PET-based radiomics signature can predict durable responses to CAR T-cell therapy in patients with large B-cell lymphoma.

Chimeric antigen receptor (CAR) T-cell therapy is a promising treatment option for relapsed or refractory (R/R) large B-cell lymphoma (LBCL). However, only a subset of patients will present long-term benefit. In this study, we explored the potential of PET-based radiomics to predict treatment outcomes with the aim of improving patient selection for CAR T-cell therapy. We conducted a single-center study including 93 consecutive R/R LBCL patients who received a CAR T-cell infusion from 2018 to 2021, split in training set (73 patients) and test set (20 patients). Radiomics features were extracted from baseline PET scans and clinical benefit was defined based on median progression-free survival (PFS). Cox regression models including the radiomics signature, conventional PET biomarkers and clinical variables were performed for most relevant outcomes. A radiomics signature including 4 PET-based parameters achieved an AUC = 0.73 for predicting clinical benefit in the test set, outperforming the predictive value of conventional PET biomarkers (total metabolic tumor volume [TMTV]: AUC = 0.66 and maximum standardized uptake value [SUVmax]: AUC = 0.59). A high radiomics score was also associated with longer PFS and OS in the multivariable analysis. In conclusion, the PET-based radiomics signature predicted efficacy of CAR T-cell therapy and outperformed conventional PET biomarkers in our cohort of LBCL patients.

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis.

OBJECTIVE: To identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications. METHODS: CT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction. RESULTS: Fifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness-kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20). CONCLUSION: CT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy. KEY POINTS: • The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability. • Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings. • ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).

Biomarker profiling beyond amyloid and tau: cerebrospinal fluid markers, hippocampal atrophy, and memory change in cognitively unimpaired older adults.

Brain changes occurring in aging can be indexed by biomarkers. We used cluster analysis to identify subgroups of cognitively unimpaired individuals (n = 99, 64-93 years) with different profiles of the cerebrospinal fluid biomarkers beta amyloid 1-42 (Aß42), phosphorylated tau (P-tau), total tau, chitinase-3-like protein 1 (YKL-40), fatty acid binding protein 3 (FABP3), and neurofilament light (NFL). Hippocampal volume and memory were assessed across multiple follow-up examinations covering up to 6.8 years. Clustering revealed one group (39%) with more pathological concentrations of all biomarkers, which could further be divided into one group (20%) characterized by tauopathy and high FABP3 and one (19%) by brain ß-amyloidosis, high NFL, and slightly higher YKL-40. The clustering approach clearly outperformed classification based on Aß42 and P-tau alone in prediction of memory decline, with the individuals with most tauopathy and FABP3 showing more memory decline, but not more hippocampal volume change. The results demonstrate that older adults can be classified based on biomarkers beyond amyloid and tau, with improved prediction of memory decline.

Differential age-related gray and white matter impact mediates educational influence on elders' cognition.

High education, as a proxy of cognitive reserve (CR), has been associated with cognitive advantage amongst old adults and may operate through neuroprotective and/or compensation mechanisms. In neuromaging studies, indirect evidences of neuroprotection can be inferred from positive relationships between CR and brain integrity measures. In contrast, compensation allows high CR elders to sustain greater brain damage. We included 100 cognitively normal old-adults and investigated the associations and interactions between education, speed of processing (SP), memory and two brain integrity measures: cortical thickness (CTh) of gray matter (GM) and fractional anisotropy (FA) in the white matter (WM). High education was associated with better cognitive performance, enlarged CTh in frontal lobe areas and reduced measures of FA in several areas. Better SP performance in higher educated subjects was related to more preserved GM and WM, while memory status amongst high educated elders was better explained by a putative compensatory mechanism and independently from cerebrovascular risk indicators. Moreover, we analyzed the direct effect of age on measures of brain integrity and found a stronger negative effect on WM than in CTh, which was accentuated amongst the high CR sample. Our study suggests that the cognitive advantage associated to high education among healthy aging is related to the coexistence of both neuroprotective and compensatory mechanisms. In particular, high educated elders seem to have greater capacity to counteract a more abrupt age impact on WM integrity.