Texture analysis of 18F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy

Nakajo, Masatoyo; Jinguji, Megumi; Nakabeppu, Yoshiaki; Nakajo, Masayuki; Higashi, Ryutarou; Fukukura, Yoshihiko; Sasaki, Ken; Uchikado, Yasuto; Natsugoe, Shoji; Yamada, Takashi

doi:10.1007/s00259-016-3506-2

Cited by 104 publications

(84 citation statements)

References 35 publications

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“…Kaplan-Meier analysis (univariate analysis) was performed using thresholds established by ROC analysis in cases in which ROC showed statistical significant results. A multivariate Cox hazard analysis was conducted to determine independent prognostic parameters as well as relative risks (RR) [26,30]. Non-parametric logrank tests were used to assess the differences in the Kaplan-Meier curves; statistical significance was considered with a p value G 0.05.…”

Section: Discussionmentioning

confidence: 99%

“…In case of multiorgan involvement, a maximum of three lesions per organ (largest in size and metabolically most active lesion) was segmented. A manual segmentation method was preferred in order to exclude adjacent physiological SSTR-avid structures on PET/CT images [26]. TF analysis was performed by using the Interview Fusion Workstation (Mediso Medical Imaging Systems Ltd., Budapest, Hungary) [13].…”

Section: Pet/ct Imaging Imaging Interpretationmentioning

confidence: 99%

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Pre-therapy Somatostatin Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy

et al. 2018

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Purpose: Early identification of aggressive disease could improve decision support in pancreatic neuroendocrine tumor (pNET) patients prior to peptide receptor radionuclide therapy (PRRT). The prognostic value of intratumoral textural features (TF) determined by baseline somatostatin receptor (SSTR)-positron emission tomography (PET) before PRRT was analyzed. Procedures: Thirty-one patients with G1/G2 pNET were enrolled (G2, n = 23/31). Prior to PRRT with [ 177 Lu]DOTATATE (mean, 3.6 cycles), baseline SSTR-PET computed tomography was performed. By segmentation of 162 (median per patient, 5) metastases, intratumoral TF were computed. The impact of conventional PET parameters (SUV mean/max ), imaging-based TF, and clinical parameters (Ki67, CgA) for prediction of both progression-free survival (PFS) and overall survival (OS) after PRRT were evaluated. Correspondence to: Ralph Bundschuh; e-mail: ralph.bundschuh@ukbonn.deResults: Within a median follow-up of 3.7 years, tumor progression was detected in 21 patients (median, 1.5 years) and 13/31 deceased (median, 1.9 years). In ROC analysis, the TF entropy, reflecting derangement on a voxel-by-voxel level, demonstrated predictive capability for OS (cutoff = 6.7, AUC = 0.71, p = 0.02). Of note, increasing entropy could predict a longer survival (9 6.7, OS = 2.5 years, 17/31), whereas less voxel-based derangement portended inferior outcome (G 6.7, OS = 1.9 years, 14/31). These findings were supported in a G2 subanalysis (9 6.9, OS = 2.8 years, 9/23 vs. G 6.9, OS = 1.9 years, 14/23). Kaplan-Meier analysis revealed a significant distinction between high-and low-risk groups using entropy (n = 31, p G 0.05). For those patients below the ROC-derived threshold, the relative risk of death after PRRT was 2.73 (n = 31, p = 0.04). Ki67 was negatively associated with PFS (p = 0.002); however, SUV mean/max failed in prognostication (n.s.). Conclusions: In contrast to conventional PET parameters, assessment of intratumoral heterogeneity demonstrated superior prognostic performance in pNET patients undergoing PRRT. This novel PET-based strategy of outcome prediction prior to PRRT might be useful for patient risk stratification.

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Section: Discussionmentioning

confidence: 99%

Section: Pet/ct Imaging Imaging Interpretationmentioning

confidence: 99%

Pre-therapy Somatostatin Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy

et al. 2018

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“…Other more recent works explored the potential of texture analysis on PET images for the prediction of tumour response to CRT on patients with oesophageal cancer. Nakajo et al 87 found that intensity variability and size-zone variability were higher in patients who were non-responders than responders. Yip et al 37 studied the impact of different image registration methods for the definition of tumour volume after CRT on the estimation of several textural features.…”

Section: Application Of Texture Analysis In Radiotherapymentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

134

108

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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“…Hence, not surprisingly, combined 18 F-FDG PET-based approaches (i.e., volumetric plus tumor texture assessment) for outcome prediction have also been investigated, e.g. in esophageal cancer [20].…”

Section: Introductionmentioning

confidence: 99%

Volumetric and texture analysis of pretherapeutic 18F-FDG PET can predict overall survival in medullary thyroid cancer patients treated with Vandetanib

Werner

Bundschuh²,

Higuchi

et al. 2018

Endocrine

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Purpose The metabolically most active lesion in 2-deoxy-2-( 18 F)fluoro-D-glucose ( 18 F-FDG) PET/CT can predict progression-free survival (PFS) in patients with medullary thyroid carcinoma (MTC) starting treatment with the tyrosine kinase inhibitor (TKI) vandetanib. However, this metric failed in overall survival (OS) prediction. In the present proof of concept study, we aimed to explore the prognostic value of intratumoral textural features (TF) as well as volumetric parameters (total lesion glycolysis, TLG) derived by pre-therapeutic 18 F-FDG PET. Methods Eighteen patients with progressive MTC underwent baseline 18 F-FDG PET/CT prior to and 3 months after vandetanib initiation. By manual segmentation of the tumor burden at baseline and follow-up PET, intratumoral TF and TLG were computed. The ability of TLG, imaging-based TF, and clinical parameters (including age, tumor marker doubling times, prior therapies and RET (rearranged during transfection) mutational status) for prediction of both PFS and OS were evaluated. Results The TF Complexity and the volumetric parameter TLG obtained at baseline prior to TKI initiation successfully differentiated between low-and high-risk patients. Complexity allocated 10/18 patients to the high-risk group with an OS of 3.3 y (vs. low-risk group, OS = 5.3 y, 8/18, AUC = 0.78, P = 0.03). Baseline TLG designated 11/18 patients to the high-risk group (OS = 3.5 y vs. low-risk group, OS = 5 y, 7/18, AUC = 0.83, P = 0.005). The Hazard Ratio for cancer-related death was 6.1 for Complexity (TLG, 9.5). Among investigated clinical parameters, the age at initiation of TKI treatment reached significance for PFS prediction (P = 0.02, OS, n.s.). Conclusions The TF Complexity and the volumetric parameter TLG are both independent parameters for OS prediction.

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Texture analysis of 18F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy

Abstract: Texture features IV and SZV, and volumetric parameters MTV and TLG can predict tumour response, but all of them have limited value in prediction of prognosis of patients with esophageal cancer treated by CRT.

Cited by 104 publications

References 35 publications

Pre-therapy Somatostatin Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy

Pre-therapy Somatostatin Receptor-Based Heterogeneity Predicts Overall Survival in Pancreatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radionuclide Therapy

Texture analysis of medical images for radiotherapy applications

Volumetric and texture analysis of pretherapeutic 18F-FDG PET can predict overall survival in medullary thyroid cancer patients treated with Vandetanib

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