Radiogenomics and Its Role in Lymphoma

The art of war: using genetic insights to understand and harness radiation sensitivity in hematologic malignancies

It is well established that hematologic malignancies are often considerably radiosensitive, which enables usage of far lower doses of therapeutic radiotherapy. This review summarizes the currently known genomic landscape of hematologic malignancies, particularly as it relates to radiosensitivity and the field of radiation oncology. By tracing the historical development of the modern understanding of radiosensitivity, we focus on the discovery and implications of pivotal mutated genes in hematologic malignancies such as TP53, ATM, and other genes critical to DNA repair pathways. These genetic insights have contributed significantly to the advancement of personalized medicine, aiming to enhance treatment precision and outcomes, and there is an opportunity to extend these insights to personalized radiotherapy. We explore the transition from early discoveries to the current efforts in integrating comprehensive genomic data into clinical practice. Specific examples from Hodgkin lymphoma, non-Hodgkin lymphoma, and plasma cell neoplasms illustrate how genetic mutations could influence radiosensitivity and impact subsequent radiotherapeutic response. Despite the advancements, challenges remain in translating these genetic insights into routine clinical practice, particularly due to the heterogeneity of alterations and the complex interactions within cancer signaling pathways. We emphasize the potential of radiogenomics to address these challenges by identifying genetic markers that predict radiotherapy response and toxicity, thereby refining treatment strategies. The need for robust decision support systems, standardized protocols, and ongoing education for healthcare providers is critical to the successful integration of genomic data into radiation therapy. As research continues to validate genetic markers and explore novel therapeutic combinations, the promise of personalized radiotherapy becomes increasingly attainable, offering the potential to significantly improve outcomes for patients with hematologic malignancies.

Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL.

RESULTS

A total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403).

CONCLUSIONS

Radiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.

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.

PET/CT in Non-Hodgkin Lymphoma: An Update

Non-Hodgkin lymphomas represents a heterogeneous group of lymphoproliferative disorders characterized by different clinical courses, varying from indolent to highly aggressive. ¹⁸F-FDG-PET/CT is the current state-of-the-art diagnostic imaging, for the staging, restaging and evaluation of response to treatment in lymphomas with avidity for ¹⁸F-FDG, despite it is not routinely recommended for surveillance. PET-based response criteria (using five-point Deauville Score) are nowadays uniformly applied in FDG-avid lymphomas. In this review, a comprehensive overview of the role of ¹⁸F-FDG-PET in Non-Hodgkin lymphomas is provided, at each relevant point of patient management, particularly focusing on recent advances on diffuse large B-cell lymphoma and follicular lymphoma, with brief updates also on other histotypes (such as marginal zone, mantle cell, primary mediastinal- B cell lymphoma and T cell lymphoma). PET-derived semiquantitative factors useful for patient stratification and prognostication and emerging radiomics research are also presented.

Radiomics Features of the Spleen as Surrogates for CT-Based Lymphoma Diagnosis and Subtype Differentiation

Simple Summary

In malignant lymphoma an early and accurate diagnosis is essential for therapy initiation and patient outcome. Within the diagnostic process, imaging plays a crucial role in disease staging. However, an invasive biopsy is required for subtype classification. Involvement of the spleen, a major lymphoid organ, is frequent in malignant lymphoma; this may be reactive or due to infiltration by malignant cells. Using radiomics features of the spleen in a machine learning approach, we investigated the possibility of distinguishing malignant lymphoma patients from other cancer patients and to classify lymphoma subtypes in the case of disease presence. Recent studies have proven the value of radiomics analysis in differentiating lymphoma from non-lymphoma groups on involved sites. Supported by machine learning, imaging could gain importance as a noninvasive diagnostic tool for future lymphoma classification, offering more precise radiological information for an interdisciplinary approach regarding treatment planning.

Abstract

The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence—in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.1 +/− 18.7 years) were generated and 1317 radiomics features per patient were extracted. For subtype classification, we created four different binary differentiation tasks and addressed them with a Random Forest classifier using 10-fold cross-validation. To detect the most relevant features, permutation importance was analyzed. Classifier results using all features were: malignant lymphoma vs. non-lymphoma AUC = 0.86 (p < 0.01); HL vs. NHL AUC = 0.75 (p < 0.01); DLBCL vs. other NHL AUC = 0.65 (p < 0.01); MCL vs. FL AUC = 0.67 (p < 0.01). Classifying malignant lymphoma vs. non-lymphoma was also possible using only shape features AUC = 0.77 (p < 0.01), with the most important feature being sphericity. Based on only shape features, a significant AUC could be achieved for all tasks, however, best results were achieved combining shape and textural features. This study demonstrates the value of splenic imaging and radiomic analysis in the diagnostic process in malignant lymphoma detection and subtype classification.

CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma

Purpose

MYCN amplification plays a critical role in defining high-risk subgroup of patients with neuroblastoma. We aimed to develop and validate the CT-based machine learning models for predicting MYCN amplification in pediatric abdominal neuroblastoma.

Methods

A total of 172 patients with MYCN amplified (n = 47) and non-amplified (n = 125) were enrolled. The cohort was randomly stratified sampling into training and testing groups. Clinicopathological parameters and radiographic features were selected to construct the clinical predictive model. The regions of interest (ROIs) were segmented on three-phrase CT images to extract first-, second- and higher-order radiomics features. The ICCs, mRMR and LASSO methods were used for dimensionality reduction. The selected features from the training group were used to establish radiomics models using Logistic regression, Support Vector Machine (SVM), Bayes and Random Forest methods. The performance of four different radiomics models was evaluated according to the area under the receiver operator characteristic (ROC) curve (AUC), and then compared by Delong test. The nomogram incorporated of clinicopathological parameters, radiographic features and radiomics signature was developed through multivariate logistic regression. Finally, the predictive performance of the clinical model, radiomics models, and nomogram was evaluated in both training and testing groups.

Results

In total, 1,218 radiomics features were extracted from the ROIs on three-phrase CT images, and then 14 optimal features, including one original first-order feature and eight wavelet-transformed features and five LoG-transformed features, were identified and selected to construct the radiomics models. In the training group, the AUC of the Logistic, SVM, Bayes and Random Forest model was 0.940, 0.940, 0.780 and 0.927, respectively, and the corresponding AUC in the testing group was 0.909, 0.909, 0.729, 0.851, respectively. There was no significant difference among the Logistic, SVM and Random Forest model, but all better than the Bayes model (p <0.005). The predictive performance of the Logistic radiomics model based on three-phrase is similar to nomogram, but both better than the clinical model and radiomics model based on single venous phase.

Conclusion

The CT-based radiomics signature is able to predict MYCN amplification of pediatric abdominal NB with high accuracy based on SVM, Logistic and Random Forest classifiers, while Bayes classifier yields lower predictive performance. When combined with clinical and radiographic qualitative features, the clinics-radiomics nomogram can improve the performance of predicting MYCN amplification.