FDG-PET/CT in Lymphoma: Where Do We Go Now?

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features

BACKGROUND

Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset.

METHOD

This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer.

RESULT

Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86.

CONCLUSION

The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.

Emerging Role of [18F]FLT PET/CT in Lymphoid Malignancies: A Review of Clinical Results

Fluorine-18 fluorodeoxyglucose ([18F]FDG) is nowadays the leading positron emission tomography (PET) tracer for routine clinical work-ups in hematological malignancies; however, it is limited by false positive findings. Notably, false positives can occur in inflammatory and infective cases or in necrotic tumors that are infiltrated by macrophages and other inflammatory cells. In this context, 3'-deoxy-3'-[18F]fluorothymidine ([18F]FLT) has been shown to be a promising imaging biomarker of hematological malignant cell proliferation. In this review, a total of 15 papers were reviewed to collect literature data regarding the clinical application of [18F]FLT PET/CT in hematological malignancies. This imaging modality seems to be a suitable tool for noninvasive assessment of tumor grading, also showing a correlation with Ki-67 immunostaining. Moreover, [18F]FLT PET/CT demonstrated high sensitivity in detecting aggressive lymphoma lesions, especially when applying a standardized uptake value (SUV) cutoff of 3. At baseline, the potential of [18F]FLT imaging as a predictive tool is demonstrated by the low tracer uptake in patients with a complete response. However, its use is limited in evaluating bone diseases due to its high physiological uptake in bone marrow. Interim [18F]FLT PET/CT (iFLT) has the potential to identify high-risk patients with greater precision than [18F]FDG PET/CT, optimizing risk-adapted therapy strategies. Moreover, [18F]FLT uptake showed a greater ability to differentiate tumor from inflammation compared to [18F]FDG, allowing the reduction of false-positive findings and making the first one a more selective tracer. Finally, FLT emerges as a superior independent predictor of PFS and OS compared to FDG and ensures a reliable early response assessment with greater accuracy and predictive value.

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.

Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial

The prognostic value of 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) at baseline or the predictive value of minimal residual disease (MRD) detection appear as potential tools to improve mantle cell lymphoma (MCL) patients' management. The LyMa-101, a phase 2 trial of the LYSA group (ClinicalTrials.gov:NCT02896582) reported induction therapy with obinutuzumab, a CD20 monoclonal antibody. Herein, we investigated the added prognostic value of radiomic features (RF) derived from FDG-PET/CT at diagnosis for MRD value prediction. FDG-PET/CT of 59 MCL patients included in the LyMa-101 trial have been independently, blindly and centrally reviewed. RF were extracted from the disease area with the highest uptake and from the total metabolic tumor volume (TMTV). Two models of machine learning were used to compare several combinations for prediction of MRD before autologous stem cell transplant consolidation (ASCT). Each algorithm was generated with or without constrained feature selections for clinical and laboratory parameters. Both algorithms showed better discrimination performances for negative vs positive MRD in the lesion with the highest uptake than in the TMTV. The constrained use of clinical and biological features showed a clear loss in sensitivity for the prediction of MRD status before ASCT, regardless of the machine learning model. These data plead for the importance of FDG-PET/CT RF compared to clinical and laboratory parameters and also reinforced the previously made hypothesis that the prognosis of the disease in MCL patients is linked to the most aggressive contingent, within the lesion with the highest uptake.

Prognostic value of whole-body dynamic 18F-FDG PET/CT Patlak in diffuse large B-cell lymphoma

Objective

This study aims to investigate the significance of interim whole-body dynamic 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) Patlak parameters for predicting the prognosis of patients with diffuse large B-cell lymphoma. To estimate the predictive value of the whole-body dynamic 18F-FDG PET/CT Patlak parameter for 2-year progression-free survival (PFS) and 2-year overall survival (OS).

Methods

This study reports the findings of 67 patients with diffuse large B-cell lymphoma (DLBCL). These patients underwent interim whole-body dynamic 18F-FDG PET/CT scans from June 2021 to January 2023 at the Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University. The predictive values of maximum standard uptake value (SUVmax), maximum of net glucose uptake rate (Kimax) and the predictive model combining Kimax and interim treatment response on the prognosis of patients was analyzed using receiver operating characteristic (ROC) curves. Kaplan-Meier survival curves and log-rank tests were used for survival analysis. Univariate and multivariate analyses were performed to screen for independent prognostic risk factors.

Results

After a median follow-up of 18 months, 21 patients (31.3%) experienced disease recurrence or death. The cut-off values for the SUVmax and the Kimax were 6.1 and 0.13 μmol min-1·ml-1, respectively. Ann Arbor stage, IPI, SUVmax, Kimax and interim treatment response were associated with PFS and OS in the univariate analysis. However, only Kimax and interim treatment response were independent influences on PFS and OS in multivariate analysis.

Conclusion

Interim whole-body dynamic 18F-FDG PET/CT Patlak imaging has significant prognostic value in patients with DLBCL. Among them, the interim dynamic parameter Kimax showed the best predictive value for prognosis compared with the interim SUVmax and interim treatment response. The predictive model established by Kimax and the interim treatment response allowed for the accurate stratification of the prognostic risk of DLBCL.

[18F]FDG-PET-Based Personalized Radiotherapy Dose Prescription

PET imaging with 2'-deoxy-2'-[18F]fluoro-D-glucose ([18F]FDG) has become one of the pillars in the management of malignant diseases. It has proven value in diagnostic workup, treatment policy, follow-up, and as prognosticator for outcome. [18F]FDG is widely available and standards have been developed for PET acquisition protocols and quantitative analyses. More recently, [18F]FDG-PET is also starting to be appreciated as a decision aid for treatment personalization. This review focuses on the potential of [18F]FDG-PET for individualized radiotherapy dose prescription. This includes dose painting, gradient dose prescription, and [18F]FDG-PET guided response-adapted dose prescription. The current status, progress, and future expectations of these developments for various tumor types are discussed.

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.

PET/CT in Hodgkin Lymphoma: An Update

¹⁸F-FDG-PET/CT is now an integral part of the workup and management of patients with Hodgkin's lymphoma (HL). PET/CT is currently routinely performed for staging and for response assessment at the end of treatment. Interim PET/CT is typically performed after 1-4 of 6-8 chemo/chemoimmunotherapy cycles ± radiation for prognostication and potential treatment escalation or de-escalation early in the course of therapy, a concept known as response-or risk-adapted treatment. Quantitative PET is an area of growing interest. Metrics such as the standardized uptake value (SUV), metabolic tumor volume, total lesion glycolysis, and their changes with treatment 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. This review highlights the most relevant applications of PET/CT in HL, its strengths and limitations, as well as recent efforts to implement PET/CT-based metrics as promising tools for precision medicine. Finally, the value of PET/CT for response assessment to immunotherapy is discussed.

FDG-PET/CT in the Monitoring of Lymphoma Immunotherapy Response: Current Status and Future Prospects

Cancer immunotherapy has been extensively investigated in lymphoma over the last three decades. This new treatment modality is now established as a way to manage and maintain several stages and subtypes of lymphoma. The establishment of this novel therapy has necessitated the development of new imaging response criteria to evaluate and follow up with cancer patients. Several FDG PET/CT-based response criteria have emerged to address and encompass the various most commonly observed response patterns. Many of the proposed response criteria are currently being used to evaluate and predict responses. The purpose of this review is to address the efficacy and side effects of cancer immunotherapy and to correlate this with the proposed criteria and relevant patterns of FDG PET/CT in lymphoma immunotherapy as applicable. The latest updates and future prospects in lymphoma immunotherapy, as well as PET/CT potentials, will be discussed.

Foreign body-type giant cell reaction with extensive granulation tissue and intense inflammation after chemotherapy mimicking residual lymphoma on FDG PET

Foreign body-type giant cell reaction is typically a biological and immunological reaction to the presence of foreign bodies such as catheters, parasites or biomaterials with a collection of fused macrophages (giant cell). We reported an unusual case of [¹⁸F]FDG PET findings in diffuse large B cell lymphoma in the urinary bladder following incomplete resection and chemotherapy. As the restaging [¹⁸F]FDG PET showed intense [¹⁸F]FDG uptake in the urinary bladder at the resection site concerning for recurrence, the lesion was subsequently resected and histopathology showed extensive granulation tissue with foreign body-type giant cell reaction with no suspected foreign bodies or neoplasia.

Tumor Size Measurements for Predicting Hodgkin’s and Non-Hodgkin’s Lymphoma Response to Treatment

The purpose of this study was to investigate the value of tumor size measurements as prognostic indicators of treatment outcome of Hodgkin’s and Non-Hodgkin’s lymphomas. ¹⁸F-FDG PET/CT exams before and after treatment were analyzed and metabolic and anatomic parameters—tumor maximum diameter, tumor maximum area, tumor volume, and maximum standardized uptake value (SUVmax)—were determined manually by an expert and automatically by a computer algorithm on PET and CT images. Results showed that the computer algorithm measurements did not correlate well with the expert’s standard maximum tumor diameter measurements but yielded better three dimensional metrics that could have clinical value. SUVmax was the strongest prognostic indicator of the clinical outcome after treatment, followed by the automated metabolic tumor volume measurements and the expert’s metabolic maximum diameter measurements. Anatomic tumor measurements had poor prognostic value. Metabolic volume measurements, although promising, did not significantly surpass current standard of practice, but automated measurements offered a significant advantage in terms of time and effort and minimized biases and variances in the PET measurements. Overall, considering the limited value of tumor size in predicting response to treatment, a paradigm shift seems necessary in order to identify robust prognostic markers in PET/CT; radiomics, namely combinations of anatomy, metabolism, and imaging, may be an option.