Functional imaging using radiomic features in assessment of lymphoma

The Diagnostic Performance of 2-[18F]FDG PET/CT in Identifying Richter Transformation in Chronic Lymphocytic Leukemia: An Updated Systematic Review and Bivariate Meta-Analysis

Richter transformation is a rare phenomenon characterized by the transformation of cell chronic lymphocytic leukemia (CLL) into a more aggressive lymphoma variant. The early identification of CLLs with a high risk of RT is fundamental. In this field, 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) has been shown to be a non-invasive and promising tool, but apparently, unclear data seem to be present in the literature. This systematic review and bivariate meta-analysis aimed to investigate the diagnostic performance of 2-[18F]FDG PET/CT and its parameters in predicting RT. Between 2006 and 2024, 15 studies were published on this topic, including 1593 CLL patients. Among semiquantitative variables, SUVmax was the most investigated, and the best threshold derived for detecting RT was five. With this cut-off value, a pooled sensitivity of 86.8% (95% CI: 78.5-93.3), a pooled specificity of 48.1% (95% CI: 27-69.9), a pooled negative predictive value of 90.5% (95% CI: 88.4-92.4), a pooled negative likelihood ratio of 0.35 (95% CI: 0.17-0.70), a pooled positive likelihood ratio of 1.8 (95% CI: 1.3-2.4), and a pooled diagnostic odds ratio of 6.7 (3.5-12.5) were obtained. With a higher cut-off (SUVmax = 10), the specificity increased while the sensitivity reduced. The other metabolic features, like metabolic tumor volume, total lesion glycolysis, and radiomic features, were only marginally investigated with controversial evidence.

Leptomeningeal Metastasis: A Review of the Pathophysiology, Diagnostic Methodology, and Therapeutic Landscape

The present review aimed to establish an understanding of the pathophysiology of leptomeningeal disease as it relates to late-stage development among different cancer types. For our purposes, the focused metastatic malignancies include breast cancer, lung cancer, melanoma, primary central nervous system tumors, and hematologic cancers (lymphoma, leukemia, and multiple myeloma). Of note, our discussion was limited to cancer-specific leptomeningeal metastases secondary to the aforementioned primary cancers. LMD mechanisms secondary to non-cancerous pathologies, such as infection or inflammation of the leptomeningeal layer, were excluded from our scope of review. Furthermore, we intended to characterize general leptomeningeal disease, including the specific anatomical infiltration process/area, CSF dissemination, manifesting clinical symptoms in patients afflicted with the disease, detection mechanisms, imaging modalities, and treatment therapies (both preclinical and clinical). Of these parameters, leptomeningeal disease across different primary cancers shares several features. Pathophysiology regarding the development of CNS involvement within the mentioned cancer subtypes is similar in nature and progression of disease. Consequently, detection of leptomeningeal disease, regardless of cancer type, employs several of the same techniques. Cerebrospinal fluid analysis in combination with varied imaging (CT, MRI, and PET-CT) has been noted in the current literature as the gold standard in the diagnosis of leptomeningeal metastasis. Treatment options for the disease are both varied and currently in development, given the rarity of these cases. Our review details the differences in leptomeningeal disease as they pertain through the lens of several different cancer subtypes in an effort to highlight the current state of targeted therapy, the potential shortcomings in treatment, and the direction of preclinical and clinical treatments in the future. As there is a lack of comprehensive reviews that seek to characterize leptomeningeal metastasis from various solid and hematologic cancers altogether, the authors intended to highlight not only the overlapping mechanisms but also the distinct patterning of disease detection and progression as a means to uniquely treat each metastasis type. The scarcity of LMD cases poses a barrier to more robust evaluations of this pathology. However, as treatments for primary cancers have improved over time, so has the incidence of LMD. The increase in diagnosed cases only represents a small fraction of LMD-afflicted patients. More often than not, LMD is determined upon autopsy. The motivation behind this review stems from the increased capacity to study LMD in spite of scarcity or poor patient prognosis. In vitro analysis of leptomeningeal cancer cells has allowed researchers to approach this disease at the level of cancer subtypes and markers. We ultimately hope to facilitate the clinical translation of LMD research through our discourse.

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.

The application of machine learning to imaging in hematological oncology: A scoping review

Background

Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.

Methods

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle-Ottawa scale was used to assess the quality of observational studies.

Results

Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case-control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.

Conclusion

To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.

The added prognostic values of baseline PET dissemination parameter in patients with angioimmunoblastic T-cell lymphoma

To explore the prognostic values of baseline 2-deoxy-2-[¹⁸F] fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) dissemination parameter in angioimmunoblastic T-cell lymphoma (AITL) and its added values to total metabolic tumour volume (TMTV). Eighty-one AITL patients with at least two FDG-avid lesions in baseline PET/CT were retrospectively included. PET parameters including TMTV and the distance between the two lesions that are the furthest apart (Dmax) were obtained. Univariate Cox analysis showed that both Dmax and TMTV were risk factors for progression-free survival (PFS) and overall survival (OS). Multivariate Cox analysis models of different combinations showed that high Dmax (> 65.7 cm) could independently predict both PFS and OS, while high TMTV (>456.6 cm³) was only significant for OS. A concise PET model based on TMTV and Dmax can effectively risk-stratify patients. PFS and OS rates were significantly lower in patients with high Dmax and high TMTV than in patients with low Dmax and low TMTV (3-year PFS rate: 15.0% vs. 48.7%, p = 0.001; 3-year OS rate: 27.6% vs. 79.0%, p < 0.001). Dmax can directly reflect the disease dissemination characteristic and has a significant prognostic value for FDG-avid AITL patients. It has the potential to be introduced into new risk stratification models for tailored treatment.

CT radiomics to predict Deauville score 4 positive and negative Hodgkin lymphoma manifestations

18F-FDG-PET/CT is standard to assess response in Hodgkin lymphoma by quantifying metabolic activity with the Deauville score. PET/CT, however, is time-consuming, cost-extensive, linked to high radiation and has a low availability. As an alternative, we investigated radiomics from non-contrast-enhanced computed tomography (NECT) scans. 75 PET/CT examinations of 43 patients on two different scanners were included. Target lesions were classified as Deauville score 4 positive (DS4+) or negative (DS4-) based on their SUVpeak and then segmented in NECT images. From these segmentations, 107 features were extracted with PyRadiomics. All further statistical analyses were then performed scanner-wise: differences between DS4+ and DS4- manifestations were assessed with the Mann-Whitney-U-test and single feature performances with the ROC-analysis. To further verify the reliability of the results, the number of features was reduced using different techniques. The feature median showed a high sensitivity for DS4+ manifestations on both scanners (scanner A: 0.91, scanner B: 0.85). It furthermore was the only feature that remained in both datasets after applying different feature reduction techniques. The feature median from NECT concordantly has a high sensitivity for DS4+ Hodgkin manifestations on two different scanners and thus could provide a surrogate for increased metabolic activity in PET/CT.

Bruton's tyrosine kinase inhibitors in the treatment of primary central nervous system lymphoma: A mini-review

Primary central nervous system lymphoma (PCNSL) is a highly aggressive brain tumor with poor prognosis if no treatment. The activation of the NF-κB (nuclear factor kappa-B) is the oncogenic hallmark of PCNSL, and it was driven by B cell receptor (BCR) and Toll-like receptor (TLR) signaling pathways. The emergence of Bruton's tyrosine kinase inhibitors (BTKis) has brought the dawn of life to patients with PCNSL. This review summarizes the management of PCNSL with BTKis and potential molecular mechanisms of BTKi in the treatment of PCNSL. And the review will focus on the clinical applications of BTKi in the treatment of PCNSL including the efficacy and adverse events, the clinical trials currently being carried out, the underlying mechanisms of resistance to BTKi and possible solutions to drug resistance.

Two-Year Event-Free Survival Prediction in DLBCL Patients Based on In Vivo Radiomics and Clinical Parameters

Purpose

For the identification of high-risk patients in diffuse large B-cell lymphoma (DLBCL), we investigated the prognostic significance of in vivo radiomics derived from baseline [¹⁸F]FDG PET/CT and clinical parameters.

Methods

Pre-treatment [¹⁸F]FDG PET/CT scans of 85 patients diagnosed with DLBCL were assessed. The scans were carried out in two clinical centers. Two-year event-free survival (EFS) was defined. After delineation of lymphoma lesions, conventional PET parameters and in vivo radiomics were extracted. For 2-year EFS prognosis assessment, the Center 1 dataset was utilized as the training set and underwent automated machine learning analysis. The dataset of Center 2 was utilized as an independent test set to validate the established predictive model built by the dataset of Center 1.

Results

The automated machine learning analysis of the Center 1 dataset revealed that the most important features for building 2-year EFS are as follows: max diameter, neighbor gray tone difference matrix (NGTDM) busyness, total lesion glycolysis, total metabolic tumor volume, and NGTDM coarseness. The predictive model built on the Center 1 dataset yielded 79% sensitivity, 83% specificity, 69% positive predictive value, 89% negative predictive value, and 0.85 AUC by evaluating the Center 2 dataset.

Conclusion

Based on our dual-center retrospective analysis, predicting 2-year EFS built on imaging features is feasible by utilizing high-performance automated machine learning.

Novel insights into the biomarkers and therapies for primary central nervous system lymphoma

Primary central nervous system lymphoma (PCNSL) is a rare and highly aggressive extranodal type of non-Hodgkin lymphoma. After the introduction and widespread use of high-dose-methotrexate (HD-MTX)-based polychemotherapy, treatment responses of PCNSL have been improved. However, long-term prognosis for patients who have failed first-line therapy and relapsed remains poor. Less invasive diagnostic markers, including the circulating tumor DNAs (ctDNAs), microRNAs, metabolomic markers, and other novel biomarkers, such as a proliferation inducing ligand (APRIL) and B-cell activating factor of the TNF family (BAFF), have shown potential to distinguish PCNSL at an early stage, and some of them are related with prognosis to a certain extent. Recent insights into novel therapies, including Bruton tyrosine kinase (BTK) inhibitors, immunomodulatory drugs, immune checkpoint inhibitors, PI3K/mTOR inhibitors, and chimeric antigen receptor (CAR) T cells, have revealed encouraging efficacy in treatment response, whereas the duration of response and long-term survival of patients with relapsed or refractory PCNSL (r/r PCNSL) need further improvement. In addition, the diagnostic efficiency of novel markers and the antitumor efficacy of novel therapies are needed to be assessed further in larger clinical trials. This review provides an overview of recent research on novel diagnostic markers and therapeutic strategies for PCNSL.

Automatic classification of lymphoma lesions in FDG-PET–Differentiation between tumor and non-tumor uptake

Introduction

The automatic classification of lymphoma lesions in PET is a main topic of ongoing research. An automatic algorithm would enable the swift evaluation of PET parameters, like texture and heterogeneity markers, concerning their prognostic value for patients outcome in large datasets. Moreover, the determination of the metabolic tumor volume would be facilitated. The aim of our study was the development and evaluation of an automatic algorithm for segmentation and classification of lymphoma lesions in PET.

Methods

Pre-treatment PET scans from 60 Hodgkin lymphoma patients from the EuroNet-PHL-C1 trial were evaluated. A watershed algorithm was used for segmentation. For standardization of the scan length, an automatic cropping algorithm was developed. All segmented volumes were manually classified into one of 14 categories. The random forest method and a nested cross-validation was used for automatic classification and evaluation.

Results

Overall, 853 volumes were segmented and classified. 203/246 tumor lesions and 554/607 non-tumor volumes were classified correctly by the automatic algorithm, corresponding to a sensitivity, a specificity, a positive and a negative predictive value of 83%, 91%, 79% and 93%. In 44/60 (73%) patients, all tumor lesions were correctly classified. In ten out of the 16 patients with misclassified tumor lesions, only one false-negative tumor lesion occurred. The automatic classification of focal gastrointestinal uptake, brown fat tissue and composed volumes consisting of more than one tissue was challenging.

Conclusion

Our algorithm, trained on a small number of patients and on PET information only, showed a good performance and is suitable for automatic lymphoma classification.

Role of Radiomics-Based Baseline PET/CT Imaging in Lymphoma: Diagnosis, Prognosis, and Response Assessment

Radiomic analysis provides information on the underlying tumour heterogeneity in lymphoma, reflecting the real-time evolution of malignancy. 2-Deoxy-2-[¹⁸F] fluoro-D-glucose positron emission tomography ([¹⁸F] FDG PET/CT) imaging is recommended before, during, and at the end of treatment for almost all lymphoma patients. This methodology offers high specificity and sensitivity, which can aid in accurate staging and assist in prompt treatment. Pretreatment [¹⁸F] FDG PET/CT-based radiomics facilitates improved diagnostic ability, guides individual treatment regimens, and boosts outcome prognosis based on heterogeneity as well as the biological, pathological, and metabolic status of the lymphoma. This technique has attracted considerable attention given its numerous applications in medicine. In the current review, we will briefly describe the basic radiomics workflow and types of radiomic features. Details of current applications of baseline [¹⁸F] FDG PET/CT-based radiomics in lymphoma will be discussed, such as differential diagnosis from other primary malignancies, diagnosis of bone marrow involvement, and response and prognostic prediction. We will also describe how this technique provides a unique noninvasive platform to assess tumour heterogeneity. Newly emerging PET radiotracers and multimodality technology will improve diagnostic specificity and further clarify tumor biology and even genetic variations in lymphoma, potentially promoting the development of precision medicine.

Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions)

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.

Positron emission tomography/computed tomography in the management of Hodgkin and B-cell non-Hodgkin lymphoma: An update

¹⁸ F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is now an integral part of lymphoma staging and management. Because of its greater accuracy compared with CT alone, PET/CT is currently routinely performed for staging and for response assessment at the end of treatment in the vast majority of FDG-avid lymphomas and is the cornerstone of response classification for these lymphomas according to the Lugano classification. Interim PET/CT, typically performed after 2 to 4 of 6 to 8 chemotherapy/chemoimmunotherapy cycles with or without radiation, is commonly performed for prognostication and potential treatment escalation or de-escalation early in the course of therapy, a concept known as response-adapted or risk-adapted treatment. Quantitative PET is an area of growing interest. Metrics, such as the standardized uptake value, changes (Δ) in the standardized uptake value, metabolic tumor volume, and total lesion glycolysis, are being investigated as more reproducible and potentially more accurate predictors of response and prognosis. Despite the progress made in standardizing the use of PET/CT in lymphoma, challenges remain, particularly with respect to its limited positive predictive value, emphasizing the need for more specific molecular probes. This review highlights the most relevant applications of PET/CT in Hodgkin and B-cell non-Hodgkin lymphoma, its strengths and limitations, as well as recent efforts at implementing PET/CT-based metrics as promising tools for precision medicine.

Positron emission tomography and magnetic resonance imaging in primary central nervous system lymphoma-a narrative review

This review addresses the challenges of primary central nervous system (CNS) lymphoma diagnosis, assessment of treatment response, and detection of recurrence. Primary CNS lymphoma is a rare form of extra-nodal non-Hodgkin lymphoma that can involve brain, spinal cord, leptomeninges, and eyes. Primary CNS lymphoma lesions are most commonly confined to the white matter or deep cerebral structures such as basal ganglia and deep periventricular regions. Contrast-enhanced magnetic resonance imaging (MRI) is the standard diagnostic modality employed by neuro-oncologists. MRI often shows common morphological features such as a single or multiple uniformly well-enhancing lesions without necrosis but with moderate surrounding edema. Other brain tumors or inflammatory processes can show similar radiological patterns, making differential diagnosis difficult. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) has selected utility in cerebral lymphoma, especially in diagnosis. Primary CNS lymphoma can sometimes present with atypical findings on MRI and FDG PET, such as disseminated disease, non-enhancing or ring-like enhancing lesions. The complementary strengths of PET and MRI have led to the development of combined PET-MR systems, which in some cases may improve lesion characterization and detection. By highlighting active developments in this field, including advanced MRI sequences, novel radiotracers, and potential imaging biomarkers, we aim to spur interest in sophisticated imaging approaches.

Prognostic Value of Baseline Radiomic Features of 18F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma

This study investigates whether baseline ¹⁸F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent ¹⁸F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinkage and selection operator regression were used to reduce the dimensionality within radiomic features. Cox proportional hazards model was used to determine the prognostic factors for progression-free survival (PFS) and overall survival (OS). A prognostic stratification model was built in the training cohort and validated in the validation cohort using Kaplan-Meier survival analysis. In the training cohort, run length non-uniformity (RLN), extracted from a gray level run length matrix (GLRLM), was independently associated with PFS (hazard ratio (HR) = 15.7, p = 0.007) and OS (HR = 8.64, p = 0.040). The International Prognostic Index was an independent prognostic factor for OS (HR = 2.63, p = 0.049). A prognostic stratification model was devised based on both risk factors, which allowed identification of three risk groups for PFS and OS in the training (p < 0.001 and p < 0.001) and validation (p < 0.001 and p = 0.020) cohorts. Our results indicate that the baseline ¹⁸F-FDG PET radiomic feature, RLN_GLRLM, is an independent prognostic factor for survival outcomes. Furthermore, we propose a prognostic stratification model that may enable tailored therapeutic strategies for patients with DLBCL.