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

A high biocompatible near-infrared fluorescent probe for tracking cysteine in multi-biosystem and its application in cervical cancer imaging

Cysteine (Cys) plays a crucial role in the biological system and many related diseases. However, the detection of Cys in living organisms are still hindered by shortage of small molecule fluorophores that exhibit excitation and emission in the near-infrared region. Herein, we designed and synthesized a high water-soluble Cys probe (Cy7-SS) based on heptamethine cyanine scaffold. The prepared Cy7-SS displayed an enhanced emission at near-infrared region (NIR) after the recognition of Cys. Moreover, Cy7-SS not only exhibited high selectivity and sensitivity on the detection of in vitro Cys, but also could be used for endogenous Cys in living cells and C.elegans. The prepared strategy for the design of fluorophores expands the in vivo sensing toolkit for the precise analysis in clinical diagnosis.

Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP

OBJECTIVE

To investigate the predictive value of machine learning-based PET/CT radiomics and clinical risk factors in predicting interim efficacy in patients with follicular lymphoma (FL).

METHODS

This study retrospectively analyzed data from 97 patients with FL diagnosed via histopathological examination between July 2012 and November 2023. Lesion segmentation was performed using LIFEx software, and radiomics features were extracted through the uAI Research Portal (uRP) platform, including first-order features, shape features, and texture features. Fourteen filters were applied to the raw images to extract higher-order features from the derived images. Univariate analysis was employed to identify clinical risk factors, and correlation coefficients, MRMR, and LASSO algorithms were used for dimensionality reduction and selection of radiomics features. Finally, a logistic regression machine learning model was developed to predict the interim efficacy of FL using a five-fold cross-validation strategy. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve, accuracy, and the Delong test to compare AUC differences.

RESULT

Among the 97 patients, 42 (43.30%) achieved complete response (CR) for interim efficacy, while 55 (56.70%) had non-complete response (non-CR). A total of 2264 radiomics features were extracted from the images. Seven clinical risk factors and ten radiomics features associated with interim efficacy were selected to construct the clinical, radiomics, and radiomics-clinical combined models. Among the three logistic regression machine learning models developed, the radiomics-clinical combined model demonstrated the best performance, achieving a mean AUC of 0.849 (95% CI, 0.676-1.000) and an accuracy of 0.795, outperforming the other two models.

CONCLUSION

Our preliminary results demonstrate that a radiomics-clinical combined model, based on baseline [18F]FDG PET/CT radiomics features and clinical risk factors, may contribute to predicting interim efficacy in FL patients.

Prognostic value of interim [18F]FDG PET/CT after immunotherapy-based combinations in extranodal NK/T-cell lymphoma, nasal type

PURPOSE

To evaluate the prognostic value of interim [18F]Fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) after immunotherapy-based systemic therapies in extranodal natural killer/T-cell lymphoma (ENKTL).

PATIENTS AND METHODS

We retrospectively recruited 133 newly diagnosed nasal-type ENKTL patients who underwent interim [18F]FDG PET/CT scans after 2-4 cycles of immunotherapy-based treatments. Interim PET/CT was interpreted by maximum standardized uptake value (SUVmax), Deauville 5-point scale (DS), and early treatment response. The prognostic value of overall survival (OS) and progression-free survival (PFS) was assessed with survival curves generated using Kaplan-Meier analysis and compared using the log-rank test. Univariate and multivariate Cox proportional hazards analyses were performed to evaluate the independent effects for survival. Model performance was assessed with a time-dependent area under the curve (time-AUC), concordance index (C-index), and the Akaike information criterion (AIC).

RESULTS

Patients with high SUVmax (> 9.2), DS 5, or with stable disease (SD) or relapsed/progressive disease (PD) on interim PET/CT showed significantly unfavorable OS and PFS with the Kaplan-Meier estimate, respectively. The interim PET/CT parameters remained independent predictors for both OS and PFS after univariate and multivariate analysis. We combined interim DS with the prognostic index for natural killer cell lymphoma-Epstein-Barr virus (PINK-E) model to stratify our cohort into 3 risk categories: low-risk (0-2 risk factors), intermediate-risk (3 risk factors), and high-risk (≥ 4 risk factors), which showed significant and superior stratifications of OS and PFS than PINK-E.

CONCLUSION

Interim PET/CT after immunotherapy-based systemic treatments showed independent prognostic value for ENKTL. The model combining interim PET/CT with PINK-E might be an effective prognostic tool.

KEY POINTS

Question The prognostic value of interim [18F]FDG PET/CT in extranodal natural killer/T-cell lymphoma remains uncertain, especially after the introduction of immunotherapy. Findings Deauville 5-point scale on interim [18F]FDG PET/CT after immunotherapy-based systemic therapies was an independent predictor for newly diagnosed extranodal natural killer/T-cell lymphomas. Clinical relevance Interim [18F]FDG PET/CT alone or combined with the prognostic index for natural killer cell lymphoma-Epstein-Barr virus (PINK-E) model is an effective and superior prognostic tool for clinical application in the era of immunotherapy.

Longitudinal FDG-PET Radiomics for Early Prediction of Treatment Response to Chemoradiation in Locally Advanced Cervical Cancer: A Pilot Study

Objectives: This study aimed to assess the capacity of longitudinal FDG-PET radiomics for early distinguishing between locally advanced cervical cancer (LACC) patients who responded to treatment and those who did not. Methods: FDG-PET scans were obtained before and midway through concurrent chemoradiation for a study cohort of patients with LACC. Radiomics features related to image textures were extracted from the primary tumor volumes and stratified for relevance to treatment response status with the aid of random forest recursive feature elimination. Predictive models based on the k-nearest neighbors time series classifier were developed using the top-selected features to differentiate between responders and non-responders. The performance of the developed models was evaluated using receiver operating characteristic (ROC) curve analysis and n-fold cross-validation. Results: The top radiomics features extracted from scans taken midway through treatment showed significant differences between the two responder groups (p-values < 0.0005). In contrast, those from pretreatment scans did not exhibit significant differences. The AUC of the mean ROC curve for the predictive model based on the top features from pretreatment scans was 0.8529, while it reached 0.9420 for those derived midway through treatment scans. Conclusions: The study highlights the potential of longitudinal FDG-PET radiomics extracted midway through treatment for predicting response to chemoradiation in LACC patients and emphasizes that interim PET scans could be crucial in personalized medicine, ultimately enhancing therapeutic outcomes for LACC.

Feasibility of Using 18F-FDG PET/CT Radiomics and Machine Learning to Detect Drug-Induced Interstitial Lung Disease

BACKGROUND

Bleomycin is an oncolytic and antibiotic agent used to treat various human cancers because of its antitumor activity. Unfortunately, up to 46% of the patients treated with bleomycin develop drug-induced interstitial lung disease (DIILD) and potentially life-threatening interstitial pulmonary fibrosis. Tools and biomarkers for predicting and detecting DIILD are limited. Therefore, we aimed to evaluate the feasibility of 18F-FDG PET/CT, PET radiomics, and machine learning in distinguishing DIILD in an explorative pilot study.

METHODS

Eighteen Hodgkin's lymphoma (HL) patients, of whom 10 developed DIILD after treatment with bleomycin, were retrospectively included. Five diffuse large B-cell lymphoma (DLBCL) patients were included as a control group since they were not treated with bleomycin. All patients underwent 18F-FDG PET/CT scans before (baseline) and during treatment (interim). Structural changes were assessed by changes in Hounsfield Units (HUs). The 18F-FDG PET scans were used to assess metabolic changes by examining the feasibility of 504 radiomics features, including the mean activity of the lungs (SUVmean). A Random Forest (RF) classifier evaluated the identification and prediction of DIILD based on PET radiomics features.

RESULTS

HL patients who developed DIILD showed a significant increase in standard SUV metrics (SUVmean; p = 0.012, median increase 37.4%), and in some regional PET radiomics features (texture strength; p = 0.009, median increase 101.6% and zone distance entropy; p = 0.019, median increase 18.5%), while this was not found in HL patients who did not develop DIILD and DLBCL patients. The RF classifier correctly identified DIILD in 72.2% of the patients and predicted the development of DIILD correctly in 50% of the patients. There were no significant differences in HUs over time within all three patient groups.

CONCLUSIONS

Our explorative longitudinal pilot study suggests that certain regional 18F-FDG PET radiomics features can effectively identify DIILD in HL patients treated with bleomycin, as significant longitudinal increases were observed in SUVmean, texture strength, and zone distance entropy after the development of DIILD. The metabolic activity of these features did not significantly increase over time in DLBCL patients and HL patients who did not develop DIILD. This indicates that 18F-FDG PET radiomics, with and without machine learning, might serve as potential biomarkers for detecting DIILD.

Recent Updates of PET in Lymphoma: FDG and Beyond

Lymphoma is one of the most common cancers worldwide, categorized into Hodgkin lymphoma and non-Hodgkin lymphoma. 18F-fluorodeoxyglucose positron emission tomography (FDG PET) has become an essential imaging tool for evaluating patients with lymphoma in terms of initial diagnosis, staging, prognosis, and treatment response assessment. Recent advancements in imaging technology and methodologies, along with the development of artificial intelligence, have revolutionized the evaluation of complex imaging data, enhancing the diagnostic and predictive power of PET in lymphoma. However, FDG is not cancer-specific, but it primarily reflects glucose metabolism, which has prompted the investigation of alternative PET tracers to address this limitation. Novel PET radiotracers, such as fibroblast activation protein inhibitors targeting the tumor microenvironment, have recently shown promising results in evaluating various malignancies compared to FDG PET. Furthermore, with the rapid advancements in immunotherapy and the favorable imaging properties of 89Zr, immunoPET has emerged as a promising modality, offering insights into the functional and molecular status of the immune system. ImmunoPET can also facilitate the development of new antibody therapeutics and radioimmunotherapy by providing pharmacokinetic and pharmacodynamic data. This review provides comprehensive insights into the current clinical applications of FDG PET in lymphoma, while also exploring novel PET imaging radiotracers beyond FDG, discussing their mechanisms of action and potential impact on patient management.

Lymphoma: The Added Value of Radiomics, Volumes and Global Disease Assessment

Lymphoma represents a condition that holds promise for cure with existing treatment modalities; nonetheless, the primary clinical obstacle lies in advancing therapeutic outcomes by pinpointing high-risk individuals who are unlikely to respond favorably to standard therapy. In this article, the authors will delineate the significant strides achieved in the lymphoma field, with a particular emphasis on the 3 prevalent subtypes: Hodgkin lymphoma, diffuse large B-cell lymphomas, and follicular lymphoma.

Molecular biomarkers in classic Hodgkin lymphoma

Classic Hodgkin lymphoma is a unique B-cell derived malignancy featuring rare malignant Hodgkin and Reed Sternberg (HRS) cells that are embedded in a quantitively dominant tumor microenvironment (TME). Treatment of classic Hodgkin lymphoma has significantly evolved in the past decade with improving treatment outcomes for newly diagnosed patients and the minority of patients suffering from disease progression. However, the burden of toxicity and treatment-related long-term sequelae remains high in a typically young patient population. This highlights the need for better molecular biomarkers aiding in risk-adapted treatment strategies and predicting response to an increasing number of available treatments that now prominently involve multiple immunotherapy options. Here, we review modern molecular biomarker approaches that reflect both the biology of the malignant HRS cells and cellular components in the TME, while holding the promise to improve diagnostic frameworks for clinical decision-making and be feasible in clinical trials and routine practice. In particular, technical advances in sequencing and analytic pipelines using liquid biopsies, as well as deep phenotypic characterization of tissue architecture at single-cell resolution, have emerged as the new frontier of biomarker development awaiting further validation and implementation in routine diagnostic procedures.

Dual-Layer Spectral Detector Computed Tomography Quantitative Parameters: A Potential Tool for Lymph Node Activity Determination in Lymphoma Patients

Dual-energy CT has shown promising results in determining tumor characteristics and treatment effectiveness through spectral data by assessing normalized iodine concentration (nIC), normalized effective atomic number (nZeff), normalized electron density (nED), and extracellular volume (ECV). This study explores the value of quantitative parameters in contrast-enhanced dual-layer spectral detector CT (SDCT) as a potential tool for detecting lymph node activity in lymphoma patients. A retrospective analysis of 55 lymphoma patients with 289 lymph nodes, assessed through 18FDG-PET/CT and the Deauville five-point scale, revealed significantly higher values of nIC, nZeff, nED, and ECV in active lymph nodes compared to inactive ones (p < 0.001). Generalized linear mixed models showed statistically significant fixed-effect parameters for nIC, nZeff, and ECV (p < 0.05). The area under the receiver operating characteristic curve (AUROC) values of nIC, nZeff, and ECV reached 0.822, 0.845, and 0.811 for diagnosing lymph node activity. In conclusion, the use of g nIC, nZeff, and ECV as alternative imaging biomarkers to PET/CT for identifying lymph node activity in lymphoma holds potential as a reliable diagnostic tool that can guide treatment decisions.

PET/CT Biomarkers Enable Risk Stratification of Patients with Relapsed/Refractory Diffuse Large B-cell Lymphoma Enrolled in the LOTIS-2 Clinical Trial

PURPOSE

Significant progress has occurred in developing quantitative PET/CT biomarkers in diffuse large B-cell lymphoma (DLBCL). Total metabolic tumor volume (MTV) is the most extensively studied, enabling assessment of FDG-avid tumor burden associated with outcomes. However, prior studies evaluated the outcome of cytotoxic chemotherapy or chimeric antigen receptor T-cell therapy without data on recently approved FDA agents. Therefore, we aimed to assess the prognosis of PET/CT biomarkers in patients treated with loncastuximab tesirine.

EXPERIMENTAL DESIGN

We centrally reviewed screening PET/CT scans of patients with relapsed/refractory DLBCL enrolled in the LOTIS-2 (NCT03589469) study. MTV was obtained by computing individual volumes using the SUV ≥4.0 threshold. Other PET/CT metrics, clinical factors, and the International Metabolic Prognostic Index (IMPI) were evaluated. Logistic regression was used to assess the association between biomarkers and treatment response. Cox regression was used to determine the effect of biomarkers on time-to-event outcomes. We estimated biomarker prediction as continuous and binary variables defined by cutoff points.

RESULTS

Across 138 patients included in this study, MTV with a cutoff point of 96 mL was the biomarker associated with the highest predictive performance in univariable and multivariable models to predict failure to achieve complete metabolic response (OR, 5.42; P = 0.002), progression-free survival (HR, 2.68; P = 0.002), and overall survival (HR, 3.09; P < 0.0001). IMPI demonstrated an appropriate performance, however, not better than MTV alone.

CONCLUSIONS

Pretreatment MTV demonstrated robust risk stratification, with those patients demonstrating high MTV achieving lower responses and survival to loncastuximab tesirine in relapsed/refractory DLBCL.