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.

Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques.

This study aims to develop a robust pipeline for classifying invasive ductal carcinomas and benign tumors in histopathological images, addressing variability within and between centers. We specifically tackle the challenge of detecting atypical data and variability between common clusters within the same database. Our feature engineering-based pipeline comprises a feature extraction step, followed by multiple harmonization techniques to rectify intra- and inter-center batch effects resulting from image acquisition variability and diverse patient clinical characteristics. These harmonization steps facilitate the construction of more robust and efficient models. We assess the proposed pipeline's performance on two public breast cancer databases, BreaKHIS and IDCDB, utilizing recall, precision, and accuracy metrics. Our pipeline outperforms recent models, achieving 90-95% accuracy in classifying benign and malignant tumors. We demonstrate the advantage of harmonization for classifying patches from different databases. Our top model scored 94.7% for IDCDB and 95.2% for BreaKHis, surpassing existing feature engineering-based models (92.1% for IDCDB and 87.7% for BreaKHIS) and attaining comparable performance to deep learning models. The proposed feature-engineering-based pipeline effectively classifies malignant and benign tumors while addressing variability within and between centers through the incorporation of various harmonization techniques. Our findings reveal that harmonizing variabilities between patches from different batches directly impacts the learning and testing performance of classification models. This pipeline has the potential to enhance breast cancer diagnosis and treatment and may be applicable to other diseases.

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.

18F-FDG-Based Radiomics and Machine Learning: Useful Help for Aortic Prosthetic Valve Infective Endocarditis Diagnosis?

BACKGROUND: Fluorine-18 fluorodeoxyglucose (18F-FDG)-positron emission tomography (PET)/computed tomography (CT) results in better sensitivity for prosthetic valve endocarditis (PVE) diagnosis, but visual image analysis results in relatively weak specificity and significant interobserver variability. OBJECTIVES: The primary objective of this study was to evaluate the performance of a radiomics and machine learning-based analysis of 18F-FDG PET/CT (PET-ML) as a major criterion for the European Society of Cardiology score using machine learning as a major imaging criterion (ESC-ML) in PVE diagnosis. The secondary objective was to assess performance of PET-ML as a standalone examination. METHODS: All 18F-FDG-PET/CT scans performed for suspected aortic PVE at a single center from 2015 to 2021 were retrospectively included. The gold standard was expert consensus after at least 3 months' follow-up. The machine learning (ML) method consisted of manually segmenting each prosthetic valve, extracting 31 radiomics features from the segmented region, and training a ridge logistic regressor to predict PVE. Training and hyperparameter tuning were done with a cross-validation approach, followed by an evaluation on an independent test database. RESULTS: A total of 108 patients were included, regardless of myocardial uptake, and were divided into training (n = 68) and test (n = 40) cohorts. Considering the latter, PET-ML findings were positive for 13 of 22 definite PVE cases and 3 of 18 rejected PVE cases (59% sensitivity, 83% specificity), thus leading to an ESC-ML sensitivity of 72% and a specificity of 83%. CONCLUSIONS: The use of ML for analyzing 18F-FDG-PET/CT images in PVE diagnosis was feasible and beneficial, particularly when ML was included in the ESC 2015 criteria. Despite some limitations and the need for future developments, this approach seems promising to optimize the role of 18F-FDG PET/CT in PVE diagnosis.

Hybrid simultaneous whole-body 2-[18F]FDG-PET/MRI imaging in newly diagnosed multiple myeloma: first diagnostic performance and clinical added value results.

OBJECTIVES: Mixing diagnostic and prognostic data provided by whole-body MRI (WB-MRI) and 2-18F-fluorodeoxyglucose (2-[18F]FDG) positron emission tomography (2-[18F]FDG-PET) from a single simultaneous imaging technique for newly diagnosed multiple myeloma (NDMM) initial workup seems attractive. However, to date, the published data are scarce and this possibility has not been fully explored. In this prospective study, we aimed to explore the diagnostic performance and added clinical value of WB-2-[18F]FDG-PET/MRI imaging in NDMM. METHODS: All patients with confirmed NDMM at the Nantes University Hospital were prospectively enrolled in this study and underwent WB-2-[18F]FDG-PET/MRI imaging on a 3-T Biograph mMR before receiving treatment. Before imaging, they were considered either as symptomatic or as smoldering MM (SMM). Diagnostic performance of global WB-2-[18F]FDG-PET/MRI imaging, as well as PET and MRI separately for FL and diffuse BMI detection, was assessed and compared in each group. PET-based (maximal standardized uptake value, SUVmax) and MRI-based (mean apparent diffusion coefficient value, ADCmean) quantitative features were collected for FL/para-medullary disease (PMD)/bone marrow and were compared. RESULTS: A total of 52 patients were included in this study. PET and MRI were equally effective at detecting patients with FL (69% vs. 75%) and with diffuse BMI (62% for both) in the symptomatic MM group. WB-2-[18F]FDG-PET/MRI imaging detected FL in 22% of patients with SMM (with a higher diagnostic performance for MRI), resulting in a significant impact on clinical management in this population. SUVmax and ADCmean quantitative features were weakly or not correlated. CONCLUSIONS: WB-2-[18F]FDG-PET/MRI could represent the next-generation imaging modality for MM. KEY POINTS: • Whole-body 2-[18F]FDG-PET/MRI imaging detected at least one focal bone lesion in 75% of patients with symptomatic multiple myeloma, and PET and MRI were equally effective at identifying patients with a focal bone lesion. • Whole-body 2-[18F]FDG-PET/MRI imaging detected a focal bone lesion in 22% of patients with smoldering multiple myeloma (with a higher diagnostic performance for MRI). • MRI had a significant impact on clinical management of smoldering multiple myeloma.

Design of a generic method for single dual-tracer PET imaging acquisition in clinical routine.

Using different tracers in positron emission tomography (PET) imaging can bring complementary information on tumor heterogeneities. Ideally, PET images of different tracers should be acquired simultaneously to avoid the bias induced by movement and physiological changes between sequential acquisitions. Previous studies have demonstrated the feasibility of recovering separated PET signals or parameters of two or more tracers injected (quasi-)simultaneously in a single acquisition. In this study, a generic framework in the context of dual-tracer PET acquisition is proposed where no strong kinetic assumptions nor specific tuning of parameters are required. The performances of the framework were assessed through simulations involving the combination of [18F]FCH and [18F]FDG injections, two protocols (90 and 60 min acquisition durations) and various activity ratios between the two injections. Preclinical experiments with the same radiotracers were also conducted. Results demonstrate the ability of the method both to extract separated arterial input functions (AIF) from noisy image-derived input function and to separate the dynamic signals and further estimate kinetic parameters. The compromise between bias and variance associated with the estimation of net influx rateKishows that it is preferable to use the second injected radiotracer with twice the activity of the first for both 90 min [18F]FCH+[18F]FDG and 60 min [18F]FDG+[18F]FCH protocols. In these optimal settings, the weighted mean-squared-error of the estimated AIF was always less than 7%. TheKibias was similar to the one of single-tracer acquisitions; below 5%. Compared to single-tracer results, the variance ofKiwas twice more for 90 min dual-tracer scenario and four times more for the 60 min scenario. The generic design of the method makes it easy to use for other pairs of radiotracers and even for more than two tracers. The absence of strong kinetic assumptions and tuning parameters makes it suitable for a possible use in clinical routine.

Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation.

PURPOSE: This study aimed to investigate the impact of several ComBat harmonization strategies, intra-tumoral sub-volume characterization, and automatic segmentations for progression-free survival (PFS) prediction through radiomics modeling for patients with head and neck cancer (HNC) in PET/CT images. METHODS: The HECKTOR MICCAI 2021 challenge set containing PET/CT images and clinical data of 325 oropharynx HNC patients was exploited. A total of 346 IBSI-compliant radiomic features were extracted for each patient's primary tumor volume defined by the reference manual contours. Modeling relied on least absolute shrinkage Cox regression (Lasso-Cox) for feature selection (FS) and Cox proportional-hazards (CoxPH) models were built to predict PFS. Within this methodological framework, 8 different strategies for ComBat harmonization were compared, including before or after FS, in feature groups separately or all features directly, and with center or clustering-determined labels. Features extracted from tumor sub-volume clustering were also investigated for their prognostic additional value. Finally, 3 automatic segmentations (2 threshold-based and a 3D U-Net) were also compared. All results were evaluated with the concordance index (C-index). RESULTS: Radiomics features without harmonization, combined with clinical factors, led to models with C-index values of 0.69 in the testing set. The best version of ComBat harmonization, i.e., after FS, for feature groups separately and relying on clustering-determined labels, achieved a C-index of 0.71. The use of features extracted from tumor sub-volumes further improved the C-index to 0.72. Models that relied on the automatic segmentations yielded close but slightly lower prognostic performance (0.67-0.70) compared to reference contours. CONCLUSION: A standard radiomics pipeline allowed for prediction of PFS in a multicenter HNC cohort. Applying a specific strategy of ComBat harmonization improved the performance. The extraction of intra-tumoral sub-volume features and automatic segmentation could contribute to the improvement and automation of prognosis modeling, respectively.

A Multicenter Study on Observed Discrepancies Between Vendor-Stated and PET-Measured 90Y Activities for Both Glass and Resin Microsphere Devices.

Dosimetry-guided treatment planning in selective internal radiation therapy relies on accurate and reproducible measurement of administered activity. This 4-center, 5-PET-device study compared the manufacturer-declared 90Y activity in vials with quantitative 90Y PET/CT assessment of the same vials. We compared 90Y PET-measured activity (APET) for 56 90Y-labeled glass and 18 90Y-labeled resin microsphere vials with the calibrated activity specified by the manufacturer (AM). Additionally, the same analysis was performed for 4 90Y-chloride vials. The mean APET/AM ratio was 0.79 ± 0.04 (range, 0.71-0.89) for glass microspheres and 1.15 ± 0.06 (range, 1.05-1.25) for resin microspheres. The mean APET/AM ratio for 90Y-chloride vials was 1.00 ± 0.04 (range, 0.96-1.06). Thus, we found an average difference of 46% between glass and resin microsphere activity calibrations, whereas close agreement was found for chloride solutions. We expect that the reported discrepancies will promote further investigations to establish reliable and accurate patient dosimetry and dose-effect assessments.

Spatially-Localized Functionalization on Nanostructured Surfaces for Enhanced Plasmonic Sensing Efficacy.

This work demonstrates the enhancement in plasmonic sensing efficacy resulting from spatially-localized functionalization on nanostructured surfaces, whereby probe molecules are concentrated in areas of high field concentration. Comparison between SERS measurements on nanostructured surfaces (arrays of nanodisks 110 and 220 nm in diameter) with homogeneous and spatially-localized functionalization with thiophenol demonstrates that the Raman signal originates mainly from areas with high field concentration. TERS measurements with 10 nm spatial resolution confirm the field distribution profiles predicted by the numerical modeling. Though this enhancement in plasmonic sensing efficacy is demonstrated with SERS, results apply equally well to any type of optical/plasmonic sensing on functionalized surfaces with nanostructuring.

Molecular Signature of 18F-FDG PET Biomarkers in Newly Diagnosed Multiple Myeloma Patients: A Genome-Wide Transcriptome Analysis from the CASSIOPET Study.

The International Myeloma Working Group recently fully incorporated 18F-FDG PET into multiple myeloma (MM) diagnosis and response evaluation. Moreover, a few studies demonstrated the prognostic value of several biomarkers extracted from this imaging at baseline. Before these 18F-FDG PET biomarkers could be fully endorsed as risk classifiers by the hematologist community, further characterization of underlying molecular aspects was necessary. Methods: Reported prognostic biomarkers (18F-FDG avidity, SUVmax, number of focal lesions, presence of paramedullary disease [PMD] or extramedullary disease) were extracted from 18F-FDG PET imaging at baseline in a group of 139 patients from CASSIOPET, a companion study of the CASSIOPEIA cohort (ClinicalTrials.gov identifier NCT02541383). Transcriptomic analyses using RNA sequencing were realized on sorted bone marrow plasma cells from the same patients. An association with a high-risk gene expression signature (IFM15), molecular classification, progression-free survival, a stringent clinical response, and minimal residual disease negativity were explored. Results:18F-FDG PET results were positive in 79.4% of patients; 14% and 11% of them had PMD and extramedullary disease, respectively. Negative 18F-FDG PET results were associated with lower levels of expression of hexokinase 2 (HK2) (fold change, 2.1; adjusted P = 0.04) and showed enrichment for a subgroup of patients with a low level of bone disease. Positive 18F-FDG PET results displayed 2 distinct signatures: either high levels of expression of proliferation genes or high levels of expression of GLUT5 and lymphocyte antigens. PMD and IFM15 were independently associated with a lower level of progression-free survival, and the presence of both biomarkers defined a group of "double-positive" patients at very high risk of progression. PMD and IFM15 were related neither to minimal residual disease assessment nor to a stringent clinical response. Conclusion: Our study confirmed and extended the association between imaging biomarkers and transcriptomic programs in MM. The combined prognostic value of PMD and a high-risk IFM15 signature may help define MM patients with a very high risk of progression.

Anti-CEA Pretargeted Immuno-PET Shows Higher Sensitivity Than DOPA PET/CT in Detecting Relapsing Metastatic Medullary Thyroid Carcinoma: Post Hoc Analysis of the iPET-MTC Study.

Pretargeting parameters for the use of anti-carcinoembryonic antigen (CEA) bispecific monoclonal antibody TF2 and the 68Ga-labeled IMP288 peptide for immuno-PET have been optimized in a first-in-humans study performed on medullary thyroid carcinoma (MTC) patients (the iPET-MTC study). The aim of this post hoc analysis was to determine the sensitivity of immuno-PET in relapsing MTC patients, in comparison with conventional imaging and 18F-l-dihydroxyphenylalanine (18F-DOPA) PET/CT. Methods: Twenty-five studies were analyzed in 22 patients. All patients underwent immuno-PET 1 and 2 h after 68Ga-IMP288 injection pretargeted by TF2, in addition to neck, thoracic, abdominal, and pelvic CT; bone and liver MRI; and 18F-DOPA PET/CT. The gold standard was histology or confirmation by one other imaging method or by imaging follow-up. Results: In total, 190 lesions were confirmed by the gold standard: 89 in lymph nodes, 14 in lungs, 46 in liver, 37 in bone, and 4 in other sites (subcutaneous tissue, heart, brain, and pancreas). The number of abnormal foci detected by immuno-PET was 210. Among these, 174 (83%) were confirmed as true-positive by the gold standard. Immuno-PET showed a higher overall sensitivity (92%) than 18F-DOPA PET/CT (65%). Regarding metastatic sites, immuno-PET had a higher sensitivity than CT, 18F-DOPA PET/CT, or MRI for lymph nodes (98% vs. 83% for CT and 70% for 18F-DOPA PET/CT), liver (98% vs. 87% for CT, 65% for 18F-DOPA PET/CT, and 89% for MRI), and bone (92% vs. 64% for 18F-DOPA PET/CT and 86% for MRI), whereas sensitivity was lower for lung metastases (29% vs. 100% for CT and 14% for 18F-DOPA PET/CT). Tumor SUVmax at 60 min ranged from 1.2 to 59.0, with intra- and interpatient variability. Conclusion: This post hoc study demonstrates that anti-carcinoembryonic antigen immuno-PET is an effective procedure for detecting metastatic MTC lesions. Immuno-PET showed a higher overall sensitivity than 18F-DOPA PET/CT for disclosing metastases, except for the lung, where CT remains the most effective examination.

Head-to-Head Comparison of 18F-DOPA PET/CT and 68Ga-DOTANOC PET/CT in Patients With Midgut Neuroendocrine Tumors.

PURPOSE: The aim of this study was to compare retrospectively 18F-DOPA PET/CT versus 68Ga-DOTANOC PET/CT in a group of patients affected by midgut NET. PATIENTS AND METHODS: Patients with histologically proven grade 1 or grade 2 midgut NET were explored after injection of 150 MBq of 68Ga-DOTANOC and 210 MBq of 18F-DOPA. The PET/CTs were analyzed visually and semiquantitatively at the patient level, regional level (7 defined regions), and lesion level (maximum of 5 lesions/organ). The criterion standard was determined on the basis of histology and imaging follow-up. RESULTS: Thirty patients (17 males and 13 females; median age, 63.5 years [37-82 years]) were included. Both PET/CTs were negative in 3 patients and positive in 25 patients. PET/CTs were discordant in 2 patients, with 18F-DOPA positive and 68Ga-DOTANOC negative. 18F-DOPA PET/CT detected more involved regions and more metastatic lesions than 68Ga-DOTANOC PET/CT in 6 (20%) and 10 (33.3%) patients, respectively. Of the 81 confirmed affected regions, 77 (95%) were detected by 18F-DOPA PET/CT and 71 (87.7%) by 68Ga-DOTANOC PET/CT (P < 0.0001). 18F-DOPA PET/CT detected significantly more lesions (211/221) than 68Ga-DOTANOC PET/CT (195/221), corresponding to a sensitivity of 95.5% and 88.2%, respectively (P < 0.0001). Tumor-to-background ratios were more favorable in liver for 18F-DOPA than for 68Ga-DOTANOC. Interestingly, a correlation was found between 18F-DOPA SUVmax and tumor burden and especially with the number of regions involved by the disease (P = 0.019). CONCLUSIONS: 18F-DOPA PET/CT is superior to 68Ga-DOTANOC PET/CT for the detection of lesions, and when available, this tracer may be recommended as the first-line examination for an accurate staging of midgut NET.

Standardization of 18F-FDG-PET/CT According to Deauville Criteria for Metabolic Complete Response Definition in Newly Diagnosed Multiple Myeloma.

PURPOSE: 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is currently the standard technique to define minimal residual disease (MRD) status outside the bone marrow (BM) in patients with multiple myeloma (MM). This study aimed to define criteria for PET complete metabolic response after therapy, jointly analyzing a subgroup of newly diagnosed transplantation-eligible patients with MM enrolled in two independent European randomized phase III trials (IFM/DFCI2009 and EMN02/HO95). PATIENTS AND METHODS: Two hundred twenty-eight patients were observed for a median of 62.9 months. By study design, PET/CT scans were performed at baseline and before starting maintenance (premaintenance [PM]). The five-point Deauville scale (DS) was applied to describe BM (BM score [BMS]) and focal lesion (FL; FL score [FS]) uptake and tested a posteriori in uni- and multivariable analyses for their impact on clinical outcomes. RESULTS: At baseline, 78% of patients had FLs (11% extramedullary), 80% with an FS ≥ 4. All patients had BM diffuse uptake (35.5% with BMS ≥ 4). At PM, 31% of patients had visually detectable FLs (2% extramedullary), 24% and 67.7% of them with an FS of 3 and ≥ 4, respectively. At PM, 98% of patients retained residual BM diffuse uptake, which was significantly lower than at baseline (mainly between BMS 2 and 3, BMS was ≥ 4 in only 8.7% of patients). By both uni- and multivariable analysis, FS and BMS < 4 were associated with prolonged progression-free survival (PFS) and overall survival (OS) at PM (OS: hazard ratio [HR], 0.6 and 0.47, respectively; PFS: HR, 0.36 and 0.24, respectively). CONCLUSION: FL and BM FDG uptake lower than the liver background after therapy was an independent predictor for improved PFS and OS and can be proposed as the standardized criterion of PET complete metabolic response, confirming the value of the DS for patients with MM.

Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials.

PURPOSE: Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is included in the International Myeloma Working Group (IMWG) imaging guidelines for the work-up at diagnosis and the follow-up of multiple myeloma (MM) notably because it is a reliable tool as a predictor of prognosis. Nevertheless, none of the published studies focusing on the prognostic value of PET-derived features at baseline consider tumor heterogeneity, which could be of high importance in MM. The aim of this study was to evaluate the prognostic value of baseline PET-derived features in transplant-eligible newly diagnosed (TEND) MM patients enrolled in two prospective independent European randomized phase III trials using an innovative statistical random survival forest (RSF) approach. METHODS: Imaging ancillary studies of IFM/DFCI2009 and EMN02/HO95 trials formed part of the present analysis (IMAJEM and EMN02/HO95, respectively). Among all patients initially enrolled in these studies, those with a positive baseline FDG-PET/CT imaging and focal bone lesions (FLs) and/or extramedullary disease (EMD) were included in the present analysis. A total of 17 image features (visual and quantitative, reflecting whole imaging characteristics) and 5 clinical/histopathological parameters were collected. The statistical analysis was conducted using two RSF approaches (train/validation + test and additional nested cross-validation) to predict progression-free survival (PFS). RESULTS: One hundred thirty-nine patients were considered for this study. The final model based on the first RSF (train/validation + test) approach selected 3 features (treatment arm, hemoglobin, and SUVmaxBone Marrow (BM)) among the 22 involved initially, and two risk groups of patients (good and poor prognosis) could be defined with a mean hazard ratio of 4.3 ± 1.5 and a mean log-rank p value of 0.01 ± 0.01. The additional RSF (nested cross-validation) analysis highlighted the robustness of the proposed model across different splits of the dataset. Indeed, the first features selected using the train/validation + test approach remained the first ones over the folds with the nested approach. CONCLUSION: We proposed a new prognosis model for TEND MM patients at diagnosis based on two RSF approaches. TRIAL REGISTRATION: IMAJEM: NCT01309334 and EMN02/HO95: NCT01134484.

Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.

PURPOSE: Lymphoma lesion detection and segmentation on whole-body FDG-PET/CT are a challenging task because of the diversity of involved nodes, organs or physiological uptakes. We sought to investigate the performances of a three-dimensional (3D) convolutional neural network (CNN) to automatically segment total metabolic tumour volume (TMTV) in large datasets of patients with diffuse large B cell lymphoma (DLBCL). METHODS: The dataset contained pre-therapy FDG-PET/CT from 733 DLBCL patients of 2 prospective LYmphoma Study Association (LYSA) trials. The first cohort (n = 639) was used for training using a 5-fold cross validation scheme. The second cohort (n = 94) was used for external validation of TMTV predictions. Ground truth masks were manually obtained after a 41% SUVmax adaptive thresholding of lymphoma lesions. A 3D U-net architecture with 2 input channels for PET and CT was trained on patches randomly sampled within PET/CTs with a summed cross entropy and Dice similarity coefficient (DSC) loss. Segmentation performance was assessed by the DSC and Jaccard coefficients. Finally, TMTV predictions were validated on the second independent cohort. RESULTS: Mean DSC and Jaccard coefficients (± standard deviation) in the validations set were 0.73 ± 0.20 and 0.68 ± 0.21, respectively. An underestimation of mean TMTV by - 12 mL (2.8%) ± 263 was found in the validation sets of the first cohort (P = 0.27). In the second cohort, an underestimation of mean TMTV by - 116 mL (20.8%) ± 425 was statistically significant (P = 0.01). CONCLUSION: Our CNN is a promising tool for automatic detection and segmentation of lymphoma lesions, despite slight underestimation of TMTV. The fully automatic and open-source features of this CNN will allow to increase both dissemination in routine practice and reproducibility of TMTV assessment in lymphoma patients.

Glucose Metabolism Quantified by SUVmax on Baseline FDG-PET/CT Predicts Survival in Newly Diagnosed Multiple Myeloma Patients: Combined Harmonized Analysis of Two Prospective Phase III Trials.

Background: Multiple myeloma is a hematological neoplasm characterized by a clonal proliferation of malignant plasma cells in the bone marrow, and is associated with high morbidity and mortality and variable survival. Positron emission tomography combined with computed tomography using 18F-deoxyfluoroglucose (FDG-PET/CT) is a promising technique for initial staging of symptomatic multiple myeloma patients. The objective of this study was to assess the prognostic value of this technique at baseline in symptomatic multiple myeloma patients included in two large European prospective studies (French and Italian). Methods: We retrospectively performed a combined harmonized analysis of 227 newly diagnosed transplant eligible multiple myeloma patients from two separate phase III trials. All images were centrally reviewed and analyzed using visual criteria and maximal standardized uptake value. An ad-hoc approach (called modified Combat) was applied to harmonize the data and then remove the "country effect" in order to strengthen the reliability of the final conclusions. Results: Using a multivariate analysis including treatment arm, R-ISS score, presence of extra-medullary disease and bone SUVmax, only bone SUVmax (p = 0.016) was an independent prognosis factor with an OS threshold of 7.1. For PFS, treatment arm and presence of extra-medullary disease were both independent prognosis biomarkers (p = 0.022 and 0.006 respectively). Conclusions: Our results show that bone SUVmax is a simple and reliable biomarker to analyze FDG-PET/CT at baseline that strongly correlates with a poorer prognosis for MM patients.

From a PMT-based to a SiPM-based PET system: a study to define matched acquisition/reconstruction parameters and NEMA performance of the Biograph Vision 450.

BACKGROUND: The purpose of this work was to propose an approach based on noise measurement to adapt present clinical acquisition and reconstruction parameters adapted to a PMT-based system (Biograph mCT) to a SiPM-based system (Biograph Vision 450) sharing identical geometrical properties. The NEMA performance (NEMA) of the recently released Biograph Vision 450 PET/CT (Vision) was also derived. METHODS: All measurements were conducted on Vision and Biograph mCT with TrueV (mCT). A full NEMA-based performance was derived for Vision only. The adaptation of acquisition and reconstruction parameters from mCT to Vision was done using the NEMA image quality phantom. The noise level reached using mCT was set as the reference value for six different numbers of net true coincidences. The noise level computed using Vision was matched to the reference noise level (within 0.01%) using a different reconstruction set-up to determine the potential reduction of count numbers for the same noise level. RESULTS: Vision sensitivity was 9.1 kcps/MBq for a timing resolution of 213 ps at 5.3 kBq/mL. The NEMA-based CR for the 10-mm sphere was better than 75% regardless the reconstruction set-up studied. The mCT reference noise properties could be achieved using Vision with a scan time reduction (STR) of 1.34 with four iterations and a 440 × 440 matrix size (or STR = 1.89 with a 220 × 220 matrix size) together with a 3D CR improvement of 53% for the 10-mm sphere (24% using 220 × 220). CONCLUSION: The Vision exhibited improved NEMA performances compared to mCT. Using the proposed approach, the time acquisition could be divided by almost two, while keeping the same noise properties as that of mCT with a marked improvement of contrast recovery.