Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer

Abstract

Background Maximum, mean and peak SUV of primary tumor at baseline FDG-PET scans, have often been found predictive for overall survival in non-small cell lung cancer (NSCLC) patients. In this study we further investigated the prognostic power of advanced metabolic metrics derived from Intensity-Volume Histograms (IVH) extracted from PET imaging. Methods A cohort of 220 NSCLC patients (mean age, 66.6 years; 149 men, 71 women), stages I-IIIB, treated with radiotherapy with curative intent were included (NCT00522639). Each patient underwent standardized pre-treatment CT-PET imaging. Primary GTV was delineated by an experienced radiation oncologist on CT-PET images. Common PET descriptors such as maximum, mean and peak SUV, and metabolic tumor volume (MTV) were quantified. Advanced descriptors of metabolic activity were quantified by IVH. These comprised 5 groups of features: Absolute and Relative Volume above Relative Intensity threshold (AVRI and RVRI), Absolute and Relative Volume above Absolute Intensity threshold (AVAI and RVAI), and Absolute Intensity above Relative Volume threshold (AIRV). MTV was derived from the IVH curves for volumes with SUV above 2.5, 3 and 4, and of 40% and 50% maximum SUV. Univariable analysis using Cox Proportional Hazard Regression was performed for overall survival assessment. Results Relative volume above higher SUV (80 %) was an independent predictor of OS (p = 0.05). None of the possible surrogates for MTV based on volumes above SUV of 3, 40% and 50% of maximum SUV showed significant associations with OS (p (AVAI3) = 0.10, p (AVAI4) = 0.22, p (AVRI40%) = 0.15, p (AVRI50%) = 0.17). Maximum and peak SUV (r = 0.99) revealed no prognostic value for OS (p (maximum SUV) = 0.20, p (peak SUV) = 0.22). Conclusions New methods using more advanced imaging features extracted from PET were analyzed. Best prognostic value for OS of NSCLC patients was found for relative portions of the tumor above higher uptakes (80% SUV).

Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells

“…Radiomics, an advanced analysis platform, extracts features from medical images by using mathematical algorithms based on tumor intensity, gray-level intensities, and texture [6][7][8][9]15,[18][19][20][21]. We have previously identified radiomics-based signatures that are prognostic and associated with proliferation-related gene signatures [7].…”

PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology

“…Compared with Adk, Sqc is characterized by higher SUV max , AUC-IVH, energy GLMC , entropy GLCM , sum entropy, difference entropy, and inverse different moment and by lower homogeneity GLCM , sum of squares, angular second moment, ratio of non-zero Gr , and difference variance 60–62 . Prognostic and predictive role Heterogeneity (i.e., AUC-CSH) can predict recurrence in pN0 Adk patients who have undergone curative surgery but not in Sqk patients (high heterogeneity is associated with a shorter DFS) 61 .Best prognostic value for overall survival is found for relative portions of the tumor above higher uptakes defined as SUV max  > 80% (i.e., V 80 ) in patients who received radiation therapy (sequential chemoradiation, concurrent chemoradiation, or only radiation). The higher the portion above higher uptake (V 80 ), the better the prognosis 29, 80 .Heterogeneity (i.e., low AUC-CSH) identifies patients with inoperable stage III NSCLC with poor PFS 75 .High SUV max , large MTV, and high heterogeneity (i.e., high entropy GLCM , high asphericity, homogeneity GLCM , and high dissimilarity, size-zone variability, and low zone percentage) are associated with poorer OS and RFS in stage I–III NSCLC 35, 73, 74, 83 .Tumor heterogeneity (i.e., entropy GLCM ) is associated with response to radiation therapy in NSCLC (DSS is lower for patients with high entropy GLCM ) 79 .Lesions in responders (complete or partial response) to chemoradiotherapy are characterized by lower coarseness, contrast NGTDM , and busyness than non-responders (stable or progressive disease). High coarseness values are associated with an increased risk of progression (increased risk of death), whereas high contrast NGTDM and busyness values are associated with a lower risk of progression (PFS and LPFS) 14 .Large primary tumors with low SumAverage (i.e., more heterogeneous) have a poor prognosis following chemoradiotherapy 76 .Lesions in responders to erlotinib are characterized by lower heterogeneity than those in non-responders.…”

Developing and validating a novel metabolic tumor volume risk stratification system for supplementing non-small cell lung cancer staging

“…Therefore, the FDG PET/CT scanners used in the study and clinical variables in the modeling and validation cohorts mimic real world situations, suggesting that the MTVwb risk stratification system developed and validated in this study may be generalizable to other centers. There are other reasons to expect that the MTVwb risk stratification system can be used in other centers and be practical in clinical use including: 1) measurement of MTVwb is practical with commercially available software, and semi-automatic tumor segmentation is as accurate as manual segmentation for primary NSCLC tumors [25]; 2) MTVwb estimates are relatively insensitive to different FDG PET/CT scanners and image-reconstruction algorithms [26]; 3) a number of studies have demonstrated consistently significant correlation between survival and MTVwb in NSCLC, in different parts of the world, and with different FDG PET/CT scanners[2–11, 13, 14, 27–31], 4) the variability of metabolic tumor volume in NSCLC primary tumors is less than that of SUV for NSCLC primary tumor in PET scans of 1-hour vs. 2-hour FDG uptake time [32]; and 5) MTVwb measurements are relatively immune to inter-observer variability[2, 5]. …”