FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy

Abstract

Our aim was to analyse whether biomarkers extracted from baseline 18F-FDG PET before anti-PD1 treatment contribute to prognostic survival information for early risk stratification in metastatic melanoma. Fifty-six patients, without prior systemic treatment, BRAF wild type, explored using 18F-FDG PET were included retrospectively. Our primary endpoint was overall survival (OS). Total metabolic tumoral volume (MTV) and forty-one IBSI compliant parameters were extracted from PET. Parameters associated with outcome were evaluated by a cox regression model and when significant helped build a prognostic score. Median follow-up was 22.1 months and 21 patients died. Total MTV and long zone emphasis (LZE) correlated with shorter OS and served to define three risk categories for the prognostic score. For low, intermediate and high risk groups, survival rates were respectively 91.1% (IC 95 80–1), 56.1% (IC 95 37.1–85) and 19% (IC 95 0.06–60.2) and hazard ratios were respectively 0.11 (IC 95 0.025–0.46), P = 0.0028, 1.2 (IC 95 0.48–2.8), P = 0.74 and 5.9 (IC 95 2.5–14), P < 0.0001. To conclude, a prognostic score based on total MTV and LZE separated metastatic melanoma patients in 3 categories with dramatically different outcomes. Innovative therapies should be tested in the group with the lowest prognosis score for future clinical trials.

Immunotherapy Monitoring with Immune Checkpoint Inhibitors Based on [18F]FDG PET/CT in Metastatic Melanomas and Lung Cancer

“…What emerges from initial reports published so far on radiomics and AI in the context of immunotherapy setting is that no unique parameter or feature can be defined as superior ( Table 2 ). While features like “skewness” and “kurtosis”, well known from other types of treatment, might represent a marker of treatment failure during ICI in lung cancer [ 90 ], for other authors either Small Run Emphasis (SRE), multiparametric radiomics signature (mpRS), cytolytic activity score (CytAct), deeply learned score (DLS), or long zone emphasis (LZE) [ 89 , 91 , 92 , 93 , 94 ] can be as effective. What is missing in this clinical scenario is a solid ground truth, which can only be obtainable from preliminary reports validating imaging parameters with targets specifically relevant for immunotherapy, as in the case of PD-L1 expression.…”

Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment

“…The choice of the best radiomics parameters from the pretherapeutic 18F-FDG-PET to predict the outcome is still a matter of debate as results from previous studies are heterogeneous. One study reporting on SUVmax found SUVmean and tumor heterogeneity index were not correlated with OS or PFS in a heterogeneous cohort of 55 melanoma patients before anti-PD1 treatment [ 9 ]; however, other studies showed significant correlations between conventional or second order 18F-FDG PET parameters and melanoma patients’ outcomes after immunotherapy [ 8 , 10 , 29 ]. Authors showed conventional parameters, such as SUV max, SUVpeak, TLG and MTV, were associated with OS [ 8 , 10 , 29 ].…”

“…One study reporting on SUVmax found SUVmean and tumor heterogeneity index were not correlated with OS or PFS in a heterogeneous cohort of 55 melanoma patients before anti-PD1 treatment [ 9 ]; however, other studies showed significant correlations between conventional or second order 18F-FDG PET parameters and melanoma patients’ outcomes after immunotherapy [ 8 , 10 , 29 ]. Authors showed conventional parameters, such as SUV max, SUVpeak, TLG and MTV, were associated with OS [ 8 , 10 , 29 ]. Second order features, such as tumor heterogeneity index, as well as long-zone emphasis from the GLZLM matrix were associated with OS [ 8 , 10 ].…”

“…Sixty minutes after injection, whole-body PET and unenhanced CT images were acquired. The CT attenuation-corrected acquired data were reconstructed using a three-dimensional ordered subset expectation–maximization algorithm as previously described [ 10 ]. Each scanner used consistent reconstruction settings, matrix and voxel size.…”

“…Few studies have examined the prognostic value of 18F-FDG PET radiomic features for metastatic melanoma before immunotherapy treatment, but associations have been found between 18F-FDG PET parameters and outcome at a group level [ 8 , 9 , 10 ]. However, to our knowledge, no study has previously investigated patient-level prediction performances before treating them with anti-PD1 as a first line therapy.…”