Benedetta Urbini

Gefitinib in patients with progressive high-grade gliomas: a multicentre phase II study by Gruppo Italiano Cooperativo di Neuro-Oncologia (GICNO)

To investigate the role of gefitinib in patients with high-grade gliomas (HGGs), a phase II trial (1839IL/0116) was conducted in patients with disease recurrence following surgery plus radiotherapy and first-line chemotherapy. Adult patients with histologically confirmed recurrent HGGs following surgery, radiotherapy and first-line chemotherapy, were considered eligible. Patients were treated with gefitinib (250 mgday−1) continuously until disease progression. The primary end point was progression-free survival at 6 months progression-free survival at 6 months (PFS-6). Tissue biomarkers (epidermal growth factor receptor (EGFR) gene status and expression, phosphorylated Akt (p-Akt) expression) were assessed. Twenty-eight patients (median age, 55 years; median ECOG performance status, 1) were enrolled; all were evaluable for drug activity and safety. Sixteen patients had glioblastoma, three patients had anaplastic oligodendrogliomas and nine patients had anaplastic astrocytoma. Five patients (17.9%, 95% CI 6.1–36.9%) showed disease stabilisation. The overall median time to progression was 8.4 (range 2–104+) weeks and PFS-6 was 14.3% (95% CI 4.0–32.7%). The median overall survival was 24.6 weeks (range 4–104+). No grade 3–4 gefitinib-related toxicity was found. Gefitinib showed limited activity in patients affected by HGGs. Epidermal growth factor receptor expression or gene status, and p-Akt expression do not seem to predict activity of this drug.

Correction to: Which elderly newly diagnosed glioblastoma patients can benefit from radiotherapy and temozolomide? A PERNO prospective study

The members of the PERNO Study Group were not individually captured in the metadata of the original publication. They are included in the metadata of this publication.

A generalized parametric response mapping method for analysis of multi‐parametric imaging: A feasibility study with application to glioblastoma

Purpose: Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well‐suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub‐volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single‐parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi‐parametric data while maintaining the key advantages of the original PRM method. Methods: MRI‐derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3‐months post‐RT for 19 patients with high‐grade glioma were used to demonstrate the algorithm. Images were first co‐registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four‐dimensional Cartesian space with coordinate values equal to a voxels image intensity in each of the image volumes and an origin defined as the multi‐parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre‐determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non‐responding patients within a training dataset. Voxel classifications were visualized via familiar three‐class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast‐enhancing lesion (CEL) and a 1 cm shell of surrounding peri‐tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave‐one‐out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P‐values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months. Results: Single‐parameter PRM and multi‐parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single‐parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri‐tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01). Conclusions: The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single‐parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi‐parametric response into consideration and enables visualization. MPRM analysis of peri‐tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset.

Erratum to: Survival prediction in high-grade gliomas using CT perfusion imaging

Baruzzi A. (Chair), Albani F., Calbucci F., D’Alessandro R., Michelucci R. (IRCCS Institute of Neurological Sciences, Bologna, Italy), Brandes A. (Department of Medical Oncology, Bellaria-Maggiore Hospitals, Bologna, Italy), Eusebi V. (Department of Hematology and Oncological Sciences ‘‘L. & A. Seragnoli’’, Section of Anatomic Pathology at Bellaria Hospital, Bologna, Italy), Ceruti S., Fainardi E., Tamarozzi R. (Neuroradiology Unit, Department of Neurosciences and Rehabilitation, S. Anna Hospital, Ferrara, Italy), Emiliani E. (Istituto Oncologico Romagnolo, Department of Medical Oncology, Santa Maria delle Croci Hospital, Ravenna, Italy), Cavallo M. (Division of Neurosurgery, Department of Neurosciences and Rehabilitation, S. Anna Hospital, Ferrara, Italy).