A statistically optimized regional thresholding method (SORT) for bone lesion detection in ¹⁸F-NaF PET/CT imaging

Abstract

Identification of individual lesions on 18 F-NaF PET bone scans is a time-consuming and often subjective process that makes accurate characterization of disease burden challenging. Current automated methods either underestimate disease or struggle with high false positive rates. We developed a statistically optimized regional thresholding (SORT) method that optimizes detection of bone lesions.This study assessed 18 F-NaF PET/CT scans of 37 bone metastatic prostate cancer patients. Each PET image was divided into 19 skeletal regions. Areas of disease in each skeletal region were identified by an experienced nuclear medicine physician. A region of interest (ROI) was placed at each disease location and local maxima were extracted for both healthy and diseased ROIs. Secondary physician review was performed after identification of suspicious local maxima. Region-specific SUV thresholds were determined based on receiver operating characteristic (ROC) analysis optimized for detection of malignant disease. The detection performance of the SORT thresholds were compared to commonly used SUV > 10 g ml −1 (SUV 10 ) and SUV > 15 g ml −1 (SUV 15 ) global thresholds. The sensitivity of the SORT thresholds to various factors was evaluated, such as the number of subjects evaluated or image reconstruction settings.1751 lesions were manually identified by the nuclear medicine physician. SORT identified different thresholds in each skeletal region (SUV range: 3-13 g ml −1 ). Region-specific SORT thresholding resulted in higher sensitivity (95.8%) than commonly used global thresholds (82.8% for SUV 10 and 58.4% for SUV 15 ) while maintaining a high specificity (97.1%, compared to 97.3% for SUV 10 and 100.0% for SUV 15 ). Factors, such as reconstruction settings, had minimal impact on threshold optimization, resulting in an average change of 10% (range: 2%-17%) in thresholds for each factor.Region-specific SUV thresholding of NaF PET images for bone lesion detection in metastatic prostate patients was found to be superior to current global thresholding methods.

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

“…( 98 ) conducted a similar analysis on 18 F-NaF PET/CT images in a cohort of 37 mCRPC subjects. Bone lesions were delineated by an automated algorithm that determines the lesion boundaries based on statistically optimised regional thresholding (SORT) which uses a different threshold based on the location of the lesion in the patients skeleton ( 123 ), which were subsequently assigned a classification label by a nuclear medicine physician between 0 and 5 depending on the likelihood of malignancy (0; background ROI, 1; Definite Benign, 5; Definite Malignant). Radiomics features were extracted from both the PET and CT images and used as the input for ML analysis with nine separate learning methods, where the random forest model performed the best under 10-fold cross-validation conditions at discriminating between the 0 + 1 vs. 5 class labels (AUC = 0.95, 95% CI 0.93 – 0.96).…”

Prostate cancer GTV delineation with biparametric MRI and 68Ga-PSMA-PET: comparison of expert contours and semi-automated methods

“…9 However, a wide range of cut-off values for this purpose is present in the literature: ADC values from approximately 700-1300 mm 2 /s x 10 −6 10-17 and %SUV max values from 20-50%, 18,19,25,26,29 have been variably suggested. Similarly to the issue of optimising image scaling for manual delineations, experimental factors such as acquisition parameters -b-values in the case of DWI, and other reconstruction factors in PET 30 -significantly impact the accuracy of derived thresholds. Specific to 68 Ga-PSMA, intravesicular accumulation of radioligand may obscure lesions at the prostate base and complicate approaches based on %SUV max .…”

Optimal 68Ga-PSMA and 18F-PSMA PET window levelling for gross tumour volume delineation in primary prostate cancer

“…In addition, future research on a larger cohort should consider whether certain tumour or imaging characteristics in high-intermediate and high risk patients are correlated with discreetly varying optimal window levels as we still observe variations in accuracy in our cohort using the trained window levels. Furthermore, special attention should be paid to the method of image acquisition as it has been demonstrated that several reconstruction parameters may impact the choice of an optimal threshold for threshold-based contouring and window level setting [19].…”