Morphological MRI-based features provide pretreatment survival prediction in glioblastoma.

OBJECTIVES: We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients. METHODS: A cohort of 404 glioblastoma patients (311 discoveries and 93 validations) was used in the study. Pretreatment volumetric postcontrast T1-weighted magnetic resonance images were segmented to obtain the relevant morphological measures. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell's concordance indexes (c-indexes) were used for the statistical analysis. RESULTS: A linear prognostic model based on the outstanding variables (age, contrast-enhanced (CE) rim width, and surface regularity) identified a group of patients with significantly better survival (p < 0.001, HR = 2.57) with high accuracy (discovery c-index = 0.74; validation c-index = 0.77). A similar model applied to totally resected patients was also able to predict survival (p < 0.001, HR = 3.43) with high predictive value (discovery c-index = 0.81; validation c-index = 0.92). Biopsied patients with better survival were well identified (p < 0.001, HR = 7.25) by a model including age and CE volume (c-index = 0.87). CONCLUSIONS: Simple linear models based on small sets of meaningful MRI-based pretreatment morphological features and age predicted survival of glioblastoma patients to a high degree of accuracy. The partition of the population using the extent of resection improved the prognostic value of those measures. KEY POINTS: • A combination of two MRI-based morphological features (CE rim width and surface regularity) and patients' age outperformed previous prognosis scores for glioblastoma. • Prognosis models for homogeneous surgical procedure groups led to even more accurate survival prediction based on Kaplan-Meier analysis and concordance indexes.

Correction to: Morphological MRI-based features provide pretreatment survival prediction in glioblastoma.

The original version of this article, published on 15 October 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The name of Mariano Amo-Salas and the affiliation of Ismael Herruzo were presented incorrectly.

Molecular Study of Long-Term Survivors of Glioblastoma by Gene-Targeted Next-Generation Sequencing.

Glioblastoma (GBM) is the most common malignant adult primary brain tumor. Despite its high lethality, a small proportion of patients have a relatively long overall survival (OS). Here we report a study of a series of 74 GBM samples from 29 long-term survivors ([LTS] OS ≥36 months) and 45 non-LTS. Using next-generation sequencing, we analyzed genetic alterations in the genes most frequently altered in gliomas. Approximately 20% of LTS had a mutation in the IDH1 or IDH2 (IDH) genes, denoting the relevance of this molecular prognostic factor. A new molecular group of GBMs harbored alterations in ATRX or DAXX genes in the absence of driver IDH or H3F3A mutations. These patients tended to have a slightly better prognosis, to be younger at diagnosis, and to present frontal or temporal tumors, and, morphologically, to present giant tumor cells. A significant fraction of LTS GBM patients had tumors with 1 or more alterations in the relevant GBM signaling pathways (RTK/PI3K, TP53 and RB1). In these patients, the PDGFRA alteration is suggested to be a favorable molecular factor. Our findings here are relevant for developing future targeted therapies and for identifying molecular prognostic factors in GBM patients.

Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma.

Purpose To evaluate the prognostic and predictive value of surface-derived imaging biomarkers obtained from contrast material-enhanced volumetric T1-weighted pretreatment magnetic resonance (MR) imaging sequences in patients with glioblastoma multiforme. Materials and Methods A discovery cohort from five local institutions (165 patients; mean age, 62 years ± 12 [standard deviation]; 43% women and 57% men) and an independent validation cohort (51 patients; mean age, 60 years ± 12; 39% women and 61% men) from The Cancer Imaging Archive with volumetric T1-weighted pretreatment contrast-enhanced MR imaging sequences were included in the study. Clinical variables such as age, treatment, and survival were collected. After tumor segmentation and image processing, tumor surface regularity, measuring how much the tumor surface deviates from a sphere of the same volume, was obtained. Kaplan-Meier, Cox proportional hazards, correlations, and concordance indexes were used to compare variables and patient subgroups. Results Surface regularity was a powerful predictor of survival in the discovery (P = .005, hazard ratio [HR] = 1.61) and validation groups (P = .05, HR = 1.84). Multivariate analysis selected age and surface regularity as significant variables in a combined prognostic model (P < .001, HR = 3.05). The model achieved concordance indexes of 0.76 and 0.74 for the discovery and validation cohorts, respectively. Tumor surface regularity was a predictor of survival for patients who underwent complete resection (P = .01, HR = 1.90). Tumors with irregular surfaces did not benefit from total over subtotal resections (P = .57, HR = 1.17), but those with regular surfaces did (P = .004, HR = 2.07). Conclusion The surface regularity obtained from high-resolution contrast-enhanced pretreatment volumetric T1-weighted MR images is a predictor of survival in patients with glioblastoma. It may help in classifying patients for surgery.

Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study.

BACKGROUND: The potential of a tumour's volumetric measures obtained from pretreatment MRI sequences of glioblastoma (GBM) patients as predictors of clinical outcome has been controversial. Mathematical models of GBM growth have suggested a relation between a tumour's geometry and its aggressiveness. METHODS: A multicenter retrospective clinical study was designed to study volumetric and geometrical measures on pretreatment postcontrast T1 MRIs of 117 GBM patients. Clinical variables were collected, tumours segmented, and measures computed including: contrast enhancing (CE), necrotic, and total volumes; maximal tumour diameter; equivalent spherical CE width and several geometric measures of the CE "rim". The significance of the measures was studied using proportional hazards analysis and Kaplan-Meier curves. RESULTS: Kaplan-Meier and univariate Cox survival analysis showed that total volume [p = 0.034, Hazard ratio (HR) = 1.574], CE volume (p = 0.017, HR = 1.659), spherical rim width (p = 0.007, HR = 1.749), and geometric heterogeneity (p = 0.015, HR = 1.646) were significant parameters in terms of overall survival (OS). Multivariable Cox analysis for OS provided the later two parameters as age-adjusted predictors of OS (p = 0.043, HR = 1.536 and p = 0.032, HR = 1.570, respectively). CONCLUSION: Patients with tumours having small geometric heterogeneity and/or spherical rim widths had significantly better prognosis. These novel imaging biomarkers have a strong individual and combined prognostic value for GBM patients. KEY POINTS: • Three-dimensional segmentation on magnetic resonance images allows the study of geometric measures. • Patients with small width of contrast enhancing areas have better prognosis. • The irregularity of contrast enhancing areas predicts survival in glioblastoma patients.

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.

OBJECTIVE:: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. METHODS:: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan-Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. RESULTS:: Kaplan-Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. CONCLUSION:: Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. ADVANCES IN KNOWLEDGE:: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour.

Multidisciplinary Rectal Cancer Management: 2nd European Rectal Cancer Consensus Conference (EURECA-CC2).

BACKGROUND AND PURPOSE: During the first decade of the 21st century a number of important European randomized studies were published. In order to help shape clinical practice based on best scientific evidence from the literature, the International Conference on 'Multidisciplinary Rectal Cancer Treatment: Looking for an European Consensus' (EURECA-CC2) was organized in Italy under the endorsement of European Society of Medical Oncology (ESMO), European Society of Surgical Oncology (ESSO), and European Society of Therapeutic Radiation Oncology (ESTRO). METHODS: Consensus was achieved using the Delphi method. The document was available to all Committee members as a web-based document customized for the consensus process. Eight chapters were identified: epidemiology, diagnostics, pathology, surgery, radiotherapy and chemotherapy, treatment toxicity and quality of life, follow-up, and research questions. Each chapter was subdivided by a topic, and a series of statements were developed. Each member commented and voted, sentence by sentence thrice. Sentences upon which an agreement was not reached after voting round # 2 were openly debated during a Consensus Conference in Perugia (Italy) from 11 December to 13 December 2008. A hand-held televoting system collected the opinions of both the Committee members and the audience after each debate. The Executive Committee scored percentage consensus based on three categories: "large consensus", "moderate consensus", and "minimum consensus". RESULTS: The total number of the voted sentences was 207. Of the 207, 86% achieved large consensus, 13% achieved moderate consensus, and only 3 (1%) resulted in minimum consensus. No statement was disagreed by more than 50% of the members. All chapters were voted on by at least 75% of the members, and the majority was voted on by >85%. CONCLUSIONS: This Consensus Conference represents an expertise opinion process that may help shape future programs, investigational protocols, and guidelines for staging and treatment of rectal cancer throughout Europe.

Plasma d-dimer levels correlate with outcomes in patients with community-acquired pneumonia.

STUDY OBJECTIVES: The aim of this study was to investigate the prognostic value of plasma d-dimer levels in patients with community-acquired pneumonia (CAP). DESIGN: Prospective observational study. SETTING: Hospital Lluis Alcanyis of Xativa, Spain. PATIENTS: Consecutive adult patients admitted to the hospital with CAP from January 2000 to October 2002. MEASUREMENTS AND RESULTS: A total of 302 patients were included. Plasma d-dimer was measured using an automated latex assay. The relationships between plasma d-dimer and prognostic variables included in the pneumonia severity index (PSI) were examined using univariate and multivariate linear and logistic regression analyses. d-Dimer levels were negative (ie, < 500 ng/mL) in 16.9% of the patients. In nonsurvivors, the d-dimer plasma level mean value was 3,786 ng/mL, while in survivors it was 1,609 ng/mL (p < 0.0001). A significant relationship was found between the presence of elevated d-dimer levels and the PSI and APACHE (acute physiology and chronic health evaluation) II score. Elevated d-dimer levels were associated with radiologic pneumonia extension. The d-dimer predictive value for mechanical ventilation therapy showed an area under the curve of 0.78 (95% confidence interval, 0.71 to 0.81). CONCLUSIONS: d-Dimer plasma levels could be useful for predicting clinical outcome in patients with CAP.

[Validation of the Pneumonia Severity Index for hospitalizing patients with community-acquired pneumonia]. / Validación del Pneumonia Severity Index en la hospitalización de los pacientes con neumonía adquirida en la comunidad.

BACKGROUND AND OBJECTIVE: Our main objective was to assess the utility of the Pneumonia Severity Index (PSI) to decide the site of care home or hospital of patients with community-acquired pneumonia (CAP). PATIENTS AND METHOD: All CAP patients who came to the emergency department from 1 January to 31 December, 2000, were prospectively assessed with a protocol based on the PSI and additional admission criteria applied to classes I, II and III. Mortality within 30 days and poor outcome were used as endpoints. We tested the diagnostic efficacy of the PSI scale in predicting mortality or unfavourable events by calculating the area below the ROC curve. RESULTS: Of the 243 CAP patients included, 124 (51%) belonged to classes I, II and III, and 119 (49%) belonged to classes IV and V. One hundred and fifty six (64%) patients were admitted. Fifteen (6.2%) patients died, all of them belonging to classes IV and V. Forty four (18%) patients showed a poor outcome. Only one patient who was initially sent home had a poor outcome. The prognostic value of the PSI scale to predict mortality (ROC = 0.92; CI 95%, 0.88-0.95) was high. CONCLUSIONS: Our results confirm that the PSI scale is a good prognostic index in clinical practice for predicting mortality due to CAP. In order to use the PSI to decide the site of care of patients with CAP, not only the score obtained but also additional factors should be taken into account.