Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: A computer aided system process.
Purpose: Accuracy and consistency in RECIST (Response evaluation criteria in solid tumors) measurements are crucial for treatment planning. Manual RECIST measurement is tedious, prone-to-error and operator-subjective. Objective was to develop a fully automated system for tumor segmentation and RECIS...
| Publicado en: | European Journal of Radiology Vol. 133 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Dec2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147295324&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147295324 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0720048X 3S7 jtl: European Journal of Radiology issn: 0720048X maglogo: N pubinfo: dt: Dec2020 vid: 133 pid: 1004 pub: Elsevier B.V. artinfo: ui: 147295324 147295324 NLM33129104 10.1016/j.ejrad.2020.109359 NLM33129104 147295324 ppct: 1 formats: tig: atl: Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: A computer aided system process. aug: au: Baidya Kayal, Esha Kandasamy, Devasenathipathy Yadav, Richa Bakhshi, Sameer Sharma, Raju Mehndiratta, Amit affil: Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India sug: subj: Bone Neoplasms Osteosarcoma Young Adult Child Treatment Outcomes Computers and Computerization Adolescence Male Female Magnetic Resonance Imaging Adult Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Purpose: Accuracy and consistency in RECIST (Response evaluation criteria in solid tumors) measurements are crucial for treatment planning. Manual RECIST measurement is tedious, prone-to-error and operator-subjective. Objective was to develop a fully automated system for tumor segmentation and RECIST score estimation with reasonable accuracy, consistency and speed.Methods: Diffusion weight images (DWI) of forty patients (N = 40; Male:Female = 30:10; Age = 17.7 ± 5.9years) with Osteosarcoma was acquired using 1.5 T MRI scanner before (baseline) and after neoadjuvant chemotherapy (follow-up). 3D tumor volume was segmented applying Simple-linear-iterative-clustering Superpixels (SLIC-S) and Fuzzy-c-means-clustering (FCM) separately. Connected-component-analysis was performed to identify image-slice with maximum tumor-burden (Max-burden-sliceno) and measure tumor-sizes (Tumor-diameter(cm) & Tumor-volume(cc)). Relative-percentage-changes in tumor-sizes across time-points were scored using RECIST1.1 and Volumetric-response criterion. Segmentation accuracy was estimated by Dice-coefficient (DC), Jaccard-Index (JI), Precision (P) and Recall (R). Evaluated Apparent-diffusion-coefficient (ADC), Tumor-diameter, Max-burden-sliceno and Tumor-volume in segmented tumor-mask and ground-truth tumor-mask were compared using paired-t-test (p < 0.05), Pearson-correlation-coefficient(PCC) and Bland-Altman plots. Misclassification-error-rate (MER) was evaluated for automated RECIST1.1 and Volumetric-response scoring methods.Results: Automated SLIC-S and FCM produced satisfactory tumor segmentation (DC:∼70-83%;JI:∼55-72%;P:∼64-85%;R:∼73-83%) and showed excellent correlation with ground-truth measurements in estimating ADC (p > 0.05; PCC=0.84-0.89), Tumor-diameters (p > 0.05; PCC=0.90-0.95; bias=0.3-2.41), Max-burden-sliceno (p > 0.05; PCC=0.87-0.96) and Tumor-volumes (p > 0.05; PCC=0.89-0.94; bias=15.19-131.81) at baseline and follow-up. MER for SLIC-S and FCM were comparable for RECIST1.1 (15-18 %) and Volumetric-response (18-20 %) scores and assessment times were 2-3s and 4-6s per patient respectively.Conclusions: Proposed method produced promising segmentation and RECIST score measurements in current bone tumor dataset and might be useful as decision-support-tool for response evaluation in other tumors. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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