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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: A computer aided system

Esha Baidya Kayal¹, Devasenathipathy Kandasamy², Richa Yadav²

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

European Journal of Radiology

|October 31, 2020

Summary

This study introduces an automated system for tumor segmentation and RECIST score estimation, offering accurate and efficient tumor response evaluation. The developed method shows promise for improving treatment planning in oncology.

Keywords:

Computer assisted decision making Diffusion magnetic resonance imaging Osteosarcoma Response evaluation criteria in solid tumors Treatment outcome

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Area of Science:

Radiology
Medical Imaging
Oncology

Background:

Manual RECIST measurements are time-consuming and prone to errors.
Accurate tumor response assessment is vital for effective cancer treatment planning.

Purpose of the Study:

To develop a fully automated system for tumor segmentation and RECIST score estimation.
To achieve reasonable accuracy, consistency, and speed in tumor measurements.

Main Methods:

Utilized Diffusion Weighted Images (DWI) from 40 Osteosarcoma patients.
Employed Simple-Linear-Iterative-Clustering Superpixels (SLIC-S) and Fuzzy-C-Means Clustering (FCM) for 3D tumor segmentation.
Measured tumor diameters and volumes, then calculated RECIST 1.1 and volumetric response scores.

Main Results:

Automated segmentation achieved satisfactory accuracy (Dice: ~70-83%, Jaccard: ~55-72%).
Excellent correlation was found between automated and ground-truth measurements for ADC, tumor dimensions, and volumes (PCC: 0.84-0.95).
Automated RECIST scoring demonstrated low misclassification error rates (15-18%) with rapid assessment times (2-6 seconds per patient).

Conclusions:

The proposed automated system provides promising tumor segmentation and RECIST score measurements.
This tool may aid in decision-making for response evaluation in bone tumors and potentially other cancer types.