PET-Derived Quantitative Metrics for Response and Prognosis in Lymphoma

PET radiomics in lung cancer: advances and translational challenges

Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice.

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.

Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [18F]FDG PET/CT predicts survival in multiple myeloma

PURPOSE

Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)-based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool.

MATERIALS AND METHODS

Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients' progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated.

RESULTS

Median follow-up [95% CI] of the patient cohort was 110 months [105-123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS.

CONCLUSIONS

The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study

OBJECTIVE

To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL).

METHODS

A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits.

CONCLUSIONS

The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

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.

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.

A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT)-derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression-free survival (PFS) and overall survival (OS) predictions. Baseline FDG-PET scans were available for 1263 patients, 832 patients of these were cell-of-origin (COO)-evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low-, intermediate- and high-risk groups. The random forest model with COO subgroups identified a clearer high-risk population (45% 2-year PFS [95% confidence interval (CI) 40%-52%]; 65% 2-year OS [95% CI 59%-71%]) than the IPI (58% 2-year PFS [95% CI 50%-67%]; 69% 2-year OS [95% CI 62%-77%]). This study confirms that standard clinical risk factors can be combined with PET-derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.

FDG PET/CT and Dosimetric Studies of 177Lu-Lilotomab Satetraxetan in a First-in-Human Trial for Relapsed Indolent non-Hodgkin Lymphoma-Are We Hitting the Target?

Purpose

[¹⁷⁷Lu]Lu-lilotomab satetraxetan, a novel CD37 directed radioimmunotherapy (RIT), has been investigated in a first-in-human phase 1/2a study for relapsed indolent non-Hodgkin lymphoma. In this study, new methods were assessed to calculate the mean absorbed dose to the total tumor volume, with the aim of establishing potential dose–response relationships based on 2-deoxy-2-[18F]fluoro-d-glucose (FDG) positron emission tomography (PET) parameters and clinical response. Our second aim was to study if higher total tumor burden induces reduction in the ¹⁷⁷Lu-lilotomab satetraxetan accumulation in tumor.

Procedures

Fifteen patients with different pre-dosing (non-radioactive lilotomab) regimens were included and the cohort was divided into low and high non-radioactive lilotomab pre-dosing groups for some of the analyses. ¹⁷⁷Lu-lilotomab satetraxetan was administered at dosage levels of 10, 15, or 20 MBq/kg. Mean absorbed doses to the total tumor volume (tTAD) were calculated from posttreatment single-photon emission tomography (SPECT)/computed tomography (CT) acquisitions. Total values of metabolic tumor volume (tMTV), total lesion glycolysis (tTLG) and the percent change in these parameters were calculated from FDG PET/CT performed at baseline, and at 3 and 6 months after RIT. Clinical responses were evaluated at 6 months as complete remission (CR), partial remission (PR), stable disease (SD), or progressive disease (PD).

Results

Significant decreases in tMTV and tTLG were observed at 3 months for patients receiving tTAD ≥ 200 cGy compared to patients receiving tTAD < 200 cGy (p = .03 for both). All non-responders had tTAD < 200 cGy. Large variations in tTAD were observed in responders. Reduction in ¹⁷⁷Lu-lilotomab satetraxetan uptake in tumor volume was not observed in patients with higher baseline tumor burden (tTMV).

Conclusion

tTAD of ≥ 200 cGy may prove valuable to ensure clinical response, but further studies are needed to confirm this in a larger patient population. Furthermore, this work indicates that higher baseline tumor burden (up to 585 cm³) did not induce reduction in radioimmunoconjugate accumulation in tumor.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11307-022-01731-3.

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.

In situ lymphoma imaging in a spontaneous mouse model using the Cerenkov Luminescence of F-18 and Ga-67 isotopes

Cerenkov luminescence imaging (CLI) is a promising approach to image-guided surgery and pathological sampling. It could offer additional advantages when combined to whole-body isotope tomographies. We aimed to obtain evidence of its applicability in lymphoma patho-diagnostics, thus we decided to investigate the radiodiagnostic potential of combined PET or SPECT/CLI in an experimental, novel spontaneous high-grade B-cell lymphoma mouse model (Bc.DLFL1). We monitored the lymphoma dissemination at early stage, and at clinically relevant stages such as advanced stage and terminal stage with in vivo 2-deoxy-2-[¹⁸F]fluoro-d-glucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) and ⁶⁷Ga-citrate single photon emission computed tomography (SPECT)/MRI. In vivo imaging was combined with ex vivo high resolution CLI. The use of CLI with ¹⁸F-Fluorine (F-18) and ⁶⁷Ga-Gallium isotopes in the selection of infiltrated lymph nodes for tumor staging and pathology was thus tested. At advanced stage, FDG PET/MRI plus ex vivo CLI allowed accurate detection of FDG accumulation in lymphoma-infiltrated tissues. At terminal stage we detected tumorous lymph nodes with SPECT/MRI and we could report in vivo detection of the Cerenkov light emission of ⁶⁷Ga. CLI with ⁶⁷Ga-citrate revealed lymphoma accumulation in distant lymph node locations, unnoticeable with only MRI. Flow cytometry and immunohistochemistry confirmed these imaging results. Our study promotes the combined use of PET and CLI in preclinical studies and clinical practice. Heterogeneous FDG distribution in lymph nodes, detected at sampling surgery, has implications for tissue pathology processing and it could direct therapy. The results with ⁶⁷Ga also point to the opportunities to further apply suitable SPECT radiopharmaceuticals for CLI.

Intraperitoneal Glucose Transport to Micrometastasis: A Multimodal In Vivo Imaging Investigation in a Mouse Lymphoma Model

Bc-DLFL.1 is a novel spontaneous, high-grade transplantable mouse B-cell lymphoma model for selective serosal propagation. These cells attach to the omentum and mesentery and show dissemination in mesenteric lymph nodes. We aimed to investigate its early stage spread at one day post-intraperitoneal inoculation of lymphoma cells (n = 18 mice), and its advanced stage at seven days post-inoculation with in vivo [18F]FDG-PET and [18F]PET/MRI, and ex vivo by autoradiography and Cherenkov luminescence imaging (CLI). Of the early stage group, nine animals received intraperitoneal injections, and nine received intravenous [18F]FDG injections. The advanced stage group (n = 3) received intravenous FDG injections. In the early stage, using autoradiography we observed a marked accumulation in the mesentery after intraperitoneal FDG injection. Using other imaging methods and autoradiography, following the intravenous injection of FDG no accumulations were detected. At the advanced stage, tracer accumulation was clearly detected in mesenteric lymph nodes and in the peritoneum after intravenous administration using PET. We confirmed the results with immunohistochemistry. Our results in this model highlight the importance of local FDG administration during diagnostic imaging to precisely assess early peritoneal manifestations of other malignancies (colon, stomach, ovary). These findings also support the importance of applying topical therapies, in addition to systemic treatments in peritoneal cancer spread.

Comparison of 11 automated PET segmentation methods in lymphoma

Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesions and quantifying patient disease burden. Simple thresholding methods remain common despite the large heterogeneity in lymphoma lesion location, size, and contrast. Here, we assess 11 automated PET segmentation methods for their use in two scenarios: individual lesion segmentation and patient-level disease quantification in lymphoma. Lesions on ¹⁸F-FDG PET/CT scans of 90 lymphoma patients were contoured by a nuclear medicine physician. Thresholding, active contours, clustering, adaptive region-growing, and convolutional neural network (CNN) methods were implemented on all physician-identified lesions. Lesion-level segmentation was evaluated using multiple segmentation performance metrics (Dice, Hausdorff Distance). Patient-level quantification of total disease burden (SUV_total) and metabolic tumor volume (MTV) was assessed using Spearman's correlation coefficients between the segmentation output and physician contours. Lesion segmentation and patient quantification performance was compared to inter-physician agreement in a subset of 20 patients segmented by a second nuclear medicine physician. In total, 1223 lesions with median tumor-to-background ratio of 4.0 and volume of 1.8 cm³, were evaluated. When assessed for lesion segmentation, a 3D CNN, DeepMedic, achieved the highest performance across all evaluation metrics. DeepMedic, clustering methods, and an iterative threshold method had lesion-level segmentation performance comparable to the degree of inter-physician agreement. For patient-level SUV_total and MTV quantification, all methods except 40% and 50% SUV_max and adaptive region-growing achieved a performance that was similar the agreement of the two physicians. Multiple methods, including a 3D CNN, clustering, and an iterative threshold method, achieved both good lesion-level segmentation and patient-level quantification performance in a population of 90 lymphoma patients. These methods are thus recommended over thresholding methods such as 40% and 50% SUV_max, which were consistently found to be significantly outside the limits defined by inter-physician agreement.

Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma

PURPOSE

To automatically detect lymph nodes involved in lymphoma on fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/CT images using convolutional neural networks (CNNs).

MATERIALS AND METHODS

In this retrospective study, baseline disease of 90 patients with lymphoma was segmented on ¹⁸F-FDG PET/CT images (acquired between 2005 and 2011) by a nuclear medicine physician. An ensemble of three-dimensional patch-based, multiresolution pathway CNNs was trained using fivefold cross-validation. Performance was assessed using the true-positive rate (TPR) and number of false-positive (FP) findings. CNN performance was compared with agreement between physicians by comparing the annotations of a second nuclear medicine physician to the first reader in 20 of the patients. Patient TPR was compared using Wilcoxon signed rank tests.

RESULTS

Across all 90 patients, a range of 0-61 nodes per patient was detected. At an average of four FP findings per patient, the method achieved a TPR of 85% (923 of 1087 nodes). Performance varied widely across patients (TPR range, 33%-100%; FP range, 0-21 findings). In the 20 patients labeled by both physicians, a range of 1-49 nodes per patient was detected and labeled. The second reader identified 96% (210 of 219) of nodes with an additional 3.7 per patient compared with the first reader. In the same 20 patients, the CNN achieved a 90% (197 of 219) TPR at 3.7 FP findings per patient.

CONCLUSION

An ensemble of three-dimensional CNNs detected lymph nodes at a performance nearly comparable to differences between two physicians' annotations. This preliminary study is a first step toward automated PET/CT assessment for lymphoma.© RSNA, 2020.