Segmentation of gliomas in 18F-fluorocholine PET/CT. A multiapproach study. / Segmentación de gliomas con PET/TC con 18F-fluorocolina. Estudio multiaproximación.

AIM: Our aim was two-fold, to study the interobserver agreement in tumour segmentation and to search for a reliable methodology to segment gliomas using 18F-fluorocholine PET/CT. METHODS: 25 patients with glioma, from a prospective and non-randomized study (Functional and Metabolic Glioma Analysis), were included.Interobserver variability in tumour segmentation was assessed using fixed thresholds. Different strategies were used to segment the tumours. First, a semi-automatic tumour segmentation was performed, selecting the best SUVmax-% threshold for each lesion. Next we determined a variable SUVmax-% depending on the SUVmax. Finally a segmentation using a fixed SUVmax threshold was performed. To do so, a sampling of 10 regions of interest (ROI of 2.8cm2) located in the normal brain was performed. The upper value of the sample mean SUVmax±3 SD was used as cut-off. All procedures were tested and classified as effective or not for tumour segmentation by two observer's consensus. RESULTS: In the pilot segmentation, the mean±SD of SUVmax, SUVmean and optimal SUVmax-% threshold were: 3.64±1.77, 1.32±0.57 and 21.32±8.39, respectively. Optimal SUVmax-% threshold showed a significant association with the SUVmax (Pearson=-0.653, p=.002). However, the linear regression model for the total sample was not good, that supported the division in two homogeneous groups, defining two formulas for predicting the optimal SUVmax-% threshold. As to the third procedure, the obtained value for the mean SUVmax background+3 SD was 0.33. This value allowed segmenting correctly a significant fraction of tumours, although not all. CONCLUSION: A great interobserver variability in the tumour segmentation was found. None of the methods was able to segment correctly all the gliomas, probably explained by the wide tumour heterogeneity on 18F-fluorocholine PET/CT.

Global heterogeneity assessed with 18F-FDG PET/CT. Relation with biological variables and prognosis in locally advanced breast cancer. / Medidas de heterogeneidad global y esfericidad con 18F-FDG PET/TC en el cáncer de mama: relación con la biología tumoral, valor predictivo y pronóstico.

AIM: To analyze the relationship between measurements of global heterogeneity, obtained from 18F-FDG PET/CT, with biological variables, and their predictive and prognostic role in patients with locally advanced breast cancer (LABC). MATERIAL AND METHODS: 68 patients from a multicenter and prospective study, with LABC and a baseline 18F-FDG PET/CT were included. Immunohistochemical profile [estrogen receptors (ER) and progesterone receptors (PR), expression of the HER-2 oncogene, Ki-67 proliferation index and tumor histological grade], response to neoadjuvant chemotherapy (NC), overall survival (OS) and disease-free survival (DFS) were obtained as clinical variables. Three-dimensional segmentation of the lesions, providing SUV, volumetric [metabolic tumor volume (MTV) and total lesion glycolysis (TLG)] and global heterogeneity variables [coefficient of variation (COV) and SUVmean/SUVmax ratio], as well as sphericity was performed. The correlation between the results obtained with the immunohistochemical profile, the response to NC and survival was also analyzed. RESULTS: Of the patients included, 62 received NC. Only 18 responded. 13 patients relapsed and 11 died during follow-up. ER negative tumors had a lower COV (p=0.018) as well as those with high Ki-67 (p=0.001) and high risk phenotype (p=0.033) compared to the rest. No PET variable showed association with the response to NC nor OS. There was an inverse relationship between sphericity with DFS (p=0.041), so, for every tenth that sphericity increases, the risk of recurrence decreases by 37%. CONCLUSIONS: Breast tumors in our LABC dataset behaved as homogeneous and spherical lesions. Larger volumes were associated with a lower sphericity. Global heterogeneity variables and sphericity do not seem to have a predictive role in response to NC nor in OS. More spherical tumors with less variation in gray intensity between voxels showed a lower risk of recurrence.

Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.

BACKGROUND AND PURPOSE: Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously. MATERIALS AND METHODS: A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclusion criteria of the study and classified as having multifocal glioblastomas. Tumors were segmented and morphologic features were computed using different methodologies: 1) measured on the largest focus, 2) aggregating the different foci as a whole, and 3) recording the extreme value obtained for each focus. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell concordance indices (c-indices) were used for the statistical analysis. RESULTS: Age (P < .001, hazard ratio = 2.11, c-index = 0.705), surgery (P < .001, hazard ratio = 2.04, c-index = 0.712), contrast-enhancing rim width (P < .001, hazard ratio = 2.15, c-index = 0.704), and surface regularity (P = .021, hazard ratio = 1.66, c-index = 0.639) measured on the largest focus were significant independent predictors of survival. Maximum contrast-enhancing rim width (P = .002, hazard ratio = 2.05, c-index = 0.668) and minimal surface regularity (P = .036, hazard ratio = 1.64, c-index = 0.600) were also significant. A multivariate model using age, surgery, and contrast-enhancing rim width measured on the largest foci classified multifocal glioblastomas into groups with different outcomes (P < .001, hazard ratio = 3.00, c-index = 0.853, median survival difference = 10.55 months). Moreover, quartiles with the highest and lowest individual prognostic scores based on the focus with the largest volume and surgery were identified as extreme groups in terms of survival (P < .001, hazard ratio = 18.67, c-index = 0.967). CONCLUSIONS: A prognostic model incorporating imaging findings on pretreatment postcontrast T1-weighted MRI classified patients with glioblastoma into different prognostic groups.

A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours.

Glioblastoma (GBM) is the most frequent and aggressive type of primary brain tumour. The development of image-based biomarkers from magnetic resonance images (MRIs) has been a topic of recent interest. GBMs on pre-treatment post-contrast T1-weighted (w) MRIs often appear as rim-shaped regions. In this research, we wanted to define rim-shape complexity (RSC) descriptors and study their value as indicators of the tumour's biological aggressiveness. We constructed a set of widths characterizing the rim-shaped contrast-enhancing areas in T1w MRIs, defined measures of the RSC and computed them for 311 GBM patients. Survival analysis, correlations and sensitivity studies were performed to assess the prognostic value of the measurements. All measures obtained from the histograms were found to depend on the class width to some extent. Several measures (FWHM and ßR) had high prognostic value. Some histogram-independent measures were predictors of survival: maximum rim width, mean rim width and spherically averaged rim width. The later quantity allowed patients to be classified into subgroups with different rates of survival (mean difference 6.28 months, p = 0.006). In conclusion, some of the morphological quantifiers obtained from pre-treatment T1w MRIs provided information on the biological aggressiveness of GBMs. The results can be used to define prognostic measurements of clinical applicability.

Predictive and prognostic potential of volume-based metabolic variables obtained by a baseline 18F-FDG PET/CT in breast cancer with neoadjuvant chemotherapy indication. / Papel predictivo y pronóstico de las variables volumétricas metabólicas obtenidas en la 18F-FDG PET/TC en el cáncer de mama con indicación de quimioterapia neoadyuvante.

AIM: To investigate the usefulness of metabolic variables using 18F-FDG PET/CT in the prediction of neoadjuvant chemotherapy (NC) response and the prognosis in locally advanced breast cancer (LABC). MATERIAL AND METHODS: Prospective study including 67 patients with LABC, NC indication and a baseline 18F-FDG PET/CT. After breast tumor segmentation, SUV variables (SUVmax, SUVmean and SUVpeak) and volume-based variables, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were obtained. Tumors were grouped into molecular phenotypes, and classified as responders or non-responders after completion of NC. Disease-free status (DFs), disease-free survival (DFS), and overall survival (OS) were assessed. A univariate and multivariate analysis was performed to study the potential of all variables to predict DFs, DFS, and OS. RESULTS: Fourteen patients were classified as responders. Median±SD of DFS and OS was 43±15 and 46±13 months, respectively. SUV and TLG showed a significant correlation (p<0.005) with the histological response, with higher values in responders compared to non-responders. MTV and TLG showed a significant association with DFs (p=0.015 and p=0.038 respectively). Median, mean and SD of MTV and TLG for patients with DFs were: 8.90, 13.73, 15.10 and 33.78, and 90.54 and 144.64, respectively. Median, mean and SD of MTV and TLG for patients with non-DFs were: 16.72, 29.70 and 31.09 and 90.89, 210.98 and 382.80, respectively. No significant relationships were observed with SUV variables and DFs. Volume-based variables were significantly associated with OS and DFS, although in multivariate analysis only MTV was related to OS. No SUV variables showed an association with the prognosis. CONCLUSION: Volume-based metabolic variables obtained with 18F-FDG PET/CT, unlike SUV based variables, were good predictors of both neoadjuvant chemotherapy response and prognosis.