Association of pre-radiotherapy tumour burden and overall survival in newly diagnosed glioblastoma adjusted for MGMT promoter methylation status.

PURPOSE: We retrospectively evaluated the association between postoperative pre-radiotherapy tumour burden and overall survival (OS) adjusted for the prognostic value of O6-methylguanine DNA methyltransferase (MGMT) promoter methylation in patients with newly diagnosed glioblastoma treated with radio-/chemotherapy with temozolomide. MATERIALS AND METHODS: Patients were included from the CENTRIC (EORTC 26071-22072) and CORE trials if postoperative magnetic resonance imaging scans were available within a timeframe of up to 4weeks before radiotherapy, including both pre- and post-contrast T1w images and at least one T2w sequence (T2w or T2w-FLAIR). Postoperative (residual) pre-radiotherapy contrast-enhanced tumour (CET) volumes and non-enhanced T2w abnormalities (NT2A) tissue volumes were obtained by three-dimensional segmentation. Cox proportional hazard models and Kaplan Meier estimates were used to assess the association of pre-radiotherapy CET/NT2A volume with OS adjusted for known prognostic factors (age, performance status, MGMT status). RESULTS: 408 tumour (of which 270 MGMT methylated) segmentations were included. Median OS in patients with MGMT methylated tumours was 117 weeks versus 61weeks in MGMT unmethylated tumours (p < 0.001). When stratified for MGMT methylation status, higher CET volume (HR 1.020; 95% confidence interval CI [1.013-1.027]; p < 0.001) and older age (HR 1.664; 95% CI [1.214-2.281]; p = 0.002) were significantly associated with shorter OS while NT2A volume and performance status were not. CONCLUSION: Pre-radiotherapy CET volume was strongly associated with OS in patients receiving radio-/chemotherapy for newly diagnosed glioblastoma stratified by MGMT promoter methylation status.

Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI.

BACKGROUND: Well differentiated liposarcoma (WDLPS) can be difficult to distinguish from lipoma. Currently, this distinction is made by testing for MDM2 amplification, which requires a biopsy. The aim of this study was to develop a noninvasive method to predict MDM2 amplification status using radiomics features derived from MRI. METHODS: Patients with an MDM2-negative lipoma or MDM2-positive WDLPS and a pretreatment T1-weighted MRI scan who were referred to Erasmus MC between 2009 and 2018 were included. When available, other MRI sequences were included in the radiomics analysis. Features describing intensity, shape and texture were extracted from the tumour region. Classification was performed using various machine learning approaches. Evaluation was performed through a 100 times random-split cross-validation. The performance of the models was compared with the performance of three expert radiologists. RESULTS: The data set included 116 tumours (58 patients with lipoma, 58 with WDLPS) and originated from 41 different MRI scanners, resulting in wide heterogeneity in imaging hardware and acquisition protocols. The radiomics model based on T1 imaging features alone resulted in a mean area under the curve (AUC) of 0·83, sensitivity of 0·68 and specificity of 0·84. Adding the T2-weighted imaging features in an explorative analysis improved the model to a mean AUC of 0·89, sensitivity of 0·74 and specificity of 0·88. The three radiologists scored an AUC of 0·74 and 0·72 and 0·61 respectively; a sensitivity of 0·74, 0·91 and 0·64; and a specificity of 0·55, 0·36 and 0·59. CONCLUSION: Radiomics is a promising, non-invasive method for differentiating between WDLPS and lipoma, outperforming the scores of the radiologists. Further optimization and validation is needed before introduction into clinical practice.

ANTECEDENTES: Es difícil distinguir los liposarcomas bien diferenciados (well-differentiated liposarcomas, WDLPS) de los lipomas. En la actualidad, esta distinción se realiza mediante la prueba de amplificación del gen MDM2 por biopsia. El objetivo de este estudio fue predecir de forma no invasiva el estado de amplificación del gen MDM2 para diferenciar los lipomas de los WDLPS utilizando características radiómicas a partir de la resonancia magnética. MÉTODOS: Se incluyeron los pacientes remitidos al instituto Erasmus MC entre 2009-2018 por un lipoma MDM2 negativo o WDLPS MDM2 positivo y las resonancias magnéticas potenciadas en T1 correspondientes antes del tratamiento. Cuando estaban disponibles, se incluyeron otras secuencias de MRI en el análisis radiómico. Se describieron la intensidad, forma y textura de la región tumoral. Para la clasificación se utilizaron varios modelos de aprendizaje automático (machine learning). La evaluación se realizó mediante una validación cruzada aleatoria 100x. Se comparó el rendimiento de los modelos con la clasificación realizada por tres radiólogos expertos. RESULTADOS: Se incluyeron 116 pacientes (58 lipomas, 58 WDLPS) y 41 aparatos de MRI, con una gran heterogeneidad en las técnicas y protocolos para la adquisición de imágenes. El modelo radiómico basado únicamente en las características de las imagen en T1 dio como resultado una AUC media de 0,83, con una sensibilidad de 0,68 y una especificidad de 0,84. Un análisis adicional incorporando las imágenes ponderadas en T2 mejoró el modelo con una AUC media de 0,89, una sensibilidad de 0,74 y una especificidad de 0,88. Los tres radiólogos obtuvieron una AUC de 0,74/0,72/0,61, una sensibilidad de 0,74/0,91/0,64 y una especificidad de 0,55/0,36/0,59, respectivamente. CONCLUSIÓN: La radiómica es un método prometedor y no invasivo para diferenciar entre WDLPS y lipomas, superando la valoración de los radiólogos. Sin embargo, se necesita la optimización y validación de esta técnica antes de su introducción en la práctica clínica diaria.