ALBERT

Description: The hallmark for Chroniclymphocytic leukemia (CLL) is a highly variable clinical course. An international prognostic index (CLL-IPI) is a promising tool to improve the precision of prognostic counseling and to identify patients who deserve closed monitoring. The CLL-IPI is a risk-weighted model comprising the risk factors age, stage, del(17p)/TP53 mutation, IGHV mutation status, and β2-microglobulin (β2-M)(LancetOncol 2016). The aims of the study were 1) to determine the IGHV status as well as the IGHV repertoire 2) to assess/validate the applicability of the CLL-IPI in general practice. We included all patients diagnosed of CLL according to the National Cancer Institute Working Group guidelines in our institution between 1986 and 2014 who had at least a 24 months follow-up. Amplification and sequence analysis of IGH rearrangements were performed on either DNA or cDNAusing the BIOMED-2 protocol. Sequence data were analyzed using the IMGT database and tools. Clinical and biological data were extracted from medical records and included age, stage, CD38 and ZAP-70 expression, serum LDH, β2-M, cytogenetics and lines of treatment. Overall survival (OS) was calculated from diagnosis to last follow-up or death, time to first treatment (TFT) from diagnosis to first treatment administration or last follow-up. 209 CLL patients were originally included but complete data to calculate the CLL-IPI was only available in 176 pts. Median age of the series was 65 years (range, 33 to 92), and a slight male predominance 102 (58.5%). The main clinical characteristics are detailed in Table 1. Median follow-up of patients was 71.5 months (range, 24-315). We identified 105/209 patients (50%) with unmutated IGHV. Somatic mutations among IGHV gene subgroups display a hierarchy of mutations (IGHV3〉IGHV1〉IGHV4). Among the functional IGHV genes, the most frequently encountered were IGHV1-69 (31; 14.6%). It was the most recurrently used in the unmutated group. The most represented IGHV gene within the mutated subset was IGHV4-34, which was used in 15 cases (7.1%). We have observed 39 IGHV genes. The most frequents are showed in Figure1. As previously described, patients with unmutated status showed a higher expression of CD38 and ZAP-70, unfavorable cytogenetics and a higher proportion of treated patients. The CLL-IPI index identified four groups of patients: low risk (0-1 points) n=74 (42%), intermediate (2-3) 67 (38.1%), high (H) (4-6) 29 (16.5%) and very high (VH) 6 (3.4 %). The 5-year OS and 5-year TFT of the CLL-IPI risk groups differed significantly (p〈 0.0001, log-rank test) between the low (OS 92.2%, TFT 74.1%), intermediate (OS 83.2%, TFT 34.6%) and high-very high groups (OS 61.5%, TFT 22.8%). We only identified 6 patients (3.4%) with a VH, with no difference in terms of OS and TFT between the VH and high (H) risk groups, probably due to the small number of patients. When we considered the H and VH altogether, the CLL-IPI identified three groups with significantly different TFT and OS (Figure 2) In summary, in our cohort the frequencies of the IGHV genes used in BCR rearrangements were similar to those described in the Mediterranean area and confirm a geographical-dependent leukaemic repertoire. We confirm that the CLL-IPI is a useful tool for real-life practice as it identifies three risk groups with significantly different time to first treatment and overall survival curves. In our experience, the CLL-IPI applied to the whole CLL population at diagnosis discriminates a smaller proportion of patients in the high (16.4%) and very high groups (3.4 %) compared to the original training cohort based on treated patients included in clinical trials. Our results are closely similar to the MAYO cohort that included patients consecutively diagnosed and observed. Table 1 Patient characteristics at diagnosis included in CLL-IPI analysis (n=176) Table 1. Patient characteristics at diagnosis included in CLL-IPI analysis (n=176) Figure 1 Distribution of rearrangements of the 12 most frequent IGHV genes according to mutational status. Figure 1. Distribution of rearrangements of the 12 most frequent IGHV genes according to mutational status. Figure 2 a) Overall survival b) Time to first chronic lymphocytic leukaemia treatment according to the CLL-IPI risk groups Figure 2. a) Overall survival b) Time to first chronic lymphocytic leukaemia treatment according to the CLL-IPI risk groups Disclosures No relevant conflicts of interest to declare.

Description: BACKGROUND Although the overall prognosis of patients with aggressive non-Hodgkin's lymphoma (NHL) has improved, nearly a third of patients will have refractory disease or relapse. Identification of these high-risk patients using traditional prognostic factors is limited. PET is the recommended imaging modality for the staging of FDG-avid lymphoma but the value of a comprehensive new imaging biomarkers analysis applied to PET for the prediction of patients outcome has still not been deeply investigated. New metrics estimating the overall tumor burden such as metabolic tumor volume (MTV) and those that may capture intratumoral biological heterogeneity such as total lesion glycolysis (TLG) have been used to predict progression-free survival. AIM The goal of the present work was to characterize Lymphoma lesions by extracting several metabolic volume and textural properties as radiomics features and evaluate their performance as surrogate indicators of the number of treatment cycles, and treatment response. Materials and methods In this retrospective, observational study, we included aggressive non-Hodgkin's lymphoma patients consecutively diagnosed according to the WHO 2016 between January of 2015 to December of 2017. A diagnostic PET/CT scan were essential. 1 patient without treatment was excluded. Clinical and biological data were extracted from medical records. PET/CT examinations were exported from the PACS and loaded into QUIBIM Precision 2.3 analysis platform (QUIBIM, Valencia, Spain) for the calculation of metabolic volumes and textural properties. The SUV values of the PET images were normalized to the average liver SUV, and the lesions were automatically segmented considering a threshold of 41% of the maximum SUV (SUVmax). Physiological uptakes in organs and tissues like bowel, bladder, brain, among others, were manually removed. In the lesions volumetry analysis, the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated. For the extraction of texture features, first order histogram descriptors (SUV values distribution, skewness, kurtosis) as well as second order descriptors were extracted after computing the Gray-Level Co-Occurrence Matrix (GLCM). For the statistical analysis, the Z-score of all imaging features obtained was calculated and a multi-variate analysis was performed by first calculating the intra-class correlation (ICC) to reduce redundant variables. Second, data hierarchy clustering was applied to automatically obtain patient groups according to different imaging signatures. The prognostic performance of IPI with and without the imaging signature was evaluated by a Discriminant Analysis for the number of treatment cycles and treatment response. Prognostic value of OS was performed through Kaplan-Meier analysis. Results A total of 41 patients were included. The descriptive analysis of patients recruited with demographic and clinical data can be appreciated in Table 1. Radiomics features extracted allowed to clusterize patients in different groups that were later introduced in the classifier (Figure 1). The classifier based on discriminant model including the IPI factors predicted number of treatment cycles with a 65.9% of accuracy, being the age the factor with the highest weight (0.818). Adding information about imaging features from PET increased the accuracy to 86.5%. For the treatment response assessment, the IPI factors predicted response correctly in 71.4% of cases, being ECOG the parameter with the highest weight (0.974). Prediction was fully accurate when adding the imaging features, with a 100% of accuracy. The texture feature with the highest importance was 'dissimilarity' of the pixels (weight of 15.919). Conclusion The addition of radiomics features to the conventional IPI evaluation of patients allows for a significant increase in predictive performance, both for determining which patients will have more than 1 treatment lines and those who will respond to treatment. The results of this study would have an impact in disease management with a combined IPI and radiomics-based prognostic evaluation of patients at diagnosis. Disclosures No relevant conflicts of interest to declare.