High metabolic heterogeneity on baseline 18FDG-PET/CT scan as a poor prognostic factor for newly diagnosed diffuse large B-cell lymphoma

Prognostic Value of 18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study

The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.

Clinical Application of 18F-FDG-PET Quantification in Hematological Malignancies: Emphasizing Multiple Myeloma, Lymphoma and Chronic Lymphocytic Leukemia

Most hematological malignancies display heightened glycolytic activity, leading to their detectability through 18F-FDG-PET imaging. PET quantification enables the extraction of metabolic information from tumors. Among various PET measurements, maximum standardized uptake value (SUVmax), which indicates the highest value of 18F-FDG uptake within the tumor, has emerged as the commonly used parameter in clinical oncology. This is because of SUVmax ease of calculation using most available commercial workstations, as well as its simplicity and independence from observer interpretation. Nonetheless, SUVmax represents the increase in activity within a specific small area, which may not fully capture the overall tumor uptake. Volumetric PET parameters have been identified as a potential solution to overcome certain limitations associated with SUVmax. However, these parameters are influenced by the low spatial resolution of PET when assessing small lesions. Another challenge is the high number of lesions observed in some patients, leading to a time-consuming process for evaluating all focal lesions. Some institutions recently have started advocating for CT-based segmentation as a method for measuring radiotracer uptake in the bone marrow and overall bone of the patients. This review article aims to provide insights into clinical application of PET quantification specifically focusing on 3 major hematologic malignancies: multiple myeloma, lymphoma, and chronic lymphocytic leukemia.

Integration of PET in DLBCL

F-fluorodeoxyglucose positron emission tomography-computerized tomography (18FDG-PET/CT) is the gold-standard imaging modality for staging and response assessment for most lymphomas. This review focuses on the utility of 18FDG-PET/CT, and its role in staging, prognostication and response assessment in diffuse large B-cell lymphoma (DLBCL), including emerging possibilities for future use.

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.

The Role of PET-CT-Guided Metabolic Biopsies in Improving Yield of Inconclusive Anatomical Biopsies: A Review of 5 Years in a Teaching Hospital

Tumour sampling is indispensable to diagnostic and therapeutic decision making. Thus, 18F-FDG PET/CT has the potential to accurately discriminate between viable and non-viable tissues due to its ability to characterise the metabolism of visible tissues. This study's objective was to evaluate the incremental utility of 18F-FDG PET-CT-guided metabolic biopsy in individuals with suspected lesions and a previous negative anatomical biopsy. This study included a total of 190 consecutive patients with probable malignancy and who had experienced a previous unsuccessful anatomical biopsy who underwent PET-CT-guided metabolic biopsy. We retrospectively analysed the patients' medical records and imaging investigations to assess demographics, complications, pathologies, and final clinical diagnoses. Using multivariate logistic regression, correlation between several confounding factors that lead to post-procedural problems was evaluated. Adequate material was obtained in all patients, and 162 (85%) were found to be positive for malignancy with a diagnostic yield of 96.9%. In 25 (13.1%) patients, post-procedural complications were reported, with pneumothorax being the most prevalent issue. In evaluating oncological patients, metabolic biopsy provides a safer alternative therapy with a high diagnostic yield and comparable complications. PET-CT, being an essential component of cancer staging, may serve as a one-stop shop for the management of these patients' conditions.

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma

The objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-year time to progression (TTP). The metabolic tumor volume (MTV), maximum distance between the largest lesion and another lesion (Dmaxbulk), and peak standardized uptake value (SUVpeak) were extracted. The predictive values of the IPI and clinical PET model (MTV, Dmaxbulk, SUVpeak, performance status, and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance, using the positive predictive value (PPV). The IPI yielded an AUC of 0.62. The clinical PET model yielded a significantly higher AUC of 0.71 (P < .001). Patients with high-risk IPI had a 2-year PFS of 61.4% vs 51.9% for those with high-risk clinical PET, with an increase in PPV from 35.5% to 49.1%, respectively. A total of 66.4% of patients with high-risk IPI were free from progression or relapse vs 55.5% of patients with high-risk clinical PET scores, with an increased PPV from 33.7% to 44.6%, respectively. The clinical PET model remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.

Safety and efficacy of tisagenlecleucel in patients with relapsed or refractory B-cell lymphoma: the first real-world evidence in Japan

BACKGROUND

Tisagenlecleucel, an autologous CD19-directed T-cell immunotherapy, can induce a durable response in adult patients with relapsed/refractory (r/r) B-cell lymphoma.

METHODS

To elucidate the outcome of chimeric antigen receptor (CAR) T-cell therapy in Japanese, we retrospectively analyzed the outcomes of 89 patients who received tisagenlecleucel for r/r diffuse large B-cell lymphoma (n = 71) or transformed follicular lymphoma (n = 18).

RESULTS

With a median follow-up of 6.6-months, 65 (73.0%) patients achieved a clinical response. The overall survival (OS) and event-free survival (EFS) rates at 12 months were 67.0% and 46.3%, respectively. Overall, 80 patients (89.9%) had cytokine release syndrome (CRS), and 6 patients (6.7%) had a grade ≥ 3 event. ICANS occurred in 5 patients (5.6%); only 1 patient had grade 4 ICANS. Representative infectious events of any grade were cytomegalovirus viremia, bacteremia and sepsis. The most common other adverse events were ALT elevation, AST elevation, diarrhea, edema, and creatinine elevation. No treatment-related mortality was observed. A Sub-analysis showed that a high metabolic tumor volume (MTV; ≥ 80 ml) and stable disease /progressive disease before tisagenlecleucel infusion were both significantly associated with a poor EFS and OS in a multivariate analysis (P < 0.05). Notably, the combination of these 2 factors efficiently stratified the prognosis of these patients (HR 6.87 [95% CI 2.4-19.65; P < 0.05] into a high-risk group).

CONCLUSION

We report the first real-world data on tisagenlecleucel for r/r B-cell lymphoma in Japan. Tisagenlecleucel is feasible and effective, even in late line treatment. In addition, our results support a new algorithm for predicting the outcomes of tisagenlecleucel.

Prognostic value of metabolic tumor volume of extranodal involvement in diffuse large B cell lymphoma

Extranodal involvement predicts poor outcomes of diffuse large B cell lymphoma (DLBCL), but the impact of the metabolic tumor burden (MTV) of extranodal sites using positron emission tomography has not been clarified. This study aimed to assess the impact of extranodal MTV on overall survival (OS). We retrospectively analyzed 145 newly diagnosed DLBCL patients and verified the prognostic impact of each extranodal and nodal MTV. Multivariate Cox hazards modelling using both extranodal and nodal MTV as covariables identified extranodal MTV as a significant factor for OS (hazard ratio [HR] 1.072, 95% confidence interval [CI] 1.019-1.129, P = 0.008), but not nodal MTV. Multivariate Cox modelling using restricted cubic splines demonstrated that the impact of total MTV depends on the MTV of extranodal sites, not of nodal sites. When both the number and MTV of extranodal involvements were used as covariables, extranodal MTV remained a significant predictor of OS (HR 1.070, 95%CI 1.017-1.127, P = 0.009), but the number of extranodal sites did not. Extranodal MTV potentially had a more significant role on prognosis than nodal MTV. When considering prognostic impacts, the MTV of extranodal involvement is significantly more important than the number.

Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer

Complete pathological response to neoadjuvant systemic treatment (NAST) in some subtypes of breast cancer (BC) has been used as a surrogate of long-term outcome. The possibility of predicting BC pathological response to NAST based on the baseline 18F-Fluorodeoxyglucose positron emission tomography (FDG PET), without the need of an interim study, is a focus of recent discussion. This review summarises the characteristics and results of the available studies regarding the potential impact of heterogeneity features of the primary tumour burden on baseline FDG PET in predicting pathological response to NAST in BC patients. Literature search was conducted on PubMed database and relevant data from each selected study were collected. A total of 13 studies were eligible for inclusion, all of them published over the last 5 years. Eight out of 13 analysed studies indicated an association between FDG PET-based tumour uptake heterogeneity features and prediction of response to NAST. When features associated with predicting response to NAST were derived, these varied between studies. Therefore, definitive reproducible findings across series were difficult to establish. This lack of consensus may reflect the heterogeneity and low number of included series. The clinical relevance of this topic justifies further investigation about the predictive role of baseline FDG PET.

PET-CT in Clinical Adult Oncology: I. Hematologic Malignancies

PET-CT is an advanced imaging modality with many oncologic applications, including staging, assessment of response to therapy, restaging and evaluation of suspected recurrence. The goal of this 6-part series of review articles is to provide practical information to providers and imaging professionals regarding the best use of PET-CT for the more common adult malignancies. In the first article of this series, hematologic malignancies are addressed. The classification of these malignancies will be outlined, with the disclaimer that the classification of lymphomas is constantly evolving. Critical applications, potential pitfalls, and nuances of PET-CT imaging in hematologic malignancies and imaging features of the major categories of these tumors are addressed. Issues of clinical importance that must be reported by the imaging professionals are outlined. The focus of this article is on [¹⁸F] fluorodeoxyglucose (FDG), rather that research tracers or those requiring a local cyclotron. This information will serve as a resource for the appropriate role and limitations of PET-CT in the clinical management of patients with hematological malignancy for health care professionals caring for adult patients with hematologic malignancies. It also serves as a practical guide for imaging providers, including radiologists, nuclear medicine physicians and their trainees.

Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma

We investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography-computed tomography of 323 patients, which included maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUVpeak between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients.

The value of FDG PET/CT imaging in outcome prediction and response assessment of lymphoma patients treated with immunotherapy: a meta-analysis and systematic review

PURPOSE

Treatment strategies of lymphoid malignancies have been revolutionized by immunotherapy. Because of the inherent property of Hodgkin lymphoma and some subtypes of non-Hodgkin lymphoma as a highly FDG-avid tumor, functional ¹⁸F-FDG PET/CT imaging is already embedded in their routine care. Nevertheless, the question is whether it is still valuable in the context of these tumors being treated with immunotherapy. Herein, we will review the value of ¹⁸F-FDG PET/CT imaging lymphoid tumors treated with immunotherapy regimens.

METHODS

A comprehensive literature search of the PubMed database was conducted on the value of the ¹⁸F-FDG PET/CT for immunotherapy response monitoring of patients with malignant lymphoma. The articles were considered eligible if they met all of the following inclusion criteria: (a) clinical studies on patients with different types of malignant lymphoma, (b) treatment with anti-CD20 antibodies, immune checkpoint inhibitors or immune cell therapies, (c) and incorporated PET/CT with ¹⁸F-FDG as the PET tracer.

RESULTS

From the initial 1488 papers identified, 91 were ultimately included in our study. In anti-CD20 therapy, the highest pooled hazard ratios (HRs) of baseline, early, and late response monitoring parameters for progression-free survival (PFS) belong to metabolic tumor volume (MTV) (3.19 (95%CI: 2.36-4.30)), maximum standardized uptake value (SUVmax) (3.25 (95%CI: 2.08-5.08)), and Deauville score (DS) (3.73 (95%CI: 2.50-5.56)), respectively. These measurements for overall survival (OS) were MTV (4.39 (95%CI: 2.71-7.08)), DS (3.23 (95%CI: 1.87-5.58)), and DS (3.64 (95%CI: 1.40-9.43)), respectively. Early and late ¹⁸F-FDG PET/CT response assessment in immune checkpoint inhibitors (ICI) and immune cell therapy might be an effective tool for prediction of clinical outcome.

CONCLUSION

For anti-CD20 therapy of lymphoma, the MTV as a baseline ¹⁸F-FDG PET/CT-derived parameter has the highest HRs for PFS and OS. The DS as visual criteria in early and late response assessment has higher HRs for PFS and OS compared to the international harmonization project (IHP) visual criteria in anti-CD20 therapy. Early changes in ¹⁸F-FDG PET parameters may be predictive of response to ICIs and cell therapy in lymphoma patients.

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.

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.

Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma

Simple Summary

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma. Even with the improvements in the treatment of DLBCL, around a quarter of patients will experience recurrence. The aim of this single centre retrospective study was to predict which patients would have recurrence within 2 years of their treatment using machine learning techniques based on radiomics extracted from the staging PET/CT images. Our study demonstrated that in our dataset of 229 patients (training data = 183, test data = 46) that a combined radiomic and clinical based model performed better than a simple model based on metabolic tumour volume, and that it had a good predictive ability which was maintained when tested on an unseen test set.

Abstract

Background: Approximately 30% of patients with diffuse large B-cell lymphoma (DLBCL) will have recurrence. The aim of this study was to develop a radiomic based model derived from baseline PET/CT to predict 2-year event free survival (2-EFS). Methods: Patients with DLBCL treated with R-CHOP chemotherapy undergoing pre-treatment PET/CT between January 2008 and January 2018 were included. The dataset was split into training and internal unseen test sets (ratio 80:20). A logistic regression model using metabolic tumour volume (MTV) and six different machine learning classifiers created from clinical and radiomic features derived from the baseline PET/CT were trained and tuned using four-fold cross validation. The model with the highest mean validation receiver operator characteristic (ROC) curve area under the curve (AUC) was tested on the unseen test set. Results: 229 DLBCL patients met the inclusion criteria with 62 (27%) having 2-EFS events. The training cohort had 183 patients with 46 patients in the unseen test cohort. The model with the highest mean validation AUC combined clinical and radiomic features in a ridge regression model with a mean validation AUC of 0.75 ± 0.06 and a test AUC of 0.73. Conclusions: Radiomics based models demonstrate promise in predicting outcomes in DLBCL patients.

Carbonic Anhydrase IX Inhibitors as Candidates for Combination Therapy of Solid Tumors

Combination therapy is becoming imperative for the treatment of many cancers, as it provides a higher chance of avoiding drug resistance and tumor recurrence. Among the resistance-conferring factors, the tumor microenvironment plays a major role, and therefore, represents a viable target for adjuvant therapeutic agents. Thus, hypoxia and extracellular acidosis are known to select for the most aggressive and resilient phenotypes and build poorly responsive regions of the tumor mass. Carbonic anhydrase (CA, EC 4.2.1.1) IX isoform is a surficial zinc metalloenzyme that is proven to play a central role in regulating intra and extracellular pH, as well as modulating invasion and metastasis processes. With its strong association and distribution in various tumor tissues and well-known druggability, this protein holds great promise as a target to pharmacologically interfere with the tumor microenvironment by using drug combination regimens. In the present review, we summarized recent publications revealing the potential of CA IX inhibitors to intensify cancer chemotherapy and overcome drug resistance in preclinical settings.

Generation and validation of a PET radiomics model that predicts survival in diffuse large B cell lymphoma treated with R-CHOP14: A SAKK 38/07 trial post-hoc analysis

Functional parameters from positron emission tomography (PET) seem promising biomarkers in various lymphoma subtypes. This study investigated the prognostic value of PET radiomics in diffuse large B-cell lymphoma (DLBCL) patients treated with R-CHOP given either every 14 (testing set) or 21 days (validation set). Using the PyRadiomics Python package, 107 radiomics features were extracted from baseline PET scans of 133 patients enrolled in the Swiss Group for Clinical Cancer Research 38/07 prospective clinical trial (SAKK 38/07) [ClinicalTrial.gov identifier: NCT00544219]. The international prognostic indices, the main clinical parameters and standard PET metrics, together with 52 radiomics uncorrelated features (selected using the Spearman correlation test) were included in a least absolute shrinkage and selection operator (LASSO) Cox regression to assess their impact on progression-free (PFS), cause-specific (CSS), and overall survival (OS). A linear combination of the resulting parameters generated a prognostic radiomics score (RS) whose area under the curve (AUC) was calculated by receiver operating characteristic analysis. The RS efficacy was validated in an independent cohort of 107 DLBCL patients. LASSO Cox regression identified four radiomics features predicting PFS in SAKK 38/07. The derived RS showed a significant capability to foresee PFS in both testing (AUC, 0.709; p < 0.001) and validation (AUC, 0.706; p < 0.001) sets. RS was significantly associated also with CSS and OS in testing (CSS: AUC, 0.721; p < 0.001; OS: AUC, 0.740; p < 0.001) and validation (CSS: AUC, 0.763; p < 0.0001; OS: AUC, 0.703; p = 0.004) sets. The RS allowed risk classification of patients with significantly different PFS, CSS, and OS in both cohorts showing better predictive accuracy respect to clinical international indices. PET-derived radiomics may improve the prediction of outcome in DLBCL patients.

Prognostic Impact of Metabolic Heterogeneity in Patients With Newly Diagnosed Multiple Myeloma Using 18F-FDG PET/CT

PURPOSE

This study aimed to investigate the prognostic impact of metabolic heterogeneity (MH) in patients with multiple myeloma (MM).

PATIENTS AND METHODS

We retrospectively analyzed MH with 18F-FDG PET/CT in 203 patients with newly diagnosed MM. Metabolic heterogeneity was estimated using the area under the curve of the cumulative SUV volume histogram. To evaluate MH, we selected 2 lesions: "MH-SUVmax," a lesion with SUVmax, and "MH-metabolic tumor volume (MTV)," a lesion with the largest MTV.

RESULTS

Metabolic heterogeneity from an MH-SUVmax lesion showed more prognostic relevance than that from a lesion with the largest MTV. The progression-free survival (PFS) and overall survival (OS) rates were significantly lower in the high-MH-SUVmax group than in the low-MH-SUVmax group (median PFS: 25.2 vs 33.9 months; median OS: 41.6 vs 112.0 months; P = 0.004 and 0.046, respectively), whereas high MH-SUVmax retained independent prognostic power on multivariate analysis. Even among patients with high whole-body MTV, those with high MH-SUVmax tended to show poorer prognosis than those without (median PFS, 23.8 vs 30.2 months; P = 0.085). Moreover, patients with high MH-SUVmax and high-risk cytogenetic abnormalities showed dismal outcomes even with standard treatment (median PFS and OS, 10.0 and 33.3 months, respectively).

CONCLUSIONS

Our results suggested that high MH-SUVmax based on pretreatment with 18F-FDG PET/CT is a novel prognostic factor for cases of MM.

18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma

Purpose

Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment.

Methods

Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values.

Results

The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUV_peak and the maximal distance between the largest lesion and any other lesion (Dmax_bulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUV_peak and Dmax_bulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively).

Conclusion

Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance.

Trial registration number and date

EudraCT: 2006–005,174-42, 01–08-2008.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05480-3.

CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

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CT-based radiomics with machine learning classifier is able to accurately predict primary refractory Diffuse Large B Cell Lymphomas (DLBCL).

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The radiomics model exhibits a better discrimination for refractory DLBCL identification compared to available standard clinical criteria.

Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.

Quantitative radiomics features in diffuse large B-cell lymphoma: does segmentation method matter?

INTRODUCTION

Radiomics features may predict outcome in diffuse large B-cell lymphoma (DLBCL). Currently, multiple segmentation methods are used to calculate metabolic tumor volume (MTV). We assessed the influence of segmentation method on the discriminative power of radiomics features in DLBCL for patient level and for the largest lesion. Methods: 50 baseline ¹⁸F-fluorodeoxyglucose positron emission tomography computed tomography (PET/CT) scans of DLBCL patients who progressed or relapsed within 2 years after diagnosis were matched on uptake time and reconstruction method with 50 baseline PET/CT scans of DLBCL patients without progression. Scans were analysed using 6 semi-automatic segmentation methods (standardized uptake value (SUV)4.0, SUV2.5, 41% of the maximum SUV, 50% of the SUVpeak, majority vote (MV)2 and MV3, respectively). Based on these segmentations, 490 radiomics features were extracted at patient level and 486 features for the largest lesion. To quantify the agreement between features extracted from different segmentation methods, the intra-class correlation (ICC) agreement was calculated for each method compared to SUV4.0. The feature space was reduced by deleting features that had high Pearson correlations (≥0.7) with the previously established predictors MTV and/or SUVpeak. Model performance was assessed using stratified repeated cross-validation with 5 folds and 2000 repeats yielding the mean receiver-operating characteristics curve integral (CV-AUC) for all segmentation methods using logistic regression with backward feature selection. Results: The percentage of features yielding an ICC ≥0.75 compared to the SUV4.0 segmentation was lowest for A50P both at patient level and for the largest lesion, with 77.3% and 66.7% of the features yielding an ICC ≥0.75, respectively. Features were not highly correlated with MTV, with at least 435 features at patient level and 409 features for the largest lesion for all segmentation methods with a correlation coefficient <0.7. Features were highly correlated with SUVpeak (at least 190 and 134 were uncorrelated, respectively). CV-AUCs ranged between 0.69±0.11 and 0.84±0.09 for patient level, and between 0.69±0.11 and 0.73±0.10 for lesion level. Conclusion: Even though there are differences in the actual radiomics feature values derived and selected features between segmentation methods, there is no substantial difference in the discriminative power of radiomics features between segmentation methods.

Overview and recent advances in PET/CT imaging in lymphoma and multiple myeloma

Imaging in hematological diseases has evolved extensively over the past several decades. Positron emission tomography/computed tomography (PET/CT) with of 2-[18 F]-fluoro-2-deoxy-d-glucose ([18 F] FDG) is currently essential for accurate staging and for early and late therapy response assessment for all FDG-avid lymphoproliferative histologies. The widely adopted visual Deauville 5-point scale and Lugano Classification recommendations have recently standardized PET scans interpretation and improved lymphoma patient management. In addition [18 F] FDG-PET is routinely recommended for initial evaluation and treatment response assessment of Multiple Myeloma (MM) with significant contribution in risk-stratification and prognostication, although magnetic resonance imaging remains the Gold Standard for the assessment of bone marrow involvement. In this review, an overview of the role of [18 F] FDG-PET, in hematological malignancies is provided, particularly focusing on Hodgkin lymphoma (HL) and Diffuse Large B Cell Lymphoma (DLBCL), both in adult and pediatric populations, and MM, at each point of patient management. Potential alternative molecular imaging applications in this field, such as non-[18 F] FDG-tracers, whole body magnetic resonance imaging (WB-MRI), hybrid PET/MRI and emerging radiomics research are briefly presented.

Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type

Purpose

To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment ¹⁸F-FDG PET/CT results.

Methods

One hundred and sixty-seven patients with ENKTL who underwent pretreatment ¹⁸F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep learning features of images. Its discriminative ability was calculated and compared with that of a conventional deep learning (CDL) method. Univariate and multivariate analyses helped explore the significance of PSI and clinical features.

Results

PSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods.

Conclusion

The WSDL-based framework was more effective for extracting ¹⁸F-FDG PET/CT features and predicting the prognosis of ENKTL than the CDL method.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05232-3.

Quantitative FDG PET Assessment for Oncology Therapy

Simple Summary

PET enables quantitative assessment of tumour biology in vivo. Accumulation of F-18 fluorodeoxyglucose (FDG) may reflect tumour metabolic activity. Quantitative assessment of FDG uptake can be applied for treatment monitoring. Numerous studies indicated biochemical change assessed by FDG-PET as a more sensitive marker than morphological change. Those with complete metabolic response after therapy may show better prognosis. Assessment of metabolic change may be performed using absolute FDG uptake or metabolic tumour volume. More recently, radiomics approaches have been applied to FDG PET. Texture analysis quantifies intratumoral heterogeneity in a voxel-by-voxel basis. Combined with various machine learning techniques, these new quantitative parameters hold a promise for assessing tissue characterization and predicting treatment effect, and could also be used for future prognosis of various tumours.

Abstract

Positron emission tomography (PET) has unique characteristics for quantitative assessment of tumour biology in vivo. Accumulation of F-18 fluorodeoxyglucose (FDG) may reflect tumour characteristics based on its metabolic activity. Quantitative assessment of FDG uptake can often be applied for treatment monitoring after chemotherapy or chemoradiotherapy. Numerous studies indicated biochemical change assessed by FDG PET as a more sensitive marker than morphological change estimated by CT or MRI. In addition, those with complete metabolic response after therapy may show better disease-free survival and overall survival than those with other responses. Assessment of metabolic change may be performed using absolute FDG uptake in the tumour (standardized uptake value: SUV). In addition, volumetric parameters such as metabolic tumour volume (MTV) have been introduced for quantitative assessment of FDG uptake in tumour. More recently, radiomics approaches that focus on image-based precision medicine have been applied to FDG PET, as well as other radiological imaging. Among these, texture analysis extracts intratumoral heterogeneity on a voxel-by-voxel basis. Combined with various machine learning techniques, these new quantitative parameters hold a promise for assessing tissue characterization and predicting treatment effect, and could also be used for future prognosis of various tumours, although multicentre clinical trials are needed before application in clinical settings.

Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review

Purpose

To systematically review the literature evaluating clinical utility of imaging metrics derived from baseline fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) for prediction of progression-free (PFS) and overall survival (OS) in patients with classical Hodgkin lymphoma (HL) and diffuse large B cell lymphoma (DLBCL).

Methods

A search of MEDLINE/PubMed, Web of Science, Cochrane, Scopus and clinicaltrials.gov databases was undertaken for articles evaluating PET/CT imaging metrics as outcome predictors in HL and DLBCL. PRISMA guidelines were followed. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool.

Results

Forty-one articles were included (31 DLBCL, 10 HL). Significant predictive ability was reported in 5/20 DLBCL studies assessing SUVmax (PFS: HR 0.13–7.35, OS: HR 0.83–11.23), 17/19 assessing metabolic tumour volume (MTV) (PFS: HR 2.09–11.20, OS: HR 2.40–10.32) and 10/13 assessing total lesion glycolysis (TLG) (PFS: HR 1.078–11.21, OS: HR 2.40–4.82). Significant predictive ability was reported in 1/4 HL studies assessing SUVmax (HR not reported), 6/8 assessing MTV (PFS: HR 1.2–10.71, OS: HR 1.00–13.20) and 2/3 assessing TLG (HR not reported). There are 7/41 studies assessing the use of radiomics (4 DLBCL, 2 HL); 5/41 studies had internal validation and 2/41 included external validation. All studies had overall moderate or high risk of bias.

Conclusion

Most studies are retrospective, underpowered, heterogenous in their methodology and lack external validation of described models. Further work in protocol harmonisation, automated segmentation techniques and optimum performance cut-off is required to develop robust methodologies amenable for clinical utility.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05233-2.

Non-Hodgkin Lymphoma Metabolism

Non-Hodgkin lymphomas (NHLs) are a heterogeneous group of lymphoid neoplasms with different biological characteristics. About 90% of all lymphomas in the United States originate from B lymphocytes, while the remaining originate from T cells [1]. The treatment of NHLs depends on the neoplastic histology and stage of the tumor, which will indicate whether radiotherapy, chemotherapy, or a combination is the best suitable treatment [2]. The American Cancer Society describes the staging of lymphoma as follows: Stage I is lymphoma in a single node or area. Stage II is when that lymphoma has spread to another node or organ tissue. Stage III is when it has spread to lymph nodes on two sides of the diaphragm. Stage IV is when cancer has significantly spread to organs outside the lymph system. Radiation therapy is the traditional therapeutic route for localized follicular and mucosa-associated lymphomas. Chemotherapy is utilized for the treatment of large-cell lymphomas and high-grade lymphomas [2]. However, the treatment of indolent lymphomas remains problematic as the patients often have metastasis, for which no standard approach exists [2].