Literature DB >> 30859285

Can pretreatment 18F-FDG PET tumor texture features predict the outcomes of osteosarcoma treated by neoadjuvant chemotherapy?

Hongjun Song1, Yining Jiao2, Weijun Wei1,3,4, Xuhua Ren2,5,6, Chentian Shen1, Zhongling Qiu1, Qingcheng Yang7, Qian Wang2, Quan-Yong Luo8.   

Abstract

PURPOSE: To investigate whether tumor texture features derived from pretreatment with 18F-fluorodeoxyglucose positron emission tomography (FDG PET) can predict histological response or event-free survival (EFS) in patients with localized osteosarcoma of the extremities treated by neoadjuvant chemotherapy (NAC).
METHODS: We retrospectively reviewed 35 patients with American Joint Committee on Cancer stage II extremity osteosarcoma treated with NAC and surgery. Primary tumor traditional parameters and texture features were measured for all 18F-FDG PET images prior to treatment. After surgery, histological responses to NAC were evaluated on the postsurgical specimens. A receiver operating characteristic curve (ROC) was constructed to evaluate the optimal predictive performance among the various indices. EFS was calculated using the Kaplan-Meier method and prognostic significance was assessed by Cox proportional hazards analysis.
RESULTS: Pathologic examination revealed 16 (45.71%) good responders and 19 (54.29%) poor responders. Although both the texture features (least axis, dependence nonuniformity, run length nonuniformity, and size zone nonuniformity) and metabolic tumor volume (MTV) can predict tumor response of osteosarcoma to NAC, the traditional indicator MTV has the best performance according to ROC curve analysis (area under the curve = 0.918, p < 0.0001). In multivariate analysis, MTV (p < 0.0001), histological response (p = 0.0003), and texture feature of coarsenessNGTDM (neighboring gray tone difference matrix) (p = 0.005) were independently associated with EFS.
CONCLUSIONS: Intratumoral heterogeneity of baseline 18F-FDG uptake measured by PET texture analysis can predict tumor response and EFS of patients with extremity osteosarcoma treated by NAC, but the conventional parameter MTV provides better predictive power and is a strong independent prognostic factor. KEY POINTS: • The baseline 18 F-FDG PET tumor texture features can predict tumor NAC response for patients with osteosarcoma. • Coarseness NGTDM is a new and independent prognostic factor for osteosarcoma. • MTV provides the best predictive power and is a strong independent prognostic factor for patients with osteosarcoma.

Entities:  

Keywords:  Diagnosis; Neoadjuvant therapy; Osteosarcoma; Positron emission tomography-computed tomography; Prognosis

Mesh:

Substances:

Year:  2019        PMID: 30859285     DOI: 10.1007/s00330-019-06074-2

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  9 in total

1.  A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.

Authors:  Jingyu Zhong; Yangfan Hu; Liping Si; Geng Jia; Yue Xing; Huan Zhang; Weiwu Yao
Journal:  Eur Radiol       Date:  2020-09-02       Impact factor: 5.315

2.  Prediction of Histologic Neoadjuvant Chemotherapy Response in Osteosarcoma Using Pretherapeutic MRI Radiomics.

Authors:  Amine Bouhamama; Benjamin Leporq; Wassef Khaled; Angéline Nemeth; Mehdi Brahmi; Julie Dufau; Perrine Marec-Bérard; Jean-Luc Drapé; François Gouin; Axelle Bertrand-Vasseur; Jean-Yves Blay; Olivier Beuf; Frank Pilleul
Journal:  Radiol Imaging Cancer       Date:  2022-09

Review 3.  Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.

Authors:  David Morland; Elizabeth Katherine Anna Triumbari; Luca Boldrini; Roberto Gatta; Daniele Pizzuto; Salvatore Annunziata
Journal:  Diagnostics (Basel)       Date:  2022-05-27

4.  Prediction of neoadjuvant chemotherapy response in high-grade osteosarcoma: added value of non-tumorous bone radiomics using CT images.

Authors:  Lei Xu; Pengfei Yang; Kun Hu; Yan Wu; Meng Xu-Welliver; Yidong Wan; Chen Luo; Jing Wang; Jinhua Wang; Jiale Qin; Yi Rong; Tianye Niu
Journal:  Quant Imaging Med Surg       Date:  2021-04

5.  An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics.

Authors:  Jingyu Zhong; Yangfan Hu; Guangcheng Zhang; Yue Xing; Defang Ding; Xiang Ge; Zhen Pan; Qingcheng Yang; Qian Yin; Huizhen Zhang; Huan Zhang; Weiwu Yao
Journal:  Insights Imaging       Date:  2022-08-20

6.  A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours.

Authors:  Alessandro Bevilacqua; Diletta Calabrò; Silvia Malavasi; Claudio Ricci; Riccardo Casadei; Davide Campana; Serena Baiocco; Stefano Fanti; Valentina Ambrosini
Journal:  Diagnostics (Basel)       Date:  2021-05-12

7.  Early response monitoring of neoadjuvant chemotherapy using [18F]FDG PET can predict the clinical outcome of extremity osteosarcoma.

Authors:  Inki Lee; Byung Hyun Byun; Ilhan Lim; Byung Il Kim; Chang Woon Choi; Jae-Soo Koh; Won Seok Song; Wan Hyeong Cho; Chang-Bae Kong; Sang Moo Lim
Journal:  EJNMMI Res       Date:  2020-01-03       Impact factor: 3.138

Review 8.  Spatial heterogeneity of nanomedicine investigated by multiscale imaging of the drug, the nanoparticle and the tumour environment.

Authors:  Josanne Sophia de Maar; Alexandros Marios Sofias; Tiffany Porta Siegel; Rob J Vreeken; Chrit Moonen; Clemens Bos; Roel Deckers
Journal:  Theranostics       Date:  2020-01-01       Impact factor: 11.556

Review 9.  Clinical Perspectives for 18F-FDG PET Imaging in Pediatric Oncology: Μetabolic Tumor Volume and Radiomics.

Authors:  Vassiliki Lyra; Sofia Chatziioannou; Maria Kallergi
Journal:  Metabolites       Date:  2022-02-28
  9 in total

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