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30.03.2021 | Imaging Informatics and Artificial Intelligence

MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study

verfasst von: Haimei Chen, Xiao Zhang, Xiaohong Wang, Xianyue Quan, Yu Deng, Ming Lu, Qingzhu Wei, Qiang Ye, Quan Zhou, Zhiming Xiang, Changhong Liang, Wei Yang, Yinghua Zhao

Erschienen in: European Radiology | Ausgabe 10/2021

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Abstract

Objective

To develop and validate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response to neoadjuvant chemotherapy (NAC) in patients with osteosarcoma.

Methods

We retrospectively enrolled 102 patients with histologically confirmed osteosarcoma who received chemotherapy before treatment from 4 hospitals (68 in the primary cohort and 34 in the external validation cohort). Quantitative imaging features were extracted from contrast-enhanced fat-suppressed T1-weighted images (CE FS T1WI). Four classification methods, i.e., the least absolute shrinkage and selection operator logistic regression (LASSO-LR), support vector machine (SVM), Gaussian process (GP), and Naive Bayes (NB) algorithm, were compared for feature selection and radiomics signature construction. The predictive performance of the radiomics signatures was assessed with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

Thirteen radiomics features selected based on the LASSO-LR classifier were adopted to construct the radiomics signature, which was significantly associated with the pathologic response. The prediction model achieved the best performance between good and poor responders with an AUC of 0.882 (95% CI, 0.837−0.918) in the primary cohort. Calibration curves showed good agreement. Similarly, findings were validated in the external validation cohort with good performance (AUC, 0.842 [95% CI, 0.793−0.883]) and good calibration. DCA analysis confirmed the clinical utility of the selected radiomics signature.

Conclusion

The constructed CE FS T1WI-radiomics signature with excellent performance could provide a potential tool to predict pathologic response to NAC in patients with osteosarcoma.

Key Points

• The radiomics signature based on multicenter contrast-enhanced MRI was useful to predict response to NAC.

• The prediction model obtained with the LASSO-LR classifier achieved the best performance.

• The baseline clinical characteristics were not associated with response to NAC.

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Ritter J, Bielack SS (2010) Osteosarcoma. Ann Oncol 21(Suppl 7):vii320–vii325PubMedCrossRef

Goorin AM, Schwartzentruber DJ, Devidas M et al (2003) Presurgical chemotherapy compared with immediate surgery and adjuvant chemotherapy for nonmetastatic osteosarcoma: Pediatric Oncology Group Study POG-8651. J Clin Oncol 21:1574–1580PubMedCrossRef

Meyers PA, Schwartz CL, Krailo M et al (2005) Osteosarcoma: a randomized, prospective trial of the addition of ifosfamide and/or muramyl tripeptide to cisplatin, doxorubicin, and high-dose methotrexate. J Clin Oncol 23:2004–2011PubMedCrossRef

Bacci G, Ferrari S, Bertoni F et al (2000) Long-term outcome for patients with nonmetastatic osteosarcoma of the extremity treated at the istituto ortopedico rizzoli according to the istituto ortopedico rizzoli/osteosarcoma-2 protocol: an updated report. J Clin Oncol 18:4016–4027PubMedCrossRef

Luetke A, Meyers PA, Lewis I, Juergens H (2014) Osteosarcoma treatment - where do we stand? A state of the art review. Cancer Treat Rev 40:523–532PubMedCrossRef

Bacci G, Bertoni F, Longhi A et al (2003) Neoadjuvant chemotherapy for high-grade central osteosarcoma of the extremity. Histologic response to preoperative chemotherapy correlates with histologic subtype of the tumor. Cancer 97:3068–3075PubMedCrossRef

Wellings RM, Davies AM, Pynsent PB, Carter SR, Grimer RJ (1994) The value of computed tomographic measurements in osteosarcoma as a predictor of response to adjuvant chemotherapy. Clin Radiol 49:19–23PubMedCrossRef

Bajpai J, Gamnagatti S, Kumar R et al (2011) Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis. Pediatr Radiol 41:441–450PubMedCrossRef

Byun BH, Kong CB, Lim I et al (2013) Combination of 18F-FDG PET/CT and diffusion-weighted MR imaging as a predictor of histologic response to neoadjuvant chemotherapy: preliminary results in osteosarcoma. J Nucl Med 54:1053–1059PubMedCrossRef

10.

Im HJ, Kim TS, Park SY et al (2012) Prediction of tumour necrosis fractions using metabolic and volumetric 18F-FDG PET/CT indices, after one course and at the completion of neoadjuvant chemotherapy, in children and young adults with osteosarcoma. Eur J Nucl Med Mol Imaging 39:39–49PubMedCrossRef

11.

Liu J, Mao Y, Li Z et al (2016) Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging 44:445–455PubMedCrossRef

12.

Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446PubMedPubMedCentralCrossRef

13.

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMedCrossRef

14.

Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res 23:7253–7262PubMedCrossRef

15.

Wu Y, Xu L, Yang P et al (2018) Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine 34:27–34PubMedPubMedCentralCrossRef

16.

Sun C, Tian X, Liu Z et al (2019) Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: a multicentre study. EBioMedicine 46:160–169PubMedPubMedCentralCrossRef

17.

Liu Z, Li Z, Qu J et al (2019) Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res 25:3538–3547PubMedCrossRef

18.

Bieling P, Rehan N, Winkler P et al (1996) Tumor size and prognosis in aggressively treated osteosarcoma. J Clin Oncol 14:848–858PubMedCrossRef

19.

Nyúl LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19:143–150PubMedCrossRef

20.

Shafiq-Ul-Hassan M, Zhang GG, Latifi K et al (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 44:1050–1062PubMedCrossRef

21.

Biermann JS, Chow W, Reed DR et al (2017) NCCN guidelines insights: bone cancer, Version 2. 2017. J Natl Compr Canc Netw 15:155–167PubMedCrossRef

22.

Barry WT, Kernagis DN, Dressman HK et al (2010) Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome. J Clin Oncol 28:2198–2206PubMedPubMedCentralCrossRef

23.

van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics System to decode the radiographic phenotype. Cancer Res 77:e104–e107PubMedPubMedCentralCrossRef

24.

Wu S, Shen G, Mao J, Gao B (2020) CT radiomics in predicting EGFR mutation in non-small cell lung cancer: a single institutional study. Front Oncol 10:542957PubMedPubMedCentralCrossRef

25.

Liu H, Zhang C, Wang L et al (2019) MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol 29:4418–4426PubMedCrossRef

26.

Cui Y, Liu H, Ren J et al (2020) Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer. Eur Radiol 30:1948–1958PubMedCrossRef

27.

Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc B Methodol 58:267–288

28.

Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27CrossRef

29.

Seeger M (2004) Gaussian processes for machine learning. Int J Neural Syst 14:69–106PubMedCrossRef

30.

Friedman N, Geiger D, Goldszmidt M (1997) Bayesian Network Classifiers. Mach Learn 29:131–163CrossRef

31.

Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422CrossRef

32.

Kramer AA, Zimmerman JE (2007) Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited. Crit Care Med 35:2052–2056PubMedCrossRef

33.

Vickers AJ, Cronin AM, Elkin EB, Gonen M (2008) Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak 8(53)

34.

Kim JY, Park JE, Jo Y et al (2019) Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 21:404–414PubMedCrossRef

35.

Fang J, Zhang B, Wang S et al (2020) Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics 10:2284–2292PubMedPubMedCentralCrossRef

36.

Zhang X, Xu X, Tian Q et al (2017) Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging 46:1281–1288PubMedPubMedCentralCrossRef

37.

Xu X, Zhang X, Tian Q et al (2019) Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. J Magn Reson Imaging 49:1489–1498PubMedCrossRef

38.

Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2:1636–1642PubMedCrossRef

39.

Park H, Lim Y, Ko ES et al (2018) Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res 24:4705–4714PubMedCrossRef

40.

Karlo CA, Di Paolo PL, Chaim J et al (2014) Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 270:464–471

41.

Braman NM, Etesami M, Prasanna P et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19(57)

42.

Khorrami M, Khunger M, Zagouras A et al (2019) Combination of peri- and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Radiol Artif Intell 1:e180012PubMedCrossRef

43.

Polan DF, Brady SL, Kaufman RA (2016) Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study. Phys Med Biol 61:6553–6569PubMedPubMedCentralCrossRef

44.

Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23:4259–4269PubMedCrossRef

45.

Tang LL, Li WF, Chen L et al (2010) Prognostic value and staging categories of anatomic masticator space involvement in nasopharyngeal carcinoma: a study of 924 cases with MR imaging. Radiology 257:151–157PubMedCrossRef

Titel: MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study
verfasst von: Haimei Chen
Xiao Zhang
Xiaohong Wang
Xianyue Quan
Yu Deng
Ming Lu
Qingzhu Wei
Qiang Ye
Quan Zhou
Zhiming Xiang
Changhong Liang
Wei Yang
Yinghua Zhao
Publikationsdatum: 30.03.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 10/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-07748-6

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MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study