Interobserver agreement of [68Ga]Ga-PSMA-11 PET/CT images interpretation in men with newly diagnosed prostate cancer

178 Background: Bone Scan Index (BSI) derived from 2D whole-body bone scans is considered an imaging biomarker of bone metastases burden carrying prognostic information. Sodium fluoride (NaF) PET/CT is more sensitive than bone scan in detecting bone changes due to metastases. We aimed to develop a semi-quantitative PET index similar to the BSI for NaF PET/CT imaging and to study its relationship to BSI and overall survival in patients with prostate cancer. Methods: NaF PET/CT and bone scans were analyzed in 48 patients (aged 53-92 years) with prostate cancer. Thoracic and lumbar spines, sacrum, pelvis, ribs, scapulae, clavicles, and sternum were automatically segmented from the CT images, representing approximately 1/3 of the total skeletal volume. Hotspots in the PET images, within the segmented parts in the CT images, were visually classified and hotspots interpreted as metastases were included in the analysis. The PET index was defined as the quotient obtained as the hotspot volume from the PET images divided by the segmented bone tissue volume from the CT images. BSI was automatically calculated using EXINIboneBSI. Results: The correlation between the PET index and BSI was r2= 0.54. The median BSI was 0.39 (IQR 0.08-2.05). The patients with a BSI ≥ 0.39 had a significantly shorter median survival time than patients with a BSI < 0.39 (2.3 years vs. not reached after 5 years). BSI was significantly associated with overall survival (HR 1.13, 95% CI 1.13 to 1.41; p < 0.001), and the C-index was 0.68. The median PET index was 0.53 (IQR 0.02-2.62). The patients with a PET index ≥ 0.53 had a significantly shorter median survival time than patients with a PET index < 0.53 (2.5 years vs. not reached after 5 years). The PET index was significantly associated with overall survival (HR 1.18, 95% CI 1.01 to 1.30; p < 0.001) and C-index was 0.68. Conclusions: PET index based on NaF PET/CT images was correlated to BSI and significantly associated with overall survival in patients with prostate cancer. Further studies are needed to evaluate the clinical value of this novel 3D PET index as a possible future imaging biomarker.

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A randomized trial comparing fluorocholine-PET/CT with conventional imaging in prostate cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.2 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 2-2

Author(s):

Scott Williams ◽

Jean-Mathieu Beauregard ◽

Peter Roselt ◽

Kate Moody ◽

Richard Fisher ◽

...

Keyword(s):

Prostate Cancer ◽

Progressive Disease ◽

Randomised Trial ◽

Whole Body ◽

Conventional Imaging ◽

Newly Diagnosed ◽

First Line ◽

Pet Ct ◽

Significant Difference ◽

Line Imaging

2 Background: We conducted a randomised trial comparing 18Flourocholine-PET/CT (FCH) to Computed Tomography (abdomen and pelvis) plus 99mTc-Whole Body Bone Scan (Conventional Imaging [CIm]) to determine imaging performance in prostate cancer (PC). Methods: This prospective two-arm 1:1 randomised trial enrolled men with newly diagnosed or biochemically recurrent PC to first-line imaging (FLI) with either CIm or FCH. Participants without evidence of metastases proceeded to second-line imaging (SLI) using the alternative imaging strategy. The primary aim was to determine whether FCH was more effective as a FLI approach in changing management. Secondary endpoints included incremental utility of SLI and negative predictive value (NPV) based on progression-free survival (PFS). Australian New Zealand Clinical Trials Registry ACTRN12608000641392. Results: 108 men were enrolled; 44% were for staging of newly-diagnosed PC and median follow-up 43 months. Imaging impacted clinical management in 32.4% of men (95% CI=23.7-42.1%), mostly with FLI (n=30). High-impact management changes occurred in 27.8% (95% CI=16.5-41.6%) of FCH cases compared with 11.1% (95% CI=4.2-22.6%) in the CIm arm (p=0.032). The final management plan was derived using FCH in 98.1% (95% CI = 90.1-100%) of cases and 92.6% (95%CI = 82.1-97.9%) of CIm cases (p=0.242). FLI with FCH showed unequivocally N1 or M1 disease in 22.2% (95% CI = 12-35.6%), and 16.7% (95% CI = 7.9-29.3%; p= 0.531) of CIm cases. The overall NPV for stage TxN0M0 (from all imaging) was 26.3% (95% CI: 13.9 - 41.2%), with no significant difference between arms (p=0.9). For N1M0 cases, the NPV was 14.3% (95% CI: 7.1 - 35.7%). The identification of N1M0 by FCH resulted in a longer time to identification of progressive disease, with a median PFS of 32 months (95% CI=2-68months) compared with 3 months (95% CI=1-16 months) in the CIm N1M0 cohort (p=0.05). Conclusions: FCH-PET/CT identifies more high-clinical-impact lesions than CIm as first-line imaging. All imaging modalities were poor at predicting subsequent progressive disease. Isolated node-positive disease seen with FCH is associated with a longer time to - but similarly high rates of - recurrence, suggesting a lead-time bias. Clinical trial information: ACTRN12608000641392.

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Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study

British Journal of Radiology ◽

10.1259/bjr.20190286 ◽

2019 ◽

Vol 92 (1101) ◽

pp. 20190286 ◽

Cited By ~ 6

Author(s):

Emine Acar ◽

Asım Leblebici ◽

Berat Ender Ellidokuz ◽

Yasemin Başbınar ◽

Gamze Çapa Kaya

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Texture Analysis ◽

Area Under The Curve ◽

Ct Images ◽

Metastatic Lesions ◽

Psma Pet ◽

Pet Ct ◽

Sclerotic Lesions ◽

Psma Expression

Objective:Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated.Methods:We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning.Results:In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions.Conclusions:Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer.Advances in knowledge:Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.

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