End of induction [18F]FDG PET is prognostic for progression-free survival and overall survival in follicular lymphoma patients enrolled in the FOLL12 trial.

PURPOSE: To evaluate the reliability of the Deauville score (DS) in therapy response assessment and to define the prognostic value of the metabolic response of end of induction (EOI) [18F]FDG PET (PET) in follicular lymphoma patients. METHODS: Adult patients with untreated grade 1-3a FL/ stage II-IV enrolled in the multicentre, prospective, phase III FOLL12 trial (NCT02063685) were randomized to receive standard immunochemotherapy followed by rituximab maintenance (standard arm) versus standard immunochemotherapy followed by response-adapted post-induction management (experimental arm). Baseline and EOI PET were mandatory for the study. All PET scans were centralized on the WIDEN® platform and classified according to DS in a blind independent central review. DS1-3 was considered negative (CMR), whereas DS4-5 was considered positive (not CMR). The primary endpoint was PFS. The main secondary endpoint was overall survival (OS). RESULTS: Overall, 807 follicular lymphoma patients-52% women, 89% stage III-IV disease, 40% with a high-risk FLIPI-2 score (3-5)-were enrolled in the study; 729 (90.4%) baseline and EOI PET were available for the analysis. EOI PET was positive (DS4-5) in 88/729 (12.1%) cases. Overall inter-reviewer agreement on PET pos/neg result was 0.92, while agreement on positive and negative cases was 0.77 and 0.94, respectively. The median follow-up was 69 months; 247 events were registered in the 5-yr follow-up, with a 5-yr PFS of 67% (95%CI: 63%-70%). The 5-yr PFS rate for PET neg (DS1-3) and PET pos (DS4-5) patients was 71% (95%CI: 67%-75%) and 36% (95%CI: 25%-46%), respectively, with HR 3.49 (95%CI: 2.57-4.72). Five-year PFS was worse as DS increased, with 74% (70%-78%), 58% (48%-67%; HR 1.71; p = 0.001)] and 36% (25%-46%; HR 3.88; p < 0.001) in DS1-2, DS3 and DS4-5, respectively. EOI PET maintained its prognostic value in both the standard and experimental arms. In the whole population, 5-yr OS was 94% (95%CI: 92%-96%), with 96% (95%CI: 94-97) and 82% (95%CI: 72%-89%) in EOI PET negative (DS1-3) and positive (DS4-5), respectively (HR 4.48; p < 0.001). When DS was associated with FLIPI-2, patients with DS3 or DS1-2 with high FLIPI-2 (3-5) experienced worse OS than patients with DS1-2 and low FLIPI-2 (1-2) (p = 0.003). CONCLUSION: This study shows that DS is a reliable prognostic tool to evaluate EOI PET in follicular lymphoma patients, with prognostic value maintained both in the standard and experimental arms, making metabolic imaging a robust tool to assess response in FL. Moreover, although preliminary, this study provides further information on DS3 patients, who are considered as CMR but show a less favourable PFS than DS1-2 patients.

Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness.

PURPOSE: to investigate the preoperative role of ML-based classification using conventional 18F-FDG PET parameters and clinical data in predicting features of EC aggressiveness. METHODS: retrospective study, including 123 EC patients who underwent 18F-FDG PET (2009-2021) for preoperative staging. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were computed on the primary tumour. Age and BMI were collected. Histotype, myometrial invasion (MI), risk group, lymph-nodal involvement (LN), and p53 expression were retrieved from histology. The population was split into a train and a validation set (80-20%). The train set was used to select relevant parameters (Mann-Whitney U test; ROC analysis) and implement ML models, while the validation set was used to test prediction abilities. RESULTS: on the validation set, the best accuracies obtained with individual parameters and ML were: 61% (TLG) and 87% (ML) for MI; 71% (SUVmax) and 79% (ML) for risk groups; 72% (TLG) and 83% (ML) for LN; 45% (SUVmax; SUVmean) and 73% (ML) for p53 expression. CONCLUSIONS: ML-based classification using conventional 18F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.

The "digital biopsy" in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of [18F]FDG PET/CT.

PURPOSE: The present pilot study investigates the putative role of radiomics from [18F]FDG PET/CT scans to predict PD-L1 expression status in non-small cell lung cancer (NSCLC) patients. METHODS: In a retrospective cohort of 265 patients with biopsy-proven NSCLC, 86 with available PD-L1 immunohistochemical (IHC) assessment and [18F]FDG PET/CT scans have been selected to find putative metabolic markers that predict PD-L1 status (< 1%, 1-49%, and ≥ 50% as per tumor proportion score, clone 22C3). Metabolic parameters have been extracted from three different PET/CT scanners (Discovery 600, Discovery IQ, and Discovery MI) and radiomics features were computed with IBSI compliant algorithms on the original image and on images filtered with LLL and HHH coif1 wavelet, obtaining 527 features per tumor. Univariate and multivariate analysis have been performed to compare PD-L1 expression status and selected radiomic features. RESULTS: Of the 86 analyzed cases, 46 (53%) were negative for PD-L1 IHC, 13 (15%) showed low PD-L1 expression (1-49%), and 27 (31%) were strong expressors (≥ 50%). Maximum standardized uptake value (SUVmax) demonstrated a significant ability to discriminate strong expressor cases at univariate analysis (p = 0.032), but failed to discriminate PD-L1 positive patients (PD-L1 ≥ 1%). Three radiomics features appeared the ablest to discriminate strong expressors: (1) a feature representing the average high frequency lesion content in a spherical VOI (p = 0.009); (2) a feature assessing the correlation between adjacent voxels on the high frequency lesion content (p = 0.004); (3) a feature that emphasizes the presence of small zones with similar grey levels inside the lesion (p = 0.003). The tri-variate linear discriminant model combining the three features achieved a sensitivity of 81% and a specificity of 82% in the test. The ability of radiomics to predict PD-L1 positive patients was instead scarce. CONCLUSIONS: Our data indicate a possible role of the [18F]FDG PET radiomics in predicting strong PD-L1 expression; these preliminary data need to be confirmed on larger or single-scanner series.

18F-FDG PET/CT May Predict Tumor Type and Risk Score in Gestational Trophoblastic Disease.

PURPOSE: The aim of this study was to investigate the role of 18F-FDG PET/CT in predicting pathological prognostic factors, including tumor type and International Federation of Gynecology and Obstetrics (FIGO) score, in gestational trophoblastic disease (GTD). METHODS: Retrospective monocentric study including 24 consecutive patients who underwent to 18F-FDG PET/CT from May 2005 to March 2021 for GTD staging purpose. The following semiquantitative PET parameters were measured from the primary tumor and used for the analysis: maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV) and total lesion glycolisis (TLG). Statistical analysis included Spearman correlation coefficient to evaluate the correlations between imaging parameters and tumor type (nonmolar trophoblastic vs postmolar trophoblastic tumors) and risk groups (high vs low, defined according to the FIGO score), whereas area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the predictive value of the PET parameters. Mann-Whitney U test was used to further describe the parameter's potential in differentiating the populations. RESULTS: SUVmax and SUVmean resulted fair (AUC, 0.783; 95% confidence interval [CI], 0.56-0.95) and good (AUC, 0.811; 95% CI, 0.59-0.97) predictors of tumor type, respectively, showing a low (ρ = 0.489, adjusted P = 0.030) and moderate (ρ = 0.538, adjusted P = 0.027) correlation. According to FIGO score, TLG was instead a fair predictor (AUC, 0.770; 95% CI, 0.50-0.99) for patient risk stratification. CONCLUSIONS: 18F-FDG PET parameters have a role in predicting GTD pathological prognostic factors, with SUVmax and SUVmean being predictive for tumor type and TLG for risk stratification.

Clinical Application of a High Sensitivity BGO PET/CT Scanner: Effects of Acquisition Protocols and Reconstruction Parameters on Lesions Quantification.

AIMS: The aim of this retrospective study was to investigate SUVs variability with respect to lesion size, administered dose, and reconstruction algorithm. BACKGROUND: SUVmax and SUVpeak are influenced by technical factors as count statistics and reconstruction algorithms. OBJECTIVE: To fulfill the aim, we evaluated the SUVs variability with respect to lesion size, administered dose, and reconstruction algorithm (ordered - subset expectation maximization plus point spread function option - OSEM+PSF, regularized Bayesian Penalized Likelihood - BPL) in a 5 - rings BGO PET/CT scanner. METHODS: Discovery IQ scanner (GE Healthcare, Milwaukee, Wisconsin, US) was used for list mode acquisition of 25 FDG patients, 12 injected with 3.7 MBq/kg (Standard Dose protocol - SD) and 13 injected with 1.8 MBq/kg (Low Dose protocol - LD). Each acquisition was reconstructed at different time/FOV with both OSEM+PSF algorithm and BPL using seven different beta factors. SUVs were calculated in 70 lesions and analysed in function of time/FOV and Beta. Image quality was evaluated as a coefficient of variation of the liver (CV - liver). RESULTS: SUVs were not considerably affected by time/FOV. However, SUVs were influenced by beta: differences were higher in small lesions (37% for SUVmax, 15% for SUVpeak) compared to larger ones (14% and 6%). CV - liver ranged from 6% with Beta-500 (LD and SD) to 13% with Beta- 200 (LD). CV - liver of BPL with Beta-350 (optimized for clinical practice in our institution) in LD was lower than CV - liver of OSEM+PSF in SD. CONCLUSION: When a high sensitivity 5 - rings BGO PET/CT scanner is used with the same reconstruction algorithm, quantification by means of SUVmax and SUVpeak is a robust standard compared to the activity and scan duration. However, both SUVs and image quality are influenced by reconstruction algorithms and the related parameters should be considered to obtain the best compromise between detectability, quantification, and noise.

Metabolic Parameters as Biomarkers of Response to Immunotherapy and Prognosis in Non-Small Cell Lung Cancer (NSCLC): A Real World Experience.

Immune-checkpoint inhibitors (ICIs) have been proven to have great efficacy in non-small cell lung cancer (NSCLC) as single agents or in combination therapy, being capable to induce deep and durable remission. However, severe adverse events may occur and about 40% of patients do not benefit from the treatment. Predictive factors of response to ICIs are needed in order to customize treatment. The aim of this study is to evaluate the correlation between quantitative positron emission tomography (PET) parameters defined before starting ICI therapy and responses to treatment and patient outcome. We retrospectively analyzed 92 NSCLC patients treated with nivolumab, pembrolizumab or atezolizumab. Basal PET/computed tomography (CT) scan parameters (whole-body metabolic tumor volume-wMTV, total lesion glycolysis-wTLG, higher standardized uptake volume maximum and mean-SUVmax and SUVmean) were calculated for each patient and correlated with outcomes. Patients who achieved disease control (complete response + partial response + stable disease) had significantly lower MTV median values than patients who had not (progressive disease) (77 vs. 160.2, p = 0.039). Furthermore, patients with MTV and TLG values lower than the median values had improved OS compared to patients with higher MTV and TLG (p = 0.03 and 0.05, respectively). No relation was found between the other parameters and outcome. In conclusion, baseline metabolic tumor burden, measured with MTV, might be an independent predictor of treatment response to ICI and a prognostic biomarker in NSCLC patients.

Combining positron emission tomography/computed tomography, radiomics, and sentinel lymph node mapping for nodal staging of endometrial cancer patients.

OBJECTIVE: To evaluate the combination of positron emission tomography/computed tomography (PET/CT) and sentinel lymph node (SLN) biopsy in women with apparent early-stage endometrial carcinoma. The correlation between radiomics features extracted from PET images of the primary tumor and the presence of nodal metastases was also analyzed. METHODS: From November 2006 to March 2019, 167 patients with endometrial cancer were included. All women underwent PET/CT and surgical staging: 60/167 underwent systematic lymphadenectomy (Group 1) while, more recently, 107/167 underwent SLN biopsy (Group 2) with technetium-99m +blue dye or indocyanine green. Histology was used as standard reference. PET endometrial lesions were segmented (n=98); 167 radiomics features were computed inside tumor contours using standard Image Biomarker Standardization Initiative (IBSI) methods. Radiomics features associated with lymph node metastases were identified (Mann-Whitney test) in the training group (A); receiver operating characteristic (ROC) curves, area under the curve (AUC) values were computed and optimal cut-off (Youden index) were assessed in the test group (B). RESULTS: In Group 1, eight patients had nodal metastases (13%): seven correctly ridentified by PET/CT true-positive with one false-negative case. In Group 2, 27 patients (25%) had nodal metastases: 13 true-positive and 14 false-negative. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of PET/CT for pelvic nodal metastases were 87%, 94%, 93%, 70%, and 98% in Group 1 and 48%, 97%, 85%, 87%, and 85% in Group 2, respectively. On radiomics analysis a significant association was found between the presence of lymph node metastases and 64 features. Volume-density, a measurement of shape irregularity, was the most predictive feature (p=0001, AUC=0,77, cut-off 0.35). When testing cut-off in Group B to discriminate metastatic tumors, PET false-negative findings were reduced from 14 to 8 (-43%). CONCLUSIONS: PET/CT demonstrated high specificity in detecting nodal metastases. SLN and histologic ultrastaging increased false-negative PET/CT findings, reducing the sensitivity of the technique. PET radiomics features of the primary tumor seem promising for predicting the presence of nodal metastases not detected by visual analysis.

The role of positron emission tomography in the assessment of cardiac sarcoidosis.

The myocardium and the cardiovascular system are often involved in patients with sarcoidosis. As therapy should be started as early as possible to avoid complications such as left ventricular dysfunction, a prompt and reliable diagnosis by means of non-invasive tests would be highly warranted. Among other techniques, 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has emerged as a high sensitive tool to detect sites of inflammation before morphological changes are visible to conventional imaging techniques. We therefore aim at summarizing the most relevant findings in the literature on the use of 18F-fluorodeoxyglucose PET in the diagnostic workup of cardiac sarcoidosis and to underline future perspectives.