Radiomics analysis of pre-treatment F-18 FDG PET/CT for predicting response to transarterial radioembolization in liver tumors

Year 2022, Volume: 5 Issue: 4, 1156 - 1161, 20.07.2022

Nazım Coşkun , Alptuğ Özer Yüksel , Murat Canyiğit , Elif Özdemir

https://doi.org/10.32322/jhsm.1118649

Abstract

Aim: To investigate the relationship between the textural features extracted from pre-treatment fluorine-18 fluorodeoxyglucose positron emission with computed tomography (F-18 FDG PET/CT) and the response to treatment in patients undergoing transarterial radioembolization (TARE) due to primary or metastatic liver tumors.
Material and Method: A total of 25 liver lesions from the pre-treatment F-18 PET/CT images of 14 patients were segmented manually. Standard uptake value (SUV) metrics and radiomics features were extracted for each lesion. Metabolic treatment response was determined according to PERCIST criteria in 18F-FDG PET/CT imaging performed 2 months after the treatment. Feature selection was done with recursive feature elimination (RFE). The association between selected features and treatment response was evaluated with logistic regression analysis.
Results: Eventually, 13 lesions responded to TARE, while 12 lesions remain stable or progressed. All standard uptake values and 27 out of 30 textural heterogeneity indicators were significantly higher in lesions that responded to treatment. SUVmax, kurtosis and dissimilarity features were selected by the RFE algorithm for the prediction of response to TARE. Logistic regression analysis revealed that all three parameters were significantly associated with treatment outcome.
Conclusion: Textural features extracted from pre-treatment F-18 FDG PET/CT in patients undergoing TARE due to liver tumors are promising biomarkers that can be potentially used to predict metabolic treatment response.

Keywords

PET/CT, radiomics, transarterial radioembolization, PERCIST

References

• Michor F, Polyak K. The origins and implications of intratumor heterogeneity. Cancer Prev Res (Phila) 2010; 3: 1361-4.
• Nassar A, Radhakrishnan A, Cabrero IA, Cotsonis GA, Cohen C. Intratumoral heterogeneity of immunohistochemical marker expression in breast carcinoma: a tissue microarray-based study. 2010; 18: 433-41.
• Lu L-C, Hsu C-H, Hsu C, Cheng A-L. Tumor heterogeneity in hepatocellular carcinoma: facing the challenges. Liver Cancer 2016; 5: 128-38.
• Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-6.
• Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30: 1234-48.
• Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278: 563-77.
• Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 2016; 1: 207-26.
• Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol 2016; 61: R150.
• Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 2013; 40: 133-40.
• Orlhac F, Soussan M, Maisonobe J-A, Garcia CA, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med 2014; 55: 414-22.
• Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging 2017; 44: 151-65.
• Nioche C, Orlhac F, Boughdad S, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 2018; 78: 4786-9.
• Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 2009; 50: 122S-50S.
• Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 2013; 266: 177-84.
• Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 2012; 22: 796-802.
• Yip C, Landau D, Kozarski R, et al. Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy. Radiology 2014; 270: 141-8.
• Zhang H, Graham CM, Elci O, et al. Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology 2013; 269: 801-9.
• Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 2012; 67: 157-64.
• Mattonen SA, Palma DA, Haasbeek CJ, Senan S, Ward AD. Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. Med Phys 2014; 41: 033502.
• Bang J-I, Ha S, Kang S-B, et al. Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [18F] FDG PET/CT scans in locally advanced rectal cancer. Eur J Nucl Med Mol Imaging 2016; 43: 422-31.
• Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer 2011; 11: 85-95.
• Torizuka T, Tamaki N, Inokuma T, et al. In vivo assessment of glucose metabolism in hepatocellular carcinoma with FDG-PET. J Nucl Med 1995; 36: 1811-7.
• Khan MA, Combs CS, Brunt EM, et al. Positron emission tomography scanning in the evaluation of hepatocellular carcinoma. J Hepatol 2000; 32: 792-7.
• Jo IY, Son SH, Kim M, et al. Prognostic value of pretreatment (18)F-FDG PET-CT in radiotherapy for patients with hepatocellular carcinoma. Radiat Oncol J 2015; 33: 179-87.
• Kim JW, Seong J, Yun M, et al. Usefulness of positron emission tomography with fluorine-18-fluorodeoxyglucose in predicting treatment response in unresectable hepatocellular carcinoma patients treated with external beam radiotherapy. Int J Radiat Oncol Biol Phys 2012; 82: 1172-8.
• Pant V, Sen IB, Soin AS. Role of 18F-FDG PET CT as an independent prognostic indicator in patients with hepatocellular carcinoma. Nucl Med Commun 2013; 34: 749-57.
• Labgaa I, Tabrizian P, Titano J, et al. Feasibility and safety of liver transplantation or resection after transarterial radioembolization with Yttrium-90 for unresectable hepatocellular carcinoma. HPB (Oxford) 2019; 21: 1497-504.
• Jeong SO, Kim EB, Jeong SW, et al. Predictive Factors for Complete Response and Recurrence after Transarterial Chemoembolization in Hepatocellular Carcinoma. Gut Liver 2017; 11: 409-16.
• Vesselle G, Quirier-Leleu C, Velasco S, et al. Predictive factors for complete response of chemoembolization with drug-eluting beads (DEB-TACE) for hepatocellular carcinoma. Eur Radiol 2016; 26: 1640-8.
• Coskun N, Okudan B, Uncu D, Kitapci MT. Baseline 18F-FDG PET textural features as predictors of response to chemotherapy in diffuse large B-cell lymphoma. Nucl Med Commun 2021; 42: 1227-32.
• Chandarana H, Rosenkrantz AB, Mussi TC, et al. Histogram analysis of whole-lesion enhancement in differentiating clear cell from papillary subtype of renal cell cancer. Radiology 2012; 265: 790-8.
• Just N. Histogram analysis of the microvasculature of intracerebral human and murine glioma xenografts. 2011; 65: 778-89.
• Baek HJ, Kim HS, Kim N, Choi YJ, Kim YJ. Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. Radiology 2012; 264: 834-43.
• Mosconi C, Cucchetti A, Bruno A, et al. Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation. Eur Radiol 2020; 30: 4534-44.
• Nardone V, Tini P, Pastina P, et al. Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab. Oncol Lett 2020; 19: 1559-66.
• Hotta M, Minamimoto R, Miwa K. 11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier. Sci Rep 2019; 9: 1-7.
• Cheng NM, Fang YH, Yen TC. The promise and limits of PET texture analysis. Ann Nucl Med 2013; 27: 867-9.