Pet vcar

PET/CT scans were performed as previously described (14 (link)). A GE ADW4.6 workstation (GE Healthcare, Waukesha, WI, USA) was used to display images, which were interpreted by two experienced nuclear medicine physicians. For the semi-quantitative analysis, the threshold of the volumes of interest (VOIs) was set at 0.5 by PETVCAR (GE Healthcare). The maximum SUV (SUVmax) was defined as the value of the highest pixel and average SUV (SUVmean) as the mean SUV related to the tumor burden. To determine the peak SUV corrected for lean body mass (SULpeak), the reviewer placed a sphere or cube as the VOI around the hottest lesions (up to five lesions, no more than two per organ). Within this VOI, the software searched for the 1.0-cm³ sphere that encompassed the voxels with the highest average SUL. For background activity, a 3-cm-diameter spherical VOI was delineated in the right lobe of the liver or in the descending thoracic aorta for patients with liver involvement. Response of SULpeak (%) was defined as (sum of baseline SULpeak—sum of follow-up SULpeak)/(sum of baseline SULpeak) × 100. Target lesions on follow-up scans were not necessarily the same as target lesions at baseline (13 (link)).

All the PET/CT images, relevant MRI, and related contrast-enhanced CT images were reviewed using PET VCAR with Integrated Registration, a component of the Advantage Workstation (version 4.6, GE Healthcare, Waukesha, WI, USA).
Segmentation of lesions was performed by two clinical radiologists with over five years of experience. The volume of interest (VOI) was checked by radiology and nuclear medicine physicians with a career in oncological PET/CT interpretation over ten years.
Segmentation of PET volumes was based on the iterative image thresholding method (ITM), which yielded reliable PET volume estimation as previously reported [17 ]. Relevant MRI and contrast-enhanced CT were used as the reference to adjust the edge of VOIs manually. VOIs were saved and exported as the radiotherapy structure set (RTSS).

The manual segmentation of lesions was performed using semi-automatic software (PET VCAR, General Electrics^®). A volume of interest was set around each lesion on the PET images. Each FDG-avid lesion was subjected to segmentation, which allowed calculation of the SUVmax (maximum standardized uptake value) and the MTV (metabolic tumor volume). The SUVmax was measured by using a volume of interest with Standardized Uptake Value (SUV) being expressed using the following definition of SUV (g/mL) = (Tissue activity (Bq/mL)/[(injected activity (Bq)/body weight (g)]). The SUVmax represents the voxel with the highest intensity of uptake in each lesion and reflects the glucose avidity of the lesion. The MTV of each lesion, representing the volume measured in the volume of interest, was determined using margin thresholds set at 42% of the SUVmax, as recommended by the European Association of Nuclear Medicine [33 (link)]. If necessary, a manual adjustment of the segmentations was performed by adapting the intensity threshold. MTV is complementary to SUVmax as it integrates information from voxels of the whole lesion. Progression of lesions was defined as the appearance of a new lesion or an increase of at least 20% of the sum of diameters of a pre-existing lesion on the reference PET/CT, in comparison with an ¹⁸FDG PET/CT or CT scan undertaken during the 12 previous months.

The PET/CT images were analyzed by two radiologists blinded to the clinical and pathological results, (Reader 1, P.X and Reader 2, C.G with 10- and 15-years’ experience in the interpretation of PET/CT images, respectively). The metabolic parameters were measured by drawing a region-of-interest (ROI) on the axial PET image based on a threshold of 40% of SUVmax using commercial software (PET VCAR; GE Healthcare, USA). Any disagreement was resolved by consensus. SUVmax was defined at the highest value on one pixel with the highest counts within the ROI (21 (link)).

The PET, CT, and fused PET/CT images of each patient were independently reviewed and interpreted by three experienced nuclear medicine physicians. The FDG-avid esophageal tumor detected by PET was fused to the corresponding lesion and anatomical location on CT scan. Standardized uptake values (SUVs) were used as the metric for metabolic tumor quantification with ¹⁸F-FDG PET. The SUV was measured semi-automatically by SUV tools obtained in the Xeleris software (Version 4.0) as follows: SUV = activity in the region of interest (Bq/g)/(injected dose(Bq)/body weight (g)). The SUVmax is the highest SUV detected for the tumor. The metabolic tumor volume (MTV; cm³) is defined as total tumor volume with an SUV of ≥2.5. Total lesion glycolysis (TLG; g/mL cm³) is calculated by multiplying MTV by SUVmean of the delineated tumor. The PET VCAR (volume computer assisted reading; GE Healthcare) was used for imaging analysis. After drawing a cuboid volume of interest (VOI) covering the tumor, the software then automatically drew the FDG uptake of tumor margin according to the specific SUV threshold. MTV and TLG then were automatically computed and measured by the PET VCAR application (Advanced workstation 4.4, GE Medical System, Milwaukee, WI, USA).

Concerning the routine cohort, 430 patients referred for cancer assessment underwent routine thoraco-abdomino-pelvic or whole body PET/CT (according to the indication), and with at least one tumoral uptake, were included between August 2018 and February 2020.
PET/CT scans were acquired on GE 710 (General Electric, Milwaukee, WI, USA) or Biograph Vision 600 (Siemens Healthineers, Knoxville, TN, USA). Patients fasted for at least 4 h and were injected with FDG at a dose of 3.0 MBq/kg of body weight. Images were acquired 60 min after injection at 2 min per bed position (GE 710) or by continuous bed motion (Biograph Vision).
The manual segmentation of lesions was performed using another semiautomatic software (PET VCAR, General Electric®) during routine clinical activity by two different nuclear medicine physicians (PD and PP). A volume of interest was set around each lesion on the PET images according to an adaptive thresholding (28 (link)), manually adapted if necessary according to medical advice. After the database was gathered, a second reading was done in order to check and confirm the suspicious character of the different segmented foci. These values were added to compute the TMTV.
Data of the two cohorts of patients are summarized in Table 1.