Aberrant patterns of PET response during treatment for DLBCL patients with MYC gene rearrangements

Outcome prediction by interim positron emission tomography and IgM monoclonal gammopathy in diffuse large B-cell lymphoma

In diffuse large B-cell lymphoma (DLBCL), a positive interim positron emission tomography (PET) scan predicts treatment failure, but the proportion of high-risk patients thus identified is small. To improve prediction, we combined the interim PET result with the presence or absence of an associated IgM gammopathy. Of 108 DLBCL patients participating in a prospective trial, nine (8%) were interim PET positive and 19 (18%) had an IgM gammopathy. The monoclonal protein was not associated with distinguishing genetic features, and its light chain restriction was not always concordant with the light chain restriction of the lymphoma. The information provided by interim PET and IgM gammopathy was combined to dichotomize the population into sizeable high-risk (1-2 adverse factors) and low-risk groups (no adverse factor) with widely different outcomes (population size, 25% vs. 75%; 3-year risk of progression, 51% vs. 10%; 3-year overall survival, 64% vs. 95%). Multivariable analyses including established risk factors revealed the interim PET result and the IgM gammopathy status to be the only factors significantly associated with outcome. Information about interim PET response and IgM gammopathy may be useful in studies testing risk-adapted treatment strategies.

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

Interim FDG-PET/CT for Response Assessment of Lymphoma

The clinical use and prognostic value of interim FDG-PET/CT (iPET/CT), which is performed after treatment initiation but prior to its completion, varies by lymphoma subtype. Evidence supporting the prognostic value of iPET/CT is more robust for classical Hodgkin lymphoma (cHL), and in this lymphoma subtype, response-adapted treatment approaches guided by iPET/CT are a widely used standard of care for first-line therapy. The data supporting use of iPET/CT among patients with non-Hodgkin lymphoma (NHL) is less well-established, but failure to achieve complete metabolic response on iPET/CT is generally considered a poor prognostic factor with likely consequences for progression free survival. This review will present the available evidence supporting use of iPET/CT in lymphoma patients, particularly as it relates to prognostication and the ability to inform response-adapted treatment strategies. The latter will be addressed through a discussion on the major iPET-response adapted clinical trials with mention of ongoing trials. Special attention will be given to cHL and a few subtypes of NHL, including diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), and peripheral T cell lymphoma (PTCL).

Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features

PURPOSE

Biomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [¹⁸F]-FDG PET/CT scans have shown promising results. This study aims to investigate which lesion- and feature-selection approaches/methods resulted in the best prediction of progression after 2 years.

METHODS

A total of 296 patients were included. 485 radiomics features (n = 5 conventional PET, n = 22 morphology, n = 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOI. 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined.

RESULTS

Models with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.72-0.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs.

CONCLUSION

Regardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value. Trial registration number and date: EudraCT: 2006-005174-42, 01-08-2008.

Mitochondria and Their Relationship with Common Genetic Abnormalities in Hematologic Malignancies

Hematologic malignancies are known to be associated with numerous cytogenetic and molecular genetic changes. In addition to morphology, immunophenotype, cytochemistry and clinical characteristics, these genetic alterations are typically required to diagnose myeloid, lymphoid, and plasma cell neoplasms. According to the current World Health Organization (WHO) Classification of Tumors of Hematopoietic and Lymphoid Tissues, numerous genetic changes are highlighted, often defining a distinct subtype of a disease, or providing prognostic information. This review highlights how these molecular changes can alter mitochondrial bioenergetics, cell death pathways, mitochondrial dynamics and potentially be related to mitochondrial genetic changes. A better understanding of these processes emphasizes potential novel therapies.