Évaluation de l’exactitude de latéralisation du foyer épileptogène par la tomographie par émission de positons au 18f-fluorodésoxyglucose dans le bilan préopératoire de l’épilepsie temporale : analyse visuelle simple et par index d’asymétrie inter-hémisphérique par soustraction d’images,analyse quantitative par statistical parametric mapping

IntroductionInter-ictal 18F-2-fluoro-deoxy-D-glucose-positron emission tomography (FDG-PET) plays a key role for the preoperative evaluation of patients with pharmacoresistant temporal lobe epilepsy. PET images are usually analyzed visually, a way that is reported to provide a high diagnostic value but that remains subjective, depending on the expertise and experience of the observer. By contrast, the voxel-based quantitative analyses, such as statistical parametric mapping (SPM), are objective and therefore, observer independent methods of analyses. In this study, the accuracy of the analyses of brain FDG-PET images to lateralize the temporal lobe epileptogenic zone was compared between: (1) a conventional visual method, (2) a quantitative SPM analysis, and (3) a visual analysis of inter-hemispheric asymmetry (IHA) obtained after images substraction.Materials and methodsFDG-PET scans of 31 patients presenting a severe temporal epilepsy and whom the temporal foci had been accurately lateralized (successful subsequent surgical treatment) were retrospectively analysed by (1) a consensual visual analysis from two experienced observers; (2) SPM analysis with voxel-wise comparisons of FDG-PET images of patients with those of age-matched healthy controls, using various statistical threshold (P) and cluster (k) values; and (3) visual assessment by the two same observers of images obtained for assessing the IHA. For this purpose, a flipped image was initially obtained by reversing in the left-right direction the FDG-PET images, which had been previously spacially normalized with the SPM template. Then, flipped and non-flipped images were substracted.ResultsThe temporal hypometabolic area was accurately identified: (1) by the conventional visual analysis in 87 % of patients and with a satisfactory interobserver reproducibility (interobserver Cohen's coefficient = 0.79); (2) by SPM analysis, in 90 % of patients (when using optimal thresholds of 0.01 for P value and of 50 voxels (400 mm³) for k value); and (3) with the visual analysis of IHA in 97 % of patients with an excellent interobserver reproductibility (interobserver Cohen's coefficient = 1).ConclusionIn patients presenting severe temporal epilepsy, visual assessment of FDG-PET images from IHA seems more accurate for lateralizing the epileptogenic temporal areas when compared with either conventional visual or quantitative SPM analyses. Moreover, this method is very easy to use in clinical practice, contrary to the quantitative method using SPM     

A Statistical Parametric Mapping Toolbox Used for Voxel-Wise Analysis of FDG-PET Images of Rat Brain

Purpose

PET (positron emission tomography) imaging researches of functional metabolism using fluorodeoxyglucose (¹⁸F-FDG) of animal brain are important in neuroscience studies. FDG-PET imaging studies are often performed on groups of rats, so it is desirable to establish an objective voxel-based statistical methodology for group data analysis.

Material and Methods

This study establishes a statistical parametric mapping (SPM) toolbox (plug-ins) named spmratIHEP for voxel-wise analysis of FDG-PET images of rat brain, in which an FDG-PET template and an intracranial mask image of rat brain in Paxinos & Watson space were constructed, and the default settings were modified according to features of rat brain. Compared to previous studies, our constructed rat brain template comprises not only the cerebrum and cerebellum, but also the whole olfactory bulb which made the later cognitive studies much more exhaustive. And with an intracranial mask image in the template space, the brain tissues of individuals could be extracted automatically. Moreover, an atlas space is used for anatomically labeling the functional findings in the Paxinos & Watson space. In order to standardize the template image with the atlas accurately, a synthetic FDG-PET image with six main anatomy structures is constructed from the atlas, which performs as a target image in the co-registration.

Results

The spatial normalization procedure is evaluated, by which the individual rat brain images could be standardized into the Paxinos & Watson space successfully and the intracranial tissues could also be extracted accurately. The practical usability of this toolbox is evaluated using FDG-PET functional images from rats with left side middle cerebral artery occlusion (MCAO) in comparison to normal control rats. And the two-sample t-test statistical result is almost related to the left side MCA.

Conclusion

We established a toolbox of SPM8 named spmratIHEP for voxel-wise analysis of FDG-PET images of rat brain.     

Differential diagnosis between progression and radionecrosis in brain metastases after stereotactic radiosurgery using hybrid FDG-PET and MRI coregistered images

IntroductionDual phase 18 FDG brain PET is helpful to assess brain metastases (BM) as tracer will build up in metastases or tumor recurrences while its retention remains stable within normal tissue or inflammatory processes. This is useful when MRI can’t discriminate brain tumor recurrence (TR) rom radionecrosis (RN) after stereotaxic radiosurgery (SRS) for BM. Many studies have sought to improve diagnostic performance by associating FDG-PET and MRI with interesting results but many biases, mostly within image post-processing. Coregistered MRI and dual phase FDG-PET images could alleviate these biases and be used to extract prognostic biomarkers.Materials and methodsWe retrospectively evaluated patients treated with SRS for BM which developed a contrast-enhanced MRI lesion with non-conclusive diagnosis for TR or RN. All patients underwent MRI and FDG-PET at least 3 months after their last SRS session. Dual FDG-PET consisted in an “early” and “delayed” acquisition, respectively 30 minutes and 4 h after injection. MRI included permeability and perfusion sequences. PET and MRI data were all coregistered on the contrast enhanced T1 MRI images. Semi-automated Volumes of Interest (VOI) of the tumor were drawn on the BM and a reference contralateral white-matter ROI (WM) was drawn for standardization; every metric was calculated inside these ROIs, in particular the tumor SUVmax and its variation in time. A 20% increase in the tumor SUVmax was in favor of TR while a modification of less than 100% was in favor of RN. Imaging metrics were then evaluated for their association with TR or RN based on histological, radiological and clinical criteria after at least 6 months follow-up.ResultsNine patients were ruled out as TR and 6 as RN. After standardization, there was a significant difference between groups for VP (P = 0.042), Washin (P = 0.035), Peak Enhancement (P = 0.037), standardized delayed SUVmax (P = 0.008) and RI (P = 0.016). Semi-quantitative analysis found respectively for PET and MRI a Sensitivity of 100% and 87.5% and a Specificity of 100% and 85.71%.ConclusionCoregistered PET-MRI images accurately discriminate between TR and RN. With FDG being the most commonly used PET radiotracer, this protocol remains easily transposable and should be encouraged to obtain non-invasive prognostic and clinically relevant biomarkers.     

Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study

Objectives

To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying “triple-negative" breast cancers.

Materials and Methods

In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.

Results

Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.

Conclusions

Considering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.     

Relationship between Tumor Heterogeneity Measured on FDG-PET/CT and Pathological Prognostic Factors in Invasive Breast Cancer

Background

There is currently little support to understand which pathological factors led to differences in tumor texture as measured from FDG PET/CT images. We studied whether tumor heterogeneity measured using texture analysis in FDG-PET/CT images is correlated with pathological prognostic factors in invasive breast cancer.

Methods

Fifty-four patients with locally advanced breast cancer who had an initial FDG-PET/CT were retrospectively included. In addition to SUVmax, three robust textural indices extracted from 3D matrices: High-Gray-level Run Emphasis (HGRE), Entropy and Homogeneity were studied. Univariate and multivariate logistic regression was used to identify PET parameters associated with poor prognosis pathological factors: hormone receptor negativity, presence of HER-2 and triple negative phenotype. Receiver operating characteristic (ROC) curves and the (AUC) analysis, and reclassification measures, were performed in order to evaluate the performance of combining texture analysis and SUVmax for characterizing breast tumors.

Results

Tumor heterogeneity, measured with HGRE, was higher in negative estrogen receptor (p = 0.039) and negative progesterone receptor tumors (p = 0.036), and in Scarff-Bloom-Richardson grade 3 tumors (p = 0.047). None of the PET indices could identify HER-2 positive tumors. Only SUVmax was positively correlated with Ki-67 (p<0.0004). Triple negative breast cancer (TNBC) exhibited higher SUVmax (Odd Ratio = 1.22, 95%CI [1.06–1.39],p = 0.004), lower Homogeneity (OR = 3.57[0.98–12.5],p = 0.05) and higher HGRE (OR = 8.06[1.88–34.51],p = 0.005) than non-TNBC. Multivariate analysis showed that HGRE remained associated with TNBC (OR = 5.27[1.12–1.38],p = 0.03) after adjustment for SUVmax. Combining SUVmax and HGRE yielded in higher area under the ROC curves (AUC) than SUVmax for identifying TNBC: AUC =  0.83 and 0.77, respectively. Probability of correct classification also increased in 77% (10/13) of TNBC and 71% (29/41) of non-TNBC (p = 0.003), when combining SUVmax and HGRE.

Conclusions

Tumor heterogeneity measured on FDG-PET/CT was higher in invasive breast cancer with poor prognosis pathological factors. Texture analysis might be used, in addition to SUVmax, as a new tool to assess invasive breast cancer aggressiveness.     

Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features

PurposeThe purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images.MethodsTexture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design.ResultsThe phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination.ConclusionLow field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.     

Metformin abolishes increased tumor 18F-2-fluoro-2-deoxy-D-glucose uptake associated with a high energy diet

Insulin regulates glucose uptake by normal tissues. Although there is evidence that certain cancers are growth-stimulated by insulin, the possibility that insulin influences tumor glucose uptake as assessed by ¹⁸F-2-Fluoro-2-Deoxy-d-Glucose Positron Emission Tomography (FDG-PET) has not been studied in detail. We present a model of diet-induced hyperinsulinemia associated with increased insulin receptor activation in neoplastic tissue and with increased tumor FDG-PET image intensity. Metformin abolished the diet-induced increases in serum insulin level, tumor insulin receptor activation and tumor FDG uptake associated with the high energy diet but had no effect on these measurements in mice on a control diet. These findings provide the first functional imaging correlate of the well-known adverse effect of caloric excess on cancer outcome. They demonstrate that, for a subset of neoplasms, diet and insulin are variables that affect tumor FDG uptake and have implications for design of clinical trials of metformin as an antineoplastic agent.     

Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis

Background

The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.

Methods and Findings

We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10⁻⁴, hazard ratio = 1.47, 95% CI 1.17–1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26–2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.

Conclusions

To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.     

Syndromes paranéoplasiques : intérêt de la tomographie par émission de positons (TEP) au 18F-fluoro-déoxyglucose (FDG)

BackgroundWe evaluated the performance of ¹⁸F-fluorodeoxyglucose (¹⁸FDG) positon emission tomography (PET) in the diagnosis of underlying malignancy in cases of suspected paraneoplastic syndrome (PS).Methods¹⁸FDG-PET was performed in 31 patients, clinically suspected to have PS. The PS were 34, among which 12 neurological diseases, eight endocrine, seven rheumatological, one dermatological and six vascular. We compared computed tomography (CT), iodine-enhanced most of the time, and ¹⁸FDG-PET reports to clinicians definitive conclusion at the end of the work-up and a follow-up period of, at least, two months.ResultsWe obtained a histological diagnosis of cancer for ten patients, but could only identify the primary site of malignancy for nine of them. ¹⁸FDG-PET showed six primary sites among which three were not seen on CT. CT disclosed four primary sites, among which one was not seen on ¹⁸FDG-PET. In one case, ¹⁸FDG-PET disclosed regional lymph node metastases whereas these were not identified by CT. Eleven non-neoplasic causes were evidenced, among which ¹⁸FDG-PET played a major role in three cases. Ten causes were still undetermined at the end of the study.ConclusionWhole-body ¹⁸FDG-PET study plays an important role in the identification of underlying malignancy in clinically suspected paraneoplastic syndromes; either by identifying the primary tumor or by directing biopsy of metastases. Furthermore, it can identify non-neoplasic causes.     

Combined HASPIN and mTOR inhibition is synergistic against KRAS-driven carcinomas

BackgroundOncogenic mutations in the KRAS gene are very common in human cancers, resulting in cells with well-characterized selective advantages. For more than three decades, the development of effective therapeutics to inhibit KRAS-driven tumorigenesis has proved a formidable challenge and KRAS was considered ‘undruggable’. Therefore, multi-targeted therapy may provide a reasonable strategy for the effective treatment of KRAS-driven cancers. Here, we assess the efficacy and mechanistic rationale for combining HASPIN and mTOR inhibition as a potential therapy for cancers carrying KRAS mutations.MethodsWe investigated the synergistic effect of a combination of mTOR and HASPIN inhibitors on cell viability, cell cycle, cell apoptosis, DNA damage, and mitotic catastrophe using a panel of human KRAS-mutant and wild-type tumor cell lines. Subsequently, the human transplant models were used to test the therapeutic efficacy and pharmacodynamic effects of the dual therapy.ResultsWe demonstrated that the combination of mTOR and HASPIN inhibitors induced potent synergistic cytotoxic effects in KRAS-mutant cell lines and delayed the growth of human tumor xenograft. Mechanistically, we showed that inhibiting of mTOR potentiates HASPIN inhibition by preventing the phosphorylation of H3 histones, exacerbating mitotic catastrophe and DNA damage in tumor cell lines with KRAS mutations, and this effect is due in part to a reduction in VRK1.ConclusionsThese findings indicate that increased DNA damage and mitotic catastrophe are the basis for the effective synergistic effect observed with mTOR and HASPIN inhibition, and support the clinical evaluation of this dual therapy in patients with KRAS-mutant tumors.     

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

PurposeAccurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions.MethodsQUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.ResultsCore and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively.ConclusionsQUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.     

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma

Background

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.

Methods

We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.

Results

We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).

Conclusion

Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.     

Image quality assessment of an iterative reconstruction algorithm applied to abdominal CT imaging

PurposeTo compare the noise and accuracy on images of the whole porcine liver acquired with iterative reconstruction (IMR, Philips Healthcare, Cleveland, OH, USA) and filtered back projection (FBP) methods.Materials and methodsWe used non-enhanced porcine liver to simulate the human liver and acquired it 100 times to obtain the average FBP value as the ground-truth. The mean and the standard deviation (“inter-scan SD”) of the pixel values on the 100 image acquisitions were calculated for FBP and for three levels of IMR (L1, L2, and L3). We also calculated the noise power spectrum (NPS) and the normalized NPS for the 100 image acquisitions.ResultsThe spatial SD for the porcine liver parenchyma on these slices was 9.92, 4.37, 3.63, and 2.30 Hounsfield units with FBP, IMR-L1, IMR-L2, and IMR-L3, respectively. The detectability of small faint features was better on single IMR than single FBP images. The inter-scan SD value for IMR-L3 images was 53% larger at the liver edges than at the liver parenchyma; it was only 10% larger on FBP images. Assessment of the normalized NPS showed that the noise on IMR images was comprised primarily of low-frequency components.ConclusionIMR images yield the same structure informations as FBP images and image accuracy is maintained. On level 3 IMR images the image noise is more strongly suppressed than on IMR images of the other levels and on FBP images.     

Effect of slice thickness on brain magnetic resonance image texture analysis

Background  

The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients.     

Trends in advanced breast cancer incidence rates after implementation of a mammography screening program in a German population

BackgroundMammography screening programs (MSPs) aim to detect early-stage breast cancers in order to decrease the incidence of advanced-stage breast cancers and to reduce breast cancer mortality. We analyzed the time trends of advanced-stage breast cancer incidence rates in the target population before and after implementation of the MSP in a region of northwestern Germany.MethodsThe MSP in the Münster district started in October 2005. A total of 13,874 women with an incident invasive breast cancer (BC) was identified by the population-based epidemiological cancer registry between 2000 and 2013 in the target group 50–69 years. Multiple imputation methods were used to replace missing data on tumor stages (10.4%). The incidence rates for early-stage (UICC I) and advanced-stage (UICC II+) BC were determined, and Poisson regression analyses were performed to assess trends over time.ResultsThe incidence rates for UICC I breast cancers increased during the step-up introduction of the MSP and remained elevated thereafter. By contrast, after increasing from 2006 to 2008, the incidence rates of UICC II+ breast cancers decreased to levels below the pre-screening period. Significantly decreasing UICC II+ incidence rates were limited to the age group 55–69 years and reached levels that were significantly lower than incidence rates in the pre-screening period.DiscussionThe incidence rates of advanced-stage breast cancers decreased in the age groups from 55 years to the upper age limit for screening eligibility, but not in the adjacent age groups. The findings are consistent with MSP lead time effects and seem to indicate that the MSP lowers advanced-stage breast cancer rates in the target population.     

应用经皮氩氦冷冻消融术姑息性治疗恶性实体肿瘤后肿瘤进展的相关因素分析

目的:分析应用经皮氩氦冷冻消融术姑息性治疗恶性实体肿瘤后肿瘤进展的相关因素。方法:回顾性搜集2012年8月-2017年6月上海市第一人民医院收治的因患有恶性实体肿瘤行姑息性经皮氩氦冷冻消融术患者的相关临床资料,并随访至2017年11月,搜集患者随访结束时的临床资料。将总消融例数根据消融后肿瘤进展情况分为比较肿瘤进展和肿瘤非进展组,比较患者的一般临床特征。将消融后进展的病例列出,比较消融前和进展时相关检验结果差异,探寻肿瘤进展原因。结果:共82次经皮氩氦冷冻消融术在2012年8月-2017年6月间进行,35名患者所行的41次消融被纳入本研究,所有病人接受的经皮氩氦冷冻消融术次数均不大于2次,41次经皮氩氦冷冻消融术共消融42枚病灶,其中一次消融术同时消融了2枚肝内病灶。35名患者按照肿瘤全身进展与否分为肿瘤进展组(n=26)及肿瘤非进展组(n=9),有统计学差异的指标包括:消融处为原发肿瘤,随访截止/进展时间,消融前至随访截止/肿瘤进展时存在化疗相关性粒细胞缺乏,消融前粒细胞与淋巴细胞比值(ratio of peripheral neutrophils to lymphocyte,NLR)3。对于消融后判定为全身肿瘤进展的30次经皮氩氦冷冻消融术,对比消融前及进展时的各项指标,有统计学意义的指标是血浆白蛋白值和NLR3。最后,应用上述有统计学意义的计数资料通过Cox回归分析评定为肿瘤进展的30次消融的无进展生存时间,结果均无统计学意义。结论:作为综合治疗的一部分,氩氦冷冻消融术姑息性治疗恶性实体肿瘤后的肿瘤进展因素中,对于原发肿瘤的消融是肿瘤进展的不利因素,化疗相关性粒细胞缺乏、NLR3、低血浆白蛋白水平是肿瘤进展的有利因素。     

Évaluation de la TEP au 18F-FDG dans l’exploration de la récidive des carcinomes colorectaux

PurposeThe aim of the study was to evaluate the diagnostic performance, the prognosis factors and the therapeutic impact of 18F-FDG positron emission tomography (FDG-PET) in the detection of recurrent colorectal cancers.MethodsSixty PET/CT with 18F-FDG and CT were performed in 52 patients, at the Paul Papin cancer center between 2003 and 2005, following suspicion of colorectal cancer relapse. The FDG-PET impact on the clinical management was studied by examination of multidisciplinary concertations results. Survival analysis were realized with a mean follow up of 2.2 years.ResultsRecurrence was confirmed for 50 explorations by histologic (n = 32), radiologic (n = 14) or clinical (n = 4) findings. Twenty patients died during the time of the study. On a patient based analysis, FDG-PET sensitivity, specificity and overall accuracy were 90, 90, 90% respectively compared with 74, 50 and 70% for CT. FDG-PET changed the clinical management in 18 cases (30%). A positive FDG-PET signal, more than one hepatic lesion, more than two lymph node lesions detected on FDG-PET and more than two hepatic lesions on CT were characterized as bad prognostic factors for survival. Multivariate analysis showed that the only independent bad prognostic factor was the FDG-PET detection of more than two liver lesions.ConclusionThese results confirmed the important impact of FDG-PET in the clinical management of patients with a suspected recurrence of colorectal cancer.     

Detection of Synchronous Cancers by Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography during Primary Staging Workup for Esophageal Squamous Cell Carcinoma in Taiwan

Aim

The aim of this retrospective study was to investigate the ability of fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in the detection of synchronous cancers during staging workup for esophageal squamous cell carcinoma.

Materials and Methods

We performed a retrospective chart review of 426 Taiwanese patients with esophageal cancer who received FDG-PET/CT during their primary staging workup between December 2006 and December 2011. We defined synchronous cancers as those occurring within 6 months of the FDG-PET/CT scan. All of the synchronous lesions were confirmed by histology or imaging follow-up. The study patients were followed for at least 18 months or were censored on the date of last follow-up.

Results

Fifty patients were excluded from analysis because of the presence of distant metastases. Of the remaining 376 patients, 359 were diagnosed with squamous cell carcinoma (SCC). We identified 17 patients with synchronous cancers, and all of them had a diagnosis of SCC. Synchronous head and neck cancers were the most frequent (n=13, 76.4%), followed by gastrointestinal cancers (colon cancer, n=2; hepatocellular carcinoma, n=1), and renal cell carcinoma (n=1). FDG-PET/CT successfully detected 15 synchronous cancers (12 head and neck cancers, 2 colon cancers, and 1 renal cell carcinoma). In contrast, conventional workup detected only 9 synchronous cancers (7 head and neck cancers, 1 hepatocellular carcinoma and 1 renal cell carcinoma). The sensitivity of FDG-PET/CT and conventional workup in detecting synchronous cancers were 88.2% and 52.9% respectively.

Conclusion

The most frequent synchronous lesions in patients with esophageal SCC were head and neck cancers in Taiwan. Our data indicate that FDG-PET/CT is superior to conventional workup in the detection of synchronous tumors during primary staging for esophageal squamous cell carcinoma.     

Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images

PurposeWe aimed to explore the temporal stability of radiomic features in the presence of tumor motion and the prognostic powers of temporally stable features.MethodsWe selected single fraction dynamic electronic portal imaging device (EPID) (n = 275 frames) and static digitally reconstructed radiographs (DRRs) of 11 lung cancer patients, who received stereotactic body radiation therapy (SBRT) under free breathing. Forty-seven statistical radiomic features, which consisted of 14 histogram-based features and 33 texture features derived from the graylevel co-occurrence and graylevel run-length matrices, were computed. The temporal stability was assessed by using a multiplication of the intra-class correlation coefficients (ICCs) between features derived from the EPID and DRR images at three quantization levels. The prognostic powers of the features were investigated using a different database of lung cancer patients (n = 221) based on a Kaplan-Meier survival analysis.ResultsFifteen radiomic features were found to be temporally stable for various quantization levels. Among these features, seven features have shown potentials for prognostic prediction in lung cancer patients.ConclusionsThis study suggests a novel approach to select temporally stable radiomic features, which could hold prognostic powers in lung cancer patients.