Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients

OBJECTIVE

The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging.

MATERIAL AND METHODS

Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M).

RESULTS

In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%.

CONCLUSION

This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes.

KEY POINTS

• Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.

Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.

Deep learning approach for the segmentation of aneurysmal ascending aorta

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

BACKGROUND

The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model.

METHODS

Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources' requirements.

RESULTS

E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region.

CONCLUSIONS

We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method

Background

Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.

This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[¹¹C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification.

Results

For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUV_mean, SUL_peak, SUV_min, SUL_peak prod-surface-area, SUV_mean prod-sphericity, surface mean SUV 3, SUL_peak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features.

Conclusions

The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.

Choline PET/CT features to predict survival outcome in high risk prostate cancer restaging: a preliminary machine-learning radiomics study

BACKGROUND

Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18F-Cho PET/CT imaging features to predict disease progression in PCa.

METHODS

We retrospectively analyzed high-risk PCa patients who underwent restaging 18F-Cho PET/CT from November 2013 to May 2018. 18F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model has been adapted using NCA for feature selection, while DA was used as a method for feature classification and performance analysis.

RESULTS

106 imaging features were extracted for 46 lesions for a total of 4876 features analyzed. No significant differences between the training and validating sets in terms of age, sex, PSA values, lesion location and size (p > 0.05) were demonstrated by the machine-learning model. Thirteen features were able to discriminate FU disease status after NCA selection. Best performance in DA classification was obtained using the combination of the 13 selected features (sensitivity 74%, specificity 58% and accuracy 66%) compared to the use of all features (sensitivity 40%, specificity 52%, and accuracy 51%). Per-site performance of the 13 selected features in DA classification were as follow: T= sensitivity 63%, specificity 83%, accuracy 71%; N= sensitivity 87%, specificity 91% of and accuracy 90%; bone-M= sensitivity 33%, specificity 77% and accuracy 66%.

CONCLUSIONS

An artificial intelligence model demonstrated to be feasible and able to select a panel of 18F-Cho PET/CT features with valuable association with PCa patients' outcome.

Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT

BACKGROUND

Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests.

METHODS

We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis features. Next, we evaluate the relationship between pulmonary function and the HRCT features at selected HU thresholds, namely -200 HU, 0 HU, and +200 HU. We model the relationship using a Poisson approximation to identify the measure with the highest log-likelihood.

RESULTS

Our Poisson models reveal no difference at the -200 and 0 HU thresholds. However, inferential conclusions change at the +200 HU threshold. Among the HRCT features considered, the percentage of normally attenuated lung at -200 HU shows the most significant diagnostic utility.

CONCLUSIONS

The percentage of normally attenuated lung can be used together with qualitative HRCT assessment and pulmonary function tests to enhance the idiopathic pulmonary fibrosis (IPF) diagnostic process.

Development of a new fully three-dimensional methodology for tumours delineation in functional images

Delineation of tumours in Positron Emission Tomography (PET) plays a crucial role in accurate diagnosis and radiotherapy treatment planning. In this context, it is of outmost importance to devise efficient and operator-independent segmentation algorithms capable of reconstructing the tumour three-dimensional (3D) shape. In previous work, we proposed a system for 3D tumour delineation on PET data (expressed in terms of Standardized Uptake Value - SUV), based on a two-step approach. Step 1 identified the slice enclosing the maximum SUV and generated a rough contour surrounding it. Such contour was then used to initialize step 2, where the 3D shape of the tumour was obtained by separately segmenting 2D PET slices, leveraging the slice-by-slice marching approach. Additionally, we combined active contours and machine learning components to improve performance. Despite its success, the slice marching approach poses unnecessary limitations that are naturally removed by performing the segmentation directly in 3D. In this paper, we migrate our system into 3D. In particular, the segmentation in step 2 is now performed by evolving an active surface directly in the 3D space. The key points of such an advancement are that it performs the shape reconstruction on the whole stack of slices simultaneously, naturally leveraging cross-slice information that could not be exploited before. Additionally, it does not require any specific stopping condition, as the active surface naturally reaches a stable topology once convergence is achieved. Performance of this fully 3D approach is evaluated on the same dataset discussed in our previous work, which comprises fifty PET scans of lung, head and neck, and brain tumours. The results have confirmed that a benefit is indeed achieved in practice for all investigated anatomical districts, both quantitatively, through a set of commonly used quality indicators (dice similarity coefficient >87.66%, Hausdorff distance < 1.48 voxel and Mahalanobis distance < 0.82 voxel), and qualitatively in terms of Likert score (>3 in 54% of the tumours).

18F-Florbetaben PET/CT to Assess Alzheimer's Disease: A new Analysis Method for Regional Amyloid Quantification

BACKGROUND AND PURPOSE

While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD.

METHODS

Amyloid PET results using a new processing imaging algorithm (MRI-Less and AAL Atlas) were correlated with clinical, cognitive status, CSF analysis, and other imaging. The regional SUVR using the white matter of cerebellum as reference region and scores from clinical and cognitive tests were used to create ROC curves. Leave-one-out cross-validation was carried out to validate the results.

RESULTS

Forty-four consecutive patients with clinical evidence of dementia, were retrospectively evaluated. Amyloid PET scan was positive in 26/44 patients with dementia. After integration with 18F-FDG PET, clinical data and CSF protein levels, 22 of them were classified as AD, the remaining 4 as vascular or frontotemporal dementia. Amyloid and FDG PET, CDR 1, CSF Tau, and p-tau levels showed the best true positive and true negative rates (amyloid PET: AUC = .85, sensitivity .91, specificity .79). A SUVR value of 1.006 in the inferior frontal cortex and of 1.03 in the precuneus region was the best cutoff SUVR value and showed a good correlation with the diagnosis of AD. Thirteen of 44 amyloid PET positive patients have been enrolled in clinical trials using antiamyloid approaches.

CONCLUSIONS

Amyloid PET using SPM-normalized SUVR analysis showed high predictive power for the differential diagnosis of AD.

Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy-d-glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R² = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy.

Assessment of survival in patients with idiopathic pulmonary fibrosis using quantitative HRCT indexes

Background: The assessment of Idiopathic Pulmonary Fibrosis (IPF) using HRCT requires great experience and is limited by a significant inter-observer variability, even between trained radiologists. The evaluation of HRCT through automated quantitative analysis may hopefully solve this problem. The accuracy of CT-histogram derived indexes in the assessment of survival in IPF patients has been poorly studied. Methods: Forty-two patients with a diagnosis of IPF and a follow up time of 3 years were retrospectively collected; HRCT and Pulmonary Function Tests (PFTs) performed at diagnosis time were analysed; the extent of fibrotic disease was quantified on HRCT using kurtosis, skewness, Mean Lung Density (MLD), High attenuation areas (HAA%) and Fibrotic Areas (FA%). Univariate Cox regression was performed to assess hazard ratios for the explored variables and a multivariate model considering skewness, FVC, DLCO and age was created to test their prognostic value in assessing survival. Through ROC analysis, threshold values demonstrating the best sensitivity and specificity in predicting mortality were identified. They were used as cut-off points to graph Kaplan-Meier curves specific for the CT-indexes. Results: Kurtosis, skewness, MLD, HAA% and FA% were good predictors of mortality (HR 0.44, 0.74, 1.01, 1.12, 1.06; p = 0.03, p = 0.01, p = 0.02, p = 0.02 and p = 0.017 respectively). Skewness demonstrated the lowest Akaike’s information criterion value (55.52), proving to be the best CT variable for prediction of mortality. Significant survival differences considering proposed cut-off points were also demonstrated according to kurtosis (p = 0. 02), skewness (p = 0.005), MLD (p = 0.003), HAA% (p = 0.009) and FA% (p = 0.02) – obtained from quantitative HRCT analysis at diagnosis time. Conclusions: CT-histogram derived indexes may provide an accurate estimation of survival in IPF.

Assessment of survival in patients with idiopathic pulmonary fibrosis using quantitative HRCT indexes

Background

The assessment of Idiopathic Pulmonary Fibrosis (IPF) using HRCT requires great experience and is limited by a significant inter-observer variability, even between trained radiologists. The evaluation of HRCT through automated quantitative analysis may hopefully solve this problem. The accuracy of CT-histogram derived indexes in the assessment of survival in IPF patients has been poorly studied.

Methods

Forty-two patients with a diagnosis of IPF and a follow up time of 3 years were retrospectively collected; HRCT and Pulmonary Function Tests (PFTs) performed at diagnosis time were analysed; the extent of fibrotic disease was quantified on HRCT using kurtosis, skewness, Mean Lung Density (MLD), High attenuation areas (HAA%) and Fibrotic Areas (FA%). Univariate Cox regression was performed to assess hazard ratios for the explored variables and a multivariate model considering skewness, FVC, DL_CO and age was created to test their prognostic value in assessing survival. Through ROC analysis, threshold values demonstrating the best sensitivity and specificity in predicting mortality were identified. They were used as cut-off points to graph Kaplan-Meier curves specific for the CT-indexes.

Results

Kurtosis, skewness, MLD, HAA% and FA% were good predictors of mortality (HR 0.44, 0.74, 1.01, 1.12, 1.06; p = 0.03, p = 0.01, p = 0.02, p = 0.02 and p = 0.017 respectively). Skewness demonstrated the lowest Akaike's information criterion value (55.52), proving to be the best CT variable for prediction of mortality. Significant survival differences considering proposed cut-off points were also demonstrated according to kurtosis (p = 0.02), skewness (p = 0.005), MLD (p = 0.003), HAA% (p = 0.009) and FA% (p = 0.02) - obtained from quantitative HRCT analysis at diagnosis time.

Conclusions

CT-histogram derived indexes may provide an accurate estimation of survival in IPF patients. They demonstrate a correlation with PFTs, highlighting their possible use in clinical practice.

[18F]FDG and [18F]FLT PET for the evaluation of response to neo-adjuvant chemotherapy in a model of triple negative breast cancer

Rationale

Pathological response to neo-adjuvant chemotherapy (NAC) represents a commonly used predictor of survival in triple negative breast cancer (TNBC) and the need to identify markers that predict response to NAC is constantly increasing. Aim of this study was to evaluate the potential usefulness of PET imaging with [¹⁸F]FDG and [¹⁸F]FLT for the discrimination of TNBC responders to Paclitaxel (PTX) therapy compared to the response assessed by an adapted Response Evaluation Criteria In Solid Tumors (RECIST) criteria based on tumor volume (Tumor Volume Response).

Methods

Nu/nu mice bearing TNBC lesions of different size were evaluated with [¹⁸F]FDG and [¹⁸F]FLT PET before and after PTX treatment. SUV_max, Metabolic Tumor Volume (MTV) and Total Lesion Glycolysis (TLG) and Proliferation (TLP) were assessed using a graph-based random walk algorithm.

Results

We found that in our TNBC model the variation of [¹⁸F]FDG and [¹⁸F]FLT SUV_max similarly defined tumor response to therapy and that SUV_max variation represented the most accurate parameter. Response evaluation using Tumor Volume Response (TVR) showed that the effectiveness of NAC with PTX was completely independent from lesions size at baseline.

Conclusions

Our study provided interesting results in terms of sensitivity and specificity of PET in TNBC, revealing the similar performances of [¹⁸F]FDG and [¹⁸F]FLT in the identification of responders to Paclitaxel.

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

BACKGROUND AND OBJECTIVES

Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [¹¹C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation.

METHODS

A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife^® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTV_MRI. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians.

RESULTS

The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTV_MRI enhanced the CTV more accurately than BTV in 25% of cases.

CONCLUSIONS

The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTV_MRI and GTV should be considered for a comprehensive treatment planning.

Predictive and Prognostic Value of Early Disease Progression by PET Evaluation in Advanced Non-Small Cell Lung Cancer

OBJECTIVE

To assess the predictive and prognostic value of progressive metabolic disease (PMD) by the use of early 18Fluorodeoxyglucose positron emission tomography (18FDG-PET) in patients with clinical stage IV non-small cell lung cancer (NSCLC) treated with first-line chemotherapy.

METHODS

An 18FDG-PET performed following the first cycle of chemotherapy (PET-1) was compared with a pretreatment 18FDG-PET (PET-0) and a computed tomography (CT) scan after the third cycle (CT-3). The primary endpoint was the positive predictive value (PPV) of PMD. Secondary endpoints included the prognostic value of PMD.

RESULTS

Eleven of 38 patients (29%) had a PMD by PET-1, and 15 (39%), including all patients with a PMD, experienced a progressive disease by CT-3. The PPV of PMD was 100% according to both the European Organization for Research and Treatment of Cancer (EORTC) criteria and the PET Response Criteria In Solid Tumors (PERCIST) (p value for both, <0.0001). Patients with a PMD by PET-1 had a median overall survival of 7.0 months versus 14.0 months for those without a PMD (p = 0.04, according to the EORTC criteria).

CONCLUSIONS

Early 18FDG-PET assessment deserves further investigation for the identification of NSCLC patients who do not benefit from first-line chemotherapy.

Evaluation of erlotinib treatment response in non-small cell lung cancer using metabolic and anatomic criteria

BACKGROUND

In this paper the clinical value of PET for early prediction of tumor response to erlotinib in patients with advanced or metastatic non-small cell lung cancer (NSCLC) after failure of at least one prior chemotherapy regimen is evaluated. The aim was to compare the early metabolic treatment response using European Organization for Research and Treatment of Cancer (EORTC) 1999 recommendations and PET Response Criteria in Solid Tumors (PERCIST), and the standard treatment response using Response Evaluation Criteria in Solid Tumors (RECIST).

METHODS

Twenty patients with stage IV NSCLC were enrolled prospectively. PET/CT studies were performed before, then 48 hours, and 45 days after the initiation of erlotinib treatment. The lesion with the highest uptake in each patient was evaluated according to EORTC 1999 recommendations, PERCIST and RECIST to assess metabolic and anatomic response. Response classifications were compared statistically using Wilcoxon signed-rank test. Disease-free survival (DFS) and overall survival (OS) were calculated by the Kaplan-Meier Test.

RESULTS

At 48 hours, the Kaplan-Meier analysis showed that EORTC proved to be a significant prognostic factor for predicting DFS and OS. At 45 days, there was a significant difference in response evaluation between RECIST and metabolic classifications. RECIST and PERCIST were significant prognostic factors for predicting DFS and OS. EORTC was not able to discriminate responder from non-responder patients.

CONCLUSIONS

This study shows that, according to the EORTC protocol, the PET exam is able to provide early identification of patients who benefit from Erlotinib treatment. Used at the end of therapy, PERCIST could be considered an appropriate metabolic evaluation method to discriminate responders from non-responders.

Correlation between histological grade and positron emission tomography parameters in cervical carcinoma

The aim of the present study was to evaluate the changes in cervical cancer glucose metabolism for different levels of cellular differentiation. The metabolic activity was measured by standardized uptake value (SUV), SUV normalized to lean body mass, metabolic tumor volume and total lesion glycolysis using fluorine-18 fluorodeoxyglucose positron emission tomography (PET). A correlation study of these values could be used to facilitate therapeutic choice and to improve clinical practice and outcome. This study considered 32 patients with diagnosed cervical cancers, at different International Federation of Gynecology and Obstetrics stages. Glucose metabolism was assessed by PET examination, and histological specimens were examined to determine their initial grade of differentiation. A correlation study of these values was evaluated. Histological examination showed that all cases were of squamous cell carcinoma. Regarding the differentiation of the tumor, 19 well- to moderately-differentiated tumors and 13 poorly-differentiated tumors were determined. Negative findings for correlations between metabolic parameters and initial grade of histological differentiation were found, and considering that histological grade has been shown to have no consistent prognostic value in cervical cancer treatment, PET imaging could play a significant role in cervical cancer prognosis.

Metabolic impact of partial volume correction of [18F]FDG PET-CT oncological studies on the assessment of tumor response to treatment

AIM

The aim of this work is to evaluate the metabolic impact of Partial Volume Correction (PVC) on the measurement of the Standard Uptake Value (SUV) from [18F]FDG PET-CT oncological studies for treatment monitoring purpose.

METHODS

Twenty-nine breast cancer patients with bone lesions (42 lesions in total) underwent [18F]FDG PET-CT studies after surgical resection of breast cancer primitives, and before (PET-II) chemotherapy and hormone treatment. PVC of bone lesion uptake was performed on the two [18F]FDG PET-CT studies, using a method based on Recovery Coefficients (RC) and on an automatic measurement of lesion metabolic volume. Body-weight average SUV was calculated for each lesion, with and without PVC. The accuracy, reproducibility, clinical feasibility and the metabolic impact on treatment response of the considered PVC method was evaluated.

RESULTS

The PVC method was found clinically feasible in bone lesions, with an accuracy of 93% for lesion sphere-equivalent diameter >1 cm. Applying PVC, average SUV values increased, from 7% up to 154% considering both PET-I and PET-II studies, proving the need of the correction. As main finding, PVC modified the therapy response classification in 6 cases according to EORTC 1999 classification and in 5 cases according to PERCIST 1.0 classification.

CONCLUSION

PVC has an important metabolic impact on the assessment of tumor response to treatment by [18F]FDG PET-CT oncological studies.