; male:female = 24:6; age = 17.6 ± 2.7 years) with osteosarcoma was acquired using 1.5 T Philips Achieva MRI scanner before (baseline) and after 3 cycles of NACT (follow-up). After NACT, all patients underwent surgical resection followed by HPE. Simple linear iterative clustering supervoxels and Otsu multithresholding were combined to develop the proposed method—SLICs+MTh—to subsegment and quantify viable and nonviable regions within tumor using multiparametric MRI. Manually drawn ground-truth ROIs and SLICs+MTh-based segmentation of tumor, edema, and necrosis were compared using Jacquard index (JI), Dice coefficient (DC), precision (P), and recall (R). Postcontrast T1W images (PC-T1W) were used to validate the SLICs+MTh-based necrosis. SLICs+MTh-based necrosis volume at follow-up was compared with HPE necrosis using paired t test (p ≤ 0.05).

Results

Active tumor, necrosis, and edema were segmented with moderate to satisfactory accuracy (JI = 62–78%; DC = 72–87%; P = 67–87%; R = 63–88%). Qualitatively and quantitatively (DC = 74 ± 9%), the SLICs+MTh-based necrosis area correlated well with the hypointense necrosis areas in PC-T1W. No significant difference (paired t test, p = 0.26; Bland–Altman plot, bias = 2.47) between SLICs+MTh-based necrosis at follow-up and HPE necrosis was observed.

Conclusion

The proposed multiparametric MRI-based SLICs+MTh method performs noninvasive assessment of NACT response in osteosarcoma that may improve cancer treatment monitoring, planning, and overall prognosis.

Key Points

• The simple linear iterative clustering supervoxels and Otsu multithresholding-based technique (SLICs+MTh) successfully estimates the proportion of necrosis, viable tumor, and edema in osteosarcoma in the course of chemotherapy.

• The proposed technique is noninvasive and uses multiparametric MRI to measure necrosis as an indication of anticancer treatment response.

• SLICs+MTh-based necrosis was in satisfactory agreement with histological necrosis after surgery.

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Abbreviations

DC:

Dice coefficient

DWI800 :

Diffusion weighting factor of 800 s/mm2

HPE:

Histopathological examination

JI:

Jacquard index

NACT:

Neoadjuvant chemotherapy

NR:

Non responder

P:

Precision

PR:

Partial responder

R:

Recall

SLICs+MTh:

Simple linear iterative clustering supervoxels and Otsu multithresholding

T2W-fatsat:

T2-weighted with fat saturation

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Acknowledgments

The authors would like to thank and acknowledge the valuable input of the intern, Sneha Patil, in data processing and various stages of implementation of the proposed algorithm. The authors would also like to acknowledge the support from Dr. Thilaka Muthiah for copyediting the manuscript for grammar, language, and clarity and the cooperation from MRI technicians Mr. Lalit Gupta and Mr. Udit Kumar during MRI data acquisition.

Funding

This study has not received any funding.

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Authors and Affiliations

  1. Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India

    Esha Baidya Kayal & Amit Mehndiratta

  2. Department of Radiology, All India Institute of Medical Sciences, New Delhi, India

    Devasenathipathy Kandasamy & Raju Sharma

  3. Department of Pathology, All India Institute of Medical Sciences, New Delhi, India

    Mehar C. Sharma

  4. Department of Medical Oncology, Dr. B.R. Ambedkar Institute-Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences, New Delhi, India

    Sameer Bakhshi

  5. Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

    Amit Mehndiratta

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  1. Esha Baidya Kayal
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Correspondence to Amit Mehndiratta.

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Guarantor

The scientific guarantor of this publication is Dr. Amit Mehndiratta, Assistant Professor, Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, India, and Department of Biomedical Engineering, All India Institute of Medical Sciences, Delhi, India.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained with reference number: IEC-103/05.02.2016, RP-26/2016. Approving body: Institute Ethics Committee, Room No.-102, First-Floor, Old O.T.Block, All India Institute of Medical Sciences, Ansari Nagar, New Delhi-110,029, India.

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Baidya Kayal, E., Kandasamy, D., Sharma, R. et al. SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging. Eur Radiol 30, 3125–3136 (2020). https://doi.org/10.1007/s00330-019-06647-1

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