FDG PET/CT metabolic tumor volume and total lesion glycolysis predict prognosis in patients with advanced lung adenocarcinoma

Chung, Hyun Woo; Lee, Kye Young; Kim, Hee Joung; Kim, Wan Seop; So, Young

doi:10.1007/s00432-013-1545-7

Cited by 87 publications

(59 citation statements)

References 35 publications

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“…Therefore, the effect of ethnicity is reflected in the frequency of EGFR1 mutations in each cohort, where the previous studies had a higher percentage of EGFR1 mutants than in our study (23% in the cohort of Lee et al (43), 40% in Chung et al (44), 64% in Huang et al (21), and 24% in Lee et al (42) vs. only 13% in our study). However, despite the differences between each study, it is clear that the conclusions on using conventional PETbased features for prediction of mutation status remain debatable.…”

Section: Discussioncontrasting

confidence: 50%

“…Conversely, studies by Huang et al (21) and Lee et al (42) identified that higher SUV measures, such as SUV max , were a strong predictor of EGFR1 tumors. In other studies, Lee et al (43) and Chung et al (44) found that none of their PET features (e.g., SUV max , MTV, and total glycolysis lesion) was significantly associated with or predicted EGFR mutation status.…”

Section: Discussionmentioning

confidence: 88%

“…Our study contained the largest patient cohort (348 patients) compared with the other studies (206 patients in Lee et al (43), 106 patients in Chung et al (44), 77 patients in Huang et al (21), and 214 patients in Lee et al (42)). Furthermore, the previous studies were from Asian institutions whereas our study was from a North American cohort, which resulted in more than 90% of our patients being Caucasian (Table 1).…”

Section: Discussionmentioning

confidence: 99%

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Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

et al. 2016

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PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18 F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18 F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR1] vs. EGFR-negative) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared with a random guess (AUC 5 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDR Wilcoxon , FDR Noether ) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDR Wilcoxon 5 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR1 vs. EGFR-negative, AUC 5 0.67, FDR Noether 5 0.0032), as well as differentiating between KRAS-positive and EGFR1 (AUC 5 0.65, FDR Noether 5 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC 5 0.50-0.54). Conclusion: Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS-mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations.

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Section: Discussioncontrasting

confidence: 50%

Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

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Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

et al. 2016

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“…MTV indicates the volume of metabolically active tumor, and TLG is the product of mean SUV and MTV (6). Studies have shown that TLG is a better index of patient prognosis than SUV max in advanced NSCLC (7)(8)(9)(10)(11)(12). However, the prognostic role of volume-dependent parameters in stage IA NSCLC has not been reported.…”

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confidence: 99%

Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Park

Cho

et al. 2014

J Nucl Med

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Despite the favorable prognosis of stage IA non-small cell lung cancer (NSCLC), the disease recurs after complete surgical resection in 20%-30% of patients. This study determined the prognostic value of various metabolic parameters of 18 F-FDG PET/CT in surgically resected stage IA NSCLC. Methods: We retrospectively reviewed 248 patients with stage IA NSCLC who underwent lobectomy and complete lymph node dissection after PET/CT. A region of interest was drawn on the primary lesion, and metabolic indices such as metabolic tumor volume, maximum standardized uptake value (SUV max ), and total lesion glycolysis (TLG) were measured using an SUV cutoff of 2.5. Results: The patients included 134 men and 114 women, and the mean age was 63.03 ± 10.01 y; 129 were stage T1a (#2 cm) and 119 were T1b (.2 cm). The median follow-up period was 36.6 mo. Recurrence took place in 15 patients. The mean (±SD) SUV max , metabolic tumor volume, and TLG were 4.55 ± 3.75, 5.92 ± 5.57, and 14.42 ± 17.35, respectively. The cutoffs of SUV max and TLG were 3.7 and 13.76, respectively. The 5-y overall survival (OS) was 95.1% in low-SUV max patients and 82.2% in high-SUV max patients (P 5 0.02). The 5-y OS was 93.7% in low-TLG patients and 78.3% in high-TLG patients (P 5 0.01). On multivariate analysis, TLG was a risk factor for OS (hazard ratio, 3.159; P 5 0.040), but SUV max showed marginal significance (P 5 0.064). The concordance index of the TLG model was 0.676 (95% CI, 0.541-0.812). Conclusion: TLG was a significant prognostic factor for OS in patients with stage IA NSCLC. Lungcanceri s the second most common cancer in both men and women and the most common cause of cancer death in the world (1). Non-small cell lung cancer (NSCLC) accounts for 80%-85% of all lung cancer cases. Oncologic treatment options for NSCLC typically include surgery, radiation, and chemotherapy, either alone or in combination. TNM stage has been considered the primary prognostic factor in NSCLC. However, tumor-and patientspecific factors vary even within the same disease stage, creating a heterogeneous population of patients, each with an individual prognosis that requires consideration of patient-and tumor-specific factors for best estimation (2,3). Recently, the incidence of earlystage NSCLC has increased as a result of the use of low-dose CT for screening. However, despite having early-stage disease, up to 20%-30% of patients with stage IA NSCLC relapse after surgical treatment. Therefore, other prognostic factors besides TNM stages are needed to identify patients at high risk for recurrence, predict prognosis, and recommend individualized adjuvant therapy (4).18 F-FDG PET relies on the fact that most neoplasms are highly metabolically active and therefore can be detected on a background of relatively less active normal tissues. When performed in addition to qualitative evaluation by visual inspection, 18 F-FDG PET can provide several semiquantitative measurements of radioactivity concentration in each tissue compartment, such as standardized uptake value (SUV...

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“…Initially these were predominantly based on SUV max [24,25,40]. However, more and more publications come to the conclusion that volume-based PET metrics, such as MTV or TLG, are superior prognostic markers on baseline PET scans compared to SUV max [41][42][43].…”

Section: Clinical Relevance Of the Suvmentioning

confidence: 99%

PET/MRI: Reliability/Reproducibility of SUV Measurements

Zaidi

Burger

2017

PET/MRI in Oncology

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FDG PET/CT metabolic tumor volume and total lesion glycolysis predict prognosis in patients with advanced lung adenocarcinoma

Abstract: Assessment of MTV and TLG by FDG PET/CT in advanced lung adenocarcinoma patients provides useful information regarding prognosis.

Cited by 87 publications

References 35 publications

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

PET/MRI: Reliability/Reproducibility of SUV Measurements

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FDG PET/CT metabolic tumor volume and total lesion glycolysis predict prognosis in patients with advanced lung adenocarcinoma

Abstract: Assessment of MTV and TLG by FDG PET/CT in advanced lung adenocarcinoma patients provides useful information regarding prognosis.

Cited by 87 publications

References 35 publications

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

Prognostic Value of Total Lesion Glycolysis by 18F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

PET/MRI: Reliability/Reproducibility of SUV Measurements

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Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer