Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy.

PURPOSE: To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. METHODS: We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence. RESULTS: In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). CONCLUSION: Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.

[PET/CT in radiotherapy : indications and potential applications]. / TEP/TDM en radiothérapie : indications et perspectives.

Functional imaging, including positron emission tomography combined with computed tomography (PET/CT), allows the evaluation of numerous biological properties that could be considered at all steps of the therapeutic management of patients treated with radiotherapy. Indeed, it enables better initial staging of the disease, and some parameters may also be used as predictive biomarkers for treatment response, allowing better selection of patients eligible for radiotherapy. It may also improve the definition of target volumes with the aim of dose escalations by dose-painting. Finally, it could be useful during the follow-up to assess response to treatment. In this review, we report how functional imaging is integrated at the present time during the radiotherapy procedure, and what are its potential future contributions.

L'imagerie fonctionnelle, dont la tomographie par émission de positons couplée à la tomodensitométrie (TEP/TDM), permet l'évaluation de nombreuses propriétés biologiques qui pourraient être prises en compte à toutes les étapes de la prise en charge des patients traités par radiothérapie. En effet, elle permet une meilleure stadification initiale de la maladie, et certains paramètres peuvent également être utilisés comme biomarqueurs prédictifs de la réponse au traitement, permettant ainsi une meilleure sélection des patients éligibles à la radiothérapie. Elle peut également améliorer la définition des volumes cibles dans le but d'escalader la dose par dose-painting. Enfin, elle pourrait être utile lors du suivi pour évaluer la réponse au traitement. Dans cette revue, nous rapportons comment l'imagerie fonctionnelle est intégrée, à l'heure actuelle, au cours d'un traitement par radiothérapie, et nous discutons quelles sont ses futures contributions potentielles dans les principales localisations tumorales où la radiothérapie est recommandée.

Outcome analysis of HDR compared to PDR IGABT in locally advanced cervical cancer: a single-center cohort analysis.

PURPOSE: This monocentric study aimed to assess the impact of technical advancement in brachytherapy (BT) on local control (LC) and cancer-specific survival (CSS) in locally advanced cervical cancer (LACC). METHODS: Since 2010, 211 patients with LACC have been treated with 45/50.4 Gy or 60 Gy radiochemotherapy (RTCT) followed by image-guided adaptive brachytherapy (IGABT) at the authors' institution. In 2013, combined intracavitary and interstitial brachytherapy (BT IC/IS) was implemented and in 2018, pulsed-dose-rate BT (PDR-BT) was replaced by high-dose-rate BT (HDR-BT). LC, CSS, and morbidity according to the RTOG/EORTC scoring system were analyzed. Dose-volume parameters for the high-risk clinical target volume (HRCTV) and organs at risk (OAR) were reported. RESULTS: While 27 (12.8%) patients died of LACC, complete local remission was achieved in 199 (94.3%). Local relapse decreases with a high D95 in the HRCTV (hazard ratio, HR = 0.85, p = 0.0024). D95 in the HRCTV is lower after 60 Gy even if interstitial BT is used. Mean D95 in the HRCTV is 78.2 Gy, 83.3 Gy, and 83.4 Gy with PDR-BT IC, PDR-BT IC/IS, and HDR-BT IC/IS, respectively, after 45/50.4 Gy. D2 cc of OARs is significantly reduced by using interstitial BT. The mean rectum and sigmoid D2 cc are about 61.5 Gy with PDR-BT IC/IS and significantly decreased with HDR-BT IC/IS. This translates into a low fistula incidence. A very low rate of severe gastrointestinal (3.4%) and genitourinary (2.3%) toxicity was observed with HDR-BT IC/IS. CONCLUSION: This large monocentric study provides further evidence that implementation of BT IC/IS has an impact on D95 in the HRCTV, LC, and CSS. There are no differences between HDR and PDR in terms of efficacy, D95 in the HRCTV, and toxicity grade ≥ 3.

Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer.

PURPOSE: To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters. METHODS: We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared. RESULTS: In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively. CONCLUSIONS: Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.

[Lung disease associated with rheumatoid arthritis]. / La pathologie pulmonaire associée à la polyarthrite rhumatoïde.

Rheumatoid arthritis is a chronic inflammatory systemic disease. Pulmonary manifestations are the most common extra-articular involvements and can impact all components of the respiratory system: parenchyma, pleura, vessels and airways, all complications that are briefly described in this article. Interstitial lung disease is the most common of these and is associated with significant morbidity and mortality. Its detection and monitoring are based on spirometry and thoracic imaging. Specific treatments are initiated in order to reduce the risk of disease flare up but may themselves in case of toxicity be associated with respiratory manifestations, either directly or by promoting infectious complications.

La polyarthrite rhumatoïde est une pathologie systémique inflammatoire chronique. Les manifestations pulmonaires représentent l'atteinte extra-articulaire la plus fréquente et peuvent affecter tous les composants du système respiratoire : le parenchyme, la plèvre, les vaisseaux et les voies aériennes, complications décrites brièvement dans cet article. La pneumopathie interstitielle diffuse en est la plus commune et associée à une morbi-mortalité importante. Son dépistage et son suivi reposent sur les épreuves fonctionnelles et l'imagerie thoracique. Des traitements spécifiques sont initiés afin de limiter au mieux l'évolution pulmonaire, mais peuvent eux-mêmes être associés à des manifestations respiratoires, soit directement, soit en favorisant des complications infectieuses.

A review in radiomics: Making personalized medicine a reality via routine imaging.

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.

The role of PET imaging in inflammatory bowel diseases: state-of-the-art review.

Inflammatory bowel diseases (IBD), i.e. Crohn disease and ulcerative colitis, are autoimmune processes of undetermined origin characterized by the chronic inflammation of the digestive tract. There is no single gold-standard to diagnose IBD which is therefore carried out through the combination of endoscopy, biopsy, radiological and biological investigations; and the development of non-invasive technique allowing the assessment and monitoring of these diseases is necessary. In this state-of-the-art review of the literature, we present the results of PET imaging studies for the diagnosis and staging of IBD (suspected or known), response evaluation to treatment and evaluation of one the main complication, i.e. strictures; explain the reasons why this examination is currently not considered in the IBD guidelines, e.g. radiation exposure, lack of standardization and not validated performances; and finally discuss the perspectives that could possibly allow it to find a place in the future, e.g. digital PET-CT, dynamic PET images acquisition, new radiopharmaceuticals, use of radiomics and use of artificial intelligence for automatically characterize and quantify digestive [18F]FDG uptake.

The use of a visual 4-point scoring scale improves the yield of 18F-FDG PET-CT imaging in the diagnosis of renal and hepatic cyst infection in patients with autosomal dominant polycystic kidney disease.

PURPOSE: [18F]FDG PET/CT (PET/CT) proved useful in the diagnosis of renal and hepatic cyst infection (CyI) in patients with autosomal dominant polycystic kidney disease (ADPKD). However, the definition of CyI by PET/CT is unclear. Here, we characterize the [18F]FDG uptake in CyI in order to infer a visual 4-point diagnostic scale. METHODS: All ADPKD patients hospitalized between 2007 and 2019 for suspected CyI and who underwent an [18F]FDG PET/CT scan were listed. CyI was defined by 5 concomitant criteria: fever ≥ 38 °C; abdominal pain; peak plasma CRP ≥ 70 mg/L; no other cause of inflammation; and favorable outcomes after antibiotics for ≥ 21 days. First, all PET/CT images were visually interpreted. Next, the [18F]FDG uptake around the suspected CyI was scored using a semiquantitative 4-point scale in comparison to blood and liver activities. RESULTS: Sixty [18F]FDG PET/CT scans were performed for suspected CyI in 38 ADPKD patients. Twenty-nine episodes met the gold-standard criteria for CyI. The visual assessment of PET/CT images reached a sensitivity of 73.1% and a specificity of 70.6%. Using the 4-point scale, an [18F]FDG score ≥ 3 (i.e., cyst uptake > liver) improved the specificity to 85.3%. CONCLUSION: [18F]FDG PET-CT is helpful in CyI diagnosis in ADPKD, and the use of a 4-point scoring of [18F]FDG uptake improves its diagnostic yield, with positive and negative predictive values of 78.3 and 78.4%, respectively. External validation is required.

[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation.

PURPOSE: To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[18F] fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC). METHODS: One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners. RESULTS: After a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F1-score, precision and recall were respectively 0.78 (0.67-0.88), 0.49 (0.25-0.67), 0.42 (0.25-0.60) and 0.63 (0.20-0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set. CONCLUSION: [18F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient's outcome but remain subject to variability across PET/CT devices.

Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.

PURPOSE: In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics. METHODS: In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing). RESULTS: The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training. CONCLUSION: The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.

Diagnostic yield of 18 F-FDG PET/CT imaging and urinary CXCL9/creatinine levels in kidney allograft subclinical rejection.

Subclinical kidney allograft acute rejection (SCR) corresponds to "the unexpected histological evidence of acute rejection in a stable patient." SCR detection relies on surveillance biopsy. Noninvasive approaches may help avoid biopsy-associated complications. From November 2015 to January 2018, we prospectively performed positron emission tomography/computed tomography (PET/CT) after injection of F18 -fluorodeoxyglucose (18 F-FDG) in adult kidney transplant recipients with surveillance biopsy at ~3 months posttransplantation. The Banff-2017 classification was used. The ratio of the mean standard uptake value (mSUVR) between kidney cortex and psoas muscle was measured. Urinary levels of CXCL-9 were concomitantly quantified. Our 92-patient cohort was categorized upon histology: normal (n = 70), borderline (n = 16), and SCR (n = 6). No clinical or biological difference was observed between groups. The mSUVR reached 1.87 ± 0.55, 1.94 ± 0.35, and 2.41 ± 0.54 in normal, borderline, and SCR groups, respectively. A significant difference in mSUVR was found among groups. Furthermore, mSUVR was significantly higher in the SCR vs normal group. The area under the receiver operating characteristic curve (AUC) was 0.79, with 83% sensitivity using an mSUVR threshold of 2.4. The AUC of urinary CXCL-9/creatinine ratios comparatively reached 0.79. The mSUVR positively correlated with ti and acute composite Banff scores. 18 F-FDG-PET/CT helps noninvasively exclude SCR, with a negative predictive value of 98%. External validations are required.

Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

Comparative dosimetry between 99mTc-MAA SPECT/CT and 90Y PET/CT in primary and metastatic liver tumors.

INTRODUCTION: The aim of this study is to determine whether 99mTc-MAA SPECT/CT-based dosimetry could predict the actual absorbed dose in hepatocellular carcinoma (HCC) or liver metastases, treated by glass or resin microspheres. MATERIAL AND METHODS: Fifty-seven patients who underwent selective internal radiation therapy (SIRT) were retrospectively included in the study, for a total of 59 treatments. Nineteen HCC were treated by resin microspheres (HCC-SIR), 20 HCC with glass microspheres (HCC-Thera), and 20 liver metastases with resin microspheres (Metastases-SIR). The mean absorbed doses in tumoral liver (Dm) and non-tumoral liver (DmNTL) were determined on the 99mTc-MAA SPECT/CT and the 90Y PET/CT, and compared with each other. RESULTS: DmNTL was < 50 Gy in the 3 groups, with a strong correlation in all population, albeit slightly lower in Metastases-SIR than HCC-SIR and HCC-Thera (CCC 0.8, 0.94 and 0.96, respectively). In tumoral liver, Dm was higher in HCC than metastases (159 ± 117 Gy versus 63 ± 31 Gy). 99mTc-MAA SPECT/CT proved to be a better indicator of Dm in HCC compared with metastases, with similar 99mTc-MAA-90Y concordance in resin and glass microspheres (CCC HCC-SIR 0.82, CCC HCC-Thera 0.82, and CCC Metastases-SIR 0.52). CONCLUSION: 99mTc-MAA SPECT/CT is a reasonably reliable tool for predicting the dose to the non-tumoral liver in both HCC and metastases, regardless of the type of microspheres. It is also fairly reliable for predicting the tumor dose in HCC, again regardless of the type of spheres, although individual variations are observed.

Stereotactic robotic body radiotherapy for patients with oligorecurrent pulmonary metastases.

BACKGROUND: Our aim is to report treatment efficacy and toxicity of patients treated by robotic (Cyberknife®) stereotactic body radiotherapy (SBRT) for oligorecurrent lung metastases (ORLM). Additionally we wanted to evaluate influence of tumor, patient and treatment related parameters on local control (LC), lung and distant progression free- (lung PFS/Di-PFS) and overall survival (OS). METHODS: Consecutive patients with up to 5 ORLM (confirmed by FDG PET/CT) were included in this study. Intended dose was 60Gy in 3 fractions (prescribed to the 80% isodose volume). Patients were followed at regular intervals and tumor control and toxicity was prospectively scored. Tumor, patient and treatment data were analysed using competing risk- and Cox regression. RESULTS: Between May 2010 and March 2016, 104 patients with 132 lesions were irradiated from primary lung carcinoma (47%), gastro-intestinal (34%) and mixed primary histologies (19%). The mean tumor volume was 7.9 cc. After a median follow up of 22 months, the 1, 2 and 3 year LC rate (per lesion) was 89.3, 80.0 and 77.8% respectively. The corresponding (per patient) 1, 2 and 3 years lung PFS were 66.3, 50.0, 42.6%, Di-PFS were 80.5, 64.4, 60.6% and OS rates were 92.2, 80.9 and 72.0% respectively. On univariable analysis, gastro-intestinal (GI) as primary tumor site showed a significant superior local control versus the other primary tumor sites. For OS, significant variables were primary histology and primary tumor site with a superior OS for patients with metastases of primary GI origin. LC was significantly affected by the tumor volume, physical and biologically effective dose coverage. Significant variables in multivariable analysis were BED prescription dose for LC and GI as primary site for OS. The vast majority of patients developed no toxicity or grade 1 acute and late toxicity. Acute and late grade 3 radiation pneumonitis (RP) was observed in 1 and 2 patients respectively. One patient with a centrally located lesion developed grade 4 RP and died due to possible RT-induced pulmonary hemorrhage. CONCLUSIONS: SBRT is a highly effective local therapy for oligorecurrent lung metastases and could achieve long term survival in patients with favourable prognostic features.

Tumor total lesion glycolysis and number of positive pelvic lymph nodes on pretreatment positron emission tomography/computed tomography (PET/CT) predict survival in patients with locally advanced cervical cancer.

OBJECTIVE: The aim of this study was to investigate the prognostic value of metabolic parameters obtained at pretreatment [18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) in patients with locally advanced cervical cancer. We hypothesize that these metabolic parameters could optimize the treatment decision and thus favor the outcome of patients suffering locally advanced cervical cancer. METHODS: Patients with locally advanced cervical cancer underwent pretreatment PET/CT. Standard uptake values (maximum, mean, peak), metabolic tumor volume, and total lesion glycolysis were measured in the tumor and in the hypermetabolic pelvic lymph nodes. The relationship between clinical, pathological, and PET/CT metabolic parameters with recurrence-free survival and overall survival was assessed by Cox regression analysis. RESULTS: 115 patients with a median age of 52 years (range 23-77) presented with locally advanced cervical cancer. After a mean follow-up of 33.0 months after initiation of therapy, 26 patients (22.6%) recurred of which 17 patients had distant metastasis; 18 (15.7%) patients died. Recurrence-free survival at 2 and 5 years was 79.2% and 72.2%, respectively. The total lesion glycolysis of the tumor and the delay between diagnosis and treatment were significantly associated with recurrence-free survival in the multivariate analysis (HR 1.00, p=0.004, and HR 2.04, p=0.02, respectively). Only the total lesion glycolysis of the tumor ≥373.54 (HR 2.49, 95% CI 1.15 to 5.38; p=0.02) remained significant after log rank testing. Overall survival at 2 and 5 years was 91.7% and 68.8%, respectively. The number of PET-positive pelvic lymph nodes was the only independent prognostic factor for overall survival in the multivariate analysis (HR 1.43, 95% CI 1.13 to 1.81; p=0.003). CONCLUSION: Tumor total lesion glycolysis and the number of positive pelvic lymph nodes on pretreatment PET/CT appear to be independent prognostic factors for recurrence and survival in patients with locally advanced cervical cancer. This may help to select patients who may benefit from therapeutic optimization and closer surveillance.

FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer.

PURPOSE: The aim of this study was to investigate the prognostic value of baseline 18F-FDG PET/CT textural analysis in locally-advanced rectal cancer (LARC). METHODS: Eighty-six patients with LARC underwent 18F-FDG PET/CT before treatment. Maximum and mean standard uptake values (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), histogram-intensity features, as well as 11 local and regional textural features, were evaluated. The relationships of clinical, pathological and PET-derived metabolic parameters with disease-specific survival (DSS), disease-free survival (DFS) and overall survival (OS) were assessed by Cox regression analysis. Logistic regression was used to predict the pathological response by the Dworak tumor regression grade (TRG) in the 66 patients treated with neoadjuvant chemoradiotherapy (nCRT). RESULTS: The median follow-up of patients was 41 months. Seventeen patients (19.7%) had recurrent disease and 18 (20.9 %) died, either due to cancer progression (n = 10) or from another cause while in complete remission (n = 8). DSS was 95% at 1 year, 93% at 2 years and 87% at 4 years. Weight loss, surgery and the texture parameter coarseness were significantly associated with DSS in multivariate analyses. DFS was 94 % at 1 year, 86 % at 2 years and 79 % at 4 years. From a multivariate standpoint, tumoral differentiation and the texture parameters homogeneity and coarseness were significantly associated with DFS. OS was 93% at 1 year, 87% at 2 years and 79% after 4 years. cT, surgery, SUVmean, dissimilarity and contrast from the neighborhood intensity-difference matrix (contrastNGTDM) were significantly and independently associated with OS. Finally, RAS-mutational status (KRAS and NRAS mutations) and TLG were significant predictors of pathological response to nCRT (TRG 3-4). CONCLUSION: Textural analysis of baseline 18F-FDG PET/CT provides strong independent predictors of survival in patients with LARC, with better predictive power than intensity- and volume-based parameters. The utility of such features, especially coarseness, should be confirmed by larger clinical studies before considering their potential integration into decisional algorithms aimed at personalized medicine.

Methylglyoxal-Mediated Stress Correlates with High Metabolic Activity and Promotes Tumor Growth in Colorectal Cancer.

Cancer cells generally rely on aerobic glycolysis as a major source of energy. Methylglyoxal (MG), a dicarbonyl compound that is produced as a side product during glycolysis, is highly reactive and induces the formation of advanced glycation end-products that are implicated in several pathologies including cancer. All mammalian cells have an enzymatic defense against MG composed by glyoxalases GLO1 and GLO2 that converts MG to d-lactate. Colorectal cancer (CRC) is one of the most frequently occurring cancers with high morbidity and mortality. In this study, we used immunohistochemistry to examine the level of MG protein adducts, in a series of 102 CRC human tumors divided into four clinical stages. We consistently detected a high level of MG adducts and low GLO1 activity in high stage tumors compared to low stage ones suggesting a pro-tumor role for dicarbonyl stress. Accordingly, GLO1 depletion in CRC cells promoted tumor growth in vivo that was efficiently reversed using carnosine, a potent MG scavenger. Our study represents the first demonstration that MG adducts accumulation is a consistent feature of high stage CRC tumors. Our data point to MG production and detoxification levels as an important molecular link between exacerbated glycolytic activity and CRC progression.

A case of added value of 123I-mIBG SPECT/CT imaging in the diagnosis of a pheochromocytoma extending into the left adrenal vein.

We present the case of a 48-year-old patient with a left adrenal incidentaloma found on computed tomography (CT) for which the diagnosis of pheochromocytoma was confirmed by a 24-hour urinary dosage of norepinephrine. The 123I-mIBG scintigraphy showed a high uptake of 123I-mIBG in the left adrenal gland and, additionally, the single photon emission computed tomography combined with a low-dose CT (SPECT/CT) suggested the extension into the adrenal vein. The diagnostic CT and magnetic resonance images agreed with these findings and the subsequent surgery confirmed the vascular invasion.

FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy.

INTRODUCTION: With (18)F-FDG PET/CT, tumor uptake intensity and heterogeneity have been associated with outcome in several cancers. This study aimed at investigating whether (18)F-FDG uptake intensity, volume or heterogeneity could predict the outcome in patients with non-small cell lung cancers (NSCLC) treated by stereotactic body radiation therapy (SBRT). METHODS: Sixty-three patients with NSCLC treated by SBRT underwent a (18)F-FDG PET/CT before treatment. Maximum and mean standard uptake value (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), as well as 13 global, local and regional textural features were analysed. The predictive value of these parameters, along with clinical features, was assessed using univariate and multivariate analysis for overall survival (OS), disease-specific survival (DSS) and disease-free survival (DFS). Cutoff values were obtained using logistic regression analysis, and survivals were compared using Kaplan-Meier analysis. RESULTS: The median follow-up period was 27.1 months for the entire cohort and 32.1 months for the surviving patients. At the end of the study, 25 patients had local and/or distant recurrence including 12 who died because of the cancer progression. None of the clinical variables was predictive of the outcome, except age, which was associated with DFS (HR 1.1, P = 0.002). None of the (18)F-FDG PET/CT or clinical parameters, except gender, were associated with OS. The univariate analysis showed that only dissimilarity (D) was associated with DSS (HR = 0.822, P = 0.037), and that several metabolic measurements were associated with DFS. In multivariate analysis, only dissimilarity was significantly associated with DSS (HR = 0.822, P = 0.037) and with DFS (HR = 0.834, P < 0.01). CONCLUSION: The textural feature dissimilarity measured on the baseline (18)F-FDG PET/CT appears to be a strong independent predictor of the outcome in patients with NSCLC treated by SBRT. This may help selecting patients who may benefit from closer monitoring and therapeutic optimization.