How Often Do We Fail to Classify the Treatment Response with [18F]FDG PET/CT Acquired on Different Scanners? Data from Clinical Oncological Practice Using an Automatic Tool for SUV Harmonization

Radioguided surgery with β decay: A feasibility study in cervical cancer

PURPOSE

Radioguided surgery (RGS) is a technique that helps the surgeon to achieve a tumour resection as complete as possible, by means of the intraoperative detection of particles emitted by a radiotracer that bounds to tumoural cells. This study aimed to investigate the applicability of β-RGS for tumour resection and margin assessment in cervical cancer patients preoperatively injected with [18F]FDG, by means of Monte Carlo simulations.

METHODS

Patients were retrospectively included if they had a recurrent or persistent cervical cancer, underwent preoperative PET/CT to exclude distant metastases and received radical surgery. All PET/CT images were analysed extracting tumour SUVmax, background SUVmean and tumour-to-non-tumour ratio. These values were used to obtain the expected count rate in a realistic surgical scenario by means of a Monte Carlo simulation of the β probe, assuming the injection of 2 MBq/kg of [18F]FDG 60 min before surgery.

RESULTS

Thirty-eight patients were included. A measuring time of ∼2-3 s is expected to be sufficient for discriminating the tumour from background in a given lesion, being this the time the probe has to be over the sample in order to be able to discriminate tumour from healthy tissue with a sensitivity of ∼99% and a specificity of at least 95%.

CONCLUSION

This study presents the first step towards a possible application of our β-RGS technique in cervical cancer. Results suggest that this approach to β-RGS could help surgeons distinguish tumour margins from surrounding healthy tissue, even in a setting of high radiotracer background activity.

Harmonization of standard uptake values across different positron emission tomography/computed tomography systems and different reconstruction algorithms: validation in oncology patients

BACKGROUND

EQ.PET is a software package that overcomes the reconstruction-dependent variation of standard uptake values (SUV). In this study, we validated the use of EQ.PET for harmonizing SUVs between different positron emission tomography/computed tomography (PET/CT) systems and reconstruction algorithms.

METHODS

In this retrospective study, 49 patients with various cancers were scanned on a Biograph mCT (mCT) or Gemini TF 16 (Gemini) after [¹⁸F]FDG injections. Three groups of patient data were collected: Group 1, patients scanned on mCT or Gemini with data reconstructed using two parameters; Group 2, patients scanned twice on different PET scanners (interval between two scans, 68.9 ± 41.4 days); and Group 3, patients scanned twice using mCT with data reconstructed using different algorithms (interval between two scans, 109.5 ± 60.6 days). The SUVs of the lesions and background were measured, and the tumor-to-background ratios (TBRs) were calculated. In addition, the consistency between the two reconstruction algorithms and confounding factors were evaluated.

RESULTS

In Group 1, the consistency of SUV and TBR between different reconstruction algorithms improved when the EQ.PET filter was applied. In Group 2, by comparing ΔSUV, ΔSUV%, ΔTBR, and ΔTBR% with and without the EQ.PET, the results showed significant differences (P < 0.05). In Group 3, Bland-Altman analysis of ΔSUV with EQ.PET showed an improved consistency relative to that without EQ.PET.

CONCLUSIONS

EQ.PET is an efficient tool to harmonize SUVs and TBRs across different reconstruction algorithms. Patients could benefit from the harmonized SUV, ΔSUV, and ΔSUV% for therapy responses and follow-up evaluations.

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.

Harmonized pretreatment quantitative volume-based FDG-PET/CT parameters for prognosis of stage I-III breast cancer: Multicenter study

OBJECTIVES

This study investigated harmonized pretreatment volume-based quantitative FDG-PET/CT parameters in breast cancer patients for prognostic value.

RESULTS

During a median overall follow-up period of 5.3 years, 91 patients had recurrence and 40 died. Multivariate analysis of ER-positive/HER2-negative patients showed high maximum standardized uptake value (SUVmax) (p = 0.018), high total lesion glycolysis (TLG) (p = 0.010), and clinical N-classification (p = 0.0027) as independent negative predictors of RFS, while high maximum SUVmax (p = 0.037), advanced clinical T-classification (p = 0.030), and advanced TNM stage (p = 0.0067) were independent negative predictors of OS. For recurrence and death in HER2-positive patients, high total TLG (p = 0.037, p = 0.0048, respectively) and advanced TNM stage (p = 0.048, p = 0.046, respectively) were independent prediction factors. In the triple-negative group, independent factors related to recurrence and death were high maximum SUVmax (p = 0.0014, p = 0.0003, respectively) and advanced TNM stage (p < 0.0001, p < 0.0001, respectively).

MATERIALS AND METHODS

Records of 546 stage I-III invasive breast cancer patients, including 344 estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative, 110 HER2-positive, and 92 triple-negative cases, treated at four institutions were reviewed retrospectively. Harmonized primary tumor and nodal maximum SUVmax, metabolic tumor volume (MTV), and TLG indicated in pretreatment FDG-PET/CT results were analyzed. Evaluations of relationships of clinicopathological factors, volume-based quantitative parameters, recurrence-free survival (RFS), and overall survival (OS) for each subtype were performed with a Cox proportional hazards model and log-rank test.

CONCLUSIONS

The results indicated that potential surrogate markers for prognosis in patients with the three main subtypes of operable breast cancer include harmonized pretreatment quantitative volume-based FDG-PET/CT parameters, particularly whole-lesion SUVmax and TLG.

Impact of scanner harmonization on PET-based treatment response assessment in metastatic melanoma

Patients with metastatic melanoma often receive ¹⁸F-FDG PET/CT scans on different scanners throughout their monitoring period. In this study, we quantified the impact of scanner harmonization on longitudinal changes in PET standardized uptake values using various harmonization and normalization methods, including an anthropomorphic PET phantom. Twenty metastatic melanoma patients received at least two FDG PET/CT scans, each on two different scanners with an average of 4 months (range: 2-8) between. Scans from a General Electric (GE) Discovery 710 PET CT^-1 were harmonized to the GE Discovery VCT using image reconstruction settings matching recovery coefficients in an anthropomorphic phantom with bone equivalent inserts and wall-less synthetic lesions. In patient images, SUV_max was measured for each melanoma lesion and time-point. Lesions were classified as progressing, stable, or responding based on pre-defined threshold of ±30% change in SUV_max. For comparison, harmonization was also performed using simpler methods, including harmonization using a NEMA phantom, post-reconstruction filtering, reference region normalization of SUV_max, and use of SUV_peak instead of SUV_max. In the 20 patients, 90 lesions across two time-points were available for treatment response assessment. Treatment response classification changed in 47% (42/90) of cases after harmonization with anthropomorphic phantom. Before harmonization, 37% (33/90) of the lesions were classified as stable (changing less than 30% between two time-points), while the fraction of stable lesions increased to 58% (52/90) after harmonization. Harmonization with the NEMA phantom agreed with harmonization with the anthropomorphic phantom in 91% (82/90) of cases. Post-reconstruction filtering agreed with anthropomorphic phantom-based harmonization in 83% (75/90) cases. The utilization of reference regions for normalization or SUV_peak was unable to correct for changes as identified by the anthropomorphic phantom-based harmonization. Overall, PET scanner harmonization has a major impact on individual lesion treatment response classification in metastatic melanoma patients. Harmonization using the NEMA phantom yielded similar results to harmonization using anthropomorphic phantom, while the only acceptable post-reconstruction technique was post-reconstruction filtering. Phantom-based harmonization is therefore strongly recommended when comparing lesion uptake across time-points when the images have been acquired on different PET scanners.