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

Kayal, Esha Baidya; Kandasamy, Devasenathipathy; Sharma, Raju; Sharma, Mehar Chand; Bakhshi, Sameer; Mehndiratta, Amit

doi:10.1007/s00330-019-06647-1

Cited by 9 publications

(11 citation statements)

References 36 publications

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“…Tumor location is indicated by peripheral arrows.A and B, T1‐weighted (T1W) and C and D, T2‐weighted (T2W) at time points t0 and t2, respectively, E‐G, diffusion‐weighted image (DWI) (b = 800 sec/mm 2 ), H‐J, apparent diffusion coefficient ( ADC ), K‐M, diffusion coefficient ( D ), N‐P, perfusion coefficient ( D* ) and Q‐S, perfusion fraction ( f ) at time points t0, t1 and t2, respectively; and T and U, histopathological slide images Ψ depict large areas of necrosis in osteosarcoma (solid arrow) and area of viable tumor (dotted arrow). Ψ Reprinted by permission from Springer Nature: Springer, European Radiology , from an article by Baidya Kayal et al 16 …”

Section: Resultsmentioning

confidence: 99%

“…Thus, methods based on structural imaging are unable to offer information about the extent of tumor necrosis and the presence of viable tumor tissue early in the course of treatment. 8,9 Use of advanced functional magnetic resonance (MR) imaging techniques like diffusion-weighted MRI (DWI), 10,11 pharmacokinetic and precontrast and postcontrast dynamic contrast enhanced (DCE) MRI, [12][13][14] multi-parametric MRI, 15,16 glucose uptake in FDG-PET/CT 17 and changes in resistive index measurement with ultrasonography 18 have been reported for noninvasive evaluation of tumor response during NACT among patients with osteosarcoma.…”

Section: Introductionmentioning

confidence: 99%

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Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

et al. 2020

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The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tonedifference matrix (NGTDM) and run-length matrix (RLM). Receiver-operatingcharacteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/ terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D*

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

et al. 2020

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“…Functional imaging is closely related to microscopic pathological changes, such as water molecule diffusion and blood perfusion within the tumor, and thus helps to more accurately pre‐evaluate the efficacy of NACT for OS. In the study performed by Baidyas et al, 89 the authors included 34 conventional OS patients treated with cisplatin and doxorubicin. They extracted 25 texture features from conventional images and ADC, D , D *, and f parameter maps using >50% necrosis in postoperative pathology specimens as an indicator of good response.…”

Section: Methodsmentioning

confidence: 99%

“…Radiomics offers new ways to visualize and quantify areas of tumor necrosis before and after treatment. Baidya Kayal et al 89 performed a radiomics analysis using T2FS, DWI, and the corresponding ADC map in 30 patients with OS. The authors found that simple linear iterative clustering of super voxels and the Otsu multi‐threshold technique (SLICs + MTh) could quantify the proportions of edema and necrotic and viable tissue present within the tumor before and after chemotherapy.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, manual segmentation results vary greatly depending on the experts. Two of the above studies of imaging histology for the assessment of efficacy in primary OS 89,91 used automated segmentation methods, which have the advantages of good repeatability, high efficiency, and high precision. However, the robustness of the automatic outlining algorithm is affected by various factors such as image patterns, feature parameters, calculation methods, threshold settings, and human processing factors.…”

Section: Methodsmentioning

confidence: 99%

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Imaging Assessment of the Efficacy of Chemotherapy in Primary Malignant Bone Tumors: Recent Advances in Qualitative and Quantitative Magnetic Resonance Imaging and Radiomics

Liu

Duan

Fang

et al. 2023

Magnetic Resonance Imaging

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A semiautomatic segmentation method framework for pelvic bone tumors based on CT‐MR multimodal images

Xia

Yan

et al. 2023

Numer Methods Biomed Eng

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The pelvic structure is complex and the tumor is poorly defined from the surrounding tissues. Finding the exact tumor resection margin based on the surgeon's clinical experience alone is a time‐consuming and difficult task, which is a major factor leading to surgical failure. An accurate method for segmenting pelvic bone tumors is needed. In this paper, a semiautomatic segmentation method for pelvic bone tumors based on CT‐MR multimodal images is presented. The method combines multiple medical prior knowledge and image segmentation algorithms. Finally, the segmentation results are visualized in three dimensions. We tested the proposed method on a collection of 10 cases (97 tumor MR images in total). The segmentation results were compared with the manual annotation of the physicians. On average, our method has an accuracy of 0.9358, a recall of 0.9278, an IOU value of 0.8697, a Dice value of 0.9280, and an AUC value of 0.9632. The average error of the 3D model was within the allowable range of the surgery. The proposed algorithm can accurately segment bone tumors in pelvic MR images regardless of tumor location, size, and other factors. It provides the possibility to assist pelvic bone tumor preservation surgery.

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

Cited by 9 publications

References 36 publications

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging

Imaging Assessment of the Efficacy of Chemotherapy in Primary Malignant Bone Tumors: Recent Advances in Qualitative and Quantitative Magnetic Resonance Imaging and Radiomics

A semiautomatic segmentation method framework for pelvic bone tumors based on CT‐MR multimodal images

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