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Greater reduction in mid-treatment FDG-PET volume may be associated with worse survival in non-small cell lung cancer.

ABSTRACT: BACKGROUND AND PURPOSE:This study tested the hypotheses that 1) changes in mid-treatment fluorodeoxyglucose (FDG)-positron emission tomography (PET) parameters are predictive of overall survival (OS) and 2) mid-treatment FDG-PET-adapted treatment has the potential to improve survival in patients with non-small cell lung cancer (NSCLC). MATERIAL AND METHODS:Patients with stage I-III NSCLC requiring daily fractionated radiation were eligible. FDG-PET-CT scans were obtained prior to and mid-treatment with radiotherapy at 40-50?Gy. The normalized maximum standardized uptake value (NSUVmax), normalized mean SUV (NSUVmean), PET-metabolic tumor volume (MTV), total lesion glycolysis (TLG), and computed tomography-based gross tumor volume (CT-GTV) were consistently measured for all patients. The primary study endpoint was OS. RESULTS:The study is comprised of 102 patients who received 3-dimensional conformal radiotherapy, among whom 30 patients who received mid-treatment PET-adapted dose escalation radiotherapy. All PET-CT parameters decreased significantly (P?0.1). Patients receiving conventional radiation (60-70?Gy) with MTV reductions greater than the mean had a median survival of 14?months, compared to those with MTV reductions less than the mean who had a median survival of 22 months. By contrast, patients receiving mid-treatment PET-adapted radiation with MTV reductions greater than the mean had a median survival of 33 months, compared to those with MTV reductions less than the mean who had a median survival of 19?months. Overall, PET-adapted treatment resulted in a 19% better 5-year survival than conventional radiation. CONCLUSION:Changes in mid-treatment PET-volumetric parameters were significantly associated with survival in NSCLC. A greater reduction in the mid-treatment MTV was associated with worse survival in patients treated with standard radiation, but with better survival in patients who received mid-treatment PET-adapted treatment.

SUBMITTER: Kong FS

PROVIDER: S-EPMC6483381 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Greater reduction in mid-treatment FDG-PET volume may be associated with worse survival in non-small cell lung cancer.

Kong Feng-Ming Spring FS Li Ling L Wang Weili W Campbell Jeff J Waller Jennifer L JL Piert Morand M Gross Milton M Cheng Monica M Owen Dawn D Stenmark Matthew M Huang Ke Colin KC Frey Kirk A KA Ten Haken Randall K RK Lawrence Theodore S TS

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 20181030

<h4>Background and purpose</h4>This study tested the hypotheses that 1) changes in mid-treatment fluorodeoxyglucose (FDG)-positron emission tomography (PET) parameters are predictive of overall survival (OS) and 2) mid-treatment FDG-PET-adapted treatment has the potential to improve survival in patients with non-small cell lung cancer (NSCLC).<h4>Material and methods</h4>Patients with stage I-III NSCLC requiring daily fractionated radiation were eligible. FDG-PET-CT scans were obtained prior to ...[more]

PMID: 30389239

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Project description:Objective. Traditional radiotherapy (RT) treatment planning of non-small cell lung cancer (NSCLC) relies on population-wide estimates of organ tolerance to minimize excess toxicity. The goal of this study is to develop a personalized treatment planning based on patient-specific lung radiosensitivity, by combining machine learning and optimization.Approach. Sixty-nine non-small cell lung cancer patients with baseline and mid-treatment [18]F-fluorodeoxyglucose (FDG)-PET images were retrospectively analyzed. A probabilistic Bayesian networks (BN) model was developed to predict the risk of radiation pneumonitis (RP) at three months post-RT using pre- and mid-treatment FDG information. A patient-specific dose modifying factor (DMF), as a surrogate for lung radiosensitivity, was estimated to personalize the normal tissue toxicity probability (NTCP) model. This personalized NTCP was then integrated into a NTCP-based optimization model for RT adaptation, ensuring tumor coverage and respecting patient-specific lung radiosensitivity. The methodology was employed to adapt the treatment planning of fifteen NSCLC patients.Main results. The magnitude of the BN predicted risks corresponded with the RP severity. Average predicted risk for grade 1-4 RP were 0.18, 0.42, 0.63, and 0.76, respectively (p< 0.001). The proposed model yielded an average area under the receiver-operating characteristic curve (AUROC) of 0.84, outperforming the AUROCs of LKB-NTCP (0.77), and pre-treatment BN (0.79). Average DMF for the radio-tolerant (RP grade = 1) and radiosensitive (RP grade ≥ 2) groups were 0.8 and 1.63,p< 0.01. RT personalization resulted in five dose escalation strategies (average mean tumor dose increase = 6.47 Gy, range = [2.67-17.5]), and ten dose de-escalation (average mean lung dose reduction = 2.98 Gy [0.8-5.4]), corresponding to average NTCP reduction of 15% [4-27].Significance. Personalized FDG-PET-based mid-treatment adaptation of NSCLC RT could significantly lower the RP risk without compromising tumor control. The proposed methodology could help the design of personalized clinical trials for NSCLC patients.

Project description:Background The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (18F-FDG PET/CT) images based on a “rough” volume of interest (VOI) containing the tumor instead of its accurate delineation, which is a significant time-consuming bottleneck of radiomics analyses. Methods A cohort of 138 patients with stage II–III NSCLC treated with radiochemotherapy recruited retrospectively (n = 87) and prospectively (n = 51) was used. Two approaches were compared: firstly, the radiomic features were extracted from the delineated primary tumor volumes in both PET (using the automated fuzzy locally adaptive Bayesian, FLAB) and CT (using a semi-automated approach with 3D Slicer™) components. Both delineations were carried out within previously manually defined “rough” VOIs containing the tumor and the surrounding tissues, which were exploited for the second approach: the same features were extracted from this alternative VOI. Both sets for features were then combined with the clinical variables and processed through the same machine learning (ML) pipelines using the retrospectively recruited patients as the training set and the prospectively recruited patients as the testing set. Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). The resulting models were compared in terms of balanced accuracy, sensitivity, and specificity. Results Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89 vs. 0.88 and 0.78 vs. 0.77). Conclusion Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.

Project description:BackgroundRadiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset.Materials and methodsThe proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume.ResultsThe number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%).ConclusionWhen performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.

Project description:AimTo investigate the diagnostic potential of and associations between tumor 18F-FDG uptake on PET imaging and cancer-associated weight loss.Methods774 non-small cell lung cancer (NSCLC) patients with pre-treatment PET evaluated between 2006 and 2014 were identified. Using the international validated definition of cachexia, the presence of clinically significant pretreatment cancer-associated weight loss (WL) was retrospectively determined. Maximum Standardized Uptake Value (SUVMax) of 18F-FDG was recorded and dichotomized based on 3 experimental cutpoints for survival analyses. Each SUVMax cutpoint prioritized either survival differences, total cohort comparison sample sizes, or sample size by stage. Patient outcomes and associations between SUVMax and cancer-associated weight loss were assessed by multivariate, categorical, and survival analyses.ResultsPatients were found to have an increased likelihood of having WL at diagnosis associated with increasing primary tumor SUVMax after controlling for potentially confounding patient and tumor characteristics on multivariate logistic regression (OR 1.038; 95% CI: 1.012, 1.064; P=0.0037). After stratifying the cohort by WL and dichotomized SUVMax, both factors were found to be relevant in predicting survival outcomes when the alternative variable was constant. Of note, the most striking survival differences contributed by WL status occurred in high SUVMax groups, where the presence of WL predicted a median survival time detriment of up to 10 months, significant regardless of cutpoint determination method applied to categorize high SUVMax patients. SUVMax classification was found to be most consistently relevant in both WL and no WL groups.ConclusionsThe significant positive association between significant pretreatment cancer-associated weight loss and primary tumor SUVMax underscores increased glucose uptake as a component of catabolic tumor phenotypes. This substantiates 18F-FDG PET analysis as a prospective tool for assessment of cancer-associated weight loss and corresponding survival outcomes. Furthermore, the survival differences observed between WL groups across multiple SUVMax classifications supports the importance of weight loss monitoring in oncologic workups. Weight loss in the setting of NSCLCs with higher metabolic activity as determined by 18F-FDG PET signal should encourage more aggressive and earlier palliative care interventions.

Project description:BackgroundPre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).MethodsA retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction.Findings1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features.InterpretationCNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care.FundingNIH NHLBI training grant (5T35HL094308-12, John Sollee).

Project description:BACKGROUND:To identify whether early metabolic responses as determined using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) during radiotherapy (RT) predict outcomes in patients with esophageal cancer. METHODS:Twenty-one patients with esophageal cancer who received pre-treatment 18F-FDG PET/CT (PET1) and inter-fractional 18F-FDG PET/CT (PET2) after 11 fractions of RT (median 23.1?Gy, 2.1?Gy per fraction) were retrospectively reviewed. The region of interest for each calculation was delineated using "PET Edge". We calculated PET parameters including maximum and mean standardized uptake values (SUVmax and SUVmean, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The relative changes (%) were calculated using the logarithmically transformed parameter values for the PET1 and PET2 scans. Multivariate analysis of locoregional recurrence and distant failures were performed using Cox regression analysis. After identifying statistically significant PET parameters for discriminating responders from non-responders, receiver operating characteristics curve analyses were used to assess the potentials of the studied PET parameters. RESULTS:After a median follow-up of 13?months, the 1-year overall and progression-free survival rates were 79.0% and 34.4%, respectively. Four patients developed locoregional recurrences (LRRs) and 8 had distant metastases (DMs). The 1-year overall LRR-free rate was 76.9% while the DM-free rate was 60.6%. The relative changes in MTV (?MTV) were significantly associated with LRR (p =?0.03). Conversely, the relative changes in SUVmean (?SUVmean) were associated with the risk of DM (p =?0.02). An ?MTV threshold of 1.14 yielded a sensitivity of 60%, specificity of 94%, and an accuracy of 86% for predicting an LRR. Additionally, a ?SUVmean threshold of a 35% decrease yielded a sensitivity of 67%, specificity of 83%, and accuracy of 76% for the prediction DM. TRIAL REGISTRATION:Retrospectively registered. CONCLUSIONS:Changes in tumor metabolism during RT could be used to predict treatment responses, recurrences, and prognoses in patients with esophageal cancer.

Dataset Information

Greater reduction in mid-treatment FDG-PET volume may be associated with worse survival in non-small cell lung cancer.

Publications

Greater reduction in mid-treatment FDG-PET volume may be associated with worse survival in non-small cell lung cancer.

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