The Influence of the Exclusion of Central Necrosis on [18F]FDG PET Radiomic Analysis

SDH-related head and neck paragangliomas: Unraveling PET radiomics beyond 18F-FDG

Radiomics revolutionizes medical imaging by providing quantitative analysis that complements traditional qualitative assessments through advanced computational techniques. In this narrative review we have investigated the impact of succinate dehydrogenase (SDH) pathogenic variants on the radiomic profile of 18F-FDG, 18F-DOPA, and 68Ga-DOTA-peptides PET in paragangliomas, focusing on head and neck localizations (HNPGLs). This influence manifests in uptake intensity and textural heterogeneity, revealing a complex radiomic landscape that may reflect specific tumor behaviors and mutation statuses. By combining radiomic analysis with genetic data, we will gain new insights into the relationship between PET imaging features and underlying molecular changes. In the future, we envision an approach integrating macroscopic indices, such as lesion location, size, and SUV, with advanced computer-based algorithms. This comprehensive analysis could facilitate in vivo predictions of SDH pathogenic variants, thereby encouraging genetic testing, and ultimately improving patient outcomes.

Commissioning of a novel PET-Linac for biology-guided radiotherapy (BgRT)

BACKGROUND

Biology-guided radiotherapy (BgRT) is a novel radiotherapy delivery technique that utilizes the tumor itself to guide dynamic delivery of treatment dose to the tumor. The RefleXion X1 system is the first radiotherapy system developed to deliver SCINTIX® BgRT. The X1 is characterized by its split arc design, employing two 90-degree positron emission tomography (PET) arcs to guide therapeutic radiation beams in real time, currently cleared by FDA to treat bone and lung tumors.

PURPOSE

This study aims to comprehensively evaluate the capabilities of the SCINTIX radiotherapy delivery system by evaluating its sensitivity to changes in PET contrast, its adaptability in the context of patient motion, and its performance across a spectrum of prescription doses.

METHODS

A series of experimental scenarios, both static and dynamic, were designed to assess the SCINTIX BgRT system's performance, including an end-to-end test. These experiments involved a range of factors, including changes in PET contrast, motion, and prescription doses. Measurements were performed using a custom-made ArcCHECK insert which included a 2.2 cm spherical target and a c-shape structure that can be filled with a PET tracer with varying concentrations. Sinusoidal and cosine4 motion patterns, simulating patient breathing, was used to test the SCINTIX system's ability to deliver BgRT during motion-induced challenges. Each experiment was evaluated against specific metrics, including Activity Concentration (AC), Normalized Target Signal (NTS), and Biology Tracking Zone (BTZ) bounded dose-volume histogram (bDVH) pass rates. The accuracy of the delivered BgRT doses on ArcCHECK and EBT-XD film were evaluated using gamma 3%/2 mm and 3%/3 mm analysis.

RESULTS

In static scenarios, the X1 system consistently demonstrated precision and robustness in SCINTIX dose delivery. The end-to-end delivery to the spherical target yielded good results, with AC and NTS values surpassing the critical thresholds of 5 kBq/mL and 2, respectively. Furthermore, bDVH analysis consistently confirmed 100% pass rates. These results were reaffirmed in scenarios involving changes in PET contrast, emphasizing the system's ability to adapt to varying PET avidities. Gamma analysis with 3%/2 mm (10% dose threshold) criteria consistently achieved pass rates > 91.5% for the static tests. In dynamic SCINTIX delivery scenarios, the X1 system exhibited adaptability under conditions of motion. Sinusoidal and cosine4 motion patterns resulted in 3%/3 mm gamma pass rates > 87%. Moreover, the comparison with gated stereotactic body radiotherapy (SBRT) delivery on a conventional c-arm Linac resulted in 93.9% gamma pass rates and used as comparison to evaluate the interplay effect. The 1 cm step shift tests showed low overall gamma pass rates of 60.3% in ArcCHECK measurements, while the doses in the PTV agreed with the plan with 99.9% for 3%/3 mm measured with film.

CONCLUSIONS

The comprehensive evaluation of the X1 radiotherapy delivery system for SCINTIX BgRT demonstrated good agreement for the static tests. The system consistently achieved critical metrics and delivered the BgRT doses per plan. The motion tests demonstrated its ability to co-localize the dose where the PET signal is and deliver acceptable BgRT dose distributions.

Promising Candidate Prognostic Biomarkers in [18F]FDG PET Images: Evaluation in Independent Cohorts of Non-Small Cell Lung Cancer Patients

The normalized distances from the hot spot of radiotracer uptake (SUVmax) to the tumor centroid (NHOC) and to the tumor perimeter (NHOP) have recently been suggested as novel PET features reflecting tumor aggressiveness. These biomarkers characterizing the shift of SUVmax toward the lesion edge during tumor progression have been shown to be prognostic factors in breast and non-small cell lung cancer (NSCLC) patients. We assessed the impact of imaging parameters on NHOC and NHOP, their complementarity to conventional PET features, and their prognostic value for advanced-NSCLC patients. Methods: This retrospective study investigated baseline [18F]FDG PET scans: cohort 1 included 99 NSCLC patients with no treatment-related inclusion criteria (robustness study); cohort 2 included 244 NSCLC patients (survival analysis) treated with targeted therapy (93), immunotherapy (63), or immunochemotherapy (88). Although 98% of patients had metastases, radiomic features including SUVs were extracted from the primary tumor only. NHOCs and NHOPs were computed using 2 approaches: the normalized distance from the localization of SUVmax or SUVpeak to the tumor centroid or perimeter. Bland-Altman analyses were performed to investigate the impact of both spatial resolution (comparing PET images with and without gaussian postfiltering) and image sampling (comparing 2 voxel sizes) on feature values. The correlation of NHOCs and NHOPs with other features was studied using Spearman correlation coefficients (r). The ability of NHOCs and NHOPs to predict overall survival (OS) was estimated using the Kaplan-Meier method. Results: In cohort 1, NHOC and NHOP features were more robust to image filtering and to resampling than were SUVs. The correlations were weak between NHOCs and NHOPs (r ≤ 0.45) and between NHOCs or NHOPs and any other radiomic features (r ≤ 0.60). In cohort 2, the patients with short OS demonstrated higher NHOCs and lower NHOPs than those with long OS. NHOCs significantly distinguished 2 survival profiles in patients treated with immunotherapy (log-rank test, P < 0.01), whereas NHOPs stratified patients regarding OS in the targeted therapy (P = 0.02) and immunotherapy (P < 0.01) subcohorts. Conclusion: Our findings suggest that even in advanced NSCLC patients, NHOC and NHOP features pertaining to the primary tumor have prognostic potential. Moreover, these features appeared to be robust with respect to imaging protocol parameters and complementary to other radiomic features and are now available in LIFEx software to be independently tested by others.

External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer

OBJECTIVES

To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance.

METHODS

Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity.

RESULTS

Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60).

CONCLUSION

The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models.

KEY POINTS

• Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.

[18F]FDG-PET/CT radiomics for the identification of genetic clusters in pheochromocytomas and paragangliomas

Objectives

Based on germline and somatic mutation profiles, pheochromocytomas and paragangliomas (PPGLs) can be classified into different clusters. We investigated the use of [¹⁸F]FDG-PET/CT radiomics, SUV_max and biochemical profile for the identification of the genetic clusters of PPGLs.

Methods

In this single-centre cohort, 40 PPGLs (13 cluster 1, 18 cluster 2, 9 sporadic) were delineated using a 41% adaptive threshold of SUV_peak ([¹⁸F]FDG-PET) and manually (low-dose CT; ldCT). Using PyRadiomics, 211 radiomic features were extracted. Stratified 5-fold cross-validation for the identification of the genetic cluster was performed using multinomial logistic regression with dimensionality reduction incorporated per fold. Classification performances of biochemistry, SUV_max and PET(/CT) radiomic models were compared and presented as mean (multiclass) test AUCs over the five folds. Results were validated using a sham experiment, randomly shuffling the outcome labels.

Results

The model with biochemistry only could identify the genetic cluster (multiclass AUC 0.60). The three-factor PET model had the best classification performance (multiclass AUC 0.88). A simplified model with only SUV_max performed almost similarly. Addition of ldCT features and biochemistry decreased the classification performances. All sham AUCs were approximately 0.50.

Conclusion

PET radiomics achieves a better identification of PPGLs compared to biochemistry, SUV_max, ldCT radiomics and combined approaches, especially for the differentiation of sporadic PPGLs. Nevertheless, a model with SUV_max alone might be preferred clinically, weighing model performances against laborious radiomic analysis. The limited added value of radiomics to the overall classification performance for PPGL should be validated in a larger external cohort.

Key Points

• Radiomics derived from [¹⁸F]FDG-PET/CT has the potential to improve the identification of the genetic clusters of pheochromocytomas and paragangliomas.

• A simplified model with SUV_maxonly might be preferred clinically, weighing model performances against the laborious radiomic analysis.

• Cluster 1 and 2 PPGLs generally present distinctive characteristics that can be captured using [¹⁸F]FDG-PET imaging. Sporadic PPGLs appear more heterogeneous, frequently resembling cluster 2 PPGLs and occasionally resembling cluster 1 PPGLs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-09034-5.

Editorial on Special Issue "Quantitative PET and SPECT"

Since the introduction of personalized (or precision) medicine, where individually tailored treatments are designed to deliver the right treatment to the right patient at the right time, the primary focus of imaging has moved from detection and diagnosis to tissue characterization, determination of prognosis, prediction of treatment efficacy, and measurement of treatment response [...].