Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding

PSMA-PET/CT response after metastasis-directed radiotherapy of bone oligometastases in prostate cancer

OBJECTIVE

Bone metastases are very common in advanced prostate cancer and can sensitively be detected utilizing PSMA-PET/CT. Therefore, our goal was to evaluate the suitability of PSMA-PET/CT-guided metastasis-directed external beam radiotherapy (MDT) as treatment option for patients with biochemical recurrence and oligometastatic bone lesions.

MATERIALS & METHODS

We retrospectively examined 32 prostate cancer patients with biochemical recurrence and PSMA-positive oligometastatic disease limited to the bone (n = 1-3). A total of 49 bone lesions were treated with MDT. All patients received a post-radiotherapy PSMA-PET/CT-Scan. Changes in SUVmax, PSMA-positive tumor volume per lesion and PSA, as well as the correlation between the PET/CT-interval and SUVmax response were calculated.

RESULTS

MDT lead to a SUVmax decrease in 46/49 (94%) of the lesions. The median relative decline of SUVmax was 60.4%, respectively. Based on PSMA-positive lesion volume with a SUV cut-off of 4, 46/49 (94%) of lesions showed complete response, two (4%) partial response and one lesion (2%) was stable on PSMA-PET/CT after MDT. Most of the treated patients (56.3%) showed an initial PSA decline at three months and a PSA nadir of median 0.14 ng/ml after a median time of 3.6 months after MDT. The median relative PSA change at three months after MDT was 3.9%.

CONCLUSION

MDT is a very effective treatment modality for prostate cancer bone oligometastases and lesion response to MDT can be assessed using the (semi-)quantitative parameters SUVmax and PSMA-positive lesion volume with established SUV cut-offs.

Liquid Biopsy and 18F-FDG PET/CT Derived Parameters as Predictive Factors of Osimertinib Treatment in Advanced EGFR-Mutated NSCLC

OBJECTIVES

Despite the outstanding results achieved by osimertinib for the treatment of advanced EGFR-mutated NSCLC, the development of resistance is almost inevitable. While molecular mechanism responsible for osimertinib resistance are being mostly revealed, the definition of predictive biomarkers is crucial in order to identify patients at higher risk of progression and optimize treatment strategy.

MATERIALS AND METHODS

This is a prospective single-center study aimed to assess the potential role of liquid biopsy and 18F-FDG PET/CT derived metabolic parameters as noninvasive predictive biomarkers of osimertinib outcomes in advanced EGFR-mutated NSCLC patients. Patients underwent blood samples for ctDNA analysis at baseline, after 15 days and 1 month (t1) of osimertinib. 18F-FDG PET/CT was performed at baseline and after 1 month of osimertinib.

RESULTS

Seventy-two advanced EGFR-mutated NSCLC patients treated with osimertinib in first (n = 63) and in second-line (n = 9) were prospectively enrolled. Baseline positive shedding status was significantly associated with a shorter progression-free survival (PFS) (9.5 vs. 29.2 months, P = .031). Early metabolic response (MR) led to improved PFS (16.8 vs. 5.5 months, P = .038) and OS (35.2 vs. 15.3 months, P = .047). Early MR was significantly correlated with subsequent radiologic response (P = .010). All 18F-FDG PET/CT baseline parameters were significantly related to baseline EGFR activating mutation allele frequency. Both clearance and no detection of EGFR at t1 were significantly associated with MR (P = .001 and P = .004, respectively).

CONCLUSION

Molecular and 18F-FDG PET/CT derived metabolic parameters might represent a useful tool to predict osimertinib outcome in advanced EGFR-mutated NSCLC patients.

Defining the optimal segmentation method for measuring somatostatin receptor expressing tumor volume on 68 Ga-DOTATATE positron emission tomography/computed tomography to predict prognosis in patients with gastroenteropancreatic neuroendocrine tumors

OBJECTIVE

We aimed to compare different segmentation methods used to calculate prognostically valuable volumetric parameters, somatostatin receptor expressing tumor volume (SRETV), and total lesion somatostatin receptor expression (TLSRE), measured by 68 Ga-DOTATATE PET/CT and to find the optimal segmentation method to predict prognosis.

PATIENTS AND METHODS

Images of 34 patients diagnosed with gastroenteropancreatic neuroendocrine tumor (GEPNET) who underwent 68 Ga-DOTATATE PET/CT imaging were reanalyzed. Four different threshold-based methods (fixed relative threshold method, normal liver background threshold method, fixed absolute standardized uptake value (SUV) threshold method, and adaptive threshold method) were used to calculate SRETV and TLSRE values. SRETV of all lesions of a patient was summarized as whole body SRETV (WB-SRETV) and TLSRE of all lesions of a patient was computed as whole body TLSRE (WB-TLSRE).

RESULTS

WB-SRETVs calculated with all segmentation methods were statistically significantly associated with progression-free survival except WB-SRETV at which was calculated using adaptive threshold method. The fixed relative threshold methods calculated by using 45% (WB-SRETV 45% ) and 60% (WB-SRETV 60% ) of the SUV value as threshold respectively, were found to have statistically significant highest prognostic value (C-index = 0.704, CI = 0.622-0.786, P  = 0.007). Among WB-TLSRE parameters, WB-TLSRE 35% , WB-TLSRE 40% , and WB-TLSRE 50% had the highest prognostic value (C-index = 0.689, CI = 0.604-0.774, P  = 0.008).

CONCLUSION

The fixed relative threshold method was found to be the most effective and easily applicable method to measure SRETV on pretreatment 68 Ga-DOTATATE PET/CT to predict prognosis in GEPNET patients. WB-SRETV 45% (cutoff value of 11.8 cm 3 ) and WB-SRETV 60% (cutoff value of 6.3 cm 3 ) were found to be the strongest predictors of prognosis in GEPNET patients.

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

PURPOSE

This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).

METHODS

We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome.

RESULTS

From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity.

CONCLUSION

Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images.Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics-the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)-to evaluate the agreement between the manual and semi-automatic methods.Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

The Challenge of Single-Photon Emission Computed Tomography Image Segmentation in the Internal Dosimetry of 177Lu Molecular Therapies

The aim of this article is to review the single photon emission computed tomography (SPECT) segmentation methods used in patient-specific dosimetry of 177Lu molecular therapy. Notably, 177Lu-labelled radiopharmaceuticals are currently used in molecular therapy of metastatic neuroendocrine tumours (ligands for somatostatin receptors) and metastatic prostate adenocarcinomas (PSMA ligands). The proper segmentation of the organs at risk and tumours in targeted radionuclide therapy is an important part of the optimisation process of internal patient dosimetry in this kind of therapy. Because this is the first step in dosimetry assessments, on which further dose calculations are based, it is important to know the level of uncertainty that is associated with this part of the analysis. However, the robust quantification of SPECT images, which would ensure accurate dosimetry assessments, is very hard to achieve due to the intrinsic features of this device. In this article, papers on this topic were collected and reviewed to weigh up the advantages and disadvantages of the segmentation methods used in clinical practice. Degrading factors of SPECT images were also studied to assess their impact on the quantification of 177Lu therapy images. Our review of the recent literature gives an insight into this important topic. However, based on the PubMed and IEEE databases, only a few papers investigating segmentation methods in 177Lumolecular therapy were found. Although segmentation is an important step in internal dose calculations, this subject has been relatively lightly investigated for SPECT systems. This is mostly due to the inner features of SPECT. What is more, even when studies are conducted, they usually utilise the diagnostic radionuclide 99mTc and not a therapeutic one like 177Lu, which could be of concern regarding SPECT camera performance and its overall outcome on dosimetry.

Are MTV and TLG Accurate for Quantifying the Intensity of Brown Adipose Tissue Activation?

Recent research has suggested that one novel mechanism of action for anti-obesity medications is to stimulate the activation of brown adipose tissue (BAT). 18FDG PET/CT remains the gold standard for defining and quantifying BAT. SUVmax is the most often used quantification tool in clinical practice. However, this parameter does not reflect the entire BAT volume. As a potential method for precisely evaluating BAT, we have utilised metabolic tumour volume (MTV) and total lesion glycolysis (TLG) to answer the question: Are MTV and TLG accurate in quantifying the intensity of BAT activation? After analysing the total number of oncological 18F-FDG PET/CT scans between 2021-2023, we selected patients with active BAT. Based on the BAT SUVmax, the patients were divided into BAT-moderate activation (MA) vs. BAT-high activation (HA). Furthermore, we statistically analysed the accuracy of TLG and MTV in assessing BAT activation intensity. The results showed that both parameters increased their predictive value regarding BAT activation, and presented a significantly high sensitivity and specificity for the correct classification of BAT activation intensity. To conclude, these parameters could be important indicators with increased accuracy for classifying BAT expression, and could bring additional information about the volume of BAT to complement the limitations of the SUVmax.

Comparison of Multiple Segmentation Methods for Volumetric Delineation of Primary Prostate Cancer with Prostate-Specific Membrane Antigen-Targeted 18F-DCFPyL PET/CT

This study aimed to assess the accuracy of intraprostatic tumor volume measurements on prostate-specific membrane antigen-targeted 18F-DCFPyL PET/CT made with various segmentation methods. An accurate understanding of tumor volumes versus segmentation techniques is critical for therapy planning, such as radiation dose volume determination and response assessment. Methods: Twenty-five men with clinically localized, high-risk prostate cancer were imaged with 18F-DCFPyL PET/CT before radical prostatectomy. The tumor volumes and tumor-to-prostate ratios (TPRs) of dominant intraprostatic foci of uptake were determined using semiautomatic segmentation (applying SUVmax percentage [SUV%] thresholds of SUV30%-SUV70%), adaptive segmentation (using adaptive segmentation percentage [A%] thresholds of A30%-A70%), and manual contouring. The histopathologic tumor volume (TV-Histo) served as the reference standard. The significance of differences between TV-Histo and PET-based tumor volume were assessed using the paired-sample Wilcoxon signed-rank test. The Spearman correlation coefficient was used to establish the strength of the association between TV-Histo and PET-derived tumor volume. Results: Median TV-Histo was 2.03 cm3 (interquartile ratio [IQR], 1.16-3.36 cm3), and median TPR was 10.16%. The adaptive method with an A40% threshold most closely determined the tumor volume, with a median difference of +0.19 (IQR, -0.71 to +2.01) and a median relative difference of +7.6%. The paired-sample Wilcoxon test showed no significant difference in PET-derived tumor volume and TV-Histo using A40%, A50%, SUV40%, and SUV50% threshold segmentation algorithms (P > 0.05). For both threshold-based segmentation methods, use of higher thresholds (e.g., SUV60% or SUV70% and A50%-A70%) resulted in underestimation of tumor volumes, and use of lower thresholds (e.g., SUV30% or SUV40% and A30%) resulted in overestimation of tumor volumes relative to TV-Histo and TPR. Manual segmentation overestimated the tumor volume, with a median difference of +2.49 (IQR, 0.42-4.11) and a median relative difference of +130%. Conclusion: Segmentation of intraprostatic tumor volume and TPR with an adaptive segmentation approach most closely approximates TV-Histo. This information might be used to guide the primary treatment of men with clinically localized, high-risk prostate cancer.

Detection of Liver Lesions in Colorectal Cancer Patients Using 18F-FDG PET/CT Dual-Time-Point Scan Imaging

OBJECTIVE

The aim of this study was to evaluate the diagnostic performance of dual-time-point fluorine-18-fluorodeoxyglucose positron emission computed tomography/computed tomography (18F-FDG PET/CT) compared to conventional early imaging for detecting colorectal liver metastases (CRLM) in colorectal cancer (CRC) patients.

METHODS

One hundred twenty-four consecutive CRC patients underwent dual-time-point imaging scans on a retrospective basis. Histopathological confirmation and/or clinical follow-up were accepted as the gold standard. Standard uptake values (SUV), signal-to-noise ratio (SNR), retention index (RI), tumor-to-normal liver ratio (TNR), and lesion sizes were measured for early and delayed PET scans. The diagnostic performance of early and delayed images was calculated on a per-patient basis and compared using McNemar's test.

RESULTS

Among the 124 patients, 57 (46%) had CRLM, 6 (4.8%) had benign lesions, and 61 (49.2%) had no concerning lesions detected. Smaller CRLM lesions (<5 cm3) showed significantly higher uptake in the delayed scans relative to early imaging (p < 0.001). The SUV and TNR increased significantly in delayed imaging of all metastatic lesions (p < 0.001). The retention index of all CRLM was high (40.8%), especially for small lesions (54.8%). A total of 177 lesions in delayed images and 124 in standard early images were identified. In a per-patient analysis, delayed imaging had significantly higher sensitivity (100% vs. 87.7%) and specificity (91.0% vs. 94.0%) compared to early imaging (p-value = 0.04).

CONCLUSIONS

The detection of liver lesions using dual-time-point PET/CT scan improves the sensitivity and specificity for the detection of colorectal liver metastasis.

Threshold for defining PSMA-positivity prior to 177Lu-PSMA therapy: a comparison of [68Ga]Ga-PSMA-11 and [18F]F-DCFPyL in metastatic prostate cancer

BACKGROUND

In 2022, the American Food and Drug Administration and the European Medicines Agency approved [177Lu]Lu-PSMA-617 (PLUVICTO™, Novartis AG, Basel, Switzerland) for radionuclide therapy with prostate-specific membrane antigen (PSMA) ligands in metastatic prostate cancer. Theranostics require appropriate patients to be identified by positron emission tomography (PET) prior to radionuclide therapy, usually employing [68Ga]Ga-PSMA-11. Alternatively, several 18F-labelled PSMA-PET tracers are available and may increasingly replace 68Ga-labelled compounds, with respect to their image quality, availability and other practical advantages. However, alternative tracers may differ in uptake behaviour, and their comparability with regard to patient selection for [177Lu]Lu-PSMA therapy has not yet been established. Here, we analysed whether tumour-to-background ratios determined by PET using the 18F-labelled PSMA-specific radiopharmaceutical [18F]F-DCFPyL were comparable to those determined by PET using [68Ga]Ga-PSMA-11.

RESULTS

No differences could be observed between [68Ga]Ga-PSMA-11-PET and [18F]F-DCFPyL-PET regarding tumour-to-liver ratios or tumour-to-mediastinum ratios (e. g. tumour-to-liver ratios using maximum SUV of the tumour lesion for ultra-high definition reconstructed PET images with a median of 2.5 (0.6-9.0) on [68Ga]Ga-PSMA-11-PET vs. 2,0 (0.6-11.4) on [18F]F-DCFPyL-PET). However, significant differences were observed in terms of contrast-to-noise ratios, thereby demonstrating the better image quality obtained with [18F]F-DCFPyL-PET.

CONCLUSIONS

Our data showed that [18F]F-DCFPyl-PET and [68Ga]Ga-PSMA-11-PET provide comparable tumour-to-liver and tumour-to-mediastinum ratios. Therefore, a tumour uptake of [18F]F-DCFPyL above the liver background, like using [68Ga]Ga-PSMA-11, can be considered as equally suitable for defining PSMA-positivity by a semiquantitative assessment based on the liver background, e. g. prior to radioligand therapy with 177Lu-labelled PSMA ligands. In addition, our data suggest a tending advantage of [18F]F-DCFPyL in terms of lesion detectability.

Durvalumab and PET-Directed Chemoradiation in Locally Advanced Esophageal Adenocarcinoma: A Phase Ib/II Study

OBJECTIVE

To determine the safety and efficacy of adding the anti-PD-L1 antibody durvalumab to induction FOLFOX and preoperative chemotherapy in locally advanced esophageal adenocarcinoma.

BACKGROUND

Neoadjuvant induction FOLFOX followed by positron emission tomography (PET) directed chemoradiation has demonstrated improved survival for esophageal adenocarcinoma. There is clear benefit now for the addition of immune checkpoint inhibitors both in early and advanced stage disease. Given these results we investigated the safety and efficacy of adding durvalumab to induction FOLFOX and preoperative chemoradiotherapy.

METHODS

Patients with locally advanced resectable esophageal/gastroesophageal junction adenocarcinoma received PET-directed chemoradiation with durvalumab before esophagectomy. Patients who had R0 resections received adjuvant durvalumab 1500 mg every 4 weeks for 6 treatments. The primary endpoint of the study was pathologic complete response.

RESULTS

We enrolled 36 patients, 33 of whom completed all preoperative treatment and underwent surgery. Preoperative treatment was well tolerated, with no delays to surgery nor new safety signals. Pathologic complete response was identified in 8 [22% (1-sided 90% lower bound: 13.3%)] patients with major pathologic response in 22 [61% (1-sided 90% lower bound: 50%)] patients. Twelve and 24-month overall survival was 92% and 85%, respectively.

CONCLUSIONS

The addition of durvalumab to induction FOLFOX and PET-directed chemoradiotherapy before surgery is safe, with a high rate of pathologic response, as well as encouraging survival data.

Gradient-based Volumetric PET Parameters on Immediate Pre-ablation FDG-PET Predict Local Tumor Progression in Patients with Colorectal Liver Metastasis Treated by Microwave Ablation

PURPOSE

This study aimed to evaluate the optimal method of segmentation of colorectal liver metastasis (CLM) on immediate pre-ablation PET scans and assess the prognostic value of quantitative pre-ablation PET parameters with regards to local tumor control. A secondary objective was to correlate the target tumor size estimation by PET methods with the tumor measurements on anatomical imaging.

METHODOLOGY

A prospectively accrued cohort of 55 CLMs (46 patients) treated with real-time ¹⁸F-FDG-PET/CT-guided percutaneous microwave ablation was followed-up for a median of 10.8 months (interquartile: 5.5-20.2). Total lesion glycolysis (TLG) and metabolic tumor volume (MTV) values of each CLM were derived from pre-ablation ¹⁸F-FDG-PET with gradient and threshold PET segmentation methodologies. The event was defined as local tumor progression (LTP). Time-dependent receiver operating characteristic (ROC) curve analyses were used to assess area under the curves (AUCs). Intraclass correlation (ICC) and 95.0% confidence interval (CI) were performed to measure the linear relationships between the continuous variables.

RESULTS

AUCs for prediction of LTP obtained from time-dependent ROC analysis for the gradient technique were higher in comparison to the threshold methodologies (AUCs for TLG and volume were: 0.790 and 0.807, respectively). ICC between PET gradient-based and anatomical measurements were higher in comparison to threshold methodologies (ICC for the longest diameter: 733 (95.0% CI 0.538-0.846), ICC for the shortest diameter: .747 (95.0% CI 0.546-0.859), p-values < 0.001).

CONCLUSIONS

The gradient-based technique had a higher AUC for prediction of LTP after microwave ablation of CLM and showed the highest correlation with anatomical imaging tumor measurements.

Evaluation of thresholding methods for the quantification of [68Ga]Ga-PSMA-11 PET molecular tumor volume and their effect on survival prediction in patients with advanced prostate cancer undergoing [177Lu]Lu-PSMA-617 radioligand therapy

PURPOSE

The aim of this study was to systematically evaluate the effect of thresholding algorithms used in computer vision for the quantification of prostate-specific membrane antigen positron emission tomography (PET) derived tumor volume (PSMA-TV) in patients with advanced prostate cancer. The results were validated with respect to the prognostication of overall survival in patients with advanced-stage prostate cancer.

MATERIALS AND METHODS

A total of 78 patients who underwent [177Lu]Lu-PSMA-617 radionuclide therapy from January 2018 to December 2020 were retrospectively included in this study. [68Ga]Ga-PSMA-11 PET images, acquired prior to radionuclide therapy, were used for the analysis of thresholding algorithms. All PET images were first analyzed semi-automatically using a pre-evaluated, proprietary software solution as the baseline method. Subsequently, five histogram-based thresholding methods and two local adaptive thresholding methods that are well established in computer vision were applied to quantify molecular tumor volume. The resulting whole-body molecular tumor volumes were validated with respect to the prognostication of overall patient survival as well as their statistical correlation to the baseline methods and their performance on standardized phantom scans.

RESULTS

The whole-body PSMA-TVs, quantified using different thresholding methods, demonstrate a high positive correlation with the baseline methods. We observed the highest correlation with generalized histogram thresholding (GHT) (Pearson r (r), p value (p): r = 0.977, p < 0.001) and Sauvola thresholding (r = 0.974, p < 0.001) and the lowest correlation with Multiotsu (r = 0.877, p < 0.001) and Yen thresholding methods (r = 0.878, p < 0.001). The median survival time of all patients was 9.87 months (95% CI [9.3 to 10.13]). Stratification by median whole-body PSMA-TV resulted in a median survival time from 11.8 to 13.5 months for the patient group with lower tumor burden and 6.5 to 6.6 months for the patient group with higher tumor burden. The patient group with lower tumor burden had significantly higher probability of survival (p < 0.00625) in eight out of nine thresholding methods (Fig. 2); those methods were SUVmax50 (p = 0.0038), SUV ≥3 (p = 0.0034), Multiotsu (p = 0.0015), Yen (p = 0.0015), Niblack (p = 0.001), Sauvola (p = 0.0001), Otsu (p = 0.0053), and Li thresholding (p = 0.0053).

CONCLUSION

Thresholding methods commonly used in computer vision are promising tools for the semiautomatic quantification of whole-body PSMA-TV in [68Ga]Ga-PSMA-11-PET. The proposed algorithm-driven thresholding strategy is less arbitrary and less prone to biases than thresholding with predefined values, potentially improving the application of whole-body PSMA-TV as an imaging biomarker.

Automated detection, delineation and quantification of whole-body bone metastasis using FDG-PET/CT images

Non-small cell lung cancer (NSCLC) patients with the metastatic spread of disease to the bone have high morbidity and mortality. Stereotactic ablative body radiotherapy increases the progression free survival and overall survival of these patients with oligometastases. FDG-PET/CT, a functional imaging technique combining positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) and computer tomography (CT) provides improved staging and identification of treatment response. It is also associated with reduction in size of the radiotherapy tumour volume delineation compared with CT based contouring in radiotherapy, thus allowing for dose escalation to the target volume with lower doses to the surrounding organs at risk. FDG-PET/CT is increasingly being used for the clinical management of NSCLC patients undergoing radiotherapy and has shown high sensitivity and specificity for the detection of bone metastases in these patients. Here, we present a software tool for detection, delineation and quantification of bone metastases using FDG-PET/CT images. The tool extracts standardised uptake values (SUV) from FDG-PET images for auto-segmentation of bone lesions and calculates volume of each lesion and associated mean and maximum SUV. The tool also allows automatic statistical validation of the auto-segmented bone lesions against the manual contours of a radiation oncologist. A retrospective review of FDG-PET/CT scans of more than 30 candidate NSCLC patients was performed and nine patients with one or more metastatic bone lesions were selected for the present study. The SUV threshold prediction model was designed by splitting the cohort of patients into a subset of 'development' and 'validation' cohorts. The development cohort yielded an optimum SUV threshold of 3.0 for automatic detection of bone metastases using FDG-PET/CT images. The validity of the derived optimum SUV threshold on the validation cohort demonstrated that auto-segmented and manually contoured bone lesions showed strong concordance for volume of bone lesion (r = 0.993) and number of detected lesions (r = 0.996). The tool has various applications in radiotherapy, including but not limited to studies determining optimum SUV threshold for accurate and standardised delineation of bone lesions and in scientific studies utilising large patient populations for instance for investigation of the number of metastatic lesions that can be treated safety with an ablative dose of radiotherapy without exceeding the normal tissue toxicity.

MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images

In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The proposed MFCNet is verified on both the public dataset (Head and Neck cancer) and the in-house dataset (pancreas cancer). The experimental results indicate that on the public and in-house datasets, the average Dice values of the proposed multi-modal segmentation network are 74.14% and 76.20%, while the average Hausdorff distances are 6.41 and 6.84, respectively. In addition, the experimental results show that the proposed MFCNet outperforms the state-of-the-art methods on the two datasets.

Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images

Accurate lymphoma segmentation in PET/CT images is important for evaluating Diffuse Large B-Cell Lymphoma (DLBCL) prognosis. As systemic multiple lymphomas, DLBCL lesions vary in number and size for different patients, which makes DLBCL labeling labor-intensive and time-consuming. To reduce the reliance on accurately labeled datasets, a weakly supervised deep learning method based on multi-scale feature similarity is proposed for automatic lymphoma segmentation. Weak labeling was performed by randomly dawning a small and salient lymphoma volume for the patient without accurate labels. A 3D V-Net is used as the backbone of the segmentation network and image features extracted in different convolutional layers are fused with the Atrous Spatial Pyramid Pooling (ASPP) module to generate multi-scale feature representations of input images. By imposing multi-scale feature consistency constraints on the predicted tumor regions as well as the labeled tumor regions, weakly labeled data can also be effectively used for network training. The cosine similarity, which has strong generalization, is exploited here to measure feature distances. The proposed method is evaluated with a PET/CT dataset of 147 lymphoma patients. Experimental results show that when using data, half of which have accurate labels and the other half have weak labels, the proposed method performed similarly to a fully supervised segmentation network and achieved an average Dice Similarity Coefficient (DSC) of 71.47%. The proposed method is able to reduce the requirement for expert annotations in deep learning-based lymphoma segmentation.

Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer

Aim

To evaluate the role of fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features (RFs) for predicting clinicopathological factors (CPFs) and prognosis in patients with resected lung squamous cell cancer (LSCC).

Material and Methods

Patients with early-stage (stage I-III) LSCC who underwent 18F-FDG PET/CT before surgical resection between August 2012 and February 2020 were analyzed. Patients who received neoadjuvant chemotherapy or radiotherapy were excluded from the study. The maximum standard uptake value (SUVmax) and RFs were extracted from PET images for primary tumors. The diagnostic performances of PET parameters in groups of tumor differentiation, stage, and mediastinal lymph node metastasis (MLNM) status were evaluated. The study endpoints were overall survival (OS) and progression-free survival (PFS). Univariate and multivariate analyses were performed with RFs, SUVmax, and CPFs to find independent predictors of PFS and OS.

Results

A total of 77 patients (5 female, 72 male) were included in the study. SUVmax and GLCM entropy were independently associated with tumor differentiation. The only parameter with significant diagnostic performance for MLNM was GLZLM-SLZGE. Tumor diameter and NGLDM busyness were independently associated with the stage. MLNM and tumor differentiation were found to be independent predictors of PFS. NGLDM contrast and MLNM were independently associated with OS.

Conclusion

Using radiomic features in addition to CPFs to predict disease recurrence and shorter overall survival can guide precision medicine in patients with LSCC.

Cross modality fusion for modality-specific lung tumor segmentation in PET-CT images

Although positron emission tomography-computed tomography (PET-CT) images have been widely used, it is still challenging to accurately segment the lung tumor. The respiration, movement and imaging modality lead to large modality discrepancy of the lung tumors between PET images and CT images. To overcome these difficulties, a novel network is designed to simultaneously obtain the corresponding lung tumors of PET images and CT images. The proposed network can fuse the complementary information and preserve modality-specific features of PET images and CT images. Due to the complementarity between PET images and CT images, the two modality images should be fused for automatic lung tumor segmentation. Therefore, cross modality decoding blocks are designed to extract modality-specific features of PET images and CT images with the constraints of the other modality. The edge consistency loss is also designed to solve the problem of blurred boundaries of PET images and CT images. The proposed method is tested on 126 PET-CT images with non-small cell lung cancer, and Dice similarity coefficient scores of lung tumor segmentation reach 75.66 ± 19.42 in CT images and 79.85 ± 16.76 in PET images, respectively. Extensive comparisons with state-of-the-art lung tumor segmentation methods have also been performed to demonstrate the superiority of the proposed network.

Computer-aided detection and segmentation of malignant melanoma lesions on whole-body 18F-FDG PET/CT using an interpretable deep learning approach

BACKGROUND AND OBJECTIVE

In oncology, 18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) / computed tomography (CT) is widely used to identify and analyse metabolically-active tumours. The combination of the high sensitivity and specificity from 18F-FDG PET and the high resolution from CT makes accurate assessment of disease status and treatment response possible. Since cancer is a systemic disease, whole-body imaging is of high interest. Moreover, whole-body metabolic tumour burden is emerging as a promising new biomarker predicting outcome for innovative immunotherapy in different tumour types. However, this comes with certain challenges such as the large amount of data for manual reading, different appearance of lesions across the body and cumbersome reporting, hampering its use in clinical routine. Automation of the reading can facilitate the process, maximise the information retrieved from the images and support clinicians in making treatment decisions.

METHODS

This work proposes a fully automated system for lesion detection and segmentation on whole-body 18F-FDG PET/CT. The novelty of the method stems from the fact that the same two-step approach used when manually reading the images was adopted, consisting of an intensity-based thresholding on PET followed by a classification that specifies which regions represent normal physiological uptake and which are malignant tissue. The dataset contained 69 patients treated for malignant melanoma. Baseline and follow-up scans together offered 267 images for training and testing.

RESULTS

On an unseen dataset of 53 PET/CT images, a median F1-score of 0.7500 was achieved with, on average, 1.566 false positive lesions per scan. Metabolically-active tumours were segmented with a median dice score of 0.8493 and absolute volume difference of 0.2986 ml.

CONCLUSIONS

The proposed fully automated method for the segmentation and detection of metabolically-active lesions on whole-body 18F-FDG PET/CT achieved competitive results. Moreover, it was compared to a direct segmentation approach which it outperformed for all metrics.

Physiological background parenchymal uptake of 18F-FDG in normal breast tissues using dedicated breast PET: correlation with mammographic breast composition, menopausal status, and menstrual cycle

OBJECTIVE

This study aimed to quantitatively evaluate the effects of age, mammographic density, menopausal status, and menstrual cycle on background parenchymal uptake (BPU) using ring-shaped dedicated breast positron emission tomography (dbPET).

METHODS

This study included 186 adult women who underwent mammography and dbPET on the same day and had no abnormalities classified as Breast Imaging Reporting and Data System (BI-RADS) category 1 on both examinations. The volume of interest (VOI) was placed in the glandular tissue of both breasts, and the maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), and metabolic breast volume (MBV) were measured as indicators of BPU. We analyzed the correlation between BPU and age, mammographic density, menopausal status, and menstrual cycle.

RESULTS

The SUV_max and SUV_mean for normal breast tissue were inversely correlated with age (both p < 0.001). The SUV_max, SUV_mean, and MBV of mammographically dense breast tissues were significantly higher than those of non-dense breast tissues (all p < 0.001). The SUV_max, SUV_mean, and MBV of normal breast tissue in premenopausal women were significantly higher than those in postmenopausal women (p < 0.001, p < 0.001, p = 0.002, respectively). In the study, 59 premenopausal women, the SUV_max of normal breast tissue in the menstrual-follicular phase was significantly lower than that in the periovulatory-luteal phase (p = 0.02). When we sorted the premenopausal women by mammographic breast composition, the SUV_max and SUV_mean of normal breast tissues in the menstrual-follicular phase were significantly lower than those in the periovulatory-luteal phase in the 44 premenopausal women with dense breasts (p = 0.007, and p = 0.038, respectively), whereas no statistically significant difference was found between the menstrual-follicular phase and the periovulatory-luteal phase in the 15 premenopausal women with non-dense breasts.

CONCLUSIONS

BPU in normal breast tissues assessed using ring-shaped dbPET was associated with mammographic density, menopausal status, and women's menstrual cycle. The menstrual cycle was significantly associated with BPU in premenopausal women with dense breasts but not in women with non-dense breasts.

Positron Emission Tomography Image Segmentation Based on Atanassov’s Intuitionistic Fuzzy Sets

In this paper, we present an approach to fully automate tumor delineation in positron emission tomography (PET) images. PET images play a major role in medicine for in vivo imaging in oncology (PET images are used to evaluate oncology patients, detecting emitted photons from a radiotracer localized in abnormal cells). PET image tumor delineation plays a vital role both in pre- and post-treatment stages. The low spatial resolution and high noise characteristics of PET images increase the challenge in PET image segmentation. Despite the difficulties and known limitations, several image segmentation approaches have been proposed. This paper introduces a new unsupervised approach to perform tumor delineation in PET images using Atanassov’s intuitionistic fuzzy sets (A-IFSs) and restricted dissimilarity functions. Moreover, the implementation of this methodology is presented and tested against other existing methodologies. The proposed algorithm increases the accuracy of tumor delineation in PET images, and the experimental results show that the proposed method outperformed all methods tested.

Clinical Perspectives for 18F-FDG PET Imaging in Pediatric Oncology: Μetabolic Tumor Volume and Radiomics

Pediatric cancer, although rare, requires the most optimized treatment approach to obtain high survival rates and minimize serious long-term side effects in early adulthood. 18F-FDG PET/CT is most helpful and widely used in staging, recurrence detection, and response assessment in pediatric oncology. The well-known 18F-FDG PET metabolic indices of metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) have already revealed an independent significant prognostic value for survival in oncologic patients, although the corresponding cut-off values remain study-dependent and not validated for use in clinical practice. Advanced tumor “radiomic” analysis sheds new light into these indices. Numerous patterns of texture 18F-FDG uptake features can be extracted from segmented PET tumor images due to new powerful computational systems supporting complex “deep learning” algorithms. This high number of “quantitative” tumor imaging data, although not decrypted in their majority and once standardized for the different imaging systems and segmentation methods, could be used for the development of new “clinical” models for specific cancer types and, more interestingly, for specific age groups. In addition, data from novel techniques of tumor genome analysis could reveal new genes as biomarkers for prognosis and/or targeted therapies in childhood malignancies. Therefore, this ever-growing information of “radiogenomics”, in which the underlying tumor “genetic profile” could be expressed in the tumor-imaging signature of “radiomics”, possibly represents the next model for precision medicine in pediatric cancer management. This paper reviews 18F-FDG PET image segmentation methods as applied to pediatric sarcomas and lymphomas and summarizes reported findings on the values of metabolic and radiomic features in the assessment of these pediatric tumors.

A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study

Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied.

Pretherapy 18F-fluorodeoxyglucose positron emission tomography/computed tomography robust radiomic features predict overall survival in non-small cell lung cancer

OBJECTIVE

To extract robust radiomic features from staging positron emission tomography/computed tomography (18F- fluroodeoxyglucose PET/CT) in patients with non-small cell lung cancer from different segmentation methods and to assess their association with 2-year overall survival.

METHODS

Eighty-one patients with stage I-IV non-small cell lung cancer were included. All patients underwent a pretherapy 18F-FDG PET/CT. Primary tumors were delineated using four different segmentation methods: method 1, manual; method 2: manual with peripheral 1 mm erosion; method 3: absolute threshold at standardized uptake value (SUV) 2.5; and method 4: relative threshold at 40% SUVmax. Radiomic features from each method were extracted using Image Biomarker Standardization Initiative-compliant process. The study cohort was divided into two groups (exploratory and testing) in a ratio of 1:2 (n = 25 and n = 56, respectively). Exploratory cohort was used to identify robust radiomic features, defined as having a minimum concordance correlation coefficient ≥0.75 among all the 4-segmentation methods. The resulting texture features were evaluated for association with 2-year overall survival in the testing cohort (n = 56). All patients in the testing cohort had a follow-up for 2 years from the date of staging 18F-FDG PET/CT scan or till death. Cox proportional hazard models were used to evaluate the independent prognostic factors.

RESULTS

Exploratory and validation cohorts were equivalent regarding their basic characteristics (age, sex, and tumor stage). Ten radiomic features were deemed robust to the described four segmentation methods: SUV SD, SUVmax, SUVQ3, SUVpeak in 0.5 ml, total lesion glycolysis, histogram entropy log 2, histogram entropy log 10, histogram energy uniformity, gray level run length matrix-gray level non-uniformity, and gray level zone length matrix-gray level non-uniformity. At the end of 2-year follow-up, 41 patients were dead and 15 were still alive (overall survival = 26.8%; median survival = 14.7 months, 95% confidence interval: 10.2-19.2 months). Three texture features, regardless the segmentation method, were associated with 2-year overall survival: total lesion glycolysis, gray level run length matrix_gray level non-uniformity, and gray level zone length matrix_run-length non-uniformity. In the final Cox-regression model: total lesion glycolysis, and gray level zone length matrix_gray level non-uniformity were independent prognostic factors. The quartiles from the two features were combined with clinical staging in a prognostic model that allowed better risk stratification of patients for overall survival.

CONCLUSION

Ten radiomic features were robust to segmentation methods and two of them (total lesion glycolysis and gray level zone length matrix_gray level non-uniformity) were independently associated with 2-year overall survival. Together with the clinical staging, these features could be utilized towards improved risk stratification of lung cancer patients.

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

PET metabolic tumor volume as a new prognostic factor in childhood rhabdomyosarcoma

PURPOSE

Childhood RMS is a rare malignant disease in which evaluation of tumour spread at diagnosis is essential for therapeutic management. F-18 FDG-PET imaging is currently used for initial RMS disease staging.

MATERIALS AND METHODS

This multicentre retrospective study in six French university hospitals was designed to analyse the prognostic accuracy of MTV at diagnosis for patients with RMS between 1 January 2007 and 31 October 2017, for overall (OS) and progression-free survival (PFS). MTV was defined as the sum of the primitive tumour and the largest metastasis, where relevant, with a 40% threshold of the primary tumour SUVmax. Additional aims were to define the prognostic value of SUVmax, SUVpeak, and bone lysis at diagnosis.

RESULTS

Participants were 101 patients with a median age of 7.4 years (IQR [4.0-12.5], 62 boys), with localized disease (35 cases), regional nodal spread (43 cases), or distant metastases (23). 44 patients had alveolar subtypes. In a univariate analysis, a MTV greater than 200 cm3 was associated with OS (HR = 3.47 [1.79;6.74], p<0.001) and PFS (HR = 3.03 [1.51;6.07], p = 0.002). SUVmax, SUVpeak, and bone lysis also influenced OS (respectively p = 0.005, p = 0.004 and p = 0.007) and PFS (p = 0.029, p = 0.019 and p = 0.015). In a multivariate analysis, a MTV greater than 200 cm3 was associated with OS (HR = 2.642 [1.272;5.486], p = 0.009) and PFS (HR = 2.707 [1.322;5.547], p = 0.006) after adjustment for confounding factors, including SUVmax, SUVpeak, and bone lysis.

CONCLUSION

A metabolic tumor volume greater than 200 cm3, SUVmax, SUVpeak, and bone lysis in the pre-treatment assessment were unfavourable for outcome.

Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge's task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (H&N) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in H&N cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs.

Quantitative Automated Segmentation of Lipiodol Deposits on Cone Beam CT Imaging acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach

PURPOSE

The purpose of this study was to show that a deep learning-based, automated model for Lipiodol segmentation on CBCT after cTACE performs closer to the "ground truth segmentation" than a conventional thresholding-based model.

MATERIALS & METHODS

This post-hoc analysis included 36 patients with a diagnosis of HCC or other solid liver tumor who underwent cTACE with an intra-procedural CBCT. Semi-automatic segmentation of Lipiodol were obtained. Then, a convolutional U-net model was used to output a binary mask that predicts Lipiodol deposition. A threshold value of signal intensity on CBCT was used to obtain a Lipiodol mask for comparison. Dice similarity coefficient (DSC), Mean-squared error (MSE), and Center of Mass (CM), and fractional volume ratios for both masks were obtained by comparing them to the ground truth (radiologist segmented Lipiodol deposits) to obtain accuracy metrics for the two masks. These results were used to compare the model vs. the threshold technique.

RESULTS

For all metrics, the U-net outperformed the threshold technique: DSC (0.65±0.17 vs. 0.45±0.22,p<0.001) and MSE (125.53±107.36 vs. 185.98±93.82,p=0.005). Difference between the CM predicted, and the actual CM was (15.31±14.63mm vs. 31.34±30.24mm,p<0.001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22±0.84vs.2.58±3.52,p=0.048 for our model's prediction and threshold technique, respectively.

CONCLUSION

This study showed that a deep learning framework could detect Lipiodol in CBCT imaging and was capable of outperforming the conventionally used thresholding technique over several metrics. Further optimization will allow for more accurate, quantitative predictions of Lipiodol depositions intra-procedurally.

Prognostic value of [18F]FDG PET/CT in patients with CNS lymphoma receiving ibrutinib-based therapies

PURPOSE

Current clinical and imaging tools remain suboptimal for predicting treatment response and prognosis in CNS lymphomas. We investigated the prognostic value of baseline [18F]FDG PET in patients with CNS lymphoma receiving ibrutinib-based treatments.

METHODS

Fifty-three patients enrolled in a prospective clinical trial and underwent brain PET before receiving single-agent ibrutinib or ibrutinib in combination with methotrexate with or without rituximab. [18F]FDG uptake in these lesions was quantified by drawing PET volumes of interest around up to five [18F]FDG-avid lesions per patient (with uptake greater than surrounding brain). We measured standardized uptake values (SUVmax), metabolic tumor volumes, total lesion glycolysis (TLG), and the sum thereof in these lesions. We analyzed the relationship between PET parameters and mutation status, overall response rates, and progression-free survival (PFS).

RESULTS

Thirty-eight patients underwent single-agent therapy and 15 received combination therapy. On PET, 15/53 patients had no measurable disease. In the other 38 patients, a total of 71 lesions were identified on PET. High-intensity [18F]FDG uptake and a larger volume of [18F]FDG-avid disease were inversely related to treatment outcome (p ≤ 0.005). In univariable analysis, PFS was linearly correlated with all PET parameters, with stronger association when sum-values were used. A multivariable model showed that risk of progression increased by 9% for every 5-unit increase in sumSUVmax (hazard ratio = 1.09 [95% CI: 1.04 to 1.14]).

CONCLUSION

Higher lesional metabolic parameters are inversely related to outcome in patients undergoing ibrutinib-based therapies, and sumSUVmax emerged as a strong independent prognostic factor.

TRIAL REGISTRATION

NCT02315326; https://clinicaltrials.gov/ct2/show/NCT02315326?term=NCT02315326&draw=2&rank=1.

Feasibility of prognosis assessment for cancer of unknown primary origin using texture analysis of 18F-fluorodeoxyglucose PET/computed tomography images of largest metastatic lymph node

OBJECTIVE

Cancers of unknown primary origin cannot be staged using images, making the prognosis difficult. We attempted to predict prognosis of patients with unknown primary origin using tumour heterogeneity recently introduced in F-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT).

METHODS

Overall, 30 patients with unknown primary origin who underwent whole-body F-FDG PET/CT scans were retrospectively enrolled for texture analysis. The volume of interest was placed in the largest metastatic lymph nodes and conventional parameters and grey-level co-occurrence matrix (GLCM) were calculated. Statistical analysis of image-based variables was performed using Cox regression analyses. Patients were stratified into two groups based on cutoff values of GLCMentropy obtained using receiver operating characteristics (ROCs). Patients were analyzed, and overall survival (OS) was compared using Kaplan-Meier analysis.

RESULTS

Univariate Cox regression analysis showed significant differences in prognosis for parenchymal organ metastasis (P < 0.01), GLCM homogeneity (P = 0.01), GLCMcontrast (P < 0.01), GLCMentropy (P < 0.01) and GLCMdissimilarity (P < 0.01). Multivariate Cox regression analysis showed a significant difference in reduced prognosis for GLCMentropy positive (P < 0.01). Stratification was performed based on the GLCMentropy cutoff value, determined using ROCs analysis, with smaller groups showing better OS.

CONCLUSIONS

Despite previous difficulties in predicting prognosis in patients with unknown primary origin, F-FDG PET/CT texture features may enable stratification of prognosis. This could be useful for appropriate patient selection and management and help identify a subset of patients with favourable outcomes. These novel findings may be helpful for prognostication and improving patient care.

Pre-treatment prediction of early response to chemoradiotherapy by quantitative analysis of baseline staging FDG-PET/CT and MRI in locally advanced cervical cancer

BACKGROUND

Early prediction of response to concurrent chemoradiotherapy (cCRT) could aid to further optimize treatment regimens for locally advanced cervical cancer (LACC) in the future.

PURPOSE

To explore whether quantitative parameters from baseline (pre-therapy) magnetic resonance imaging (MRI) and FDG-PET/computed tomography (CT) have potential as predictors of early response to cCRT.

MATERIAL AND METHODS

Forty-six patients with LACC undergoing cCRT after staging with FDG-PET/CT and MRI were retrospectively analyzed. Primary tumor volumes were delineated on FDG-PET/CT, T2-weighted (T2W)-MRI and diffusion-weighted MRI (DWI) to extract the following quantitative parameters: T2W volume; T2W signal_mean; DWI volume; ADC_mean; ADC_SD; MTV_42%; and SUV_max. Outcome was the early treatment response, defined as the residual tumor volume on MRI 3-4 weeks after start of external beam radiotherapy with chemotherapy (before the start of brachytherapy): patients with a residual tumor volume <10 cm³ were classified as early responders. Imaging parameters were analyzed together with FIGO stage to assess their performance to predict early response, using multivariable logistic regression analysis with bi-directional variable selection. Leave-one-out cross-validation with bootstrapping was used to simulate performance in a new, independent dataset.

RESULTS

T2W volume (OR 0.94, P = 0.003) and SUV_max (OR 1.15, P = 0.18) were identified as independent predictors in multivariable analysis, rendering a model with an AUC of 0.82 in the original dataset, and AUC of 0.68 (95% CI 0.41-0.81) from cross-validation.

CONCLUSION

Although the predictive performance achieved in this small exploratory dataset was limited, these preliminary data suggest that parameters from baseline MRI and FDG-PET/CT (in particular pre-therapy tumor volume) may contribute to prediction of early response to cCRT in cervical cancer.

Comparison of endoscopically determined gross tumor volume and metabolic tumor volume in esophageal cancer

The purpose of this study is to compare the longitudinal location of endoscopically-defined gross tumor volume (GTV) and positron emission tomography-based metabolic tumor volume (MTV) of esophageal cancer.A retrospective review of medical records was performed of the nine patients who underwent endoscopic placement of fiducial markers for radiotherapy of esophageal squamous cell carcinoma. Endoscopic hemoclips were used as the fiducial markers, and GTV was newly delineated solely based on the locations of the fiducial markers. The standardized uptake value (SUV) threshold corresponding to the superior and inferior borders of GTV was defined as the highest threshold that made MTV reach each border of GTV.The median fixed relative and absolute thresholds were 32% and 3.8, respectively. The coefficient of variation of the threshold values was 0.781 for the fixed relative threshold method and 0.400 for the fixed absolute threshold method, indicating more consistent results from the fixed absolute threshold method. All but two GTV borders were included in MTV with a SUV threshold of 2.5. Esophageal tumors with a maximum SUV > 20 tended to have closer threshold values corresponding to the GTV borders to 2.5 (median 2.8 vs 3.6, P = .069).The fixed absolute threshold method was suitable for determining the MTV threshold for esophageal lesions. A SUV of 2.5 was appropriate for esophageal tumors with a maximum SUV > 20. Endoscopic hemoclips were stable enough for using as the fiducial marker.

Diffusion-weighted MRI and PET/CT reproducibility in epithelial ovarian cancers during neoadjuvant chemotherapy

PURPOSE

To investigate the reproducibility of diffusion-weighted (DW) MRI and ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG)-Positron emission tomography/CT (PET/CT) in monitoring response to neoadjuvant chemotherapy in epithelial ovarian cancer.

MATERIALS AND METHODS

Ten women (median age, 67 years; range: 41.8-77.3 years) with stage IIIC-IV epithelial ovarian cancers were included in this prospective trial (NCT02792959) between 2014 and 2016. All underwent initial laparoscopic staging, four cycles of carboplatine-paclitaxel-based chemotherapy and interval debulking surgery. PET/CT and DW-MRI were performed at baseline (C0), after one cycle (C1) and before surgery (C4). Two nuclear physicians and two radiologists assessed five anatomic sites for the presence of ≥1 lesion. Target lesions in each site were defined and their apparent diffusion coefficient (ADC), maximal standardized uptake value (SUV-max), SUV-mean, SUL-peak, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were monitored (i.e., 10 patients ×5 sites ×3 time-points). Their relative early and late changes were calculated. Intra/inter-observer reproducibilities of qualitative and quantitative analysis were estimated with Kappa and intra-class correlation coefficients (ICCs).

RESULTS

For both modalities, inter- and intra-observer agreement percentages were excellent for initial staging but declined later for DW-MRI, leading to lower Kappa values for inter- and intra-observer variability (0.949 and 1 at C0, vs. 0.633 and 0.643 at C4, respectively) while Kappa values remained>0.8 for PET/CT. Inter- and intra-observer ICCs were>0.75 for SUV-max, SUL-peak, SUV-mean and their change regardless the time-point. ADC showed lower ICCs (range: 0.013-0.811). ANOVA found significant influences of the evaluation time, the measurement used (ADC, SUV-max, SUV-mean, SUV-max, SUL-peak, MTV or TLG) and their interaction on ICC values (P=0.0023, P<0.0001 and P =0.0028, respectively).

CONCLUSION

While both modalities demonstrated high reproducibility at baseline, only SUV-max, SUL-peak, SUV-mean and their changes maintained high reproducibility during chemotherapy.

PROGNOSTIC VALUE OF POST-INDUCTION CHEMOTHERAPY VOLUMETRIC PET/CT PARAMETERS FOR STAGE IIIA/B NON-SMALL CELL LUNG CANCER PATIENTS RECEIVING DEFINITIVE CHEMORADIOTHERAPY

Purpose/Objective(s): The aim of this follow-up analysis of the ESPATUE phase-3 trial was to explore the prognostic value of post-induction chemotherapy PET metrics in patients with stage III non-small cell lung cancer (NSCLC) who were assigned to receive definitive chemoradiotherapy. Materials/Methods: All eligible patients stage IIIA (cN2) and stage IIIB of the trial received induction chemotherapy consisting of 3 cycles of cisplatin/paclitaxel and chemoradiotherapy up to 45 Gy/1.5 Gy per fraction twice-a-day, followed by a radiation-boost with 2 Gy once per day with concurrent cisplatin/vinorelbine. The protocol definition prescribed a total dose of 65-71 Gy. 18F-FDG-PET/CT (PETpre) was performed at study entry and before concurrent chemoradiotherapy (interim-PET; PETpost). Interim PETpost metrics and known prognostic clinical parameters were correlated in uni- and multivariable survival analyses. Leave-one-out cross-validation was used to show internal validity. Results: Ninety-two patients who underwent 18F-FDG-PET/CT after induction chemotherapy were enrolled. Median MTVpost value was 5.9 ml. Altogether 85 patients completed the whole chemoradiation with the planned total dose of 60-71 Gy. In univariable proportional hazard analysis, each of the parameters MTVpost, SUVmax(post) and TLGmax(post) was associated with overall survival (P < 0.05). Multivariable survival analysis, including clinical and post-induction PET parameters, found TLGmax(post) (hazard ratio: 1.032 (95%-CI: 1.013-1.052) per 100 ml increase) and total radiation dose (hazard ratio: 0.930 (0.902-0.959) per Gray increase) significantly related with overall survival in the whole group of patients, and also in patients receiving a total dose ≥ 60 Gy. The best leave-one-out cross-validated 2 parameter classifier contained TLGmax(post) and total radiation dose. TLGmax(post) was associated with time to distant metastases (P = 0.0018), and SUVmax(post) with time to loco-regional relapse (P = 0.039) in multivariable analysis of patients receiving a total dose ≥ 60 Gy. Conclusion: Post-induction chemotherapy PET parameters demonstrated prognostic significance. Therefore, an interim 18F-FDG-PET/CT is a promising diagnostic modality for guiding individualized treatment intensification.

Diagnostic and Prognostic Utility of 18F-FDG PET/CT in Recurrent Salivary Gland Cancers

OBJECTIVE. The role of ¹⁸F-FDG PET/CT in the evaluation of recurrent salivary gland tumors remains poorly defined. We investigated the diagnostic and prognostic utility of PET in this setting. MATERIALS AND METHODS. A total of 146 patients with recurrent salivary gland cancer were treated at our institution between January 2002 and December 2015. Patients who underwent FDG PET/CT and conventional imaging (CT or MRI) within 3 months of recurrence (n = 78) were included in this retrospective analysis. On FDG PET/CT, we measured the SUV_max, total body metabolic tumor volume of all lesions, and total lesion glycolysis of all lesions to determine the intensity and extent of FDG-avid disease. We assessed the correlation of FDG PET/CT findings with clinicopathologic features, progression-free survival, and overall survival. RESULTS. FDG PET/CT was positive for recurrence in 74 of 78 patients (94.9%) and falsely negative in four patients (5.1%). In comparison with conventional imaging, FDG PET/CT performed for restaging detected additional recurrent lesions in 14 patients (17.9%). The median SUV_max was 7.4, the median total body metabolic tumor volume was 30.1 cm³, and median total lesion glycolysis was 97.3 g/mL × cm³. Sixty-six patients had progressive disease, and 54 died. Univariate and multivariate Cox hazards analysis identified pathologic risk group (p = .04), total body metabolic tumor volume (p < .001), and total lesion glycolysis (p < .001) as independent prognostic factors for progression-free survival and identified age (p = .05), total body metabolic tumor volume (p < .001), and total lesion glycolysis (p < .001) as independent prognostic factors for overall survival. CONCLUSION. In patients with recurrent salivary gland cancer, FDG PET/CT is useful as a single test for defining the extent of disease and providing prognostic information, which may help in selecting appropriate treatment strategies.

Unsupervised Clustering Reveals Sarcoidosis Phenotypes Marked by a Reduction in Lymphocytes Relate to Increased Inflammatory Activity on 18FDG-PET/CT

Introduction: Sarcoidosis is a T-helper cell mediated disease characterized by granulomatous inflammation. We posited that unsupervised clustering of various features in sarcoidosis would establish phenotypes associated with inflammatory activity measured by 18FDG-PET/CT. Our goal was to identify unique features capable of distinguishing clusters and subsequently examine the relationship with FDG avidity to substantiate their potential use as markers for sarcoidosis inflammation.

Methods: We performed a retrospective study of a diverse, but primarily African American, cohort of 58 subjects with biopsy proven sarcoidosis followed at the University of Illinois Bernie Mac Sarcoidosis Center and Center for Lung Health who underwent 18FDG-PET/CT scan. Demographic, therapeutic, radiographic, and laboratory data were utilized in unsupervised cluster analysis to identify sarcoidosis phenotypes. The association between clusters, their defining features, and quantitative measurements on 18FDG-PET/CT was determined. The relevance of these features as markers of 18FDG-PET/CT inflammatory activity was also investigated.

Results: Clustering determined three distinct phenotypes: (1) a predominantly African American cluster with chronic, quiescent disease, (2) a predominantly African American cluster with elevated conventional inflammatory markers, advanced pulmonary disease and extrathoracic involvement, and (3) a predominantly Caucasian cluster characterized by reduced lymphocyte counts and acute disease. In contrast to the chronic quiescent cluster, Clusters 2 and 3 were defined by significantly greater FDG avidity on 18FDG-PET/CT. Despite similarly increased inflammatory activity on 18FDG-PET/CT, Clusters 2, and 3 differed with regards to extrathoracic FDG avidity and circulating lymphocyte profiles, specifically CD4+ T-cells. Notably, absolute lymphocyte counts and CD4+ T-cell counts were found to predict 18FDG-PET/CT inflammatory activity by receiver operating curve analysis with a 69.2 and 73.42% area under the curve, respectively.

Conclusions: Utilizing cluster analysis, three distinct phenotypes of sarcoidosis were identified with significant variation in race, disease chronicity, and serologic markers of inflammation. These phenotypes displayed varying levels of circulating inflammatory cells. Additionally, reduction in lymphocytes, specifically CD4+ T-cells, was significantly related to activity on 18FDG-PET/CT. Though future studies are warranted, these findings suggest that peripheral lymphocyte counts may be considered a determinant of sarcoidosis phenotypes and an indicator of active inflammation on 18FDG-PET/CT.

Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer

OBJECTIVE

To investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data.

METHODS

Sixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2W_volume/T2W_entropy/ADC_mean/SUV_mean/TLG/CT_mean-HU) and local texture features (histogram features derived from local entropy/mean/standard deviation maps) were calculated. These respective feature sets were combined with clinical baseline parameters (e.g. age/gender/TN-stage) to build multivariable prediction models to predict a good (Mandard TRG1-2) versus poor (Mandard TRG3-5) response to chemoradiotherapy. Leave-one-out cross-validation (LOOCV) with bootstrapping was performed to estimate performance in an 'independent' dataset.

RESULTS

When using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture).

CONCLUSION

In imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research.

KEY POINTS

• Quantification of local tumour texture on pre-therapy FDG-PET/CT and MRI has potential added value compared to global tumour features to predict response to chemoradiotherapy in rectal cancer. • However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture over global tumour features is limited. • Predictive performance of our optimal model-combining clinical baseline variables with global quantitative tumour features-was encouraging (AUC 0.83), warranting further research in this direction on a larger scale.

A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

Intensity threshold based solid tumour segmentation method for Positron Emission Tomography (PET) images: A review

Accurate, robust and reproducible delineation of tumour in Positron Emission Tomography (PET) is essential for diagnosis, treatment planning and response assessment. Since standardized uptake value (SUV) – a normalized semiquantitative parameter used in PET is represented by the intensity of the PET images and related to the radiotracer uptake, a SUV based threshold method is a natural choice to delineate the tumour. However, determination of an optimum threshold value is a challenging task due to low spatial resolution, and signal-to-noise ratio (SNR) along with finite image sampling constraint. The aim of the review is to summarize different fixed and adaptive threshold-based PET image segmentation approaches under a common mathematical framework Advantages and disadvantages of different threshold based methods are also highlighted from the perspectives of diagnosis, treatment planning and response assessment. Several fixed threshold values (30%–70% of the maximum SUV of the tumour (SUV_maxT)) have been investigated. It has been reported that the fixed threshold-based method is very much dependent on the SNR, tumour to background ratio (TBR) and the size of the tumour. Adaptive threshold-based method, an alternative to fixed threshold, can minimize these dependencies by accounting for tumour to background ratio (TBR) and tumour size. However, the parameters for the adaptive methods need to be calibrated for each PET camera system (e.g., scanner geometry, image acquisition protocol, reconstruction algorithm etc.) and it is not straight forward to implement the same procedure to other PET systems to obtain similar results. It has been reported that the performance of the adaptive methods is also not optimum for smaller volumes with lower TBR and SNR. Statistical analysis carried out on the NEMA thorax phantom images also indicates that regions segmented by the fixed threshold method are significantly different for all cases. On the other hand, the adaptive method provides significantly different segmented regions only for low TBR with different SNR. From this viewpoint, a robust threshold based segmentation method that will be less sensitive to SUVmaxT, SNR, TBR and volume needs to be developed. It was really challenging to compare the performance of different threshold-based methods because the performance of each method was tested on dissimilar data set with different data acquisition and reconstruction protocols along with different TBR, SNR and volumes. To avoid such difficulties, it will be desirable to have a common database of clinical PET images acquired with different image acquisition protocols and different PET cameras to compare the performance of automatic segmentation methods. It is also suggested to report the changes in SNR and TBR while reporting the response using threshold based methods.

Texture Feature Comparison Between Step-and-Shoot and Continuous-Bed-Motion 18F-FDG PET

Our objective was to investigate the differences in texture features between step-and-shoot (SS) and continuous-bed-motion (CBM) imaging in phantom and clinical studies. Methods: A National Electrical Manufacturers Association body phantom was filled with ¹⁸F-FDG solution at a sphere-to-background ratio of 4:1. SS and CBM were performed using the same acquisition duration, and the data were reconstructed using 3-dimensional ordered-subset expectation maximization with time-of-flight algorithms. Texture features were extracted using the software LIFEx. A volume of interest was delineated on the 22-, 28-, and 37-mm spheres with a threshold of 42% of the maximum SUV. The voxel intensities were discretized using 2 resampling methods, namely a fixed bin size and a fixed bin number discretization. The discrete resampling values were set to 64 and 128. In total, 31 texture features were calculated with gray-level cooccurrence matrix (GLCM), gray-level run length matrix, neighborhood gray-level different matrix, and gray-level zone length matrix. The texture features of the SS and CBM images were compared for all settings using the paired t test and the coefficient of variation. In a clinical study, 27 lesions from 20 patients were examined using the same acquisition and image processing as were used during the phantom study. The percentage difference (%Diff) and correlation between the texture features from SS and CBM images were calculated to evaluate agreement between the 2 scanning techniques. Results: In the phantom study, the 11 features exhibited no significant difference between SS and CBM images, and the coefficient of variation was no more than 10%, depending on resampling conditions, whereas entropy and dissimilarity from GLCM fulfilled the criteria for all settings. In the clinical study, the entropy and dissimilarity from GLCM exhibited a low %Diff and excellent correlation in all resampling conditions. The %Diff of entropy was lower than that of dissimilarity. Conclusion: Differences between the texture features of SS and CBM images varied depending on the type of feature. Because entropy for GLCM exhibits minimal differences between SS and CBM images irrespective of resampling conditions, entropy may be the optimal feature to reduce the differences between the 2 scanning techniques.

Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review

Radiomics or textural feature extraction obtained from positron emission tomography (PET) images through complex mathematical models of the spatial relationship between multiple image voxels is currently emerging as a new tool for assessing intra-tumoral heterogeneity in medical imaging. In this paper, available literature on texture analysis using FDG PET imaging in patients suffering from tumors of the gastro-intestinal tract is reviewed. While texture analysis of FDG PET images appears clinically promising, due to the lack of technical specifications, a large variability in the implemented methodology used for texture analysis and lack of statistical robustness, at present, no firm conclusions can be drawn regarding the predictive or prognostic value of FDG PET texture analysis derived indices in patients suffering from gastro-enterologic tumors. In order to move forward in this field, a harmonized image acquisition and processing protocol as well as a harmonized protocol for texture analysis of tumor volumes, allowing multi-center studies excluding statistical biases should be considered. Furthermore, the complementary and additional value of CT-imaging, as part of the PET/CT imaging technique, warrants exploration.

[Uncertainties in the current concept of radiotherapy planning target volume]

The planning target volume is an essential notion in radiotherapy, that requires a new conceptualization. Indeed, the variability and diversity of the uncertainties involved or improved with the development of the new modern technologies and devices in radiotherapy suggest that random and systematic errors cannot be currently generalized. This article attempts to discuss these various uncertainties and tries to demonstrate that a redefinition of the concept of planning target volume toward its personalization for each patient and the robustness notion are likely an improvement basis to take into account the radiotherapy uncertainties.

Comparison of different automatic methods for the delineation of the total metabolic tumor volume in I-II stage Hodgkin Lymphoma

Total metabolic tumor volume (TMTV) is a promising quantitative biomarker for therapy assessment and prognosis in Hodgkin Lymphoma affected patients that allows prediction of patient outcome. The aim of this study was to evaluate the TMTV reproducibility between different sources of variability in tumor delimitation such as SUV-based thresholds (2.5, 41% and 50%) and software tools (Beth Israel plugin (BI) and LIFEx). Effect of contouring procedure both including single and multiple regions of interest was also studied in patients with multiple lesions, and optimal cut-offs for each studied method were displayed to compare the effect on prognosis. Strong alikeness in TMTV was found for 2.5 under software choice. Best accuracy in contouring compared to visual assessment of the disease was found for BI multiple ROI and LIFEx single ROI drawing. Similar cut-offs were found between both software for all considered thresholds, but best resemblance and highest cut-off due to an overestimation of the TMTV was found for 2.5 SUV. Our findings suggest that optimal reproducibility in TMTV is found for SUV > 2.5 threshold under choice of contouring methodology or software tool, meaning that overestimation of the TMTV threshold using 2.5 looks to be preferable than underestimation with 41% and 50%.

FAPI-74 PET/CT Using Either 18F-AlF or Cold-Kit 68Ga Labeling: Biodistribution, Radiation Dosimetry, and Tumor Delineation in Lung Cancer Patients

⁶⁸Ga-fibroblast activation protein inhibitors (FAPIs) 2, 4, and 46 have already been proposed as promising PET tracers. However, the short half-life of ⁶⁸Ga (68 min) creates problems with manufacture and delivery. ¹⁸F (half-life, 110 min) labeling would result in a more practical large-scale production, and a cold-kit formulation would improve the spontaneous availability. The NOTA chelator ligand FAPI-74 can be labeled with both ¹⁸F-AlF and ⁶⁸Ga. Here, we describe the in vivo evaluation of ¹⁸F-FAPI-74 and a proof of mechanism for ⁶⁸Ga-FAPI-74 labeled at ambient temperature. Methods: In 10 patients with lung cancer, PET scans were acquired at 10 min, 1 h, and 3 h after administration of 259 ± 26 MBq of ¹⁸F-FAPI-74. Physiologic biodistribution and tumor uptake were semiquantitatively evaluated on the basis of SUV at each time point. Absorbed doses were evaluated using OLINDA/EXM, version 1.1, and QDOSE dosimetry software with the dose calculator IDAC-Dose, version 2.1. Identical methods were used to evaluate one examination after injection of 263 MBq of ⁶⁸Ga-FAPI-74. Results: The highest contrast was achieved in primary tumors, lymph nodes, and distant metastases at 1 h after injection, with an SUV_max of more than 10. The effective dose per a 100-MBq administered activity of ¹⁸F-FAPI-74 was 1.4 ± 0.2 mSv, and for ⁶⁸Ga-FAPI-74 it was 1.6 mSv. Thus, the radiation burden of a diagnostic ¹⁸F-FAPI-74 PET scan is even lower than that of PET scans with ¹⁸F-FDG and other ¹⁸F tracers; ⁶⁸Ga-FAPI-74 is comparable to other ⁶⁸Ga ligands. FAPI PET/CT supported target volume definition for guiding radiotherapy. Conclusion: The high contrast and low radiation burden of FAPI-74 PET/CT favor multiple clinical applications. Centralized large-scale production of ¹⁸F-FAPI-74 or decentralized cold-kit labeling of ⁶⁸Ga-FAPI-74 allows flexible routine use.

Prognostic Impact of Pretherapeutic FDG-PET in Localized Anal Cancer

Due to the heterogeneity of tumour mass segmentation methods and lack of consensus, our study evaluated the prognostic value of pretherapeutic positron emission tomography with fluorodeoxyglucose (FDG-PET) metabolic parameters using different segmentation methods in patients with localized anal squamous cell carcinoma (SCC). Eighty-one patients with FDG-PET before radiochemotherapy were retrospectively analyzed. Semiquantitative data were measured with three fixed thresholds (35%, 41% and 50% of Maximum Standardized Uptake Value (SUVmax)) and four segmentation methods based on iterative approaches (Black, Adaptive, Nestle and Fitting). Metabolic volumes of primary anal tumour (P-MTV) and total tumour load (T-MTV: P-MTV+ lymph node MTV) were calculated. The primary endpoint was event-free survival (EFS). Seven multivariate models were created to compare FDG-PET tumour volumes prognostic impact. For all segmentation thresholds, PET metabolic volume parameters were independent prognostic factor and T-MTV variable was consistently better associated with EFS than P-MTV. Patient's sex was an independent variable and significantly correlated with EFS. With fixed threshold segmentation methods, 35% of SUVmax threshold seemed better correlated with EFS and the best cut-off for discrimination between a low and high risk of event occurrence was 40 cm3. Determination of T-MTV by FDG-PET using fixed threshold segmentation is useful for predicting EFS for primary anal SCC. If these data are confirmed in larger studies, FDG-PET could contribute to individualized patient therapies.

Radiologic Assessment of Groin Lymph Nodes in Pelvic Malignancies

INTRODUCTION

Metastatic involvement of groin nodes can alter radiation therapy planning for pelvic tumors. ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) can identify nodal metastases; however, interpretation of PET/CT-positive nodes can be complicated by non-malignant processes. We evaluated quantitative metrics as methods to identify groin metastases in patients with pelvic tumors by comparison with standard subjective interpretive criteria, with pathology as the reference standard.

METHODS

We retrospectively identified patients with vulvar, vaginal, or anal cancers who underwent ¹⁸F-FDG PET/CT before pathologic evaluation of groin nodes between 2007 and 2017. Because patho-radiologic correlation was not possible for every node, one index node identified on imaging was selected for each groin. For each index node, standardized uptake value measurements, total lesion glycolysis, metabolic tumor volume, CT-based volume, and short and long axes were measured. Multivariate logistic regression was used to identify metrics predictive for pathologically positive groins and generate a probabilistic model. Area under the receiver-operating characteristic curves (AUCs) for the model were compared with clinical interpretation from the diagnostic report via a Wald's χ² test.

RESULTS

Of 55 patients identified for analysis, 75 groins had pathologic evaluation resulting in 75 index groin nodes for analysis with 35 groins pathologically positive for malignancy. Logistic regression identified mean standardized-uptake-value (50% threshold) and short-axis length as the most predictive imaging metrics for metastatic nodal involvement. The probabilistic model performed better at predicting pathologic involvement compared with standard clinical interpretation on analysis (AUC 0.91, 95% CI 0.84 to 0.97 vs 0.80, 95% CI 0.71 to 0.89; p<0.01).

DISCUSSION

Accuracy of ¹⁸F-FDG PET/CT for detecting groin nodal metastases in patients with pelvic tumors may be improved with the use of quantitative metrics. Improving prediction of nodal metastases can aid with appropriate selection of patients for pathologic node evaluation and guide radiation volumes and doses.

Semiautomatically Quantified Tumor Volume Using 68Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer

Prostate-specific membrane antigen (PSMA)-targeting PET imaging is becoming the reference standard for prostate cancer staging, especially in advanced disease. Yet, the implications of PSMA PET-derived whole-body tumor volume for overall survival are poorly elucidated to date. This might be because semiautomated quantification of whole-body tumor volume as a PSMA PET biomarker is an unmet clinical challenge. Therefore, in the present study we propose and evaluate a software that enables the semiautomated quantification of PSMA PET biomarkers such as whole-body tumor volume. Methods: The proposed quantification is implemented as a research prototype. PSMA-accumulating foci were automatically segmented by a percental threshold (50% of local SUV_max). Neural networks were trained to segment organs in PET/CT acquisitions (training CTs: 8,632, validation CTs: 53). Thereby, PSMA foci within organs of physiologic PSMA uptake were semiautomatically excluded from the analysis. Pretherapeutic PSMA PET/CTs of 40 consecutive patients treated with ¹⁷⁷Lu-PSMA-617 were evaluated in this analysis. The whole-body tumor volume (PSMA_TV50), SUV_max, SUV_mean, and other whole-body imaging biomarkers were calculated for each patient. Semiautomatically derived results were compared with manual readings in a subcohort (by 1 nuclear medicine physician). Additionally, an interobserver evaluation of the semiautomated approach was performed in a subcohort (by 2 nuclear medicine physicians). Results: Manually and semiautomatically derived PSMA metrics were highly correlated (PSMA_TV50: R ² = 1.000, P < 0.001; SUV_max: R ² = 0.988, P < 0.001). The interobserver agreement of the semiautomated workflow was also high (PSMA_TV50: R ² = 1.000, P < 0.001, interclass correlation coefficient = 1.000; SUV_max: R ² = 0.988, P < 0.001, interclass correlation coefficient = 0.997). PSMA_TV50 (ml) was a significant predictor of overall survival (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006, P = 0.002) and remained so in a multivariate regression including other biomarkers (hazard ratio: 1.004; 95% confidence interval: 1.001-1.006 P = 0.004). Conclusion: PSMA_TV50 is a promising PSMA PET biomarker that is reproducible and easily quantified by the proposed semiautomated software. Moreover, PSMA_TV50 is a significant predictor of overall survival in patients with advanced prostate cancer who receive ¹⁷⁷Lu-PSMA-617 therapy.

PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability

Background

PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction.

Methods

Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences.

Results

The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation.

Conclusions

FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.