Primary tumour standardised uptake value is prognostic in nonsmall cell lung cancer: a multivariate pooled analysis of individual data

Impact of preoperative [18F]FDG PET/CT vs. contrast-enhanced CT in the staging and survival of patients with clinical stage I and II non-small cell lung cancer: a 10-year follow-up study

OBJECTIVES

To elucidate the impact of [18F]FDG positron emission tomography/computed tomography (PET/CT) vs. CT workup on staging and prognostic evaluation of clinical stage (c) I-II NSCLC.

METHODS

We retrospectively identified 659 cI-II NSCLC who underwent CT (267 patients) or preoperative CT followed by PET/CT (392 patients), followed by curative-intended complete resection in our hospital from January 2008 to December 2013. Differences were assessed between preoperative and postoperative stage. Five-year disease-free survival (DFS) and overall survival (OS) rates were calculated using the Kaplan-Meier approach and compared with log-rank test. Impact of preoperative PET/CT on survival was assessed by Cox regression analysis.

RESULTS

The study included 659 patients [mean age, 59.5 years ± 10.8 (standard deviation); 379 men]. The PET/CT group was superior over CT group in DFS [12.6 vs. 6.9 years, HR 0.67 (95% CI 0.53-0.84), p < 0.001] and OS [13.9 vs. 10.5 years, HR 0.64 (95% CI 0.50-0.81), p < 0.001]. In CT group, more patients thought to have cN0 migrated to pN1/2 disease as compared with PET/CT group [26.4% (66/250) vs. 19.2% (67/349), p < 0.001], resulting in more stage cI cases being upstaged to pII-IV [24.7% (49/198) vs. 16.1% (47/292), p = 0.02], yet this was not found in cII NSCLC [27.5% (19/69) vs. 27.0% (27/100), p = 0.94]. Cox regression analysis identified preoperative PET/CT as an independent prognostic factor of OS and DFS (p = 0.002, HR = 0.69, 95% CI 0.54-0.88; p = 0.004, HR = 0.72, 95% CI 0.58-0.90).

CONCLUSION

Addition of preoperative [18F]FDG PET/CT was associated with superior DFS and OS in resectable cI-II NSCLC, which may result from accurate staging and stage-appropriate therapy.

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.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Background

Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

Methods

A 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.

Findings

1168 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.

Interpretation

CNNs 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.

Funding

NIH NHLBI training grant (5T35HL094308-12, John Sollee).

A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models

BACKGROUND

Surveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions. The purpose of this study was to utilise readily available patient, tumour, and treatment data to develop, validate and externally test machine learning models for predicting recurrence, recurrence-free survival (RFS) and overall survival (OS) at 2 years from treatment.

METHODS

A retrospective, multicentre study of patients receiving curative-intent radiotherapy for NSCLC was undertaken. A total of 657 patients from 5 hospitals were eligible for inclusion. Data pre-processing derived 34 features for predictive modelling. Combinations of 8 feature reduction methods and 10 machine learning classification algorithms were compared, producing risk-stratification models for predicting recurrence, RFS and OS. Models were compared with 10-fold cross validation and an external test set and benchmarked against TNM-stage and performance status. Youden Index was derived from validation set ROC curves to distinguish high and low risk groups and Kaplan-Meier analyses performed.

FINDINGS

Median follow-up time was 852 days. Parameters were well matched across training-validation and external test sets: Mean age was 73 and 71 respectively, and recurrence, RFS and OS rates at 2 years were 43% vs 34%, 54% vs 47% and 54% vs 47% respectively. The respective validation and test set AUCs were as follows: 1) RFS: 0·682 (0·575-0·788) and 0·681 (0·597-0·766), 2) Recurrence: 0·687 (0·582-0·793) and 0·722 (0·635-0·81), and 3) OS: 0·759 (0·663-0·855) and 0·717 (0·634-0·8). Our models were superior to TNM stage and performance status in predicting recurrence and OS.

INTERPRETATION

This robust and ready to use machine learning method, validated and externally tested, sets the stage for future clinical trials entailing quantitative personalised risk-stratification and surveillance following curative-intent radiotherapy for NSCLC.

FUNDING

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Prognostic impact of maximum standardized uptake value on 18 F-FDG PET/CT imaging of the primary lung lesion on survival in advanced non-small cell lung cancer: A retrospective study

Background

Positron emission tomography/computed tomography (PET/CT) has been recognized for diagnosing and staging lung cancer, but the prognostic value of standardized uptake value (SUV) on ¹⁸F‐FDG PET/CT imaging in patients with advanced non‐small cell lung cancer (NSCLC) remains controversial.

Methods

We performed a retrospective analysis of patients with advanced NSCLC who had undergone ¹⁸F‐FDG PET/CT before systemic treatment between June 2012 and June 2016. The relationship between the maximum SUV (SUVmax) of the pulmonary lesion and lesion size was evaluated via Spearman's correlation analysis. We collected patients' clinical and pathological data. Univariate and multivariate analyses were performed to analyze the factors influencing survival.

Results

We included 157 patients with advanced NSCLC. Among these, 135 died, 13 survived, and nine were lost to follow‐up (median follow‐up period, 69 months). SUVmax was correlated with lesion size and was significantly greater for tumors ≥3 cm than for tumors <3 cm (10.2 ± 5.4 vs. 5.6 ± 3.3, t = −6.709, p = 0.000). Univariate analysis showed that survival was associated with gender, tumor size, epidermal growth factor receptor gene mutation or anaplastic lymphoma kinase rearrangement, SUVmax of the primary lung lesion, and treatment lines. Multivariate analysis showed a significant correlation between SUVmax of the primary lung lesion and survival. The mortality risk of patients with SUVmax ≤6 was 35% lower than that of patients with SUVmax >6 (HR = 0.651, 95% confidence interval, 0.436–0.972; Wald value, 4.400; p = 0.036).

Conclusions

The SUVmax of the primary lung lesion on PET/CT is significantly correlated with survival in treatment‐naive patients with advanced NSCLC.

Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

Background

Asphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) in FDG-PET/CT is independently predictive for survival in patients with non-small cell lung cancer (NSCLC). However, comparability between PET systems may be limited. Therefore, reproducibility of ASP was evaluated at varying image reconstruction and acquisition times to assess feasibility of ASP assessment in multicenter studies.

Methods

This is a retrospective study of 50 patients with NSCLC (female 20; median age 69 years) undergoing pretherapeutic FDG-PET/CT (median 3.7 MBq/kg; 180 s/bed position). Reconstruction used OSEM with TOF_4/16 (iterations 4; subsets 16; in-plane filter 2.0, 6.4 or 9.5 mm), TOF_4/8 (4 it; 8 ss; filter 2.0/6.0/9.5 mm), PSF + TOF_2/17 (2 it; 17 ss; filter 2.0/7.0/10.0 mm) or Bayesian-penalized likelihood (Q.Clear; beta, 600/1750/4000). Resulting reconstructed spatial resolution (FWHM) was determined from hot sphere inserts of a NEMA IEC phantom. Data with approx. 5-mm FWHM were retrospectively smoothed to achieve 7-mm FWHM. List mode data were rebinned for acquisition times of 120/90/60 s. Threshold-based delineation of primary tumor MTV was followed by evaluation of relative ASP/SUVmax/MTV differences between datasets and resulting proportions of discordantly classified cases.

Results

Reconstructed resolution for narrow/medium/wide in-plane filter (or low/medium/high beta) was approx. 5/7/9 mm FWHM. Comparing different pairs of reconstructed resolution between TOF_4/8, PSF + TOF_2/17, Q.Clear and the reference algorithm TOF_4/16, ASP differences was lowest at FWHM of 7 versus 7 mm. Proportions of discordant cases (ASP > 19.5% vs. ≤ 19.5%) were also lowest at 7 mm (TOF_4/8, 2%; PSF + TOF_2/17, 4%; Q.Clear, 10%). Smoothing of 5-mm data to 7-mm FWHM significantly reduced discordant cases (TOF_4/8, 38% reduced to 2%; PSF + TOF_2/17, 12% to 4%; Q.Clear, 10% to 6%), resulting in proportions comparable to original 7-mm data. Shorter acquisition time only increased proportions of discordant cases at < 90 s.

Conclusions

ASP differences were mainly determined by reconstructed spatial resolution, and multicenter studies should aim at comparable FWHM (e.g., 7 mm; determined by in-plane filter width). This reduces discordant cases (high vs. low ASP) to an acceptable proportion for TOF and PSF + TOF of < 5% (Q.Clear: 10%). Data with better resolution (i.e., lower FWHM) could be retrospectively smoothed to the desired FWHM, resulting in a comparable number of discordant cases.

A New Prognostic Index Combines the Metabolic Response and RECIST 1.1 to Evaluate the Therapeutic Response in Patients With Non-Small Cell Lung Cancer

Aim: Response Evaluation Criteria in Solid Tumors (RECIST) is occasionally insufficient for evaluation. We proposed a new prognostic index (NPI) that combines the standardized uptake value (SUV), metabolic tumor volume (MTV), and RECIST. Methods: In total, 116 patients with lung cancer who underwent consecutive positron emission tomography-computed tomography prior to and after the initial treatment were included. We formulated the NPI by estimating the hazard ratios of overall survival for ΔMTV, ΔSUVmax, and ΔD (tumor size based on RECIST). Progression-free survival (PFS) and overall survival (OS) were compared between RECIST and the NPI. Results: ROC curve analysis identified two cutoff values based on the NPI (≤ -49.3% and ≥43.4%) to discriminate partial remission (NPR), stable disease (NSD) and progressive disease (NPD). Based on RECIST, survival analysis did not discriminate significantly on either PFS or OS between the PR, SD, and PD groups. However, according to the NPI, PFS and OS differed significantly between the NPR, NSD, and NPD groups (training set: PFS, p = 0.048; OS, p = 0.026; validation set: PFS, p = 0.004; OS, p = 0.023). Moreover, therapeutic response based on NPI was independent prognostic factor for both PFS [NPR as reference, NSD: hazard ratio (HR) 2.04; 95% confidence interval (95% CI) 1.35-3.08; p = 0.001; NPD: HR 6.87; 95% CI 3.03-15.57; p < 0.001] and OS (NPR as reference, NSD: HR 1.64; 95% CI 1.05-2.57; p = 0.031; NPD: HR 3.56; 95% CI 1.59-7.95; p = 0.002). Conclusion: The NPI showed superiority for evaluation of the therapeutic response and survival for patients with non-small cell lung cancer, overcoming the limitations of RECIST.

2-[18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease

Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[¹⁸F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[¹⁸F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[¹⁸F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.

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.

Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics

Background

Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment.

Methods

The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets.

Results

The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets.

Conclusion

The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets.

Potential Prognostic Role of 18F-FDG PET/CT in Invasive Epithelial Ovarian Cancer Relapse. A Preliminary Study

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy, with relapse occurring in about 70% of advanced cases with poor prognosis. Fluorine-18-2-fluoro-2-deoxy-d-glucose PET/CT (¹⁸F-FDGPET/CT) is the most specific radiological imaging used to assess recurrence. Some intensity-based and volume-based PET parameters, maximum standardized uptake values (SUV_max), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are indicated to have a correlation with treatment response. The aim of our study is to correlate these parameters with post relapse survival (PRS) and overall survival (OS) in Epithelial Ovarian Cancer (EOC) relapse. The study included 50 patients affected by EOC relapse who underwent ¹⁸F-FDGPET/CT before surgery. All imaging was reviewed and SUV_max, MTV and TLG were calculated and correlated to PRS and OS. PRS and OS were obtained from the first relapse and from the first diagnosis to the last follow up or death, respectively. SUV_max, MTV and TLG were tested in a univariate logistic regression analysis, only SUV_max demonstrated to be significantly associated to PRS and OS (p = 0.005 and p = 0.024 respectively). Multivariate analysis confirmed the results. We found a cut-off of SUV_max of 13 that defined worse or better survival (p = 0.003). In the first relapse of EOC, SUV_max is correlated to PRS and OS, and when SUV_max is greater than 13, it is an unfavorable prognostic factor.

Combining fluorine-18 fluorodeoxyglucose positron emission tomography and pathological risk factors to predict postoperative recurrence in stage I lung adenocarcinoma

OBJECTIVE

The aim of this study was to investigate the predictive value of qualitative assessment of tumor fluorine-18 fluorodeoxyglucose (F-FDG) uptake on PET and pathological risk factors for postoperative tumor recurrence in patients with stage I lung adenocarcinoma.

PATIENTS AND METHODS

Eighty-seven patients with stage I lung adenocarcinoma who had undergone F-FDG-PET and sequential surgical treatment without adjuvant chemotherapy were enrolled into this retrospective study. Qualitative assessment visually compared tumor F-FDG uptake with liver uptake. Tumors with one or more risk factors of tumor size of at least 4 cm, poorly differentiated, visceral pleural invasion, and lymphovascular invasion were defined as pathological high-risk tumors.

RESULTS

Patients with pathological high-risk tumors had a significantly (P=0.015) higher standardized uptake value. A multivariable Cox's proportional hazard analysis showed that tumor F-FDG uptake>liver uptake (adjusted hazard ratio: 3.54; 95% confidence interval: 1.36-9.21, P=0.010) and pathological high-risk tumors (adjusted hazard ratio: 2.34; 95% confidence interval: 1.13-4.87, P=0.023) were significant independent predictors of postoperative tumor recurrence. Patients with tumor F-FDG uptake>liver uptake and pathological high-risk tumors had significantly (P=0.001) worse 5-year disease-free survival (38.8%) and significantly (P=0.011) worse overall survival (68.5%).

CONCLUSION

Tumor F-FDG uptake>liver uptake and pathological high-risk tumors were significant independent predictors of postoperative tumor recurrence in stage I lung adenocarcinoma. Combining the two factors improves the prediction of disease-free and overall survivals, which could offer a feasible prediction model for clinically recommending adjuvant chemotherapy.

Prognostic value of metabolic tumour volume and total lesion glycolysis measured by 18F-fluorodeoxyglucose positron emission tomography/computed tomography in small cell lung cancer: A systematic review and meta-analysis

The aim of this study was to evaluate the prognostic value of metabolic tumour volume (MTV) and total lesion glycolysis (TLG) for small cell lung cancer (SCLC). MEDLINE, EMBASE and Cochrane Library databases were systematically searched. The pooled hazard ratio (HR) was used to measure the influence of MTV and TLG on survival. The subgroup analysis according to VALSG stage and the measured extent of MTV was performed. Patients with high MTV values experienced a significantly poorer prognosis with a HR of 2.42 (95% CI 1.46-4.03) for overall survival (OS) and a HR of 2.78 (95% CI 1.39-5.53) for progression-free survival (PFS) from the random effect model, and the pooled HR from the fixed effect model was 2.10 (95% CI 1.77-2.50) for OS and 2.27 (95% CI 1.83-2.81) for PFS. Patients with high TLG experienced a poorer prognosis with a HR of 1.61 (95% CI: 1.24-2.07) for OS from the random effect model, and the pooled HR from the fixed effect model was 1.64 (95% CI 1.37-1.96). Heterogeneity among studies was high for MTV in both OS and PFS meta-analyses (I²  = 87% and 88% respectively). After removing one outlier study the heterogeneity was substantially reduced (I²  = 0%) and the pooled HR for the effect of MTV on OS was 1.80 (1.51-2.16, P < 0.00001), and on PFS it was 1.86 (1.49-2.33, P < 0.00001), using either the fixed or random effects model. High MTV is associated with a significantly poorer prognosis OS and PFS, and high TLG is associated with a significantly poorer prognosis regarding OS for SCLC.

Effect of PET/CT standardized uptake values on complete response to treatment before definitive chemoradiotherapy in stage III non-small cell lung cancer

PURPOSE

The standard treatment for patients with stage III non-small cell lung cancer (NSCLC), unsuitable for resection and with good performance, is definitive radiotherapy with cisplatin-based chemotherapy. Our aim is to evaluate the effect of the maximum value of standardized uptake values (SUVmax) of the primary tumor in positron emission tomography-computed tomography (PET/CT) before treatment on complete response (CR) and overall survival.

METHODS

The data of 73 stage III NSCLC patients treated with concurrent definitive chemoradiotherapy (CRT) between 2008 and 2017 and had PET/CT staging in the pretreatment period were evaluated. ROC curve analysis was performed to determine the ideal cut-off value of pretreatment SUVmax to predict CR.

RESULTS

Median age was 58 years (range 27-83 years) and 66 patients were male (90.4%). Median follow-up time was 18 months (range 3-98 months); median survival was 23 months. 1-year overall survival (OS) rate and 5-year OS rate were 72 and 19%, respectively. Median progression-free survival (PFS) was 9 months; 1-year PFS rate and 5-year PFS rate were 38 and 19%, respectively. The ideal cut-off value of pretreatment SUVmax that predicted the complete response of CRT was 12 in the ROC analysis [AUC 0.699 (0.550-0.833)/P < 0.01] with a sensitivity of 83%, and specificity of 55%. In patients with SUVmax < 12, CR rate was 60%, while, in patients with SUV ≥ 12, it was only 19% (P = 0.002). Median OS was 26 months in patients with pretreatment SUVmax < 12, and 21 months in patients with SUVmax ≥ 12 (HR = 2.93; 95% CI 17.24-28.75; P = 0.087). CR rate of the whole patient population was 26%, and it was the only factor that showed a significant benefit on survival in both univariate and multivariate analyses.

CONCLUSION

Pretreatment SUVmax of the primary tumor in PET/CT may predict CR in stage III NSCLC patients who were treated with definitive CRT. Having clinical CR is the only positive predictive factor for prolonged survival.

The assessment of correlation and prognosis among 18F-FDG uptake parameters, Glut1, pStat1 and pStat3 in surgically resected non-small cell lung cancer patients

Introduction

To assess the correlation among ¹⁸F-FDG uptake, Glut1, pStat1 and pStat3, and to investigate the relationship between the prognosis and ¹⁸F-FDG uptake and these molecular markers in surgically resected non-small cell lung cancer (NSCLC) patients.

Results

Knockdown of Glut1 led to a significant increase in pStat1 expression. Glut1 expression positively correlated with the SUVmax, SUVmean, and TLG significantly (P<0.001). pStat3 expression negatively correlated with all PET parameters significantly (P<0.001). pStat1 had positive weak correlations with the SUVmax and SUVmean. All PET parameters and Glut1 were significantly associated with DFS (P<0.05). TLG, MTV, Glut1 and pStat1 were significantly associated with OS (P<0.05).

Conclusion

pStat3 and Glut1 may be associated with ¹⁸F-FDG uptake mechanism. TLG, MTV, and Glut1 may be independent prognostic factors.

Methods

The SUVmax, SUVmean, MTV and TLG of primary lesions were calculated in 140 patients. The expressions of Glut1 and Stat pathway proteins in NSCLC cell lines were examined by immune blots. Excised tumor tissue was analyzed by immunohistochemistry. OS and DFS were evaluated by the Kaplan-Meier method. The difference in survival between subgroups was analyzed by log-rank test. The prognostic significance of clinicopathological, molecular and PET parameters was assessed by Cox proportional hazard regression analysis.

Prognostic value of primary tumor SUVmax on pre-treatment 18F-FDG PET/CT imaging in patients with stage iii non-small cell lung cancer

OBJECTIVES

Concomitant chemoradiotherapy (CCRT) is widely used in the treatment of patients with stage iii non-small cell lung carcinoma (NSCLC). The early identification of patients with poor prognosis is the premise of personalized treatment for patients. The aim of the study was to evaluate the prognostic value of clinical parameters and primary tumor SUVmax on pre-treatment 18F-FDG PET/CT in patients with stage iii NSCLC.

MATERIAL AND METHODS

Clinical records of 79 stage iii-NSCLC patients with pre-treatment 18F-FDG PET/CT imaging, treated with definitive CCRT were retrospectively reviewed. The clinical endpoints in terms of progression-free survival (PFS) and overall survival (OS) were correlated with the median pre-treatment primary tumor SUVmax. Furthermore, other factors influencing patient outcome were analyzed.

RESULTS

The median age of patients was 58 years (range, 45-71) with 72 (91%) males. Squamous cell carcinoma (73%) was the most common histologic type. Performance status was very good (ECOG 0) in 64.5% of patients. Sixty (79%) patients had died at the time of this analysis. Median OS and PFS were 22.5 and 12.0 months, respectively. Patients were dichotomized according to pre-treatment primary tumor SUVmax≤15.0 vs.>15.0. There was no statistically significant difference for OS and PFS in both arms. Multivariate analysis showed that pre-treatment SUVmax was not a significant predictor of OS (HR 1.099, P=0.726) and PFS (HR 1.022, P=0.941).

CONCLUSIONS

SUVmax with threshold value of 15.0 on the primary tumor before treatment had no prognostic value in our patient group with stage iii NSCLC treated with definitive CCRT.

18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study

BACKGROUND

Lymph node stage prior to treatment is strongly related to disease progression and poor prognosis in non-small cell lung cancer (NSCLC). However, few studies have investigated metabolic imaging features derived from pre-radiotherapy 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) of metastatic hilar/mediastinal lymph nodes (LNs). We hypothesized that these would provide complementary prognostic information to FDG-PET descriptors to only the primary tumor (tumor).

METHODS

Two independent cohorts of 262 and 50 node-positive NSCLC patients were used for model development and validation. Image features (i.e. Radiomics) including shape and size, first order statistics, texture, and intensity-volume histograms (IVH) (http://www.radiomics.io/) were evaluated by univariable Cox regression on the development cohort. Prognostic modeling was conducted with a 10-fold cross-validated least absolute shrinkage and selection operator (LASSO), automatically selecting amongst FDG-PET-Radiomics descriptors from (1) tumor, (2) LNs or (3) both structures. Performance was assessed with the concordance-index. Development data are publicly available at www.cancerdata.org and Dryad (doi:10.5061/dryad.752153b).

RESULTS

Common SUV descriptors (maximum, peak, and mean) were significantly related to overall survival when extracted from LNs, as were LN volume and tumor load (summed tumor and LNs' volumes), though this was not true for either SUV metrics or tumor's volume. Feature selection exclusively from imaging information based on FDG-PET-Radiomics, exhibited performances of (1) 0.53 -external 0.54, when derived from the tumor, (2) 0.62 -external 0.56 from LNs, and (3) 0.62 -external 0.59 from both structures, including at least one feature from each sub-category, except IVH.

CONCLUSION

Combining imaging information based on FDG-PET-Radiomics features from tumors and LNs is desirable to achieve a higher prognostic discriminative power for NSCLC.