Automatic head and neck tumor segmentation through deep learning and Bayesian optimization on three-dimensional medical images

Medical imaging constitutes critical information in the diagnostic and prognostic evaluation of patients, as it serves to uncover a broad spectrum of pathologies and deviances. Clinical practitioners who carry out medical image screening are primarily reliant on their knowledge and experience for disease diagnosis. Convolutional Neural Networks (CNNs) hold the potential to serve as a formidable decision-support tool in the realm of medical image analysis due to their high capacity to extract hierarchical features and effectuate direct classification and segmentation from image data. However, CNNs contain a myriad of hyperparameters and optimizing these hyperparameters poses a major obstacle to the effective implementation of CNNs. In this work, a two-phase Bayesian Optimization-derived Scheduling (BOS) approach is proposed for hyperparameter optimization for the head and cancerous tissue segmentation tasks. We proposed this two-phase BOS approach to incorporate both rapid convergences in the first training phase and slower (but without overfitting) improvements in the last training phase. Furthermore, we found that batch size and learning rate have a significant impact on the training process, but optimizing them separately can lead to sub-optimal hyperparameter combinations. Therefore, batch size and learning rate have been coupled as the batch size to learning rate (B2L) ratio and utilized in the optimization process to optimize both simultaneously. The optimized hyperparameters have been tested for a three-dimensional V-Net model with computed tomography (CT) and positron emission tomography (PET) scans to segment and classify cancerous and noncancerous tissues. The results of 10-fold cross-validation indicate that the optimal batch size to learning rate (B2L) ratio for each phase of the training method can improve the overall medical image segmentation performance.

Mid-treatment changes in intra-tumoural metabolic heterogeneity correlate to outcomes in oropharyngeal squamous cell carcinoma patients

BACKGROUND

This study evaluated mid-treatment changes in intra-tumoural metabolic heterogeneity and quantitative FDG-PET/CT imaging parameters and correlated the changes with treatment outcomes in oropharyngeal squamous cell cancer (OPSCC) patients. 114 patients from two independent cohorts underwent baseline and mid-treatment (week 3) FDG-PET. Standardized uptake value maximum (SUVmax), standardized uptake value mean (SUVmean), metabolic tumour volume (MTV), and total lesional glycolysis (TLG) were measured. Intra-tumoural metabolic heterogeneity was quantified as the area under a cumulative SUV-volume histogram curve (AUC-CSH). Baseline and relative change (%∆) in imaging features were correlated to locoregional recurrence free survival (LRRFS) using multivariate Cox regression analysis. Patients were stratified into three risk groups utilising ∆AUC-CSH and known prognostic features, then compared using Kaplan-Meier analysis.

RESULTS

Median follow up was 39 months. 18% of patients developed locoregional recurrence at 2 years. A decrease in heterogeneity (∆AUC-CSH: 24%) was observed mid-treatment. There was no statistically significant difference in tumour heterogeneity (AUC-CSH) at baseline (p = 0.134) and change at week 3 (p = 0.306) between p16 positive and p16 negative patients. Baseline imaging features did not correlate to LRRFS. However, ∆MTV (aHR 1.04; 95% CI 1.03-1.06; p < 0.001) and ∆AUC-CSH (aHR 0.96; 95% CI 0.94-0.98; p = 0.004) were correlated to LRRFS. Stratification using ∆AUC-CSH and p16 status into three groups showed significant differences in LRR (2 year LRRFS 94%, 79%, 17%; log rank p < 0.001). Stratification using ∆AUC-CSH and ∆MTV into three groups showed significant differences in LRR (2 year LRRFS 93%, 70%, 17%; log rank p < 0.001).

CONCLUSION

Mid-treatment changes in intra-tumoural FDG-PET/CT heterogeneity correlated with treatment outcomes in OPSCC and may help with response prediction. These findings suggest potential utility in designing future risk adaptive clinical trials.

Clinico-pathological factors and [18F]FDG PET/CT metabolic parameters for prediction of progression-free survival in radioiodine refractory differentiated thyroid carcinoma

OBJECTIVE

Identifying prognostic markers for clinical outcomes is crucial in selecting appropriate treatment options for patients with radioiodine-refractory (RAI-R) differentiated thyroid carcinoma (DTC). The aim of this study was to investigate the prognostic value of clinico-pathological features and semiquantitative [18F]FDG PET/CT metabolic parameters in predicting progression-free survival (PFS) in DTC patients with RAI-R.

PATIENTS AND METHODS

This prospective cohort study included 110 consecutive RAI-R DTC patients who were referred for [18F]FDG PET/CT imaging. The lesion standard uptake values (SUV)s, including SUVmax, SUVmean, SULpeak as well astotal metabolic tumor volume (tMTV)and total lesion glycolysis (tTLG) were measured. Disease progression was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and/or Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) 1.0. PFS curves were plotted using Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses were performed to identify the prognostic factors for PFS.

RESULTS

[18F]FDG PET/CT metabolic parameters demonstrate predictive value for PFS in RAI-R DTC patients, with sensitivity ranging from 70.7% to 81% and specificity from 75% to 92.3% (p < 0.001). PFS was significantly worse in patients with SUVmax > 6.39 g/ml, SUVmean > 3.68 g/ml, SULpeak > 3.14 g/ml, tTLG > 4.23 g/ml × cm3, and tMTV > 1.24 cm3. Clinico-pathological factors including age > 55, aggressive variant and follicular histological subtype, extra-thyroidal extension of the primary tumor, stage III - IV disease at initial DTC diagnosis, distant metastases detected on [18F]FDG PET/CT, and metabolic parameters of [18F]FDG PET/CT associated with PFS in univariate analysis (p < 0.01). In multivariate analysis, extra-thyroidal extension (HR: 2.25; 95% CI: 1.22 - 4.16; p = 0.01), distant metastases on [18F]FDG PET/CT (HR: 2.98; 95%CI: 1.62 - 5.5; p < 0.001), and tMTV > 1.24 cm3 (HR: 4.17; 95% CI: 2.02 - 8.6; p < 0.001), were independent prognostic factors for PFS.

CONCLUSIONS

In addition to classic clinico-pathological factors, the semiquantitative [18F]FDG PET/CT metabolic parameters can be utilized for dynamic risk stratification for progression in RAI-R DTC patients. Furthermore, extra-thyroidal extension of the primary tumor, distant metastases, and tMTV > 1.24 cm3 are independent prognostic factors for PFS.

Three-Dimensional Margin as a Predictor of Local Tumor Progression after Microwave Ablation: Intraprocedural versus 4-8-Week Postablation Assessment

PURPOSE

To evaluate the prognostic accuracy of intraprocedural and 4-8-week (current standard) post-microwave ablation zone (AZ) and margin assessments for prediction of local tumor progression (LTP) using 3-dimensional (3D) software.

MATERIALS AND METHODS

Data regarding 100 colorectal liver metastases (CLMs) in 75 patients were collected from 2 prospective fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT)-guided microwave ablation (MWA) trials. The target CLMs and theoretical 5- and 10-mm margins were segmented and registered intraprocedurally and at 4-8 weeks after MWA contrast-enhanced CT (or magnetic resonance [MR] imaging) using the same methodology and 3D software. Tumor and 5- and 10-mm minimal margin (MM) volumes not covered by the AZ were defined as volumes of insufficient coverage (VICs). The intraprocedural and 4-8-week post-MWA VICs were compared as predictors of LTP using receiver operating characteristic curve analysis.

RESULTS

The median follow-up time was 19.6 months (interquartile range, 7.97-36.5 months). VICs for 5- and 10-mm MMs were predictive of LTP at both time assessments. The highest accuracy for the prediction of LTP was documented with the intra-ablation 5-mm VIC (area under the curve [AUC], 0.78; 95% confidence interval, 0.66-0.89). LTP for a VIC of 6-10-mm margin category was 11.4% compared with 4.3% for >10-mm margin category (P < .001).

CONCLUSIONS

A 3D 5-mm MM is a critical endpoint of thermal ablation, whereas optimal local tumor control is noted with a 10-mm MM. Higher AUCs for prediction of LTP were achieved for intraprocedural evaluation than for the 4-8-week postablation 3D evaluation of the AZ.

Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data

Reliable performance of PET segmentation algorithms on clinically relevant tasks is required for their clinical translation. However, these algorithms are typically evaluated using figures of merit (FoMs) that are not explicitly designed to correlate with clinical task performance. Such FoMs include the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the Hausdorff distance (HD). The objective of this study was to investigate whether evaluating PET segmentation algorithms using these task-agnostic FoMs yields interpretations consistent with evaluation on clinically relevant quantitative tasks. Methods: We conducted a retrospective study to assess the concordance in the evaluation of segmentation algorithms using the DSC, JSC, and HD and on the tasks of estimating the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors from PET images of patients with non-small cell lung cancer. The PET images were collected from the American College of Radiology Imaging Network 6668/Radiation Therapy Oncology Group 0235 multicenter clinical trial data. The study was conducted in 2 contexts: (1) evaluating conventional segmentation algorithms, namely those based on thresholding (SUVmax40% and SUVmax50%), boundary detection (Snakes), and stochastic modeling (Markov random field-Gaussian mixture model); (2) evaluating the impact of network depth and loss function on the performance of a state-of-the-art U-net-based segmentation algorithm. Results: Evaluation of conventional segmentation algorithms based on the DSC, JSC, and HD showed that SUVmax40% significantly outperformed SUVmax50%. However, SUVmax40% yielded lower accuracy on the tasks of estimating MTV and TLG, with a 51% and 54% increase, respectively, in the ensemble normalized bias. Similarly, the Markov random field-Gaussian mixture model significantly outperformed Snakes on the basis of the task-agnostic FoMs but yielded a 24% increased bias in estimated MTV. For the U-net-based algorithm, our evaluation showed that although the network depth did not significantly alter the DSC, JSC, and HD values, a deeper network yielded substantially higher accuracy in the estimated MTV and TLG, with a decreased bias of 91% and 87%, respectively. Additionally, whereas there was no significant difference in the DSC, JSC, and HD values for different loss functions, up to a 73% and 58% difference in the bias of the estimated MTV and TLG, respectively, existed. Conclusion: Evaluation of PET segmentation algorithms using task-agnostic FoMs could yield findings discordant with evaluation on clinically relevant quantitative tasks. This study emphasizes the need for objective task-based evaluation of image segmentation algorithms for quantitative PET.

Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer

BACKGROUND

The main problem of positron emission tomography/computed tomography (PET/CT) for lymph node (LN) staging is the high false positive rate (FPR). Thus, we aimed to explore a clinico-biological-radiomics (CBR) model via machine learning (ML) to reduce FPR and improve the accuracy for predicting the hypermetabolic mediastinal-hilar LNs status in lung cancer than conventional PET/CT.

METHODS

A total of 260 lung cancer patients with hypermetabolic mediastinal-hilar LNs (SUVmax ≥ 2.5) were retrospectively reviewed. Patients were treated with surgery with systematic LN resection and pathologically divided into the LN negative (LN-) and positive (LN +) groups, and randomly assigned into the training (n = 182) and test (n = 78) sets. Preoperative CBR dataset containing 1738 multi-scale features was constructed for all patients. Prediction models for hypermetabolic LNs status were developed using the features selected by the supervised ML algorithms, and evaluated using the classical diagnostic indicators. Then, a nomogram was developed based on the model with the highest area under the curve (AUC) and the lowest FPR, and validated by the calibration plots.

RESULTS

In total, 109 LN- and 151 LN + patients were enrolled in this study. 6 independent prediction models were developed to differentiate LN- from LN + patients using the selected features from clinico-biological-image dataset, radiomics dataset, and their combined CBR dataset, respectively. The DeLong test showed that the CBR Model containing all-scale features held the highest predictive efficiency and the lowest FPR among all of established models (p < 0.05) in both the training and test sets (AUCs of 0.90 and 0.89, FPRs of 12.82% and 6.45%, respectively) (p < 0.05). The quantitative nomogram based on CBR Model was validated to have a good consistency with actual observations.

CONCLUSION

This study presents an integrated CBR nomogram that can further reduce the FPR and improve the accuracy of hypermetabolic mediastinal-hilar LNs evaluation than conventional PET/CT in lung cancer, thereby greatly reducing the risk of overestimation and assisting for precision treatment.

Oncogenic KRAS triggers metabolic reprogramming in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with an extremely high lethality rate. Oncogenic KRAS activation has been proven to be a key driver of PDAC initiation and progression. There is increasing evidence that PDAC cells undergo extensive metabolic reprogramming to adapt to their extreme energy and biomass demands. Cell-intrinsic factors, such as KRAS mutations, are able to trigger metabolic rewriting. Here, we update recent advances in KRAS-driven metabolic reprogramming and the associated metabolic therapeutic potential in PDAC.

Pretreatment 18F-FDG uptake heterogeneity may predict treatment outcome of combined Trastuzumab and Pertuzumab therapy in patients with metastatic HER2 positive breast cancer

OBJECTIVE

Intra-tumoral heterogeneity of 18F-fluorodeoxyglucose (18F-FDG) uptake has been proven to be a surrogate marker for predicting treatment outcome in various tumors. However, the value of intra-tumoral heterogeneity in metastatic Human epidermal growth factor receptor 2(HER2) positive breast cancer (MHBC) remains unknown. The aim of this study was to evaluate 18F-FDG uptake heterogeneity to predict the treatment outcome of the dual target therapy with Trastuzumab and Pertuzumab(TP) in MHBC.

METHODS

Thirty-two patients with MHBC who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) scan before TP were enrolled retrospectively. The region of interesting (ROI) of the lesions were drawn, and maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), metabolic tumor volume (MTV) and heterogeneity index (HI) were recorded. Correlation between PET/CT parameters and the treatment outcome was analyzed by Spearman Rank Test. The ability to predict prognosis were determined by time-dependent survival receiver operating characteristic (ROC) analysis. And the survival analyses were then estimated by Kaplan-Meier method and compared by log-rank test.

RESULTS

The survival analysis showed that HI50% calculated by delineating the lesion with 50%SUVmax as threshold was a significant predictor of patients with MHBC treated by the treatment with TP. Patients with HI50% (≥ 1.571) had a significantly worse prognosis of progression free survival (PFS) (6.87 vs. Not Reach, p = 0.001). The area under curve (AUC), the sensitivity and the specificity were 0.88, 100% and 63.6% for PFS, respectively.

CONCLUSION

18F-FDG uptake heterogeneity may be useful for predicting the prognosis of MHBC patients treated by TP.

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.

Whole body MRI with Diffusion Weighted Imaging versus 18F-fluorodeoxyglucose-PET/CT in the staging of lymphomas

PURPOSE

To assess the diagnostic performance of Whole Body (WB)-MRI in comparison with 18F-Fluorodeoxyglucose-PET/CT (18F-FDG-PET/CT) in lymphoma staging and to assess whether quantitative metabolic parameters from 18F-FDG-PET/CT and Apparent Diffusion Coefficient (ADC) values are related.

MATERIALS AND METHODS

We prospectively enrolled patients with a histologically proven primary nodal lymphoma to  undergo 18F-FDG-PET/CT and WB-MRI, both performed within 15 days one from the other, either before starting treatment (baseline) or during treatment (interim). Positive and negative predictive values of WB-MRI for the identification of nodal and extra-nodal disease were measured. The agreement between WB-MRI and 18F-FDG-PET/CT for the identification of lesions and staging was assessed through Cohen's coefficient k and observed agreement. Quantitative parameters of nodal lesions derived from 18F-FDG-PET/CT and WB-MRI (ADC) were measured and the Pearson or Spearman correlation coefficient was used to assess the correlation between them. The specified level of significance was p ≤ 0.05.

RESULTS

Among the 91 identified patients, 8 refused to participate and 22 met exclusion criteria, thus images from 61 patients (37 men, mean age 30.7 years) were evaluated. The agreement between 18F-FDG-PET/CT and WB-MRI for the identification of nodal and extra-nodal lesions was 0.95 (95% CI 0.92 to 0.98) and 1.00 (95% CI NA), respectively; for staging it was 1.00 (95% CI NA). A strong negative correlation was found between ADCmean and SUVmean of nodal lesions in patients evaluated at baseline (Spearman coefficient r_s = - 0.61, p = 0.001).

CONCLUSION

WB-MRI has a good diagnostic performance for staging of patients with lymphoma in comparison with 18F-FDG-PET/CT and is a promising technique for the quantitative assessment of disease burden in these patients.

Investigating and modeling positron emission tomography factors associated with large cell transformation from low-grade lymphomas

Low-grade lymphomas have a 1%-3% annual risk of transformation to a high-grade histology, and prognostic factors remain undefined. We set to investigate the role of positron emission tomography (PET) metrics in identification of transformation in a retrospective case-control series of patients matched by histology and follow-up time. We measured PET parameters including maximum standard uptake value (SUV-max) and total lesion glycolysis (TLG), and developed a PET feature and lactate dehydrogenase (LDH)-based model to identify transformation status within discovery and validation cohorts. For our discovery cohort, we identified 53 patients with transformation and 53 controls with a similar distribution of follicular lymphoma (FL). Time to transformation and control follow-up time was similar. We observed a significant incremental increase in SUV-max and TLG between control, pretransformation and post-transformation groups (P < 0.05). By multivariable analysis, we identified a significant interaction between SUV-max and TLG such that SUV-max had highest significance for low volume cases (P = 0.04). We developed a scoring model incorporating SUV-max, TLG, and serum LDH with improved identification of transformation (area under the curve [AUC] = 0.91). Our model performed similarly for our validation cohort of 23 patients (AUC = 0.90). With external and prospective validation, our scoring model may provide a specific and noninvasive tool for risk stratification for patients with low-grade lymphoma.

Repeatability of metabolic tumor burden and lesion glycolysis between clinical readers

The Metabolic Tumor Volume (MTV) and Tumor Lesion Glycolysis (TLG) has been shown to be independent prognostic predictors for clinical outcome in Diffuse Large B-cell Lymphoma (DLBCL). However, definitions of these measurements have not been standardized, leading to many sources of variation, operator evaluation continues to be one major source. In this study, we propose a reader reproducibility study to evaluate computation of TMV (& TLG) metrics based on differences in lesion delineation. In the first approach, reader manually corrected regional boundaries after automated detection performed across the lesions in a body scan (Reader M using a manual process, or manual). The other reader used a semi-automated method of lesion identification, without any boundary modification (Reader A using a semi- automated process, or auto). Parameters for active lesion were kept the same, derived from standard uptake values (SUVs) over a 41% threshold. We systematically contrasted MTV & TLG differences between expert readers (Reader M & A). We find that MTVs computed by Readers M and A were both concordant between them (concordant correlation coefficient of 0.96) and independently prognostic with a P-value of 0.0001 and 0.0002 respectively for overall survival after treatment. Additionally, we find TLG for these reader approaches showed concordance (CCC of 0.96) and was prognostic for over -all survival (p ≤ 0.0001 for both). In conclusion, the semi-automated approach (Reader A) provides acceptable quantification & prognosis of tumor burden (MTV) and TLG in comparison to expert reader assisted measurement (Reader M) on PET/CT scans.

Development of scalable lymphatic system in the 4D XCAT phantom: Application to quantitative evaluation of lymphoma PET segmentations

BACKGROUND

Digital anthropomorphic phantoms, such as the 4D extended cardiac-torso (XCAT) phantom, are actively used to develop, optimize, and evaluate a variety of imaging applications, allowing for realistic patient modeling and knowledge of ground truth. The XCAT phantom defines the activity and attenuation for a simulated patient, which includes a complete set of organs, muscle, bone, and soft tissue, while also accounting for cardiac and respiratory motion. However, the XCAT phantom does not currently include the lymphatic system, critical for evaluating medical imaging tasks such as sentinel node detection, node density measurement, and radiation dosimetry.

PURPOSE

In this study, we aimed to develop a scalable lymphatic system in the XCAT phantom, to facilitate improved research of the lymphatic system in medical imaging. Using this scalable lymphatic system, we modeled the lymph node conglomerate pathology that is characteristically observed in primary mediastinal B-cell lymphoma (PMBCL). As an extended application, we evaluated positron emission tomography (PET) image quantification of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of these simulated lymphomas, though the phantoms may be applied to other imaging modalities and study design paradigms (e.g., image quality, detection).

METHODS

A template model for the lymphatic system was developed based on anatomical data from the Visible Human Project of the National Library of Medicine. The segmented nodes and vessels were fit with non-uniform rational basis spline surfaces, and multichannel large deformation diffeomorphic metric mapping was used to propagate the template to different XCAT anatomies. To model conglomerates observed in PMBCL, lymph nodes were enlarged, converged within the mediastinum, and tracer concentration was increased. We used the phantoms as inputs to a PET simulation tool, which generated images using ordered subsets expectation maximization reconstruction with 2-8 mm Gaussian filters. Fixed thresholding (FT) and gradient segmentation were used to determine MTV and TLG. Percent bias (%Bias) and coefficient of variation (COV) were computed as measures of accuracy and precision, respectively, for each MTV and TLG measurement.

RESULTS

Using the methodology described above, we introduced a scalable lymphatic system in the XCAT phantom, which allows for the radioactivity and attenuation ground truth to be generated in 116 ± 2.5 s using a 2.3 GHz processor. Within the Rhinoceros interface, lymph node anatomy and function were modified to create a cohort of 10 phantoms with lymph node conglomerates. Using the lymphoma phantoms to evaluate PET quantification of MTV, mean %Bias values were -9.3%, -41.3%, and 20.9%, while COV values were 4.08%, 7.6%, and 3.4% using 25% FT, 40% FT, and gradient segmentations, respectively. Comparatively for TLG, mean %Bias values were -27.4%, -45.8%, and -16.0%, while COV values were 1.9%, 5.7%, and 1.4%, for the 25% FT, 40% FT, and gradient segmentations, respectively.

CONCLUSIONS

In this work, we upgraded the XCAT phantom to include a lymphatic system, comprised of a network of 276 scalable lymph nodes and corresponding vessels. As an application, we created a cohort of phantoms with lymph node conglomerates to evaluate lymphoma quantification in PET imaging, which highlights an important application of this work.

Reliability of gradient-based segmentation for measuring metabolic parameters influenced by uptake time on 18F-PSMA-1007 PET/CT for prostate cancer

Purpose

To determine an optimal setting for functional contouring and quantification of prostate cancer lesions with minimal variation by evaluating metabolic parameters on 18F-PSMA-1007 PET/CT measured by threshold-based and gradient-based methods under the influence of varying uptake time.

Methods and materials

Dual time point PET/CT was chosen to mimic varying uptake time in clinical setting. Positive lesions of patients who presented with newly diagnosed disease or biochemical recurrence after total prostatectomy were reviewed retrospectively. Gradient-based and threshold-based tools at 40%, 50% and 60% of lesion SUVmax (MIM 6.9) were used to create contours on PET. Contouring was considered completed if the target lesion, with its hottest voxel, was delineated from background tissues and nearby lesions under criteria specific to their operations. The changes in functional tumour volume (FTV) and metabolic tumour burden (MTB, defined as the product of SUVmean and FTV) were analysed. Lesion uptake patterns (increase/decrease/stable) were determined by the percentage change in tumour SUVmax at ±10% limit.

Results

A total of 275 lesions (135 intra-prostatic lesions, 65 lymph nodes, 45 bone lesions and 30 soft tissue lesions in pelvic region) in 68 patients were included. Mean uptake time of early and delayed imaging were 94 and 144 minutes respectively. Threshold-based method using 40% to 60% delineated only 85 (31%), 110 (40%) and 137 (50%) of lesions which all were contoured by gradient-based method. Although the overall percentage change using threshold at 50% was the smallest among other threshold levels in FTV measurement, it was still larger than gradient-based method (median: 50%=-7.6% vs gradient=0%). The overall percentage increase in MTB of gradient-based method (median: 6.3%) was compatible with the increase in tumour SUVmax. Only a small proportion of intra-prostatic lesions (&lt;2%), LN (&lt;4%), bone lesions (0%) and soft tissue lesions (&lt;4%) demonstrated decrease uptake patterns.

Conclusions

With a high completion rate, gradient-based method is reliable for prostate cancer lesion contouring on 18F-PSMA-1007 PET/CT. Under the influence of varying uptake time, it has smaller variation than threshold-based method for measuring volumetric parameters. Therefore, gradient-based method is recommended for tumour delineation and quantification on 18F-PSMA-1007 PET/CT.

Liver Transplant for Non-Hepatocellular Malignancies: A Review for Radiologists

Although traditionally only performed for hepatocellular carcinoma (HCC), the last decade has seen a resurgence in the use of liver transplant (LT) for non-HCC malignancies, likely due to improvements in neoadjuvant treatment regimens as well as the establishment of well-defined eligibility criteria. Given promising survival results, patients with perihilar cholangiocarcinoma, neuroendocrine liver metastases, and hepatic hemangioendothelioma are eligible to receive Model for End Stage Liver Disease (MELD) exception for tumors that meet well-defined criteria. Additional tumors such as colorectal cancer liver metastases, intrahepatic cholangiocarcinoma, and hepatocholangiocarcinoma may undergo transplant at specialized centers with well-defined protocols, although are not yet eligible for MELD exception. Transplant eligibility criteria commonly incorporate imaging findings, yet due to the relatively novel and evolving nature of LT for non-HCC malignancies, radiologists may be unaware of relevant criteria or of the implications of their imaging interpretations. Knowledge of the allocation process, background, and liver transplant selection criteria facilitates the radiologist' active participation in multidisciplinary discussion, leading to better and more equitable care for transplant candidates with non-HCC malignancy. This review provides an overview of transplant allocation and selection criteria in patients with non-HCC malignancy, with an emphasis on imaging features and the role of the radiologist.

Significance of mid-radiotherapy 18F-fluorodeoxyglucose positron emission tomography/computed tomography in esophageal cancer

BACKGROUND AND PURPOSE

Metabolic parameters evaluated by 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) are known as prognostic markers in various cancers. We aimed to validate the predictive value of mid-radiotherapy (RT) FDG PET/CT parameters in esophageal cancer.

MATERIALS AND METHODS

Eighty-three patients treated with RT with or without chemotherapy between 2015 and 2020 were included. PET parameters including metabolic tumor volume (MTV), total lesion glycolysis, and mean (SUV_mean) and maximum standardized uptake value (SUV_max) were analyzed. Locoregional recurrence-free rate (LRFR) and distant metastasis-free rate (DMFR) were analyzed.

RESULTS

The median follow-up period was 10.5 months. Mid-RT SUV_max was significantly associated with LRFR (HR 1.07, p = 0.009) and DMFR (HR 1.13, p=0.047) while mid-RT MTV was associated with DMFR (HR 1.06, p=0.007). Treatment response after RT was associated with overall survival (HR, 1.52, p=0.025). Further, treatment response was significantly associated with mid-RT SUV_max. The optimal cutoff value for mid-RT SUV_max in predicting LRFR and DMFR was 11 while cutoff value for mid-RT MTV was 15. The patients with mid-RT SUV_max≤11 showed superior LRFR and DMFR compared to SUV_max>11 (1-year LRFR; 73.4% vs 48.4%, p=0.028, 1-year DMFR; 74.6% vs 40.7%, p=0.007). The 1-year DMFR was significantly different between patients with mid-RT MTV≤15 and >15 (1-year DMFR; 78.2% vs 31.9%, p=0.002).

CONCLUSION

Tumor metabolism changes during RT can be a useful predictive tool for treatment response and recurrence in patients with esophageal cancer. Clinicians may consider early response evaluation with PET during RT for information about prognosis.

Correlation between Tissue Cellularity and Metabolism Represented by Diffusion-Weighted Imaging (DWI) and 18F-FDG PET/MRI in Head and Neck Cancer (HNC)

Simple Summary

We report on the correlation between the diffusion-weighted imaging (DWI) and the metabolic volume parameters derived from a PET scan, to determine the correlation between these parameters and the tumor cellularity in head and neck primary tumors. Our findings implied that there was no correlation between the information derived from the DWI and the information derived from the FDG metabolic parameters. Thus, both imaging techniques might play a complementary role in HNC diagnosis and assessment. This is significant because the treatment plan of patients with HNC should be well evaluated by using all the available diagnosis techniques, for a better understanding of how the tumor will react.

Abstract

Background: This study aimed to assess the association of 18F-Fluorodeoxyglucose positron-emission-tomography (18F-FDG/PET) and DWI imaging parameters from a primary tumor and their correlations with clinicopathological factors. Methods: We retrospectively analyzed primary tumors in 71 patients with proven HNC. Primary tumor radiological parameters: DWI and FDG, as well as pathological characteristics were analyzed. Spearman correlation coefficient was used to assess the correlation between DWI and FDG parameters, ANOVA or Kruskal–Wallis, independent sample t-test, Mann–Whitney test, and multiple regression were performed on the clinicopathological features that may affect the 18F- FDG and apparent-diffusion coefficient (ADC) of the tumor. Results: No significant correlations were observed between DWI and any of the 18F-FDG parameters (p > 0.05). SUVmax correlated with N-stages (p = 0.023), TLG and MTV correlated with T-stages (p = 0.006 and p = 0.001), and ADC correlated with tumor grades (p = 0.05). SUVmax was able to differentiate between N+ and N− groups (p = 0.004). Conclusions: Our results revealed a non-significant correlation between the FDG-PET and ADC-MR parameters. FDG-PET-based glucose metabolic and DWI-MR-derived cellularity data may represent different biological aspects of HNC.

Pre-treatment total metabolic tumour volumes in lymphoma: Does quantity matter?

Positron emission tomography/computed tomography (PET/CT) is used for the staging of lymphomas. Clinical information, such as Ann Arbor stage and number of involved sites, is derived from baseline staging and correlates with tumour volume. With modern imaging software, exact measures of total metabolic tumour volumes (tMTV) can be determined, in a semi- or fully-automated manner. Several technical factors, such as tumour segmentation and PET/CT technology influence tMTV and there is no consensus on a standardized uptake value (SUV) thresholding method, or how to include the volumes in the bone marrow and spleen. In diffuse large B-cell lymphoma, follicular lymphoma, peripheral T-cell lymphoma, and Hodgkin lymphoma, tMTV has been shown to predict progression-free survival and/or overall survival, after adjustments for clinical risk scores. However, most studies have used receiver operating curves to determine the optimal cut-off for tMTV and many studies did not include a training-validation approach, which led to the risk of overestimation of the independent prognostic value of tMTV. The identified cut-off values are heterogeneous, even when the same SUV thresholding method is used. Future studies should focus on testing tMTV in homogeneously-treated cohorts and seek to validate identified cut-off values externally so that a prognostic value can be documented, over and above currently used clinical surrogates for tumour volume.

Quantitative evaluation of PSMA PET imaging using a realistic anthropomorphic phantom and shell-less radioactive epoxy lesions

BACKGROUND

Positron emission tomography (PET) with prostate specific membrane antigen (PSMA) have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]fluorodeoxyglucose ([18F]FDG) PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard ordered subsets expectation maximization (OSEM) reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10 mm) that are typically observed in PSMA images. In this study, we cast 3 mm-16-mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated.

RESULTS

The 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM's PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined "apex"), metabolic tumour volume (MTV), and total tumour uptake (TTU) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV and TTU, particularly for lesions [Formula: see text] 6 mm in diameter (R2 = 0.979-0.996 vs. R2 = 0.115-0.527, respectively).

CONCLUSION

An anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200-400 and OSEM with 2-5 iterations resulted in the most accurate and robust measurements of SUVmean, MTV, and TTU for imaging conditions in 18F-PSMA PET/CT images. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.

The metabolic parameters based on volume in PET/CT are associated with clinicopathological N stage of colorectal cancer and can predict prognosis

Background

A combination of positron emission tomography and computed tomography (PET/CT) is an important modality for the diagnosis of carcinoma. Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been reported as metabolic parameters in PET/CT since the late 1990s, and they are expected to be useful in diagnosing diverse cancers and as prognostic biomarkers. We evaluated the potential of these parameters in the prognosis of colorectal cancer (CRC) by comparing them with conventional parameters, including the maximum standardized uptake value (SUVmax). We enrolled 84 patients who underwent surgery for CRC without distal metastasis between April 2015 and April 2019. SUVmax, MTV, and TLG were measured by ¹⁸F-fluorodeoxyglucose (FDG)-PET/CT. To find an optimal threshold value related to prognosis, the volume of interest in the primary carcinoma was measured at fixed relative and absolute thresholds based on SUVmax (30%, 40%, and 50%; 2.5, 3.0, and 3.5, respectively), tumor-to-liver standardized uptake ratios, TLR (1.0, 1.5, and 2.0), and SUV normalized to lean body mass, SUL (2.0, 2.5, and 3.0). After classifying the patients into two groups according to pathological N stage, the optimal threshold values of all metabolic parameters were compared between groups using a non-parametric comparison test.

Result

The most suitable thresholds for MTV were a SUVmax of 3.5 and a TLR 2.0. TLG with a SUVmax value of 40% showed the most significant difference. The MTV standard uptake ratio of 2.0 was significantly associated with pathological N stage.

Conclusion

Our results suggest that an MTV TLR 2.0 on PET/CT reflects pathological N stage in local patients with CRC.

Chordoma: 18F-FDG PET/CT and MRI imaging features

OBJECTIVE

Examine the ¹⁸F-FDG PET/CT and MRI imaging characteristics of chordoma.

MATERIALS AND METHODS

Biopsy-proven chordoma with a pre-therapy ¹⁸F-FDG PET/CT from 2001 through 2019 in patients > 18 years old were retrospectively reviewed. Multiple PET/CT and MRI imaging parameters were assessed.

RESULTS

A total of 23 chordoma patients were included (16 M, 7 F; average age of 60.1 ± 13.0 years) with comparative MRI available in 22 cases. This included 13 sacrococcygeal, 9 mobile spine, and one clival lesions. On ¹⁸F-FDG PET/CT, chordomas demonstrated an average SUVmax of 5.8 ± 3.7, average metabolic tumor volume (MTV) of 160.2 ± 263.8 cm³, and average total lesion glycolysis (TLG) of 542.6 ± 1210 g. All demonstrated heterogeneous FDG activity. On MRI, chordomas were predominantly T2 hyperintense (22/22) and T1 isointense (18/22), contained small foci of T1 hyperintensity (17/22), and demonstrated heterogeneous enhancement (14/20). There were no statistically significant associations found between ¹⁸F-FDG PET/CT and MRI imaging features. There was no relationship of SUVmax (p = 0.53), MTV (p = 0.47), TLG (p = 0.48), maximal dimension (p = 0.92), or volume (p = 0.45) to the development of recurrent or metastatic disease which occurred in 6/22 patients over a mean follow-up duration of 4.1 ± 2.0 years.

CONCLUSION

On ¹⁸F-FDG PET/CT imaging, chordomas demonstrate moderate, heterogeneous FDG uptake. Predominant T2 hyperintensity and small foci of internal increased T1 signal are common on MRI. The inherent FDG avidity of chordomas suggests that ¹⁸F-FDG PET/CT may be a useful modality for staging, evaluating treatment response, and assessing for recurrent or metastatic disease.

The Utility of PET/CT Metabolic Parameters Measured Based on Fixed Percentage Threshold of SUVmax and Adaptive Iterative Algorithm in the New Revised FIGO Staging System for Stage III Cervical Cancer

Objectives: The main aim of this study was to evaluate the differences in metabolic parameters of positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro-D-glucose integrated with computed tomography (¹⁸F-FDG PET/CT) measured based on fixed percentage threshold of maximum standard uptake value (SUVmax) and adaptive iterative algorithm (AT-AIA) in patients with cervical cancer. Metabolic parameters in stage III patients subdivided into five groups according to FIGO and T staging (IIIB-T3B, IIIC1-T2B, IIIC1-T3B, IIIC2-T2B, IIIC2-T3B) were compared.

Methods: In total, 142 patients with squamous cell cervical cancer subjected to ¹⁸F-FDG-PET/CT before treatment were retrospectively reviewed. SUVmax, mean standard uptake value (SUVmean), maximum glucose homogenization (GNmax), mean glucose homogenization (GNmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and glucose homogenization total lesion glycolysis (GNTLG) values measured based on the above two measurement methods of all 142 patients (IIB-IVB) and 102 patients in the above five groups were compared.

Results: MTV measured based on fixed percentage threshold of SUVmax was lower than that based on AT-AIA (p < 0.05). MTV_40%, MTV_0.5, TLG_0.5, GNTLG_40%, and GNTLG_0.5 values were significantly different among the five groups (p < 0.05) while the rest parameters were comparable (p > 0.05). All metabolic parameters of group IIIB-T3B were comparable to those of the other four groups. MTV_40%, MTV_0.5, GNTLG_40%, and GNTLG_0.5 in group IIIC1-T2B relative to IIIC1-T3B and those of group IIIC2-T2B relative to group IIIC2-T3B were significantly different. All metabolic parameters of group IIIC1-T2B relative to IIIC2-T2B and those of group IIIC1-T3B relative to group IIIC2-T3B were not significantly different.

Conclusion: Metabolic parameters obtained with the two measurement methods showed a number of differences. Selection of appropriate methods for measurement of ¹⁸F-FDG-PET/CT metabolic parameters is important to facilitate advances in laboratory research and clinical applications. When stage III patients had the same T stage, their metabolic parameters of local tumor were not significantly different, regardless of the presence or absence of lymph node metastasis, location of metastatic lymph nodes in the pelvic cavity or para-abdominal aorta. These results support the utility of the revised FIGO system for stage III cervical cancer, although our T-staging of stage III disease is incomplete.

[18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma

Purpose

In this prospective exploratory study, we evaluated the feasibility of [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) PET/MRI-based chemotherapy response prediction in pancreatic ductal adenocarcinoma at two weeks upon therapy onset.

Material and methods

In a mixed cohort, seventeen patients treated with chemotherapy in neoadjuvant or palliative intent were enrolled. All patients were imaged by [¹⁸F]FDG PET/MRI before and two weeks after onset of chemotherapy. Response per RECIST1.1 was then assessed at 3 months [¹⁸F]FDG PET/MRI-derived parameters (MTV_50%, TLG_50%, MTV_2.5, TLG_2.5, SUV_max, SUV_peak, ADC_max, ADC_mean and ADC_min) were assessed, using multiple t-test, Man–Whitney-U test and Fisher’s exact test for binary features.

Results

At 72 ± 43 days, twelve patients were classified as responders and five patients as non-responders. An increase in ∆MTV_50% and ∆ADC (≥ 20% and 15%, respectively) and a decrease in ∆TLG_50% (≤ 20%) at 2 weeks after chemotherapy onset enabled prediction of responders and non-responders, respectively. Parameter combinations (∆TLG_50% and ∆ADC_max or ∆MTV_50% and ∆ADC_max) further improved discrimination.

Conclusion

Multiparametric [¹⁸F]FDG PET/MRI-derived parameters, in particular indicators of a change in tumor glycolysis and cellularity, may enable very early chemotherapy response prediction. Further prospective studies in larger patient cohorts are recommended to their clinical impact.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-021-00808-4.

Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images

Quantitative imaging biomarkers (QIBs) provide medical image-derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.

A Bayesian approach to tissue-fraction estimation for oncological PET segmentation

Tumor segmentation in oncological PET is challenging, a major reason being the partial-volume effects (PVEs) that arise due to low system resolution and finite voxel size. The latter results in tissue-fraction effects (TFEs), i.e. voxels contain a mixture of tissue classes. Conventional segmentation methods are typically designed to assign each image voxel as belonging to a certain tissue class. Thus, these methods are inherently limited in modeling TFEs. To address the challenge of accounting for PVEs, and in particular, TFEs, we propose a Bayesian approach to tissue-fraction estimation for oncological PET segmentation. Specifically, this Bayesian approach estimates the posterior mean of the fractional volume that the tumor occupies within each image voxel. The proposed method, implemented using a deep-learning-based technique, was first evaluated using clinically realistic 2D simulation studies with known ground truth, in the context of segmenting the primary tumor in PET images of patients with lung cancer. The evaluation studies demonstrated that the method accurately estimated the tumor-fraction areas and significantly outperformed widely used conventional PET segmentation methods, including a U-net-based method, on the task of segmenting the tumor. In addition, the proposed method was relatively insensitive to PVEs and yielded reliable tumor segmentation for different clinical-scanner configurations. The method was then evaluated using clinical images of patients with stage IIB/III non-small cell lung cancer from ACRIN 6668/RTOG 0235 multi-center clinical trial. Here, the results showed that the proposed method significantly outperformed all other considered methods and yielded accurate tumor segmentation on patient images with Dice similarity coefficient (DSC) of 0.82 (95% CI: 0.78, 0.86). In particular, the method accurately segmented relatively small tumors, yielding a high DSC of 0.77 for the smallest segmented cross-section of 1.30 cm². Overall, this study demonstrates the efficacy of the proposed method to accurately segment tumors in PET images.

FDG PET/CT and MRI Features of Pathologically Proven Schwannomas

PURPOSE

The aim of this study was to examine the MRI and FDG PET/CT imaging features of pathologically proven schwannomas.

PATIENTS AND METHODS

This institutional review board-approved retrospective study examined biopsy-proven schwannomas that underwent FDG PET/CT and/or MRI at our institution between January 1, 2002, and April 1, 2018. PET/CT features analyzed included SUVmax, metabolic ratios, volumetric metabolic measures, presence of calcification, and pattern of FDG activity. MRI features included T1/T2 signal, enhancement pattern, margins, perilesional edema, presence of muscular denervation, and size.

RESULTS

Ninety-five biopsy-proven schwannomas were identified (40 with both PET and MRI, 35 with PET only, and 20 with MRI only), 46 females and 49 males, average age of 57.7 ± 15.3 years. The average largest dimension was 4.6 ± 2.7 cm, the average SUVmax was 5.4 ± 2.7, and lesion SUVmax/liver SUVmean was 2.2 ± 1.2. Eleven (15%) of 75 lesions had SUVmax greater than 8.1, 26/75 (35%) had SUVmax greater than 6.1, and 14/75 (19%) had lesion SUVmax/liver SUVmean greater than 3.0. On MRI, 29/53 (55%) demonstrated internal nonenhancing areas. Twenty-eight (70%) of 40 lesions with both MRI and PET demonstrated at least 1 imaging feature concerning for malignant peripheral nerve sheath tumor (irregular margins, internal nonenhancement, perilesional edema, heterogeneous FDG uptake, or SUVmax >8.1). Lesions with heterogeneous FDG activity had higher SUVmax (6.5 ± 0.5 vs 4.7 ± 0.4, P = 0.0031) and more frequent internal nonenhancement on MRI (P = 0.0218).

CONCLUSIONS

Schwannomas may be large, be intensely FDG avid, and demonstrate significant heterogeneity, features typically associated with malignant peripheral nerve sheath tumors. A significant proportion exhibit FDG activity above cutoff levels previously thought useful in differentiating malignant from benign peripheral nerve sheath tumors.

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET

An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. To address this need, we propose an automated physics-guided deep-learning-based three-module framework to segment PET images on a per-slice basis. The framework is designed to help address the challenges of limited spatial resolution and lack of clinical training data with known ground-truth tumor boundaries in PET. The first module generates PET images containing highly realistic tumors with known ground-truth using a new stochastic and physics-based approach, addressing lack of training data. The second module trains a modified U-net using these images, helping it learn the tumor-segmentation task. The third module fine-tunes this network using a small-sized clinical dataset with radiologist-defined delineations as surrogate ground-truth, helping the framework learn features potentially missed in simulated tumors. The framework was evaluated in the context of segmenting primary tumors in 18F-fluorodeoxyglucose (FDG)-PET images of patients with lung cancer. The framework's accuracy, generalizability to different scanners, sensitivity to partial volume effects (PVEs) and efficacy in reducing the number of training images were quantitatively evaluated using Dice similarity coefficient (DSC) and several other metrics. The framework yielded reliable performance in both simulated (DSC: 0.87 (95% confidence interval (CI): 0.86, 0.88)) and patient images (DSC: 0.73 (95% CI: 0.71, 0.76)), outperformed several widely used semi-automated approaches, accurately segmented relatively small tumors (smallest segmented cross-section was 1.83 cm2), generalized across five PET scanners (DSC: 0.74 (95% CI: 0.71, 0.76)), was relatively unaffected by PVEs, and required low training data (training with data from even 30 patients yielded DSC of 0.70 (95% CI: 0.68, 0.71)). In conclusion, the proposed automated physics-guided deep-learning-based PET-segmentation framework yielded reliable performance in delineating tumors in FDG-PET images of patients with lung cancer.

Predicting per-lesion local recurrence in locally advanced non-small cell lung cancer following definitive radiation therapy using pre- and mid-treatment metabolic tumor volume

BACKGROUND

We evaluated whether pre- and mid-treatment metabolic tumor volume (MTV) predicts per lesion local recurrence (LR) in patients treated with definitive radiation therapy (RT, dose≥60 Gy) for locally advanced non-small cell lung cancer (NSCLC).

METHODS

We retrospectively reviewed records of patients with stage III NSCLC treated from 2006 to 2018 with pre- and mid-RT PET-CT. We measured the MTV of treated lesions on the pre-RT (MTVpre) and mid-RT (MTVmid) PET-CT. LR was defined per lesion as recurrence within the planning target volume. Receiver operating characteristic (ROC) curves, cumulative incidence rates, and uni- and multivariable (MVA) competing risk regressions were used to evaluate the association between MTV and LR.

RESULTS

We identified 111 patients with 387 lesions (112 lung tumors and 275 lymph nodes). Median age was 68 years, 69.4% were male, 46.8% had adenocarcinoma, 39.6% had squamous cell carcinoma, and 95.5% received concurrent chemotherapy. Median follow-up was 38.7 months. 3-year overall survival was 42.3%. 3-year cumulative incidence of LR was 26.8% per patient and 11.9% per lesion. Both MTVpre and MTVmid were predictive of LR by ROC (AUC = 0.71 and 0.76, respectively) and were significantly associated with LR on MVA (P = 0.004 and P = 7.1e-5, respectively). Among lesions at lower risk of LR based on MTVpre, higher MTVmid was associated with LR (P = 0.001).

CONCLUSION

Per-lesion, larger MTVpre and MTVmid predicted for increased risk of LR. MTVmid was more highly predictive of LR than MTVpre and if validated may allow for further discrimination of high-risk lesions at mid-RT informing dose painting strategies.

The effect of different segmentation methods on primary tumour metabolic volume assessed in 18F-FDG-PET/CT in patients with cervical cancer, for radiotherapy planning

Introduction

Gynaecological cancers, including cervical cancer, often require a multidisciplinary approach that includes external beam radiotherapy, chemotherapy, and/or surgical treatment. Biological parameters of the tumour evaluated in ¹⁸F-FDG-PET/CT are used for target volume delineation in radiotherapy planning. The choice of segmentation method may affect the assessment of metabolic tumour volume (MTV) in ¹⁸F-FDG-PET/CT.

Aim of the study

To find the optimal segmentation method for the assessment of primary MTV in ¹⁸F-FDG-PET/CT in cervical cancer patients for radiotherapy planning.

Material and methods

Retrospective analysis was performed on a group of 30 patients with newly diagnosed, histologically confirmed cervical cancer. The primary MTVs were assessed by SUV_max and SUV_mean values; three segmentation methods were used to assess the primary MTV: constant threshold of SUV_max of 2.5, threshold of SUV_max 35%, and threshold of SUV _max 45%. The MTVs were compared with the tumour volumes obtained in magnetic resonance imaging (MRI), which was the "gold standard", to select the best optimal segmentation method reflecting the tumour size. Wilcoxon-Mann-Whitney and t-test were used for statistical analysis.

Results

Depending on the segmentation method chosen, significant differences in the MTVs were obtained (p < 0.001). The highest volumes were obtained using the method based on constant SUV_max of 2.5, while the smallest in case of threshold of SUV_max of 45%. Regarding the volume determined by MRI, a 35% SUV_max threshold was chosen as the most reliable method.

Conclusions

The choice of appropriate segmentation method has a significant impact on the primary MTV assessment in ¹⁸F-FDG-PET/CT in patients with cervical cancer.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI

Objectives

To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.

Materials and methods

Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement.

Results

No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively.

Discussion

Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

Increases in Serial Pretreatment 18F-FDG PET-CT Metrics Predict Survival in Early Stage Non-Small Cell Lung Cancer Treated With Stereotactic Ablative Radiation Therapy

Purpose

Quantitative changes in positron emission tomography with computed tomography imaging metrics over serial scans may be predictive biomarkers. We evaluated the relationship of pretreatment metabolic tumor growth rate (MTGR) and standardized uptake value velocity (SUVV) with disease recurrence or death in patients with early-stage non-small cell lung cancer treated with stereotactic ablative radiation therapy (SABR).

Methods and Materials

Under institutional review board approval, we retrospectively identified patients who underwent positron emission tomography with computed tomography at diagnosis and staging and simulation for SABR. Two cohorts underwent SABR between November 2005 to October 2012 (discovery) and January 2012 to April 2016 (validation). MTGR and SUVV were calculated as the daily change in metabolic tumor volume and maximum standardized uptake value, respectively. Cox proportional hazard models identified predictors of local, regional, and distant recurrence and death for the combined cohort. MTGR and SUVV thresholds dichotomizing risk of death in the discovery cohort were applied to the validation cohort.

Results

A total of 152 lesions were identified in 143 patients (92 lesions in 83 discovery cohort patients). In multivariable models, increasing MTGR trended toward increased hazard of distant recurrence (hazard ratio, 6.98; 95% confidence interval, 0.67-72.61; P = .10). In univariable models, SUVV trended toward risk of death (hazard ratio, 11.8, 95% confidence interval, 0.85-165.1, P = .07). MTGR greater than 0.04 mL/d was prognostic of decreased survival in discovery (P = .048) and validation cohorts (P < .01).

Conclusions

MTGR greater than 0.04 mL/d is prognostic of death in patients with non-small cell lung cancer treated with SABR. Increasing SUVV trends, nonsignificantly, toward increased risk of recurrence and death. MTGR and SUVV may be candidate imaging biomarkers to study in trials evaluating systemic therapy with SABR for patients at high risk of out-of-field recurrence.

How clinical imaging can assess cancer biology

Human cancers represent complex structures, which display substantial inter- and intratumor heterogeneity in their genetic expression and phenotypic features. However, cancers usually exhibit characteristic structural, physiologic, and molecular features and display specific biological capabilities named hallmarks. Many of these tumor traits are imageable through different imaging techniques. Imaging is able to spatially map key cancer features and tumor heterogeneity improving tumor diagnosis, characterization, and management. This paper aims to summarize the current and emerging applications of imaging in tumor biology assessment.

Prognostic values of mid-radiotherapy 18F-FDG PET/CT in patients with esophageal cancer

BACKGROUND

To identify whether early metabolic responses as determined using ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) during radiotherapy (RT) predict outcomes in patients with esophageal cancer.

METHODS

Twenty-one patients with esophageal cancer who received pre-treatment ¹⁸F-FDG PET/CT (PET1) and inter-fractional ¹⁸F-FDG PET/CT (PET2) after 11 fractions of RT (median 23.1 Gy, 2.1 Gy per fraction) were retrospectively reviewed. The region of interest for each calculation was delineated using "PET Edge". We calculated PET parameters including maximum and mean standardized uptake values (SUV_max and SUV_mean, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The relative changes (%) were calculated using the logarithmically transformed parameter values for the PET1 and PET2 scans. Multivariate analysis of locoregional recurrence and distant failures were performed using Cox regression analysis. After identifying statistically significant PET parameters for discriminating responders from non-responders, receiver operating characteristics curve analyses were used to assess the potentials of the studied PET parameters.

RESULTS

After a median follow-up of 13 months, the 1-year overall and progression-free survival rates were 79.0% and 34.4%, respectively. Four patients developed locoregional recurrences (LRRs) and 8 had distant metastases (DMs). The 1-year overall LRR-free rate was 76.9% while the DM-free rate was 60.6%. The relative changes in MTV (ΔMTV) were significantly associated with LRR (p = 0.03). Conversely, the relative changes in SUV_mean (ΔSUV_mean) were associated with the risk of DM (p = 0.02). An ΔMTV threshold of 1.14 yielded a sensitivity of 60%, specificity of 94%, and an accuracy of 86% for predicting an LRR. Additionally, a ΔSUV_mean threshold of a 35% decrease yielded a sensitivity of 67%, specificity of 83%, and accuracy of 76% for the prediction DM.

TRIAL REGISTRATION

Retrospectively registered.

CONCLUSIONS

Changes in tumor metabolism during RT could be used to predict treatment responses, recurrences, and prognoses in patients with esophageal cancer.

Update 2018: 18F-FDG PET/CT and PET/MRI in Head and Neck Cancer

There are recent advances, namely, a standardized method for reporting therapy response (Hopkins criteria), a multicenter prospective cohort study with excellent negative predictive value of F-FDG PET/CT for N0 clinical neck, a phase III multicenter randomized controlled study establishing the value of a negative posttherapy F-FDG PET/CT for patient management, a phase II randomized controlled study demonstrating radiation dose reduction strategies for human papilloma virus-related disease, and Food and Drug Administration approval of nivolumab for treatment of recurrent head and neck squamous cell carcinoma.

18F-FDG PET/CT and MRI features of myxoid liposarcomas and intramuscular myxomas

OBJECTIVE

To examine the imaging characteristics of intramuscular myxomas (IM) and myxoid liposarcomas (MLS) on ¹⁸F-FDG PET/CT and MRI.

MATERIALS AND METHODS

With IRB approval, our institutional imaging database was searched for pathologically proven IM and MLS evaluated by ¹⁸F-FDG PET/CT and MRI. PET/CT and MRI imaging characteristics were recorded and correlated with pathologic diagnosis.

RESULTS

We found eight patients (2 M, 6 F) with IM (mean age 65.6 ± 10.4 years) and 16 patients (7 F, 9 M) with MLS (mean age 42.8 ± 16.3 years). MRI was available in 7/8 IM and 15/16 MLS patients. There was no significant difference between the two groups in SUVmax (IM 2.7 ± 0.8, MLS 3.0 ± 1.0; p = 0.35), SUVmean (1.7 ± 0.4, 1.5 ± 0.5; p = 0.40), total lesion glycolysis (101.8 ± 127.3, 2420.2 ± 4003.3 cm³*g/ml; p = 0.12), metabolic tumor volume (62.3 ± 71.1, 1742.9 ± 3308.0 cm³; p = 0.17) or CT attenuation (p = 0.70). MLS occurred in younger patients (p = 0.0015), were larger (16.4 ± 8.2 vs. 5.6 ± 2.5 cm; p = 0.0015), more often T1 hyperintense (p = 0.03), with nodular enhancement (p = 0.006), and macroscopic fat on CT (p = 0.0013) and MRI (p = < 0.001) compared to myxomas.

CONCLUSIONS

IM and MLS most commonly demonstrate low-grade FDG activity and overlapping metabolic measures on PET/CT. MRI is useful in differentiation, but MLS can present without macroscopic fat on MRI, underscoring the importance of radiologic-pathologic correlation for accurate diagnosis.

Impact of 90Y PET gradient-based tumor segmentation on voxel-level dosimetry in liver radioembolization

BACKGROUND

The purpose was to validate ⁹⁰Y PET gradient-based tumor segmentation in phantoms and to evaluate the impact of the segmentation method on reported tumor absorbed dose (AD) and biological effective dose (BED) in ⁹⁰Y microsphere radioembolization (RE) patients. A semi-automated gradient-based method was applied to phantoms and patient tumors on the ⁹⁰Y PET with the initial bounding volume for gradient detection determined from a registered diagnostic CT or MR; this PET-based segmentation (PS) was compared with radiologist-defined morphologic segmentation (MS) on CT or MRI. AD and BED volume histogram metrics (D90, D70, mean) were calculated using both segmentations and concordance/correlations were investigated. Spatial concordance was assessed using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). PS was repeated to assess intra-observer variability.

RESULTS

In phantoms, PS demonstrated high accuracy in lesion volumes (within 15%), AD metrics (within 11%), high spatial concordance relative to morphologic segmentation (DSC > 0.86 and MDA < 1.5 mm), and low intra-observer variability (DSC > 0.99, MDA < 0.2 mm, AD/BED metrics within 2%). For patients (58 lesions), spatial concordance between PS and MS was degraded compared to in-phantom (average DSC = 0.54, average MDA = 4.8 mm); the average mean tumor AD was 226 ± 153 and 197 ± 138 Gy, respectively for PS and MS. For patient AD metrics, the best Pearson correlation (r) and concordance correlation coefficient (ccc) between segmentation methods was found for mean AD (r = 0.94, ccc = 0.92), but worsened as the metric approached the minimum dose (for D90, r = 0.77, ccc = 0.69); BED metrics exhibited a similar trend. Patient PS showed low intra-observer variability (average DSC = 0.81, average MDA = 2.2 mm, average AD/BED metrics within 3.0%).

CONCLUSIONS

⁹⁰Y PET gradient-based segmentation led to accurate/robust results in phantoms, and showed high concordance with MS for reporting mean tumor AD/BED in patients. However, tumor coverage metrics such as D90 exhibited worse concordance between segmentation methods, highlighting the need to standardize segmentation methods when reporting AD/BED metrics from post-therapy ⁹⁰Y PET. Estimated differences in reported AD/BED metrics due to segmentation method will be useful for interpreting RE dosimetry results in the literature including tumor response data.

Prognostic Value of FDG-PET/CT Metabolic Parameters in Metastatic Radioiodine-Refractory Differentiated Thyroid Cancer

PURPOSE

There are no standardized prognostication algorithms for metastatic radioiodine-refractory (RAI-R) differentiated thyroid cancer (DTC). We hypothesize that [F]-FDG PET/CT may predict progression versus stability of disease based on quantitative analysis of metabolic tumor volume (MTV) and total lesion glycolysis (TLG).

METHODS

Retrospective study of 62 patients with metastatic RAI-R DTC to determine clinical outcomes with median follow-up from initial diagnosis of 11.1 years (8.38, 14.1) (range, 1.2-20 years). Baseline [F]-FDG PET/CT scans were evaluated qualitatively for regional and distant metastases and quantitatively for tumor burden based on MTV and TLG obtained using gradient segmentation method.

RESULTS

After diagnosis of metastatic RAI-R disease was established, the 5-year overall survival (OS) probability was 34%, and median OS was 3.56 years (2.87, infinity). The 5-year progression-free survival (PFS) probability was 19%, and median PFS was 1.31 years (1.03, 2.38). TSH-suppressed thyroglobulin (Tg) levels greater than 100 ng/mL and Tg doubling time (Tg-DT) less than 6 months were significantly associated with worse OS and PFS. Higher than median values of MTV and TLG were associated with worse OS (P = 0.06) and PFS (P = 0.007). Higher hazard of death was noted for higher values of log-MTV and log-TLG (HR, 1.17 [95% confidence interval, 0.99-1.39], P = 0.05, and HR, 1.14 [95% confidence interval, 1.00-1.31], P = 0.05, respectively).

CONCLUSIONS

[F]-FDG PET/CT metabolic parameters can help define the volume and biologic variations of metastatic tumor burden. Metabolic tumor volume and TLG can be used for dynamic risk stratification of patients with metastatic RAI-R DTC regarding PFS and complement Tg-DT for prognosis of clinical disease course.

Accurate FDG PET tumor segmentation using the peritumoral halo layer method: a study in patients with esophageal squamous cell carcinoma

Background

In a previous study, FDG PET tumor segmentation (SegPHL) using the peritumoral halo layer (PHL) was more reliable than fixed threshold methods in patients with thyroid cancer. We performed this study to validate the reliability and accuracy of the PHL method in patients with esophageal squamous cell carcinomas (ESCCs), which can be larger and more heterogeneous than thyroid cancers.

Methods

A total of 121 ESCC patients (FDG avid = 85 (70.2%); FDG non-avid = 36 (29.8%)) were enrolled in this study. In FDG avid ESCCs, metabolic tumor length (ML) using SegPHL (ML_PHL), fixed SUV 2.5 threshold (ML_2.5), and fixed 40% of maximum SUV (SUVmax) (ML_40%) were measured. Regression and Bland-Altman analyses were performed to evaluate associations between ML, endoscopic tumor length (EL), and pathologic tumor length (PL). A comparison test was performed to evaluate the absolute difference between ML and PL. Correlation with tumor threshold determined by the PHL method (PHL tumor threshold) and SUVmax was evaluated.

Results

ML_PHL, ML_2.5, and ML_40% correlated well with EL (R² = 0.6464, 0.5789, 0.3321, respectively; p < 0.001) and PL (R² = 0.8778, 0.8365, 0.6266, respectively; p < 0.001). However, ML_2.5 and ML_40% showed significant proportional error with regard to PL; there was no significant error between ML_PHL and PL. ML_PHL showed the smallest standard deviation on Bland-Altman analyses. The absolute differences between ML and PL were significantly smaller for ML_PHL and ML_40% than for ML_2.5 (p < 0.0001). The PHL tumor threshold showed an inverse correlation with SUVmax (σ = − 0.923, p < 0.0001).

Conclusions

SegPHL was more accurate than fixed threshold methods in ESCC. The PHL tumor threshold was adjusted according to SUVmax of ESCC.

Early Response Assessment in Pancreatic Ductal Adenocarcinoma Through Integrated PET/MRI

OBJECTIVE

The purpose of this study is to investigate early changes in ¹⁸F-FDG PET/MRI metrics after treatment in patients with advanced pancreatic ductal adenocarcinoma (PDAC) and to correlate those changes with eventual tumor response at standard-of-care CT.

SUBJECTS AND METHODS

Thirteen patients with advanced PDAC underwent integrated FDG PET/MRI before and 4 weeks after treatment initiation. Patients were classified as responders or nonresponders according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 at subsequent CT performed 8-12 weeks after treatment initiation. Changes in the primary tumor's maximum standardized uptake value (SUV_max), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) determined at PET and apparent diffusion coefficient (ADC) determined at DWI at 4 weeks were compared between responders and nonresponders.

RESULTS

Seven patients had a partial response according to RECIST, and six did not. Responders displayed significantly greater decreases in MTV (p = 0.003) and TLG (p = 0.006) in the primary pancreatic tumor at 4 weeks. Responders also displayed a greater increase in the mean (p = 0.004) and minimum (p = 0.024) ADC of the primary tumors. Tumor size change at 4 weeks was not significantly different between responders and nonresponders (p = 0.11). PET responders enjoyed longer progression-free survival (PFS) (p = 0.0004) and overall survival (OS) (p = 0.013) than did nonresponders, using either a 60% reduction in MTV or 65% reduction in TLG as a threshold. MRI responders had significantly longer PFS (p = 0.0002) and OS (p = 0.027) than did nonresponders, using a 20% increase in either mean or minimum ADC as a threshold.

CONCLUSION

Integrated PET/MRI can provide early response assessment in patients with advanced PDAC, thus potentially allowing early treatment adaptation in nonresponders.

Comparison of SUVmax and SUVpeak based segmentation to determine primary lung tumour volume on FDG PET-CT correlated with pathology data

PURPOSE

The aim of the study was to compare simple SUVmax and SUVpeak based segmentation methods for calculating the lung tumour volume, compared to a pathology ground truth.

METHODS

Thirty patients diagnosed with early stage Non-Small Cell lung cancer (NSCLC) underwent surgical resection in the Netherlands between 2006 and 2008. FDG PET-CT scans for these patients were acquired within a median of 20 days before surgery. The tumour volume for each percentage SUVmax and SUVpeak threshold, with and without background correction, was calculated for each patient. The percentage threshold that provided the tumour volume that corresponded best with the pathology volume was considered to be the optimal threshold. The optimal thresholds were plotted as a function of tumour volume using a power law function and cross validated using the leave-one-out technique.

RESULTS

The mean optimal percentage threshold was 50% ± 10% and 62% ± 15% for the SUVmax and SUVpeak without background correction respectively and 47% ± 10% and 60 ± 15% for the SUVmax and SUVpeak with background correction respectively. The optimal threshold curves could be fitted well with power law function. After cross validation the correlation between the effective tumour diameter in pathology and autosegmentation was 0.900 and 0.905 for the SUVmax and SUVpeak without background correction respectively and 0.913 and 0.908 for the SUVmax and SUVpeak with background correction respectively.

CONCLUSION

No benefit was shown on clinical data for the SUVpeak based segmentation method over a SUVmax based one. Both methods can be used to determine the tumour volumes in resected NSCLC tumours.

Pathological tumor volume predicts survival outcomes in oral squamous cell carcinoma

The present study examined whether the pathological tumor volume (PTV) was correlated with the survival outcomes in patients with oral squamous cell carcinoma (SCC) and clinical lymph node metastasis. Forty-seven patients who underwent radical surgery without preoperative treatment were enrolled. The PTV of the primary tumor, which was surgically resected without preoperative treatment, was calculated based on the diameters in three dimensions. A survival analysis was performed using a Cox proportional hazards model. A PTV of ≥18 cm³ was significantly correlated with shorter overall survival (P<0.01) and local recurrence-free survival (P<0.01) in a univariate analysis. A multivariate analysis with adjustment for the pathological stage (stage I–II/III–IV), primary site (tongue/others) and positive surgical margin and/or extracapsular extension (absent/present) showed that a PTV of ≥18 cm³ was significantly correlated with shorter overall survival (P<0.01) and local recurrence-free survival (P<0.01). The present findings suggested that PTV in oral SCC provides a prognostic parameter that may predict shorter or longer overall and local recurrence-free survival.

Defining the optimal method for measuring baseline metabolic tumour volume in diffuse large B cell lymphoma

PURPOSE

Metabolic tumour volume (MTV) is a promising prognostic indicator in diffuse large B cell lymphoma (DLBCL). Optimal thresholds to divide patients into 'low' versus 'high' MTV groups depend on clinical characteristics and the measurement method. The aim of this study was to compare in consecutive unselected patients with DLBCL, different software algorithms and published methods of MTV measurement using FDG PET.

METHOD

Pretreatment MTV was measured on 147 patients treated at Guy's and St Thomas' Hospital. We compared 3 methods: SUV ≥2.5, SUV ≥41% of maximum SUV and SUV ≥ mean liver uptake (PERCIST) and compared 2 software programs for measuring SUV ≥2.5; in-house 'PETTRA' software and Hermes commercial software.

RESULTS

There was strong correlation between MTV using the 4 methods, although derived thresholds were very different for the 41% method. Optimal cut-offs for predicting PFS ranged from 166-400cm³. All methods predicted survival with similar accuracy. 5y-PFS was 83-87% vs. 42-44% and 5y-OS was 85-89% vs. 55-58% for the low- and high-MTV groups, respectively. Interobserver variation in 50 patients showed excellent agreement, though variation was lowest using the SUV ≥ 2.5 method. The 41% method was the most complex and took the longest time.

CONCLUSION

All methods predicted PFS and OS with similar accuracy, but the derived cut-off separating good from poor prognosis varied markedly depending on the method. The choice of the optimal method should rely primarily on prognostic value, but for clinical use needs to take account of ease of use and reproducibility. In this study, all methods predicted prognosis, but SUV ≥ 2.5 had the best inter-observer agreement and was easiest to apply.

Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better?

Numerous methods to segment tumors using ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG PET) have been introduced. Metabolic tumor volume (MTV) refers to the metabolically active volume of the tumor segmented using FDG PET, and has been shown to be useful in predicting patient outcome and in assessing treatment response. Also, tumor segmentation using FDG PET has useful applications in radiotherapy treatment planning. Despite extensive research on MTV showing promising results, MTV is not used in standard clinical practice yet, mainly because there is no consensus on the optimal method to segment tumors in FDG PET images. In this review, we discuss currently available methods to measure MTV using FDG PET, and assess the advantages and disadvantages of the methods.

Comparison of novel multi-level Otsu (MO-PET) and conventional PET segmentation methods for measuring FDG metabolic tumor volume in patients with soft tissue sarcoma

BACKGROUND

We have previously developed a novel and highly consistent PET segmentation algorithm using a multi-level Otsu method (MO-PET). The aim of this study was to evaluate the reliability of MO-PET compared to conventional PET segmentation methods for measuring 18F-FDG (FDG) PET metabolic tumor volume (MTV) in patients with soft tissue sarcoma (STS). Clinical and imaging data were obtained from the Cancer Imaging Archive. Forty-eight STS patients with FDG PET/CT and MR prior to therapy were analyzed. MTV of the tumor using MO-PET was compared to other conventional methods (absolute SUV threshold values of 2.0, 2.5, or 3.0 and percentage of tumor SUVmax values of 30, 40, 50, or 60%) and gradient-based method (PET Edge™). The reference volume was defined as an MR-based gross tumor volume (GTV). Spearman, intra-class correlation, and Bland-Altman analysis were performed to evaluate the correlation and agreement of MTV to GTV.

RESULTS

MTVs obtained using each conventional SUV parameter, PET Edge™, and MO-PET were highly correlated with the GTV in Spearman and intra-class correlation analysis (p < 0.05). MO-PET and PET Edge™ showed high intra-class correlation coefficient of MTV to GTV (0.93 and 0.84, respectively). The Bland-Altman bias results showed the highest agreement for MTV using MO-PET with GTV (26.0 ± 489.6 cm3) compared to other methods (SUV 2.0 with - 69.3 ± 765.8, 30% SUVmax with - 255.0 ± 876.6, and PET Edge™ with - 26.46 ± 668.82 cm3).

CONCLUSIONS

PET MTV segmented with MO-PET showed higher correlation and agreement with GTV in comparison to conventional percentage SUVmax and absolute SUV threshold-based PET segmentation methods. MO-PET is comparable to PET Edge™. MO-PET is a reliable and consistent method for measuring tumor MTV.

Mid-radiotherapy PET/CT for prognostication and detection of early progression in patients with stage III non-small cell lung cancer

BACKGROUND AND PURPOSE

Pre- and mid-radiotherapy FDG-PET metrics have been proposed as biomarkers of recurrence and survival in patients treated for stage III non-small cell lung cancer. We evaluated these metrics in patients treated with definitive radiation therapy (RT). We also evaluated outcomes after progression on mid-radiotherapy PET/CT.

MATERIAL AND METHODS

Seventy-seven patients treated with RT with or without chemotherapy were included in this retrospective study. Primary tumor and involved nodes were delineated. PET metrics included metabolic tumor volume (MTV), total lesion glycolysis (TLG), and SUV_max. For mid-radiotherapy PET, both absolute value of these metrics and percentage decrease were analyzed. The influence of PET metrics on time to death, local recurrence, and regional/distant recurrence was assessed using Cox regression.

RESULTS

91% of patients had concurrent chemotherapy. Median follow-up was 14months. None of the PET metrics were associated with overall survival. Several were positively associated with local recurrence: pre-radiotherapy MTV, and mid-radiotherapy MTV and TLG (p=0.03-0.05). Ratio of mid- to pre-treatment SUV_max was associated with regional/distant recurrence (p=0.02). 5/77 mid-radiotherapy scans showed early out-of-field progression. All of these patients died.

CONCLUSIONS

Several PET metrics were associated with risk of recurrence. Progression on mid-radiotherapy PET/CT was a poor prognostic factor.

Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data

PURPOSE

Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making.

METHODS

To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis.

RESULTS

On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively.

CONCLUSION

Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

Effect of different segmentation algorithms on metabolic tumor volume measured on 18F-FDG PET/CT of cervical primary squamous cell carcinoma

Background and purpose

It is known that fluorine-18 fluorodeoxyglucose PET/computed tomography (CT) segmentation algorithms have an impact on the metabolic tumor volume (MTV). This leads to some uncertainties in PET/CT guidance of tumor radiotherapy. The aim of this study was to investigate the effect of segmentation algorithms on the PET/CT-based MTV and their correlations with the gross tumor volumes (GTVs) of cervical primary squamous cell carcinoma.

Materials and methods

Fifty-five patients with International Federation of Gynecology and Obstetrics stage Ia∼IIb and histologically proven cervical squamous cell carcinoma were enrolled. A fluorine-18 fluorodeoxyglucose PET/CT scan was performed before definitive surgery. GTV was measured on surgical specimens. MTVs were estimated on PET/CT scans using different segmentation algorithms, including a fixed percentage of the maximum standardized uptake value (20∼60% SUV_max) threshold and iterative adaptive algorithm. We divided all patients into four different groups according to the SUV_max within target volume. The comparisons of absolute values and percentage differences between MTVs by segmentation and GTV were performed in different SUV_max subgroups. The optimal threshold percentage was determined from MTV_20%∼MTV_60%, and was correlated with SUV_max. The correlation of MTV_{iterative adaptive} with GTV was also investigated.

Results

MTV_50% and MTV_60% were similar to GTV in the SUV_max up to 5 (P>0.05). MTV_30%∼MTV_60% were similar to GTV (P>0.05) in the 5<SUV_max≤10 group. MTV_20%∼MTV_60% were similar to GTV (P>0.05) in the 10<SUV_max≤15 group. MTV_20% and MTV_30% were similar to GTV (P>0.05) in the SUV_max of at least 15 group. MTV_{iterative adaptive} was similar to GTV in both total and different SUV_max groups (P>0.05). Significant differences were observed among the fixed percentage method and the optimal threshold percentage was inversely correlated with SUV_max. The iterative adaptive segmentation algorithm led to the highest accuracy (6.66±50.83%). A significantly positive correlation was also observed between MTV_{iterative adaptive} and GTV (Pearson’s correlation r=0.87, P<0.0001).

Conclusion

MTV_{iterative adaptive} is independent of SUV_max, more accurate, and correlated with GTV. Iterative adaptive algorithm segmentation may be more suitable than the fixed percentage threshold method to estimate the tumor volume of cervical primary squamous cell carcinoma.

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis.