Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool. (March 2020)

• Record Type: Journal Article
• Authors: Krarup, Marie Manon Krebs; Nygård, Lotte; Vogelius, Ivan Richter; Andersen, Flemming Littrup; Cook, Gary; Goh, Vicky; Fischer, Barbara Malene
• Is Part Of: Radiotherapy and oncology. Volume 144(2020)
• Page Start: 72
• Page End: 78
• Publication Date: 2020-03
• Subjects: RFs Radiomic features -- PFS Progression free survival -- MTV Metabolic Active Tumour volume -- GTV Gross tumour volume -- SUV Standardized uptake value -- UICC Union for International Cancer Control -- OS Overall Survival -- LFU Last Follow Up -- 3D-OP-OSEM 3D ordered-subset expectation-maximization -- PSF Point Spread Function -- TOF Time of Flight -- CARE Combined Applications to Reduce Exposure -- GLCM Grey-level co-occurrence matrix -- GLRLM Grey-level run length matrix -- HGRE High grey-level run emphasis -- GLZLM Grey-level zone length matrix -- ZP Zone Percentage -- ASP Asphericity -- TFs Texture Features -- VOI Volume of interest -- TA Texture Analysis -- DICOM Digital Imaging and Communications in Medicine -- cCRT Concomitant chemo-radio therapy -- HR Hazard ratio; Positron Emission Tomography Computed Tomography -- Carcinoma, Non Small Cell Lung -- Prognosis -- Texture features -- Heterogeneity -- Radiomics; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2019.10.012
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: PET-based radiomic features (RFs) may be able to predict survival. Initial radiomic studies on lung cancer patients show promising results. Validation of RFs on external patients cohorts is necessary. GTV and clinical stage predicted progression free survival (PFS). None of the preselected RFs predicted independently for PFS. Abstract: Aim: The aim was to validate promising radiomic features (RFs) 1 on 18 F-flourodeoxyglucose positron emission tomography/computed tomography-scans ( 18 F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy. Methods: 18 F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV) 3, gross tumour volume (GTV) 4 and maximum and mean of standardized uptake value (SUV) 5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P -value ≤ 0.05 were considered significant. Results: Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm 3 (SD 130.30 cm 3 ). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS ( p = 0.001 and p = 0.008 respectively). Conclusion: The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.