Esha Baidya Kayal1, Devasenathipathy Kandasamy2, Richa Yadav2, Sameer Bakhshi3, Raju Sharma2, Amit Mehndiratta4. 1. Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India. 2. Department of Radio Diagnosis, All India Institute of Medical Sciences, New Delhi, India. 3. Department of Medical Oncology, Dr. B.R. Ambedkar Institute-Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences, New Delhi, India. 4. Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India; Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India. Electronic address: amit.mehndiratta@keble.oxon.org.
Abstract
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.
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.
Authors: Laure Fournier; Lioe-Fee de Geus-Oei; Daniele Regge; Daniela-Elena Oprea-Lager; Melvin D'Anastasi; Luc Bidaut; Tobias Bäuerle; Egesta Lopci; Giovanni Cappello; Frederic Lecouvet; Marius Mayerhoefer; Wolfgang G Kunz; Joost J C Verhoeff; Damiano Caruso; Marion Smits; Ralf-Thorsten Hoffmann; Sofia Gourtsoyianni; Regina Beets-Tan; Emanuele Neri; Nandita M deSouza; Christophe M Deroose; Caroline Caramella Journal: Front Oncol Date: 2022-01-10 Impact factor: 6.244