Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma.

BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60-74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29-43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index = 0.472-0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692-0.750) and progression-free survival (concordance index = 0.680-0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities.

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

SU-E-J-110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen-Rényi Divergence.

PURPOSE: To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. METHODS: To present a novel joint segmentation/registration for multimodality image-guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen-Renyi (JR) divergence to achieve improved noise robustness in a multi-modality imaging space. RESULTS: It was found that JR divergence when used for segmentation has an improved robustness to noise compared to using mutual information, or other entropy-based metrics. The MI metric failed at around 2/3 the noise power than the JR divergence. CONCLUSIONS: The JR divergence metric is useful for the task of joint segmentation/registration of multimodality images and shows improved results compared entropy based metric. The algorithm can be easily modified to incorporate non-intensity based images, which would allow applications into multi-modality and texture analysis.

SU-D-BRB-03: Prediction of Tumor Outcomes Through Wavelet Image Fusion and Texture Analysis of PET/MR Imaging.

PURPOSE: To investigate the combination of PET/MR image features for the early prediction of tumor metastases to the lungs in soft-tissue sarcoma (STS) cancer. METHODS: A dataset of 24 patients with histologically proven STS was used in this study. All patients underwent pre-treatment FDG-PET and MR scans, which comprised of T1 and T2-fat suppression weighted (T2FS) sequences. The patients had a median follow-up period of 36 months (range: 6-69 months). Eight patients developed metastases to the lungs.Tumors were contoured on the T2FS scans by an expert physician. Fusion of the co-registered FDG-PET/MR scans was performed using a wavelet transform technique. A SUV feature (SUVmax) from the FDG-PET scans and 6 texture features from the co-occurrence matrix of the fused scans were extracted from the tumor region and correlation with the clinical endpoint of metastases to the lungs was investigated. Statistical analysis was performed using Spearman's rank correlation (rs) and multivariable logistic regression. RESULTS: The highest univariate prediction was found on FDG-PET/T2FS fused scans analyzed using the texture features "Sum-Mean" and "Variance". These two fused scan-texture feature combinations reached rs = -0.6838 (p = 0.0003). In comparison, SUVmax reached rs = -0.6257 (p = 0.0011). The highest multivariate prediction was found with the following 3- parameter model: -3.15*SUVmax - 5.37*FDG-PET/T2FS-Sum-Mean + 0.57*FDG-PET/T1-Variance. This model reached rs = 0.7977 (p = 0.000005). CONCLUSIONS: This work indicates the potential of PET/MR texture features of tumors as complementary metrics to existing prognostic factors. Substantial improvement in terms of prediction of metastases to the lungs in STS cancer was found with the combination of texture features from fused FDG-PET/MR scans. Potentially, this could improve patients' outcomes by allowing better adaptation of treatments. Future work will involve evaluation of the robustness of the proposed method and validation on a larger set of patients.

Modulation of inflammation by chondroitin sulfate.

OBJECTIVE AND METHODS: To evaluate the immune-modulator effect of chondroitin sulfate (CS) by means of the review of the literature. RESULTS: Inflammatory reactions are primarily originated by infectious agents, immune reactions and by sterile tissue lesions that activate membrane receptors by means of pathogen-associated molecular patterns, tissue breakdown products and cytokines. The activation of membrane receptors triggers the phosphorylation of mitogen activated protein kinases and of the nuclear factor kappaB (NF-kappaB). The binding of NF-kappaB to the promoter of target genes enhances the expression of pro-inflammatory cytokines, inducible nitric oxide synthase, cyclooxygenase 2, phospholipase A2, and matrix metalloproteases, proteins that contribute to tissue damage and to the inflammatory reaction. The activation of NF-kappaB has a key role in the immune homeostasis and the inflammatory response and therefore, in the pathogenesis of numerous diseases. Chondroitin sulfate (CS) is able to diminish NF-kappaB activation and nuclear translocation in chondrocytes and synovial membrane, effects that may explain the benefits of CS in osteoarthritis. In addition, systemic CS reduces NF-kappaB nuclear translocation in macrophages and hepatocytes, raising the hypothesis that CS might be of benefit to treat other diseases with a strong inflammatory component. There is preliminary evidence in humans that CS improves moderate to severe psoriasis. Moreover, experimental and clinical data suggest that CS might be a useful therapeutic agent in diseases such as inflammatory bowel diseases, atherosclerosis, Parkinson's and Alzheimer's diseases, multiple sclerosis, amyotrophic lateral sclerosis, rheumatoid arthritis and systemic lupus erythematosus. DISCUSSION: These results urge for double blinded placebo-controlled trials to confirm the utility of CS in diseases with immune and inflammatory components.

Designing efficient zero calibration point for phase-sensitive surface plasmon resonance biosensing.

This work is related to the development of phase-sensitive methodologies in Surface Plasmon Resonance (SPR) biosensing. We take advantage of a specific angular dependence of phase of light, reflected under SPR geometry, on parameters of the SPR-supporting metal, and propose a polarimetry-based methodology to easily determine the optimal calibration zero point, corresponding to the maximal phase sensitivity. The proposed methodology can significantly facilitate the calibration of the system in field and multi-channel sensing, broaden the dynamic range, as well as contribute to the development of feedback loops.

[Doughnut lesions of the cranial vault: an hereditary bone dysplasia]. / Lésions en beignet de la voûte crânienne: une dysplasie osseuse héréditaire.

We describe a large family of whom twelve members were shown to have a benign bone dysplasia known as "doughnut lesions of the skull". Its clinical features are pathological fractures and cranial lumps; its radiological features comprise doughnut cranial lesions, double cortical lines of the vertebral bodies ("bone in bone"), squaring of the metatarsal and metacarpal bones, osteopenia, and tubulation defects of the diaphyses of the long bones. We believe the disease to be transmitted as an autosomal dominant trait.