Prediction of survival by [18F]fluorodeoxyglucose positron emission tomography in patients with locally advanced non-small-cell lung cancer undergoing definitive chemoradiation therapy: results of the ACRIN 6668/RTOG 0235 trial

New Perspectives for Estimating Body Composition From Computed Tomography: Clothing Associated Artifacts

As the value of clinical imaging is expanded through retrospective analyses, it is imperative that all efforts are made to optimize validity. Such considerations for retrospective designs should prioritize factors like naturalistic conditions for observations and measurement replicability, while avoiding sample biases and reliance on strict clinical timelines. Valid methodological approaches are immanent for successful translation from retrospective observational designs into prospective pragmatic research with actionable potential. In particular, thousands of studies have sought to associate clinical outcomes to measures of body composition across diverse patient groups. Post-hoc use of computed tomography (CT) to quantify adiposity and lean tissue characteristics has most frequently involved just a single slice at the level of the third lumbar vertebrae (L3). Abundant in statistical significance and inconsistencies alike, such methods have yet to be implemented or deemed valuable for making real-world clinical decisions. We present herein a concerning perspective, for both magnitude and prevalence, of a widely overlooked source of data variability for this methodology: the hinderance of pants and other tightly fit clothing.

Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome

PURPOSE

Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome.

METHODS

[18F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score.

RESULTS

Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001).

CONCLUSIONS

Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome.

SAROS: A dataset for whole-body region and organ segmentation in CT imaging

The Sparsely Annotated Region and Organ Segmentation (SAROS) dataset was created using data from The Cancer Imaging Archive (TCIA) to provide a large open-access CT dataset with high-quality annotations of body landmarks. In-house segmentation models were employed to generate annotation proposals on randomly selected cases from TCIA. The dataset includes 13 semantic body region labels (abdominal/thoracic cavity, bones, brain, breast implant, mediastinum, muscle, parotid/submandibular/thyroid glands, pericardium, spinal cord, subcutaneous tissue) and six body part labels (left/right arm/leg, head, torso). Case selection was based on the DICOM series description, gender, and imaging protocol, resulting in 882 patients (438 female) for a total of 900 CTs. Manual review and correction of proposals were conducted in a continuous quality control cycle. Only every fifth axial slice was annotated, yielding 20150 annotated slices from 28 data collections. For the reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined. The SAROS dataset serves as an open-access resource for training and evaluating novel segmentation models, covering various scanner vendors and diseases.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study

OBJECTIVES

This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

METHODS

The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms.

RESULTS

The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05).

CONCLUSION

Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging

OBJECTIVES

Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT).

MATERIALS AND METHODS

This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs).

RESULTS

For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively.

CONCLUSIONS

The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.

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.

Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy

PURPOSE

Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy.

METHODS

Pretreatment 18F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported.

RESULTS

From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets.

CONCLUSION

This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care 18F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy.

Convolutional neural network-based program to predict lymph node metastasis of non-small cell lung cancer using 18F-FDG PET

PURPOSE

To develop a convolutional neural network (CNN)-based program to analyze maximum intensity projection (MIP) images of 2-deoxy-2-[F-18]fluoro-D-glucose (FDG) positron emission tomography (PET) scans, aimed at predicting lymph node metastasis of non-small cell lung cancer (NSCLC), and to evaluate its effectiveness in providing diagnostic assistance to radiologists.

METHODS

We obtained PET images of NSCLC from public datasets, including those of 435 patients with available N-stage information, which were divided into a training set (n = 304) and a test set (n = 131). We generated 36 maximum intensity projection (MIP) images for each patient. A residual network (ResNet-50)-based CNN was trained using the MIP images of the training set to predict lymph node metastasis. Lymph node metastasis in the test set was predicted by the trained CNN as well as by seven radiologists twice: first without and second with CNN assistance. Diagnostic performance metrics, including accuracy and prediction error (the difference between the truth and the predictions), were calculated, and reading times were recorded.

RESULTS

In the test set, 67 (51%) patients exhibited lymph node metastases and the CNN yielded 0.748 predictive accuracy. With the assistance of the CNN, the prediction error was significantly reduced for six of the seven radiologists although the accuracy did not change significantly. The prediction time was significantly reduced for five of the seven radiologists with the median reduction ratio 38.0%.

CONCLUSION

The CNN-based program could potentially assist radiologists in predicting lymph node metastasis by increasing diagnostic confidence and reducing reading time without affecting diagnostic accuracy, at least in the limited situations using MIP images.

Pretreatment 18-FDG-PET/CT parameters can serve as prognostic imaging biomarkers in recurrent NSCLC patients treated with reirradiation-chemoimmunotherapy

BACKGROUND AND PURPOSE

Our study aimed to assess whether quantitative pretreatment 18F-FDG-PET/CT parameters could predict prognostic clinical outcome of recurrent NSCLC patients who may benefit from ablative reirradiation.

MATERIALS AND METHODS

Forty-eight patients with recurrent NSCLC of all UICC stages who underwent ablative thoracic reirradiation were analyzed. Twenty-nine (60%) patients received immunotherapy with or without chemotherapy in addition to reirradiation. Twelve patients (25%) received reirradiation only and seven (15%) received chemotherapy and reirradiation. Pretreatment 18-FDG-PET/CT was mandatory in initial diagnosis and recurrence, based on which volumetric and intensity quantitative parameters were measured before reirradiation and their impact on overall survival, progression-free survival, and locoregional control was assessed.

RESULTS

With a median follow-up time of 16.7 months, the median OS was 21.8 months (95%-CI: 16.2-27.3). On multivariate analysis, OS and PFS were significantly influenced by MTV (p < 0.001 for OS; p = 0.006 for PFS), TLG (p < 0.001 for OS; p = 0.001 for PFS) and SUL peak (p = 0.0024 for OS; p = 0.02 for PFS) of the tumor and MTV (p = 0.004 for OS; p < 0.001 for PFS) as well as TLG (p = 0.007 for OS; p = 0.015 for PFS) of the metastatic lymph nodes. SUL peak of the tumor (p = 0.05) and the MTV of the lymph nodes (p = 0.003) were only PET quantitative parameters that significantly impacted LRC.

CONCLUSION

Pretreatment tumor and metastastic lymph node MTV, TLG and tumor SUL peak significantly correlated with clinical outcome in recurrent NSCLC patients treated with reirradiation-chemoimmunotherapy.

Metabolic patterns on [18F]FDG PET/CT in patients with unresectable stage III NSCLC undergoing chemoradiotherapy ± durvalumab maintenance treatment

PURPOSE

In patients with unresectable stage III non-small-cell lung cancer (NSCLC), durvalumab maintenance treatment after chemoradiotherapy (CRT) significantly improves survival. So far, however, metabolic changes of tumoral lesions and secondary lymphoid organs under durvalumab are unknown. Hence, we assessed changes on [18F]FDG PET/CT in comparison to patients undergoing CRT alone.

METHODS

Forty-three patients with [18F]FDG PET/CT both before and after standard CRT for unresectable stage III NSCLC were included, in 16/43 patients durvalumab maintenance treatment was initiated (CRT-IO) prior to the second PET/CT. Uptake of tumor sites and secondary lymphoid organs was compared between CRT and CRT-IO. Also, readers were blinded for durvalumab administration and reviewed scans for findings suspicious for immunotherapy-related adverse events (irAE).

RESULTS

Initial uptake characteristics were comparable. However, under durvalumab, diverging metabolic patterns were noted: There was a significantly higher reduction of tumoral uptake intensity in CRT-IO compared to CRT, e.g. median decrease of SUVmax -70.0% vs. -24.8%, p = 0.009. In contrast, the spleen uptake increased in CRT-IO while it dropped in CRT (median + 12.5% vs. -4.4%, p = 0.029). Overall survival was significantly longer in CRT-IO compared to CRT with few events (progression/death) noted in CRT-IO. Findings suggestive of irAE were present on PET/CT more often in CRT-IO (12/16) compared to CRT (8/27 patients), p = 0.005.

CONCLUSION

Durvalumab maintenance treatment after CRT leads to diverging tumoral metabolic changes, but also increases splenic metabolism and leads to a higher proportion of findings suggestive of irAE compared to patients without durvalumab. Due to significantly prolonged survival with durvalumab, survival analysis will be substantiated in correlation to metabolic changes as soon as more clinical events are present.

Waiting for Big Changes in Limited-Stage Small-Cell Lung Cancer: For Now, More of the Same

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice.Concurrent chemoradiotherapy remains central to the treatment of limited-stage small-cell lung cancer (SCLC). SCLC is one of the few tumors treated with twice-daily radiotherapy (RT) in the primary definitive setting, a regimen that was established when Intergroup 0096 demonstrated its superiority over once-daily RT. However, questions remained about the optimal chemoradiotherapy regimen given the low RT dose used in the once-daily RT arm of Intergroup 0096. CALGB 30610/RTOG 0538 and CONVERT attempted to establish whether dose-escalated once-daily RT was superior to twice-daily RT in limited-stage SCLC. Although both studies showed similar survival between treatment regimens, once-daily RT was not found to be superior to twice-daily RT, and trial design limited the ability to conclude dose-escalated once-daily RT as noninferior to twice-daily RT. Thus, twice-daily RT with concurrent chemotherapy remains a standard of care in limited-stage SCLC.

Biology-Guided Radiation Therapy

Biology-guided radiation therapy is an emerging field whereby delivery of external beam radiotherapy incorporates biological/molecular imaging to inform radiation treatment. At present, there is evidence for the use of functional imaging such as PET to evaluate treatment response in patients both during and after radiation treatment as well as to provide a method of adapting or selecting patient-specific treatments. Examples in thoracic, gastrointestinal, and hematologic malignancies are provided. Improvements in PET metrics, thresholds, and novel radiotracers will further move this novel field forward.

Cervical body composition on radiotherapy planning computed tomography scans predicts overall survival in glioblastoma patients

Background and purpose

Glioblastoma (GBM) patients face a strongly unfavorable prognosis despite multimodal therapy regimens. However, individualized mortality prediction remains imprecise. Harnessing routine radiation planning cranial computed tomography (CT) scans, we assessed cervical body composition measures as novel biomarkers for overall survival (OS) in GBM patients.

Materials and methods

We performed threshold-based semi-automated quantification of muscle and subcutaneous fat cross-sectional area (CSA) at the levels of the first and second cervical vertebral body. First, we tested this method's validity by correlating cervical measures to established abdominal body composition in an open-source whole-body CT cohort. We then identified consecutive patients undergoing radiation planning for recent GBM diagnosis at our institution from 2010 to 2020 and quantified cervical body composition on radiation planning CT scans. Finally, we performed univariable and multivariable time-to-event analyses, adjusting for age, sex, body mass index, comorbidities, performance status, extent of surgical resection, extent of tumor at diagnosis, and MGMT methylation.

Results

Cervical body composition measurements were well-correlated with established abdominal markers (Spearman's rho greater than 0.68 in all cases). Subsequently, we included 324 GBM patients in our study cohort (median age 63 years, 60.8% male). 293 (90.4%) patients died during follow-up. Median survival time was 13 months. Patients with below-average muscle CSA or above-average fat CSA demonstrated shorter survival. In multivariable analyses, continuous cervical muscle measurements remained independently associated with OS.

Conclusion

This exploratory study establishes novel cervical body composition measures routinely available on cranial radiation planning CT scans and confirms their association with OS in patients diagnosed with GBM.

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

PURPOSE

Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.

METHODS

Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) ¹⁸F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center).

RESULTS

In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R² = 0.94), FL-SQ (R² = 0.93), and FL-PL (R² = 0.92), while CB model achieved a far lower coefficient of determination (R² = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed.

CONCLUSION

Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.

Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET

Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these methods, assessing their performance on clinically relevant tasks is important. However, these methods are typically evaluated using metrics that may not correlate with the task performance. One such widely used metric is the Dice score, a figure of merit that measures the spatial overlap between the estimated segmentation and a reference standard (e.g., manual segmentation). In this work, we investigated whether evaluating AI-based segmentation methods using Dice scores yields a similar interpretation as evaluation on the clinical tasks of quantifying metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumor from PET images of patients with non-small cell lung cancer. The investigation was conducted via a retrospective analysis with the ECOG-ACRIN 6668/RTOG 0235 multi-center clinical trial data. Specifically, we evaluated different structures of a commonly used AI-based segmentation method using both Dice scores and the accuracy in quantifying MTV/TLG. Our results show that evaluation using Dice scores can lead to findings that are inconsistent with evaluation using the task-based figure of merit. Thus, our study motivates the need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.

Prognostic Role of Biologically Active Volume of Disease in Patients With Metastatic Lung Adenocarcinoma

BACKGROUND

Number of metastatic sites can identify patient populations with non-small cell lung cancer (NSCLC) that benefit from aggressive therapy. Total volume of disease is also relevant. We evaluated the prognostic impact of biologically active volume of disease (BaVD) on patients with metastatic lung adenocarcinoma.

MATERIALS AND METHODS

Positron emission tomography/computerized tomography (PET/CT) scans from patients with newly diagnosed lung adenocarcinoma prior to starting any therapy were identified. SUV thresholds of 3 and 4 were used to auto-contour all FDG avid areas. Kaplan-Meier analysis and Cox regression were performed to examine influence on OS.

RESULTS

One hundred forty-eight patients were included in the analysis. The median BaVD when using an SUV threshold of 3 was 122.8 mL. The median BaVD when using an SUV threshold of 4 was 46.2 mL When stratified by median BaVD using an SUV of 3, median OS was higher for patients with <=122.8 mL (2.12 years) compared to patients with >122.8 mL (1.46 years) (log-rank P = .001). Similarly, when stratified by median BaVD using an SUV of 4, median OS was higher for patients with <=46.2 mL (1.91 years; 95% CI: 1.65-3.22 years) compared to patients with >46.2 mL (1.48 years; 95% CI: 1.07-1.80 years) (log-rank P = .007). On multivariable analysis, BaVD was significantly associated with OS when using an SUV threshold of 3 (HR: 20.169, P < .001) and 4 (HR: 4.117, P < .001).

CONCLUSION

BaVD is an important prognostic factor in metastatic lung adenocarcinoma and may aid identification of patients with limited disease who may be candidates for more aggressive therapies.

Automated segmentation of five different body tissues on computed tomography using deep learning

PURPOSE

To develop and validate a computer tool for automatic and simultaneous segmentation of five body tissues depicted on computed tomography (CT) scans: visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), skeletal muscle (SM), and bone.

METHODS

A cohort of 100 CT scans acquired on different subjects were collected from The Cancer Imaging Archive-50 whole-body positron emission tomography-CTs, 25 chest, and 25 abdominal. Five different body tissues (i.e., VAT, SAT, IMAT, SM, and bone) were manually annotated. A training-while-annotating strategy was used to improve the annotation efficiency. The 10-fold cross-validation method was used to develop and validate the performance of several convolutional neural networks (CNNs), including UNet, Recurrent Residual UNet (R2Unet), and UNet++. A grid-based three-dimensional patch sampling operation was used to train the CNN models. The CNN models were also trained and tested separately for each body tissue to see if they could achieve a better performance than segmenting them jointly. The paired sample t-test was used to statistically assess the performance differences among the involved CNN models RESULTS: When segmenting the five body tissues simultaneously, the Dice coefficients ranged from 0.826 to 0.840 for VAT, from 0.901 to 0.908 for SAT, from 0.574 to 0.611 for IMAT, from 0.874 to 0.889 for SM, and from 0.870 to 0.884 for bone, which were significantly higher than the Dice coefficients when segmenting the body tissues separately (p < 0.05), namely, from 0.744 to 0.819 for VAT, from 0.856 to 0.896 for SAT, from 0.433 to 0.590 for IMAT, from 0.838 to 0.871 for SM, and from 0.803 to 0.870 for bone.

CONCLUSION

There were no significant differences among the CNN models in segmenting body tissues, but jointly segmenting body tissues achieved a better performance than segmenting them separately.

Comparison of Conventional Imaging and 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in the Diagnostic Accuracy of Staging in Patients with Intrahepatic Cholangiocarcinoma

We aimed to examine the accuracy of tumor staging of intrahepatic cholangiocarcinoma (ICC) by using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT). From January 2001 to December 2021, 202 patients underwent PET-CT, CT, and MRI for the initial staging of ICC in two institutions. Among them, 102 patients had undergone surgical treatment. Ninety patients who had a histopathological diagnosis of ICC were retrospectively reviewed. The sensitivity and specificity of 18F-FDG PET-CT, CT, and magnetic resonance imaging (MRI) in detecting tumors, satellite focus, vascular invasion, and lymph node metastases were analyzed. Ninety patients with histologically diagnosed ICC were included. PET-CT demonstrated no statistically significant advantage over CT and MR in the diagnosis of multiple tumors and macrovascular invasion, and bile duct invasion. The overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET-CT in lymph node metastases were 84%, 86%, 91%, 84%, and 86%, respectively. PET-CT revealed a significantly higher accuracy compared to CT or MRI (86%, 67%, and 76%, p < 0.01, respectively) in the diagnosis of regional lymph node metastases. The accuracy of tumor staging by PET-CT was higher than that by CT/MRI (PET-CT vs. CT vs. MRI: 68/90 vs. 47/90 vs. 51/90, p < 0.05). 18F-FDG PET-CT had sensitivity and specificity values for diagnosing satellite focus and vascular and bile duct invasion similar to those of CT or MRI; however, PET-CT showed higher accuracy in diagnosing regional lymph node metastases. 18F-FDG PET-CT exhibited higher tumor staging accuracy than that of CT/MRI. Thus, 18FDG PET-CT may support tumor staging in ICC.

New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies

Not only visual interpretation for lesion detection, staging, and characterization, but also quantitative treatment response assessment are key roles for ¹⁸F-FDG PET in oncology. In multicenter oncology PET studies, image quality standardization and SUV harmonization are essential to obtain reliable study outcomes. Standards for image quality and SUV harmonization range should be regularly updated according to progress in scanner performance. Accordingly, the first aim of this study was to propose new image quality reference levels to ensure small lesion detectability. The second aim was to propose a new SUV harmonization range and an image noise criterion to minimize the inter-scanner and intra-scanner SUV variabilities. We collected a total of 37 patterns of images from 23 recent PET/CT scanner models using the NEMA NU2 image quality phantom. PET images with various acquisition durations of 30-300 s and 1800 s were analyzed visually and quantitatively to derive visual detectability scores of the 10-mm-diameter hot sphere, noise-equivalent count (NEC_phantom), 10-mm sphere contrast (Q_H,10 mm), background variability (N_10 mm), contrast-to-noise ratio (Q_H,10 mm/N_10 mm), image noise level (CV_BG), and SUVmax and SUVpeak for hot spheres (10-37 mm diameters). We calculated a reference level for each image quality metric, so that the 10-mm sphere can be visually detected. The SUV harmonization range and the image noise criterion were proposed with consideration of overshoot due to point-spread function (PSF) reconstruction. We proposed image quality reference levels as follows: Q_H,10 mm/N_10 mm ≥ 2.5 and CV_BG ≤ 14.1%. The 10th-90th percentiles in the SUV distributions were defined as the new SUV harmonization range. CV_BG ≤ 10% was proposed as the image noise criterion, because the intra-scanner SUV variability significantly depended on CV_BG. We proposed new image quality reference levels to ensure small lesion detectability. A new SUV harmonization range (in which PSF reconstruction is applicable) and the image noise criterion were also proposed for minimizing the SUV variabilities. Our proposed new standards will facilitate image quality standardization and SUV harmonization of multicenter oncology PET studies. The reliability of multicenter oncology PET studies will be improved by satisfying the new standards.

Differential role of residual metabolic tumor volume in inoperable stage III NSCLC after chemoradiotherapy ± immune checkpoint inhibition

BACKGROUND

The PET-derived metabolic tumor volume (MTV) is an independent prognosticator in non-small cell lung cancer (NSCLC) patients. We analyzed the prognostic value of residual MTV (rMTV) after completion of chemoradiotherapy (CRT) in inoperable stage III NSCLC patients with and without immune checkpoint inhibition (ICI).

METHODS

Fifty-six inoperable stage III NSCLC patients (16 female, median 65.0 years) underwent ¹⁸F-FDG PET/CT after completion of standard CRT. rMTV was delineated on ¹⁸F-FDG PET/CT using a standard threshold (liver SUV_mean + 2 × standard deviation). 21/56 patients underwent additional ICI (CRT-IO, 21/56 patients) thereafter. Patients were divided in volumetric subgroups using median split dichotomization (MTV ≤ 4.3 ml vs. > 4.3 ml). rMTV, clinical features, and ICI-application were correlated with clinical outcome parameters (progression-free survival (PFS), local PFS (LPFS), and overall survival (OS).

RESULTS

Overall, median follow-up was 52.0 months. Smaller rMTV was associated with longer median PFS (29.3 vs. 10.5 months, p = 0.015), LPFS (49.9 vs. 13.5 months, p = 0.001), and OS (63.0 vs. 23.0 months, p = 0.003). CRT-IO patients compared to CRT patients showed significantly longer median PFS (29.3 vs. 11.2 months, p = 0.034), LPFS (median not reached vs. 14.0 months, p = 0.016), and OS (median not reached vs. 25.2 months, p = 0.007). In the CRT subgroup, smaller rMTV was associated with longer median PFS (33.5 vs. 8.6 months, p = 0.001), LPFS (49.9 vs. 10.1 months, p = 0.001), and OS (63.0 vs. 16.3 months, p = 0.004). In the CRT-IO subgroup, neither PFS, LPFS, nor OS were associated with MTV (p > 0.05 each). The findings were confirmed in subsequent multivariate analyses.

CONCLUSION

In stage III NSCLC, smaller rMTV is highly associated with superior clinical outcome, especially in patients undergoing CRT without ICI. Patients with CRT-IO show significantly improved outcome compared to CRT patients. Of note, clinical outcome in CRT-IO patients is independent of residual MTV. Hence, even patients with large rMTV might profit from ICI despite extensive tumor load.

Implementation of FDG-PET/CT imaging methodology for quantification of inflammatory response in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial

We measured changes in 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) images in the lung parenchyma to quantify the degree of lung inflammation in patients with locally advanced non-small cell lung cancer (NSCLC) who received radiotherapy (RT). The goal of this study was to demonstrate successful implementation of this imaging methodology on NSCLC patients and to report quantitative statistics between pre-RT and post-RT. Seventy-one patients with NSCLC underwent FDG-PET/CT imaging before and after RT in a prospective study (ACRIN 6668/RTOG 0235). Comparisons between pre-RT and post-RT PET/CT were conducted for partial volume corrected (PVC)-mean standardized uptake value (SUVmean), PVC-global lung parenchymal glycolysis (GLPG), and lung volume for both ipsilateral and contralateral lungs using the nonparametric Wilcoxon signed-rank test. Regression modeling was conducted to associate clinical characteristics with post-RT PET/CT parameters. There was a significant increase in average SUVmean and GLPG of the ipsilateral lung (relative change 40% and 20%) between pre-RT and post-RT PET/CT scans (P<0.0001 and P=0.004). Absolute increases in PVC-SUVmean and PVC-GLPG were more pronounced (ΔPVC-SUVmean 0.32 versus ΔSUVmean 0.28; ΔPVC-GLPG 463.34 cc versus ΔGLPG 352.90 cc) and highly significant (P<0.0001). In contrast, the contralateral lung demonstrated no significant difference between pre-RT to post-RT in either GLPG (P=0.12) or SUVmean (P=0.18). The only clinical feature significantly associated with post-RT PET/CT parameters was clinical staging. Our study demonstrated inflammatory response in the ipsilateral lung of NSCLC patients treated with photon RT, suggesting that PET/CT parameters may serve as biomarkers for radiation pneumonitis (RP).

Update on Molecular Imaging and Precision Medicine in Lung Cancer

Precision medicine integrates molecular pathobiology, genetic make-up, and clinical manifestations of disease in order to classify patients into subgroups for the purposes of predicting treatment response and suggesting outcome. By identifying those patients who are most likely to benefit from a given therapy, interventions can be tailored to avoid the expense and toxicity of futile treatment. Ultimately, the goal is to offer the right treatment, to the right patient, at the right time. Lung cancer is a heterogeneous disease both functionally and morphologically. Further, over time, clonal proliferations of cells may evolve, becoming resistant to specific therapies. PET is a sensitive imaging technique with an important role in the precision medicine algorithm of lung cancer patients. It provides anatomo-functional insight during diagnosis, staging, and restaging of the disease. It is a prognostic biomarker in lung cancer patients that characterizes tumoral heterogeneity, helps predict early response to therapy, and may direct the selection of appropriate treatment.

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.

Targeting of GLUT5 for Transporter-Mediated Drug-Delivery Is Contingent upon Substrate Hydrophilicity

Specific link between high fructose uptake and cancer development and progression highlighted fructose transporters as potential means to achieve GLUT-mediated discrimination between normal and cancer cells. The gained expression of fructose-specific transporter GLUT5 in various cancers offers a possibility for developing cancer-specific imaging and bioactive agents. Herein, we explore the feasibility of delivering a bioactive agent through cancer-relevant fructose-specific transporter GLUT5. We employed specific targeting of GLUT5 by 2,5-anhydro-D-mannitol and investigated several drug conjugates for their ability to induce cancer-specific cytotoxicity. The proof-of-concept analysis was carried out for conjugates of chlorambucil (CLB) in GLUT5-positive breast cancer cells and normal breast cells. The cytotoxicity of conjugates was assessed over 24 h and 48 h, and significant dependence between cancer-selectivity and conjugate size was observed. The differences were found to relate to the loss of GLUT5-mediated uptake upon increased conjugate size and hydrophobicity. The findings provide information on the substrate tolerance of GLUT5 and highlight the importance of maintaining appropriate hydrophilicity for GLUT-mediated delivery.

One Versus Up-to-5 Lesion Measurements for Response Assessment by PERCIST in Patients with Lung Cancer

Purpose

The optimal number of lesions to measure for response assessment from fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/computed tomography (CT) is not validated for lung cancer. We compared 1 lesion and up-to-5 lesion measurements for response assessment in lung cancer per PET Response Criteria in Solid Tumors (PERCIST).

Methods

Patients with lung cancer with pre- and post-treatment PET/CT images were included. The standard uptake value corrected for lean body mass (SUL_peak) of up-to-5 hottest target lesions was measured at each time point. The percent changes of SUL_peak of the single hottest lesion and the sum of up-to-5 hottest lesions were computed. Pearson correlation coefficient evaluated the strength of association between the percent changes of SUL_peak values from the 1 lesion and up-to-5 lesion analyses. Response categories were complete metabolic response (CMR) with no perceptible lesion; partial metabolic response (PMR), stable metabolic disease (SMD), or progressive metabolic disease (PMD) using the threshold of 30% and 0.8 unit change in SUL_peak; and unequivocal new lesion meant PMD. The concordance for response categorization was assessed by kappa statistics.

Results

A total of 40 patients (25 non-small cell lung cancer; 15 small cell lung cancer) were analyzed, all with ¹⁸F-FDG-avid lung cancer. Average of 3 target lesions were measured for up-to-5 lesion analysis. Pearson's r was 0.74 (P < 0.001) and increased to 0.96 (P < 0.001) when two outliers were excluded. Response categorization with 1 lesion and up-to-5 lesion analyses was concordant in 37 patients (92.5%, weighted kappa = 0.89).

Conclusion

Analyzing 1 lesion and up-to-5 lesions for response assessment by PERCIST showed high concordance in patients with lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13139-021-00697-4.

PET/CT imaging for evaluation of multimodal treatment efficacy and toxicity in advanced NSCLC-current state and future directions

Purpose

The advent of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of advanced NSCLC, leading to a string of approvals in recent years. Herein, a narrative review on the role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in the ever-evolving treatment landscape of advanced NSCLC is presented.

Methods

This comprehensive review will begin with an introduction into current treatment paradigms incorporating ICIs; the evolution of CT-based criteria; moving onto novel phenomena observed with ICIs and the current state of hybrid imaging for diagnosis, treatment planning, evaluation of treatment efficacy and toxicity in advanced NSCLC, also taking into consideration its limitations and future directions.

Conclusions

The advent of ICIs marks the dawn of a new era bringing forth new challenges particularly vis-à-vis treatment response assessment and observation of novel phenomena accompanied by novel systemic side effects. While FDG PET/CT is widely adopted for tumor volume delineation in locally advanced disease, response assessment to immunotherapy based on current criteria is of high clinical value but has its inherent limitations. In recent years, modifications of established (PET)/CT criteria have been proposed to provide more refined approaches towards response evaluation. Not only a comprehensive inclusion of PET-based response criteria in prospective randomized controlled trials, but also a general harmonization within the variety of PET-based response criteria is pertinent to strengthen clinical implementation and widespread use of hybrid imaging for response assessment in NSCLC.

A PET/CT volumetric parameter predicts prognosis of non-small cell lung cancer treated using preoperative chemoradiotherapy and surgery: A retrospective case series study

The purpose of the present study was to clarify whether positron emission tomography/computed tomography (PET/CT) volumetric parameters were prognostic predictors of non-small cell lung cancer (NSCLC) treatment in patients who had undergone preoperative concurrent chemoradiotherapy (CCRT) and surgery. In the present study, retrospectively surveyed the data of patients with NSCLC who underwent preoperative CCRT and surgery at Okayama University Hospital (Okayama, Japan) between April 2006 and March 2018. The maximum standardized uptake value (SUV_max) and volumetric parameters, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were calculated using PET/CT and the percentage decrease (Δ) in each parameter value post-CCRT. The SUV_max threshold for defining MTV was set at 2.5. Furthermore, the association between survival and PET parameter values was analyzed. A total of 52 patients were included in the present study. The median follow-up period was 50.65 months. In univariate analysis, ΔTLG was identified to be a significant predictor of progression-free survival (PFS; P=0.03). The 5-year PFS rates were 48.6 and 76.6% for patients with low ΔTLG and high ΔTLG, respectively. High ΔTLG was indicative of a higher overall survival rate (P=0.08). The present results suggest that ΔTLG calculated using PET/CT is a prognostic predictor of NSCLC treated using preoperative CCRT and surgery, and may help physicians determine treatment strategies.

Update on Quantitative Imaging for Predicting and Assessing Response in Oncology

Molecular imaging has revolutionized clinical oncology by imaging-specific facets of cancer biology. Through noninvasive measurements of tumor physiology, targeted radiotracers can serve as biomarkers for disease characterization, prognosis, response assessment, and predicting long-term response/survival. In turn, these imaging biomarkers can be utilized to tailor therapeutic regimens to tumor biology. In this article, we review biomarker applications for response assessment and predicting long-term outcomes. 18F-fluorodeoxyglucose (FDG), a measure of cellular glucose metabolism, is discussed in the context of lymphoma and breast and lung cancer. FDG has gained widespread clinical acceptance and has been integrated into the routine clinical care of several malignancies, most notably lymphoma. The novel radiotracers 16α-18F-fluoro-17β-estradiol and 18F-fluorothymidine are reviewed in application to the early prediction of response assessment of breast cancer. Through illustrative examples, we explore current and future applications of molecular imaging biomarkers in the advancement of precision medicine.

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

OBJECTIVES

The aim of our study was to investigate the value of a simple metabolic heterogeneity parameter, COV (coefficient of variation), by 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in the prognosis prediction of central lung cancer in early and locally advanced non-small-cell lung cancer (NSCLC).

METHODS

Seventy-three patients with NSCLC manifesting as central lung cancer were included retrospectively, and we used the COV to evaluate metabolic heterogeneity. Univariate and multivariate analyses were used to evaluate the predictive value in terms of overall survival (OS) and progression-free survival (PFS).

RESULT

For all 73 patients with pathologically confirmed NSCLC, 69.9 % had SCC, and 30.1 % had ADC or other types of NSCLC. The COV was a statistically significant factor in the univariate analysis for the OS rate. The optimal cut-off value was 23.1366, with sensitivity = 0.737 and specificity = 0.771. The COV values were dichotomized by this value and included with atelectasis in the Cox multivariate analysis. Both COV and atelectasis were independent risk factors for OS as follows: for COV (HR, 3.162, P = 0.0002), the 2-year OS rate was 62.5 % and 26.9 % in the low and high COV groups, respectively. For atelectasis (HR 2.047, P = 0.041), the 2-year OS rate was 30.6 % and 65.2 % in the groups with and without atelectasis, respectively (P = 0.017). For PFS, only COV (HR, 2.636, P = 0.001) was a significant predictor. The 2-year PFS rate was 29.7 % in the low COV group and 8% in the high COV group.

CONCLUSION

The pre-treatment metabolic heterogeneity parameter COV is a simple and easy way to predict the OS and PFS of patients with NSCLC manifesting as central lung cancer. Therefore, COV plays an important role in prognostic risk classification in NSCLC. The presence of atelectasis could also be a risk factor for poor prognosis of OS.

18F-FDG PET is Superior to WHO Grading as a Prognostic Tool in Neuroendocrine Neoplasms and Useful in Guiding PRRT: A Prospective 10-Year Follow-up Study

Accurate grading of patients with neuroendocrine neoplasms (NENs) is essential for risk stratification and optimal choice of therapy. Currently, grading is based on histologically assessed degree of tumor proliferation. The aim of the present study was to assess the long-term prognostic value of ¹⁸F-FDG PET imaging for risk stratification of NENs and compare it with tumor grading (World Health Organization 2010 classification). Methods: We conducted a prospective cohort study evaluating the prognostic value of ¹⁸F-FDG PET imaging and compared it with histologic grading. Enrolled were 166 patients of all grades and with histologically confirmed NENs of gastroenteropancreatic origin. The primary endpoint was overall survival (OS). Progression-free survival (PFS) was a secondary endpoint. In addition, OS in relation to peptide receptor radionuclide therapy (PRRT) was analyzed as an exploratory endpoint. The median follow-up time was 9.8 y. Results: Analysis of the whole cohort revealed that a positive ¹⁸F-FDG PET scan was associated with a shorter OS than a negative ¹⁸F-FDG PET scan (hazard ratio: 3.8; 95% CI: 2.4-5.9; P < 0.001). In G1 and G2 patients (n = 140), a positive ¹⁸F-FDG PET scan was the only identifier of high risk for death (hazard ratio: 3.6; 95% CI, 2.2-5.9; P < 0.001). In multivariate analysis, ¹⁸F-FDG PET, G3 tumor, ≥2 liver metastases, and ≥2 prior therapies were independent prognostic factors for OS, and ¹⁸F-FDG PET, G3 tumor, and ≥3 liver metastases were independent prognostic factors for PFS. For patients receiving PRRT, ¹⁸F-FDG-negative cases had a significantly longer survival than ¹⁸F-FDG-positive cases, whereas no difference was identified for tumor grading. ¹⁸F-FDG-positive patients receiving PRRT had a significantly longer median survival than patients not receiving PRRT (4.4 vs. 1.4 y, P = 0.001), whereas no difference was seen for ¹⁸F-FDG-negative patients. Conclusion: ¹⁸F-FDG PET is useful for risk stratification of all NEN grades and is superior to histologic grading. ¹⁸F-FDG PET could differentiate G1 and G2 tumors into low- and high-risk groups. In the selection of therapy and for risk stratification of NEN patients, ¹⁸F-FDG PET status should be considered.

Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer

Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUV_max value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

Lower tumor volume is associated with increased benefit from immune checkpoint inhibitors in patients with advanced non-small-cell lung cancer

AIM

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment for advanced non-small-cell lung cancer (NSCLC), yet many patients do not benefit from Programmed cell death protein 1 (PD-1) axis inhibitors, emphasizing the need for additional markers for better patient selection. Our aim was to evaluate the association between tumor volume and response to ICI.

METHODS

This retrospective ethically-approved study included all consecutive patients with advanced NSCLC who were evaluated with a fluorodeoxyglucose-positron emission tomography scan, prior to the first administration of a single-agent ICI between 1/2016 and 6/2017. Tumor burden was calculated based on total body metabolic tumor volume and sum of all measurable lesions (SOML).

RESULTS

Median SOML was 88 mm, and was inversely and significantly associated with progression-free survival (PFS) (hazard ratio [HR] 2, CI 1.28-3.37, P = .003) and overall survival (OS) (HR 2.36, CI 1.13-4.94, P = .02). SOML≤80 mm had a significantly longer PFS compared to patients with a SOML≥80 mm (median PFS 9.7 vs 3.7 months, respectively, HR for progression 2.26, CI 1.1-4.5, P = .02). Patients with a SOML≤80 also had longer median OS compared to patients with SOML≥80 (median OS 12 vs 9.8 months, respectively, HR for death 3.1, CI 1.2-8, P = .018).

CONCLUSIONS

Low tumor burden was associated with higher response rates (RR), and better PFS and OS in advanced NSCLC patients treated with ICI. These results may improve the selection of patients for treatment with single-agent ICI, as opposed to the combination with chemotherapy, which might be more appropriate for patients with high tumor burden. Prospective analysis is warranted.

18F-Fluorodeoxyglucose PET in Locally Advanced Non-small Cell Lung Cancer: From Predicting Outcomes to Guiding Therapy

PET using 18-fluorodeoxyglucose (FDG) has become an important part of the work-up for non-small cell lung cancer (NSCLC). This article summarizes advancements in using FDG-PET for patients with locally advanced NSCLC treated with definitive radiation therapy (RT). This article discusses prognostication of outcome based on pretreatment or midtreatment PET metrics, using textural image features to predict treatment outcomes, and using PET to define RT target volumes and inform RT dose modifications. The role of PET is evolving and is moving toward using quantitative image information, with the overarching goal of individualizing therapy to improve outcomes for patients with NSCLC.

Benefits of positron emission tomography scans for the evaluation of radiotherapy

The assessment of tumour response during and after radiotherapy determines the subsequent management of patients (adaptation of treatment plan, monitoring, adjuvant treatment, rescue treatment or palliative care). In addition to its role in extension assessment and therapeutic planning, positron emission tomography combined with computed tomography provides useful functional information for the evaluation of tumour response. The objective of this article is to review published data on positron emission tomography combined with computed tomography as a tool for evaluating external radiotherapy for cancers. Data on positron emission tomography combined with computed tomography scans acquired at different times (during, after initial and after definitive [chemo-]radiotherapy, during post-treatment follow-up) in solid tumours (lung, head and neck, cervix, oesophagus, prostate and rectum) were collected and analysed. Recent recommendations of the National Comprehensive Cancer Network are also reported. Positron emission tomography combined with computed tomography with (¹⁸F)-labelled fluorodeoxyglucose has a well-established role in clinical routine after chemoradiotherapy for locally advanced head and neck cancers, particularly to limit the number of neck lymph node dissection. This imaging modality also has a place for the evaluation of initial chemoradiotherapy of oesophageal cancer, including the detection of distant metastases, and for the post-therapeutic evaluation of cervical cancer. Several radiotracers for positron emission tomography combined with computed tomography, such as choline, are also recommended for patients with prostate cancer with biochemical failure. (¹⁸F)-fluorodeoxyglucose positron emission tomography combined with computed tomography is optional in many other circumstances and its clinical benefits, possibly in combination with MRI, to assess response to radiotherapy remain a very active area of research.

Local and regional treatment response by 18FDG-PET-CT-scans 4 weeks after concurrent hypofractionated chemoradiotherapy in locally advanced NSCLC

BACKGROUND AND PURPOSE

To investigate associations of early post-treatment ¹⁸Fluorodeoxyglucose-positron-emission-tomography (FDG-PET)-scans with local (LF), regional (RF), distant failure (DF) and overall survival (OS) in locally advanced non-small cell lung cancer (LA-NSCLC)-patients treated with concurrent chemoradiotherapy.

MATERIALS AND METHODS

Forty-seven stage IIIA-B NSCLC-patients included in a randomized phase II-trial (NTR2230) received 66 Gy (24x2.75 Gy) with low dose Cisplatin +/- Cetuximab. FDG-PET-scans were performed at baseline and 4 weeks post-treatment (range, 1.6-10.1). SUV_max, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and gross tumor volume were calculated separately for the primary tumor and the involved lymph nodes to generate baseline, post-treatment, and relative response metrics defined as (metric_pre-metric_post)/metric_pre. Univariable cox regression analyses were performed to investigate associations between PET-metrics and outcomes.

RESULTS

Metrics resulted from the post-treatment scan and relative response were associated with outcome, but baseline metrics were not. Primary tumor metrics were stronger associated with all outcomes than lymph node metrics. Both the volumetric (TLG/MTV) and intensity (SUV_max/SUV_mean) PET-metrics were associated with OS. The intensity metrics were associated with LF, while the volumetric PET-metrics were associated with RF/DF. This was in contrast to the nodal metrics, demonstrating only an association between RF and the relative response of TLG/MTV. No preference was found between PET volumetric and intensity metrics associated with outcome.

CONCLUSION

Early post-treatment PET-metrics are associated with treatment outcome in LA-NSCLC patients treated with chemoradiotherapy. Both volumetric and intensity PET-metrics are useful, but more for the primary tumor than for lymph nodes.

Prognostic importance of 18F-fluorodeoxyglucose uptake by positron emission tomography for stage III non-small cell lung cancer treated with definitive chemoradiotherapy

OBJECTIVES

Survival heterogeneity exists among patients with non-small cell lung cancer (NSCLC), even within the same stage. We aimed to evaluate the prognostic role of pre-treatment maximum standardized uptake value (SUV_max) in patients treated with definitive concurrent chemoradiotherapy for stage III NSCLC.

MATERIALS AND METHODS

Between 2010 and 2017, 103 patients with stage III NSCLC who underwent ¹⁸F-FDG PET/CT at the time of diagnosis were included in the study.

RESULTS

Higher tumor stages were correlated with higher pre-treatment SUV_max of lymph nodes (LNs) (p=0.005) but were not correlated with higher SUV_max of primary tumor (p=0.2). The median SUV_max of LNs was 2.84, 8.06, and 11.11 in stage IIIa, IIIb and IIIc, respectively. Higher nodal stage was also correlated with higher SUV_max of LNs (p=0.01). According to ROC analysis, there was no significant cut-off value of SUV_max observed for primary tumor, therefore continuous variables were used for survival analyses. The best SUV_max cut-off was 3.5 for the LNs, therefore the SUV_max of LNs was evaluated as both a dichotomous and a continuous variable. Pre-treatment SUV_max of primary tumor did not predict survival outcomes but both the continuous and dichotomous variables of SUV_max of LNs predicted recurrence free survival and overall survival. Nodal stage (N0-2 vs. N3) and AJCC stage (IIIa vs IIIb vs. IIIc) were the other prognostic factors.

CONCLUSIONS

Pre-treatment SUV_max of LNs had prognostic value in patients treated with definitive concurrent chemoradiotherapy for stage III NSCLC. In future trials, pre-treatment SUV_max of the LNs would serve as a guide for patients who might benefit from more aggressive treatments.

Prognostic value of post-induction chemotherapy 18F-FDG PET-CT in stage II/III non-small cell lung cancer before (chemo-) radiation

INTRODUCTION

The purpose of our present study was to assess the prognostic impact of FDG PET-CT after induction chemotherapy for patients with inoperable non-small-cell lung cancer (NSCLC).

MATERIAL AND METHODS

This retrospective study included 50 patients with inoperable stage II/III NSCLC from January 2012 to July 2015. They were treated for curative intent with induction chemotherapy, followed by concomitant chemoradiation therapy or sequential radiation therapy. FDG PET-CT scans were acquired at initial staging (PET1) and after the last cycle of induction therapy (PET2). Five parameters were evaluated on both scans: SUVmax, SUVpeak, SUVmean, TLG, MTV, and their respective deltas. The prognostic value of each parameter for overall survival (OS) and progression-free survival (PFS) was evaluated with Cox proportional-hazards regression models.

RESULTS

Median follow-up was 19 months. PET1 parameters, clinical and histopathological data were not predictive of the outcome. TLG2 and ΔTLG were prognostic factors for OS. TLG2 was the only prognostic factor for PFS. For OS, log-rank test showed that there was a better prognosis for patients with TLG2< 69g (HR = 7.1, 95%CI 2.8-18, p = 0.002) and for patients with ΔTLG< -81% after induction therapy (HR = 3.8, 95%CI 1.5-9.6, p = 0.02). After 2 years, the survival rate was 89% for the patients with low TLG2 vs 52% for the others. We also evaluated a composite parameter considering both MTV2 and ΔSUVmax. Patients with MTV2> 23cc and ΔSUVmax> -55% had significantly shorter OS than the other patients (HR = 5.7, 95%CI 2.1-15.4, p< 0.01).

CONCLUSION

Post-induction FDG PET might be an added value to assess the patients' prognosis in inoperable stage II/III NSCLC. TLG, ΔTLG as well as the association of MTV and ΔSUVmax seemed to be valuable parameters, more accurate than clinical, pathological or pretherapeutic imaging data.

Predictive and Prognostic Value of 18F-fluorodeoxyglucose Uptake Combined with Thymidylate Synthase Expression in Patients with Advanced Non-Small Cell Lung Cancer

We investigated the relationship between tumor ¹⁸F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) scans and thymidylate synthase (TS) expression. In addition, we evaluated the value of FDG uptake in predicting treatment response and prognosis when combined with TS expression in patients with advanced non-small cell lung cancer (NSCLC). We measured the maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis (TLG) of tumor lesions on pretreatment scan in 234 patients (age: 60.1 ± 9.4 years; males: 56.4%) with stage IV non-squamous NSCLC who were enrolled in the prospective phase II clinical trial. We investigated the correlation of the parameters with TS expression and the predictive values of the parameters compared with other clinical factors. Among these parameters, TLG was the most relevant parameter that had a significant correlation with TS expression (ρ = 0.192, P = 0.008). A multivariable Cox proportional-hazards model revealed that high TLG was a significant independent predictor for treatment response (hazard ratio [HR]: 2.05; P = 0.027), progression-free survival (HR: 1.39; P = 0.043), and overall survival (HR: 1.65; P = 0.035) with other factors. In patients with advanced non-squamous NSCLC, tumor TLG on pretreatment PET/CT scan has predictive and prognostic value.

Incorporation of the SUVmax Measured From FDG PET and Neutrophil-to-lymphocyte Ratio Improves Prediction of Clinical Outcomes in Patients With Locally Advanced Non-small-cell Lung Cancer

INTRODUCTION

The aim of the present study was to investigate the value of incorporation 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) maximum standardized uptake value (SUVmax) and neutrophil-to-lymphocyte ratio (NLR) for improving prediction of clinical outcomes of patients with locally advanced non-small-cell lung cancer (LA NSCLC).

MATERIALS AND METHODS

We retrospectively enrolled 138 patients with unresectable LA NSCLC at our institution from July 2010 to August 2017. Spearman correlation analyses were used to estimate the correlations between SUVmax and NLR level. The univariate and multivariate Cox survival analyses were used to evaluate the prognostic indicators, including the incorporation of SUVmax and NLR. We defined the SUVmax and NLR grade (SNG = 0, 1, or 2) score as the number of risk factors among (1) SUVmax > 11.95 and (2) NLR > 3.82. The SNG score prognostic value was evaluated for overall survival (OS) and progression-free survival (PFS).

RESULTS

Univariate analysis showed that tumor stage, SUVmax, SUVmean, NLR, and SNG score were significantly associated with OS and PFS in patients with LA NSCLC. Kaplan-Meier analysis and log-rank test demonstrated significant differences in both OS and PFS among patients in SNG score (OS, P < .001; PFS, P < .001). Spearman correlation analyses showed that SUVmax had a correlation with the NLR (r = 0.237; P = .005). In subgroup analyses for patients with tumor pathologic stage IIIA/IIIB, we found that the SNG score was significantly associated with OS and PFS in each subgroup (P < .001, P < .001 for OS and P = .027, P < .001 for PFS, respectively). Multivariate analysis showed that the SNG score was a significantly independent prognostic factor for OS (hazard ratio, 1.612; 95% confidence interval, 1.157-2.246; P = .005) and PFS (hazard ratio, 2.241; 95% confidence interval, 1.486-3.379; P < .001).

CONCLUSION

Incorporation of the SUVmax and NLR improves prediction of clinical outcomes in patients with LA NSCLC.

Quantification of global lung inflammation using volumetric 18F-FDG PET/CT parameters in locally advanced non-small-cell lung cancer patients treated with concurrent chemoradiotherapy: a comparison of photon and proton radiation therapy

INTRODUCTION

Radiation pneumonitis is a major dose-limiting complication in thoracic radiation therapy (RT) and presents clinically in the first few months after RT. We evaluated the feasibility of quantifying pulmonary parenchymal glycolysis (PG) as a surrogate of global lung inflammation and radiation-induced pulmonary toxicity using a novel semiautomatic lung segmentation technique in non-small-cell lung cancer (NSCLC) patients and compared PG in patients treated with photon or proton RT.

PATIENTS AND METHODS

We evaluated 18 consecutive locally advanced NSCLC patients who underwent pretreatment and post-treatment F-FDG PET/CT treated with definitive (median: 66.6 Gy; 1.8 Gy fractions) photon or proton RT between 2010 and 2014. Lung volume segmentation was conducted using 3D Slicer by performing simple thresholding. Pulmonary PG was calculated by summing F-FDG uptake in the whole lung.

RESULTS

In nine patients treated with photon RT, significant increases in PG in both ipsilateral (mean difference: 1400±510; P=0.02) and contralateral (mean difference: 1200±450; P=0.03) lungs were noted. In nine patients treated with proton therapy, no increase in pulmonary PG was observed in either the ipsilateral (P=0.30) or contralateral lung (P=0.98).

CONCLUSION

We observed a significant increase in global lung inflammation bilaterally as measured by quantification of PG. However, no significant change in global lung inflammation was noted after proton therapy. Future larger studies are needed to determine whether this difference correlates with lower risks of radiation pneumonitis in NSCLC patients treated with proton therapy.

Prognostic Value of Serum Tumor Markers in Patients With Stage III NSCLC Treated With Chemoradiotherapy

BACKGROUND/AIM

Serum tumor markers such as carcinoembryonic antigen and cytokeratin subunit 19 fragment are generally monitored in non-small cell lung cancer (NSCLC) patients in the clinical practice. However, their clinical relevance in stage III NSCLC treated with concurrent chemoradiotherapy (CCRT) remains unclear. Herein, we examined the clinical relevance of tumor markers in those patients.

PATIENTS AND METHODS

We retrospectively reviewed 62 consecutive patients with stage III NSCLC who received CCRT. We examined the associations of tumor marker levels with their prognosis.

RESULTS

There was no correlation between pretreatment tumor marker levels and prognosis. Normal tumor marker levels post-CCRT were significantly associated with favorable progression-free survival (54.8 versus 14.5 months, p=0.02) and overall survival (71.7 versus 40.4 months, p=0.06) compared with high tumor marker levels post-CCRT.

CONCLUSION

We revealed that normal tumor markers levels post-CCRT in stage III NSCLC might be a useful surrogate marker for curing those patients.

Prognostic factors of advanced or postoperative recurrent non-small cell lung cancer targeted with immune check point inhibitors

Background

Although immune checkpoint inhibitors (ICIs) for non-small cell lung cancer (NSCLC) have been established as one of standard therapy, the prognostic factors of ICIs remain unclear, aside from the programed cell death-ligand 1 (PD-L1) expression of tumor cells. The aim of this study was to determine the prognostic factors of ICIs.

Methods

We analyzed the clinicopathological data of 44 cases of advanced NSCLC targeted with ICIs in our hospital, between February 2016 and February 2018, in order to determine the prognostic factors of ICIs. We also reviewed the literature regarding ICIs.

Result

We retrospectively analyzed the 44 cases (26 nivolumab and 18 pembrolizumab cases). These patients were 38 men and 6 women, comprising 13 cases of adenocarcinoma, 29 squamous cell carcinoma and 2 unclassified types. Seven patients were using first-line therapy and while the others were using second-line therapy or later. Epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) mutations were negative in all the cases. The response rate and disease control rate were 20.5% and 51.3%, respectively. The median progression-free survival time and median survival time were 146 days and 257 days, respectively. We observed five severe adverse effects (AEs) (three cases of interstitial pneumonia, one of liver dysfunction and one of adrenal failure), that were resolved by steroid pulse therapy. In multivariate analyses, the Eastern Cooperative Oncology Group performance status (ECOG PS), pathological type, standardized uptake value (SUV) on positron emission tomography (PET), white blood cell (WBC) count, neutrophil, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH) and albumin were independently prognostic factors. There were no significant differences in the prognosis between nivolumab and pembrolizumab.

Conclusions

ICIs were effective in 44 treated NSCLC cases. Our analysis suggests that while ICIs are effective in treating patients, candidates must be carefully selected and cautiously observed.

Interobserver variability of image-derived arterial blood SUV in whole-body FDG PET

Background

Today, the standardized uptake value (SUV) is essentially the only means for quantitative evaluation of static [¹⁸F-]fluorodeoxyglucose (FDG) positron emission tomography (PET) investigations. However, the SUV approach has several well-known shortcomings which adversely affect the reliability of the SUV as a surrogate of the metabolic rate of glucose consumption. The standard uptake ratio (SUR), i.e., the uptake time-corrected ratio of tumor SUV to image-derived arterial blood SUV, has been shown in the first clinical studies to overcome most of these shortcomings, to decrease test-retest variability, and to increase the prognostic value in comparison to SUV. However, it is unclear, to what extent the SUR approach is vulnerable to observer variability of the additionally required blood SUV (BSUV) determination. The goal of the present work was the investigation of the interobserver variability of image-derived BSUV.

Methods

FDG PET/CT scans from 83 patients (72 male, 11 female) with non-small cell lung cancer (N = 46) or head and neck cancer (N = 37) were included. BSUV was determined by 8 individuals, each applying a dedicated delineation tool for the BSUV determination in the aorta. Two of the observers applied two further tools. Altogether, five different delineation tools were used. With each used tool, delineation was performed for the whole patient group, resulting in 12 distinct observations per patient. Intersubject variability of BSUV determination was assessed using the fractional deviations for the individual patients from the patient group average and was quantified as standard deviation (SD _is), 95% confidence interval, and range.

Interobserver variability of BSUV determination was assessed using the fractional deviations of the individual observers from the observer-average for the considered patient and quantified as standard deviations (SD _p, SD _d) or root mean square (RMS), 95% confidence interval, and range in each patient, each observer, and the pooled data respectively.

Results

Interobserver variability in the pooled data amounts to RMS = 2.8% and is much smaller than the intersubject variability of BSUV (SD _is= 16%). Averaged over the whole patient group, deviations of individual observers from the observer average are very small and fall in the range [ − 0.96, 1.05]%. However, interobserver variability partly differs distinctly for different patients, covering a range of [0.7, 7.4]% in the investigated patient group.

Conclusion

The present investigation demonstrates that the image-based manual determination of BSUV in the aorta is sufficiently reproducible across different observers and delineation tools which is a prerequisite for accurate SUR determination. This finding is in line with the already demonstrated superior prognostic value of SUR in comparison to SUV in the first clinical studies.

Metabolic tumour volume is prognostic in patients with non-small-cell lung cancer treated with stereotactic ablative radiotherapy

Introduction

Stereotactic ablative radiotherapy (sabr) is a relatively new technique for the curative-intent treatment of patients with inoperable early-stage non-small-cell lung cancer (nsclc). Previous studies have demonstrated a prognostic value for positron emission tomography-computed tomography (pet/ct) parameters, including maximal standardized uptake value (suv_max), metabolic tumour volume (mtv), and total lesion glycolysis (tlg) in lung cancer patients. We aimed to determine which pet/ct parameter is most prognostic of local control (lc) and overall survival (os) in patients treated with sabr for nsclc.

Methods

We conducted a retrospective review of patients treated with sabr for stage I inoperable nsclc at BC Cancer between 2009 and 2013. The Akaike information criterion was used to compare the prognostic value of the various pet/ct parameters.

Results

The study included 134 patients with a median age of 76 years. Median tumour diameter was 2.2 cm, gross tumour volume was 8.1 mL, suv_max was 7.9, mtv was 2.4 mL, and tlg was 10.9 suv·mL. The 2-year lc was 92%, and os was 66%. On univariate and multivariate analysis, imaging variables including tumour size, gross tumour volume, suv_max, mtv, and tlg were all associated with worse lc. Tumour size was not associated with significantly worse os, but other imaging variables were. The pet/ct parameter most prognostic of lc was mtv. Compared with suv_max, tlg and mtv were more prognostic of os.

Conclusions

In patients with early-stage nsclc treated with sabr, mtv appears to be prognostic of lc and os.

Pretreatment metabolic tumour volume in stage IIIA/B non-small-cell lung cancer uncovers differences in effectiveness of definitive radiochemotherapy schedules: analysis of the ESPATUE randomized phase 3 trial

PURPOSE

According to the ACRIN 6668/RTOG 0235 trial, pretreatment metabolic tumour volume (MTV) as detected by ¹⁸F-fluorodeoxyglucose PET/CT is a prognostic factor in patients with stage III non-small-cell lung cancer (NSCLC) after definitive radiochemotherapy (RCT). To validate the prognostic value of MTV in patients with stage III NSCLC after RCT, we analysed mature survival data from the German phase III trial ESPATUE.

METHODS

This analysis included patients who were staged by PET/CT and who were enrolled in the ESPATUE trial, a randomized study comparing definitive RCT (arm A) with surgery (arm B) after induction chemotherapy and RCT in patients with resectable stage IIIA/IIIB NSCLC. Patients refusing surgery and those with nonresectable disease were scheduled to receive definitive RCT. MTV was measured using a fixed threshold-based approach and a model-based iterative volume thresholding approach. Data were analysed using proportional hazards models and Kaplan-Meier survival functions.

RESULTS

MTV as a continuous variable did not reveal differences in survival between the 117 patients scheduled to receive definitive RCT and all 169 enrolled patients who underwent pretreatment PET/CT (p > 0.5). Five-year survival rates were 33% (95% CI 17-49%) in patients scheduled for definitive RCT with a high MTV (>95.4 ml) and 32% (95% CI: 22-42%) in those with a low MTV. The hazard ratio for survival was 0.997 (95% CI 0.973-1.022) per 10-ml increase in MTV and the slope was significantly shallower than that in the ACRIN 6668/RTOG 0235 trial (random effects model, p = 0.002). There were no differences in MTV size distributions between the ACRIN and ESPATUE trials (p = 0.97).

CONCLUSION

Patients with stage III NSCLC and a large MTV in whom definitive RCT had a particularly good survival in the ESPATUE trial. Treatment individualization according to MTV is not supported by this study. The ESPATUE and ACRIN trials differed by the use of cisplatin-containing induction chemotherapy and an intensified radiotherapy regimen that were particularly effective in patients with large MTV disease.