Metabolic tumor volume predicts outcome in patients with advanced stage follicular lymphoma from the RELEVANCE trial.

BACKGROUND: We investigated the prognostic value of baseline positron emission tomography (PET) parameters for patients with treatment-naïve follicular lymphoma (FL) in the phase III RELEVANCE trial, comparing the immunomodulatory combination of lenalidomide and rituximab (R2) versus R-chemotherapy (R-chemo), with both regimens followed by R maintenance therapy. PATIENTS AND METHODS: Baseline characteristics of the entire PET-evaluable population (n = 406/1032) were well balanced between treatment arms. The maximal standard uptake value (SUVmax) and the standardized maximal distance between tow lesions (SDmax) were extracted, the standardized distance between two lesions the furthest apart, were extracted. The total metabolic tumor volume (TMTV) was computed using the 41% SUVmax method. RESULTS: With a median follow-up of 6.5 years, the 6-year progression-free survival (PFS) was 57.8%, the median TMTV was 284 cm3, SUVmax was 11.3 and SDmax was 0.32 m-1, with no significant difference between arms. High TMTV (>510 cm3) and FLIPI were associated with an inferior PFS (P = 0.013 and P = 0.006, respectively), whereas SUVmax and SDmax were not (P = 0.08 and P = 0.12, respectively). In multivariable analysis, follicular lymphoma international prognostic index (FLIPI) and TMTV remained significantly associated with PFS (P = 0.0119 and P = 0.0379, respectively). These two adverse factors combined stratified the overall population into three risk groups: patients with no risk factors (40%), with one factor (44%), or with both (16%), with a 6-year PFS of 67.7%, 54.5%, and 41.0%, respectively. No significant interaction between treatment arms and TMTV or FLIPI (P = 0.31 or P = 0.59, respectively) was observed. The high-risk group (high TMTV and FLIPI 3-5) had a similar PFS in both arms (P = 0.45) with a median PFS of 68.4% in the R-chemo arm versus 71.4% in the R2 arm. CONCLUSIONS: Baseline TMTV is predictive of PFS, independently of FLIPI, in patients with advanced FL even in the context of antibody maintenance.

Differentially expressed genes associated with high metabolic tumor volume served as diagnostic markers and potential therapeutic targets for pancreatic cancer.

BACKGROUND: The lack of distinct biomarkers for pancreatic cancer is a major cause of early-stage detection difficulty. The pancreatic cancer patient group with high metabolic tumor volume (MTV), one of the values measured from positron emission tomography-a confirmatory method and standard care for pancreatic cancer, showed a poorer prognosis than those with low MTV. Therefore, MTV-associated differentially expressed genes (DEGs) may be candidates for distinctive markers for pancreatic cancer. This study aimed to evaluate the possibility of MTV-related DEGs as markers or therapeutic targets for pancreatic cancer. METHODS: Tumor tissues and their normal counterparts were obtained from patients undergoing preoperative 18F-FDG PET/CT. The tissues were classified into MTV-low and MTV-high groups (7 for each) based on the MTV2.5 value of 4.5 (MTV-low: MTV2.5 < 4.5, MTV-high: MTV2.5 ≥ 4.5). Gene expression fold change was first calculated in cancer tissue compared to its normal counter and then compared between low and high MTV groups to obtain significant DEGs. To assess the suitability of the DEGs for clinical application, the correlation of the DEGs with tumor grades and clinical outcomes was analyzed in TCGA-PAAD, a large dataset without MTV information. RESULTS: Total RNA-sequencing (MTV RNA-Seq) revealed that 44 genes were upregulated and 56 were downregulated in the high MTV group. We selected the 29 genes matching MTV RNA-seq patterns in the TCGA-PAAD dataset, a large clinical dataset without MTV information, as MTV-associated genes (MAGs). In the analysis with the TCGA dataset, MAGs were significantly associated with patient survival, treatment outcomes, TCGA-PAAD-suggested markers, and CEACAM family proteins. Some MAGs showed an inverse correlation with miRNAs and were confirmed to be differentially expressed between normal and cancerous pancreatic tissues. Overexpression of KIF11 and RCC1 and underexpression of ADCY1 and SDK1 were detected in ~ 60% of grade 2 pancreatic cancer patients and associated with ~ 60% mortality in stages I and II. CONCLUSIONS: MAGs may serve as diagnostic markers and miRNA therapeutic targets for pancreatic cancer. Among the MAGs, KIF11, RCC1, ADCY, and SDK1 may be early diagnostic markers.

A single-center, multi-factor, retrospective study to improve the diagnostic accuracy of primary prostate cancer using [68Ga]Ga-PSMA-11 total-body PET/CT imaging.

PURPOSE: To improve the diagnostic accuracy of initial detection in patients with suspected primary prostate cancer (PCa). METHODS: Eighty-four patients who underwent Gallium-68-labeled prostate-specific membrane antigen ([68Ga]Ga-PSMA-11) total-body positron emission tomography/computed tomography (PET/CT) imaging before treatment in our department were enrolled. The maximum standard uptake value (SUVmax) of the prostate (SUVmax-PSMA), liver (SUVmax-PSMA-L), and mediastinal blood pool (SUVmax-PSMA-M) was measured using [68Ga]Ga-PSMA-11 total-body PET/CT imaging. The [68Ga]Ga-PSMA-11 derived metabolic tumor volume (MTV), the total lesion (TLP), and the cross-sectional areas of focal concentration in the prostate (CAP) were also determined. Besides, the prostate-specific antigen (PSA) levels and the above imaging characteristics were analyzed using receiver operating characteristic curves to identify the cutoff value to improve the diagnostic accuracy of suspected PCa. Finally, a multivariate regression analysis was conducted to discover the independent predictor to improve the diagnostic accuracy on [68Ga]Ga-PSMA-11 total-body imaging. RESULTS: There was no significant difference between the PCa and Non-PCa groups in age, height, weight, injected dose, except for the PSA levels, the SUVmax-PSMA, TLP, MTV, and CAP. Besides, the SUVmax-PSMA-T/L and SUVmax-PSMA-T/M derived from SUVmax-PSMA were both significantly different. In addition, the areas under the curve of PSA levels, SUVmax-PSMA, SUVmax-PSMA-T/L, SUVmax-PSMA-T/M, TLP, MTV, and CAP to predict PCa on [68Ga]Ga-PSMA-11 imaging were 0.620 (95% confidence interval (CI) 0.485-0.755), 0.864 (95% CI 0.757-0.972), 0.819 (95% CI 0.704-0.935), 0.876 (95% CI 0.771-0.980), 0.845 (95% CI 0.741-0.949), 0.820 (95% CI 0.702-0.938), 0.627 (95% CI 0.499-0.754), respectively. However, a multivariate regression analysis showed that SUVmax-PSMA was an independent predictor, with a cutoff value of 11.5 and an odds ratio of 1.221. CONCLUSION: The SUVmax-PSMA with a cutoff value of 11.5 was an independent predictor to improve the diagnostic accuracy of PCa on [68Ga]Ga-PSMA-11 total-body imaging.

Prognostic value of baseline and interim [18F]FDG PET metabolic parameters in pediatric Hodgkin's lymphoma.

BACKGROUND: Hodgkin lymphoma (HL) in pediatric populations has a high survival rate but poses risks for long-term morbidities. Although [18F]fluoro­2­deoxy­2­d­glucose positron emission tomography ([18F]FDG PET) scans offer potential for improved risk stratification, the definitive prognostic value of quantitative [18F]FDG PET parameters remains unclear for pediatric HL. METHODS: A single-center, retrospective study included pediatric patients diagnosed with HL between 2016 and 2023 treated according to EuroNet-PHL-C1 and DAL/GPOH-HD protocols. Patients underwent baseline and interim PET/CT scans after two chemotherapy cycles. Event-free survival (EFS) was the primary endpoint, Deauville score was the secondary endpoint. Quantitative [18F]FDG PET parameters included SUVmax, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) that were evaluated using two segmentation methods (SUV 2.5, 41% SUVmax). Survival outcomes were assessed using Cox regression analysis. RESULTS: A total of 115 patients (50 males, median age 14.2 years) were studied, with a median follow-up period of 35 months. During this period, 16 cases (13.9%) of relapse or progression were noted. Baseline and interim MTV 2.5, MTV 41%, TLG 2.5, and TLG 41%, along with interim SUVmax, were significantly associated with worse EFS and correlated with post-treatment Deauville scores. In multivariable analysis, interim MTV 2.5 > 0 ml (adj. hazard ratio, HR: 3.89, p = 0.009) and interim TLG 41% ≥ 30 g (adj. HR: 7.98, p = 0.006) were independent risk factors for EFS. CONCLUSION: Baseline and interim [18F]FDG PET parameters can serve as significant prognostic indicators for EFS and treatment response in pediatric HL. These quantitative measures could enhance individualized, risk-adapted treatment strategies for children and adolescents with HL.

TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis.

PURPOSE: Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach. METHODS: Our study included 1418 2-[18F]FDG PET/CT scans from four different centers. The dataset was divided into 900 scans for development/validation/testing phases and 518 for multi-center external testing. The former consisted of 450 lymphoma, lung cancer, and melanoma scans, along with 450 negative scans, while the latter consisted of lymphoma patients from different centers with diffuse large B cell, primary mediastinal large B cell, and classic Hodgkin lymphoma cases. Our approach involves resampling PET/CT images into different voxel sizes in the first step, followed by training multi-resolution 3D U-Nets on each resampled dataset using a fivefold cross-validation scheme. The models trained on different data splits were ensemble. After applying soft voting to the predicted masks, in the second step, we input the probability-averaged predictions, along with the input imaging data, into another 3D U-Net. Models were trained with semi-supervised loss. We additionally considered the effectiveness of using test time augmentation (TTA) to improve the segmentation performance after training. In addition to quantitative analysis including Dice score (DSC) and TMTV comparisons, the qualitative evaluation was also conducted by nuclear medicine physicians. RESULTS: Our cascaded soft-voting guided approach resulted in performance with an average DSC of 0.68 ± 0.12 for the internal test data from developmental dataset, and an average DSC of 0.66 ± 0.18 on the multi-site external data (n = 518), significantly outperforming (p < 0.001) state-of-the-art (SOTA) approaches including nnU-Net and SWIN UNETR. While TTA yielded enhanced performance gains for some of the comparator methods, its impact on our cascaded approach was found to be negligible (DSC: 0.66 ± 0.16). Our approach reliably quantified TMTV, with a correlation of 0.89 with the ground truth (p < 0.001). Furthermore, in terms of visual assessment, concordance between quantitative evaluations and clinician feedback was observed in the majority of cases. The average relative error (ARE) and the absolute error (AE) in TMTV prediction on external multi-centric dataset were ARE = 0.43 ± 0.54 and AE = 157.32 ± 378.12 (mL) for all the external test data (n = 518), and ARE = 0.30 ± 0.22 and AE = 82.05 ± 99.78 (mL) when the 10% outliers (n = 53) were excluded. CONCLUSION: TMTV-Net demonstrates strong performance and generalizability in TMTV segmentation across multi-site external datasets, encompassing various lymphoma subtypes. A negligible reduction of 2% in overall performance during testing on external data highlights robust model generalizability across different centers and cancer types, likely attributable to its training with resampled inputs. Our model is publicly available, allowing easy multi-site evaluation and generalizability analysis on datasets from different institutions.

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.

[18F]FDG-PET/CT-based risk stratification in women with locally advanced uterine cervical cancer.

BACKGROUND: [18F]FDG-PET/CT is used for staging and treatment planning in patients with locally advanced cervical cancer (LACC). We studied if a PET-based prediction model could provide additional risk stratification beyond International Federation of Gynaecology and Obstetrics (FIGO) staging in our population with LACC to aid treatment decision making. METHODS: In total, 183 patients with LACC treated with chemoradiation between 2013 and 2018 were included. Patients were treated according to FIGO 2009 and retrospectively reclassified according to FIGO 2018 staging system. After validation of an existing PET-based prediction model, the predicted recurrent free survival (RFS), disease specific survival (DSS) and overall survival (OS) at 1, 3, and 5 years, based on metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and highest level of [18F]FDG-positive node was calculated. Then the observed survival was compared to the predicted survival. An area under the curve (AUC) close to or higher than 0.7 was considered adequate for accurate prediction. The Youden (J) index defined survival chance cutoff values for low and high risk groups. RESULTS: All AUC values for the comparison between predicted and observed outcomes were > 0.7 except for 5-year RFS and for 5-year OS which were close to 0.7 (0.684 and 0.650 respectively). Cutoff values for low and high risk survival chance were 0.44 for the 3-year RFS and 0.47 for the 5-year OS. The FIGO 2009 system could not differentiate between the risk profiles. After reclassification according to FIGO 2018, all patients with stage IIIC2 and IVB fell in the high risk and almost all patients with stages IB2-IIIB and IVA in the low risk group. In patients with stage IIIC1 disease the FIGO stage cannot discriminate between the risk profiles. CONCLUSIONS: Low and high risk patients with LACC can be identified with the PET-based prediction model. In particular patients with stage IIIC1 need additional risk stratification besides the FIGO 2018 staging. The Kidd model could be a useful tool to aid treatment decision making in these patients. Our results also support the choice of [18F]FDG-PET/CT imaging in patients with LACC.

A low total metabolic tumor volume independently predicts for a longer time to first treatment in initially observed, low tumor burden follicular lymphoma.

Watchful waiting is an acceptable management strategy for advanced-stage, low tumor burden (LTB) patients with follicular lymphoma (FL). However, the prediction of how long this treatment-free observation period will last remains imperfect. We explored whether total metabolic tumor volume (TMTV) and other positron emission tomography parameters were predictive of time to first treatment (TTFT). We analyzed 97 grade 1-3A advanced-stage LTB FL patients and found that a high TMTV was associated with other tumor burden features at diagnosis. Patients with a TMTV above our established cutoff of 50 mL had a significantly shorter median duration of observation (2.6 vs. 8.8 years; p = 0.001). At 5 years, 77% of patients with a high TMTV and 46% of patients with a low TMTV required treatment. In the multivariable analysis, a high TMTV was the only independent factor predicting TTFT (hazard ratio = 2.09; p = 0.017). Overall, TMTV is a strong predictor of the duration of observation in LTB FL patients. Upon validation of our cutoff in external series and standardization of the methodology, the TMTV could become an additional factor to consider deferring or initiating treatment in otherwise LTB patients.

Impact of baseline and interim quantitative PET parameters on outcomes of classical Hodgkin Lymphoma.

Currently, analysis of interim PET (iPET) according to the Deauville score (DS) is the most important predictive factor in Hodgkin lymphoma (HL); however, there is room for improvement in its prognostic power. This study aimed to evaluate the prognostic value of quantitative PET analysis (maximum standard uptake value [SUVmax], total metabolic tumor volume [TMTV] and total lesion glicolysis [TLG]) at baseline (PET0) and iPET in a retrospective cohort of newly diagnosed classical HL. For positive iPET (+ iPET), the reduction of quantitative parameters in relation to PET0 (ΔSUVmax, ΔTMTV and ΔTLG) was calculated. Between 2011 and 2017, 234 patients treated with ABVD were analyzed. Median age was 30 years-old, 59% had advanced stage disease, 57% a bulky mass and 25% a + iPET (DS 4-5). At baseline, high TLG was associated with an increased cumulative incidence of failure (CIF) (p = 0.032) while neither SUVmax, TMTV or TLG were associated with overall survival (OS) or progression-free survival (PFS). In multivariate analysis, only iPET was associated with CIF (p < 0.001). Among ΔSUVmax, ΔTMTV and ΔTLG, only a ΔSUVmax ≥ 68.8 was significant for PFS (HR: 0.31, CI95%: 0.11-0.86, p = 0.024). A subset of patients with improved PFS amongst + iPET was identified by the quantitative (ΔSUVmax ≥ 68.8%) analysis. In this real-world Brazilian cohort, with prevalent high-risk patients, quantitative analysis of PET0 did not demonstrate to be prognostic, while a dynamic approach incorporating the ΔSUVmax to + iPET succeeded in refining a subset with better prognosis. These findings warrant validation in larger series and indicate that not all patients with + iPET might need treatment intensification.

Metabolic tumor volume from baseline [18 F]FDG PET/CT at diagnosis improves the IPI stratification in patients with diffuse large B-cell lymphoma.

PURPOSE: Although several different parameters of PET/CT were reported to be predictive of survival in DLBCL, the best parameter remains to be elucidated and whether it could improve the risk stratification of IPI in patients with DLBCL. PROCEDURES: 262 DLBCL patients including in the training and validation cohort were retrospectively analyzed in this study. RESULTS: Among different parameters, MTV was identified as the optimal prognostic parameter with a maximum area under the curve (AUC) of 0.652 ± 0.112 than TLG and SDmax (0.645 ± 0.113 and 0.600 ± 0.117, respectively). Patients with high MTV were associated with inferior PFS (p < 0.001 and p = 0.021, respectively) and OS (p < 0.001 and p < 0.001, respectively) in both the training and validation cohort. The multivariate analysis revealed that high MTV was an unfavorable factor for PFS (relative ratio [RR], 2.295; 95% confidence interval [CI], 1.457-3.615; p < 0.01) and OS (RR, 2.929; 95% CI 1.679-5.109; p < 0.01) independent of IPI. CONCLUSIONS: Further analysis showed MTV could improve the risk stratification of IPI for both PFS and OS (p < 0.01 and p < 0.01, respectively). In conclusion, our study suggests that MTV was an optimal prognostic parameter of PET/CT for survival and it could improve the risk stratification of IPI in DLBCL, which may help to guide treatment in clinical trial.

New predictors of microvascular invasion for small hepatocellular carcinoma ≤ 3 cm.

BACKGROUND: Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3 cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3 cm. METHODS: We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3 cm who had undergone 18F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort. RESULTS: In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively. CONCLUSIONS: Our scoring system can accurately predict MVI in small HCC ≤ 3 cm, and could contribute to establishing an appropriate treatment strategy.

Usefulness of pre- and post-treatment volumetric PET/CT parameters in predicting prognosis and evaluating recurrence for chemoradiotherapy-treated hypopharyngeal cancer.

OBJECTIVE: To investigate the usefulness of pre- and post-treatment metabolic tumor volume (MTV) obtained from positron emission tomography (PET) in predicting prognosis and evaluating recurrence in patients with hypopharyngeal cancer (HPC). MATERIALS AND METHODS: Forty-three consecutive HPC patients treated with chemoradiotherapy were retrospectively analyzed. Maximum standard uptake value (SUVmax) and MTV of tumor (T) and lymph node (N) were analyzed. RESULTS: On multivariate analysis using pre-treatment parameters, MTV-T (p = 0.049) and MTV-TN (p = 0.043) were significantly associated with local control (LC), and MTV-N (p = 0.049) was significantly associated with disease-specific survival (DSS). Post-treatment MTV-TN was also significantly associated with prognosis (p < 0.001 in LC; p = 0.002 in DSS) and recurrence (area under curve 0.95). Neither pre- nor post-treatment SUVmax was significantly associated with prognosis. CONCLUSION: Pre- and post-treatment MTV appears useful for predicting prognosis and evaluating recurrence.

Baseline metabolic tumour burden improves risk stratification in Hodgkin lymphoma: A Children's Oncology Group study.

The Children's Oncology Group AHOD0831 study used a positron emission tomography (PET) response-adapted approach in high-risk Hodgkin lymphoma, whereby slow early responders (SERs) received more intensive therapy than rapid early responders (RERs). We explored if baseline PET-based characteristics would improve risk stratification. Of 166 patients enrolled in the COG AHOD0831 study, 94 (57%) had baseline PET scans evaluable for quantitative analysis. For these patients, total body metabolic tumour volume (MTV), total lesion glycolysis (TLG), maximum standardized uptake value (SUVmax ) and peak SUV (SUVpeak ) were obtained. MTV/TLG thresholds were an SUV of 2.5 (MTV2.5 /TLG2.5 ) and 40% of the tumour SUVmax (MTV40% /TLG40% ). TLG2.5 was associated with event-free survival (EFS) in the complete cohort (p = 0.04) and in RERs (p = 0.01), but not in SERs (p = 0.8). The Youden index cut-off for TLG2.5 was 1841. Four-year EFS was 92% for RER/TLG2.5  up to 1841, 60% for RER/TLG2.5  greater than 1841, 74% for SER/TLG2.5  up to 1841 and 79% for SER/TLG2.5  greater than 1841. Second EFS for RER/TLG2.5  up to 1841 was 100%. Thus, RERs with a low baseline TLG2.5 experienced excellent EFS with less intensive therapy, whereas RERs with a high baseline TLG2.5 experienced poor EFS. These findings suggest that patients with a high upfront tumour burden may benefit from intensified therapy, even if they achieve a RER.

[18F] AlF-NOTA-FAPI-04 PET/CT can predict treatment response and survival in patients receiving chemotherapy for inoperable pancreatic ductal adenocarcinoma.

PURPOSE: We investigated whether uptake of [18F] AlF-NOTA-FAPI-04 on positron emission tomography/computed tomography (PET/CT) could predict treatment response and survival in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: We prospectively evaluated 47 patients with histopathologically confirmed primary PDAC who provided pretreatment [18F] AlF-NOTA-FAPI-04 scans to detect fibroblast activation protein (FAP) on the tumor surface by uptake of [18F] AlF-NOTA-FAPI-04. PDAC specimens were immunohistochemically stained with cancer-associated fibroblast (CAF) markers. We obtained a second PET scan after one cycle of chemotherapy to study changes in FAPI uptake variables from before to during treatment. Correlations between baseline PET variables and CAF-related immunohistochemical markers were assessed with Spearman's rank test. Cox regression and Kaplan-Meier methods were used to assess relationships between disease progression and potential predictors. Receiver operating characteristic (ROC) curve analysis was used to define the optimal cut-off points for distinguishing patients according to good response vs. poor response per RECIST v.1.1. RESULTS: The FAPI PET variables maximum and mean standardized uptake values (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion FAP expression (TLF) were positively correlated with CAF markers (FAP, α-smooth muscle actin, vimentin, S100A4, and platelet-derived growth factor receptor α/ß, all P < 0.05). MTV was associated with survival in patients with inoperable PDAC (all P < 0.05). Cox multivariate regression showed that MTV was associated with overall survival (MTV hazard ratio [HR] = 1.016, P = 0.016). Greater changes from before to during chemotherapy in SUVmax, MTV, and TLF were associated with good treatment response (all P < 0.05). ΔMTV, ΔTLF, and ΔSUVmax had larger areas under the curve than ΔCA19-9 for predicting treatment response. Kaplan-Meier analysis showed that the extent of change in MTV and TLF from before to after treatment predicted progression-free survival, with cut-off values (based on medians) of - 4.95 for ΔMTV (HR = 8.09, P = 0.013) and - 77.83 for ΔTLF (HR = 4.62, P = 0.012). CONCLUSIONS: A higher baseline MTV on [18F] AlF-NOTA-FAPI-04 scans was associated with poorer survival in patients with inoperable PDAC. ΔMTV was more sensitive for predicting response than ΔCA19-9. These results are clinically meaningful for identifying patients with PDAC who are at high risk of disease progression.

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [Formula: see text]F]FDG PET/CT.

PURPOSE: PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients. METHODS: Automated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [Formula: see text]F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [Formula: see text]F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and lymph node (LN) metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively. RESULTS: In the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) ([Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in cross-validation and [Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in external testing). CONCLUSION: To the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma.

PURPOSE: [18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients. MATERIALS AND METHODS: Whole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1-6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian × 1.1 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 2: liver SUVmedian × 1.5 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 3: liver SUVmedian × 2 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients. RESULTS: BM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of ß2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5. CONCLUSIONS: The automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.

Revisiting the predictive role of 18F-fluorodeoxyglucose positron emission tomography/computerized tomography on treatment outcome in early-stage favorable Hodgkin lymphoma.

In the present manuscript Gallamini et al. comment the results of three large, phase III, randomized clinical trials in early-stage favorable Hodgkin Lymphoma (HL), aimed at exploring the non-inferiority of ABVD chemotherapy alone compared to combined-modality treatment with ABVD and Involved Field/Node Radiotherapy (INRT). The authors also report the preliminary results of risk-stratification in the first 60 enrolled patients in the phase 2, prospective, international study RAFTING: RAdiotherapy Free Treatment IN Good-prognosis early-stage HL (National Trial Identifier 04866654). In this trial patients are stratified, before treatment onset, according to the risk of therapy failure in a single patient basis, taking into account non only the results of interim and End-of-Therapy PET, but also the value of new metrics extracted from the baseline PET/CT such as the Large Nodal Mass (LNM) and Total Metabolic Tumor Volume (TMTV). Treatment intensity, consisting in ABVD chemotherapy, INRT and Nivolumab maintenance, is modulated on the presence/absence of the above factors, in a personalized-medicine approach. The most frequently detected factors driving treatment intensity were LNM and TMTV, while the results of interim and end-of-treatment PET were also determinant, albeit in a lower percentage of cases.

Prognostic value of baseline metabolic tumor volume (MTV) for forecasting chemotherapy outcome in early-stage unfavorable Hodgkin lymphoma: Data from the phase III HD17 trial.

OBJECTIVES: The prognostic relevance of metabolic tumor volume (MTV) having recently been demonstrated in patients with early-stage favorable and advanced-stage Hodgkin lymphoma. The current study aimed to assess the potential prognostic value of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in early-stage unfavorable Hodgkin lymphoma patients treated within the German Hodgkin Study Group HD17 trial. METHODS: 18 F-FDG PET/CT images were available for MTV analysis in 154 cases. We used three different threshold methods (SUV2.5 , SUV4.0 , and SUV41% ) to calculate MTV. Receiver-operating-characteristic analysis was performed to describe the value of these parameters in predicting an adequate therapy response. Therapy response was evaluated as PET negativity after 2 cycles of eBEACOPP followed by 2 cycles of ABVD. RESULTS: All three threshold methods analyzed for MTV showed a positive correlation with the PET response after chemotherapy. Areas under the curve (AUC) were 0.70 (95% CI 0.53-0.87) and 0.65 (0.50-0.80) using the fixed thresholds of SUV4.0 and SUV2.5 , respectively, for MTV- calculation. The calculation of MTV using a relative threshold of SUV41% showed an AUC of 0.63 (0.47-0.79). CONCLUSIONS: MTV does have predictive value after chemotherapy in early-stage unfavorable Hodgkin lymphoma, particularly when the fixed threshold of SUV4.0 is used for MTV calculation. TRIAL REGISTRATION: ClinicalTrials.gov NCT01356680.

A novel analytical approach for outcome prediction in newly diagnosed NSCLC based on [18F]FDG PET/CT metabolic parameters, inflammatory markers, and clinical variables.

OBJECTIVES: To develop a novel analytical approach based on 18F-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) metabolic parameters, serum inflammatory markers, and clinical variables to improve the outcome prediction in NSCLC. METHODS: A total of 190 newly diagnosed NSCLC patients who underwent pretreatment [18F]FDG PET/CT were retrospectively enrolled and divided into a training cohort (n = 127) and a test cohort (n = 63). Cox regression analysis was used to investigate the predictive values of PET metabolic parameters, inflammation markers, and clinical variables for progression-free survival (PFS) and overall survival (OS). Based on the results of multivariate analysis, PET-based, clinical, and combined models were constructed. The predictive performance of different models was evaluated using time-dependent ROC curve analysis, Harrell concordance index (C-index), calibration curve, and decision curve analysis. RESULTS: The combined models incorporating SULmax, MTV, NLR, and ECOG PS demonstrated significant prognostic superiority over PET-based models, clinical models, and TNM stage in terms of both PFS (C-index: 0.813 vs. 0.786 vs. 0.776 vs. 0.678, respectively) and OS (C-index: 0.856 vs. 0.792 vs. 0.781 vs. 0.674, respectively) in the training cohort. Similar results were observed in the test cohort for PFS (C-index: 0.808 vs. 0.764 vs. 0.748 vs. 0.679, respectively) and OS (C-index: 0.836 vs. 0.785 vs. 0.726 vs. 0.660, respectively) prediction. The combined model calibrated well in two cohorts. Decision curve analysis supported the clinical utility of the combined model. CONCLUSIONS: We reported a novel analytical approach combining PET metabolic information with inflammatory biomarker and clinical characteristics, which could significantly improve outcome prediction in newly diagnosed NSCLC. KEY POINTS: • The nomogram incorporating SULmax, MTV, NLR, and ECOG PS outperformed the TNM stage for outcome prediction in patients with newly diagnosed NSCLC. • The established nomogram could provide refined prognostic stratification.

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.