Faculty Bibliography

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

OBJECTIVES/OBJECTIVE:To evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling. METHODS:One hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization. RESULTS:Using unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72-0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82-0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79-0.92) was revealed. CONCLUSION/CONCLUSIONS:Radiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification. KEY POINTS/CONCLUSIONS:• Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation. • Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing. • When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation.

BACKGROUND:MR is an important imaging modality for evaluating musculoskeletal malignancies owing to its high soft tissue contrast and its ability to acquire multiparametric information. PET provides quantitative molecular and physiologic information and is a critical tool in the diagnosis and staging of several malignancies. PET/MR, which can take advantage of its constituent modalities, is uniquely suited for evaluating skeletal metastases. We reviewed the current evidence of PET/MR in assessing for skeletal metastases and provided recommendations for its use. METHODS:We searched for the peer reviewed literature related to the usage of PET/MR in the settings of osseous metastases. In addition, expert opinions, practices, and protocols of major research institutions performing research on PET/MR of skeletal metastases were considered. RESULTS:Peer-reviewed published literature was included. Nuclear medicine and radiology experts, including those from 13 major PET/MR centers, shared the gained expertise on PET/MR use for evaluating skeletal metastases and contributed to a consensus expert opinion statement. [18F]-FDG and non [18F]-FDG PET/MR may provide key advantages over PET/CT in the evaluation for osseous metastases in several primary malignancies. CONCLUSION/CONCLUSIONS:PET/MR should be considered for staging of malignancies where there is a high likelihood of osseous metastatic disease based on the characteristics of the primary malignancy, hight clinical suspicious and in case, where the presence of osseous metastases will have an impact on patient management. Appropriate choice of tumor-specific radiopharmaceuticals, as well as stringent adherence to PET and MR protocols, should be employed.

For patients diagnosed with multiple myeloma (MM) there have been significant treatment advances over the past decade, reflected in an increasing proportion of patients achieving durable remissions. Clinical trials repeatedly demonstrate that achieving a deep response to therapy, with a bone marrow assessment proving negative for minimal residual disease (MRD), confers a significant survival advantage. To accurately assess for minute quantities of residual cancer requires highly sensitive methods; either multiparameter flow cytometry or next generation sequencing are currently recommended for MM response assessment. Under optimal conditions, these methods can detect one aberrant cell amongst 1,000,000 normal cells (a sensitivity of 10^-6). Here, we will review the practical use of MRD assays in MM, including challenges in implementation for the routine diagnostic laboratory, standardisation across laboratories and clinical trials, the clinical integration of MRD status assessment into MM management and future directions for ongoing research.

Lymphomas are typically large, well-defined, and relatively homogeneous tumors, and therefore represent ideal targets for the use of radiomics. Of the available functional imaging tests, [18F]FDG-PET for body lymphoma and diffusion-weighted MRI (DWI) for central nervous system (CNS) lymphoma are of particular interest. The current literature suggests that two main applications for radiomics in lymphoma show promise: differentiation of lymphomas from other tumors, and lymphoma treatment response and outcome prognostication. In particular, encouraging results reported in the limited number of presently available studies that utilize functional imaging suggest that (1) MRI-based radiomics enables differentiation of CNS lymphoma from glioblastoma, and (2) baseline [18F]FDG-PET radiomics could be useful for survival prognostication, adding to or even replacing commonly used metrics such as standardized uptake values and metabolic tumor volume. However, due to differences in biological and clinical characteristics of different lymphoma subtypes and an increasing number of treatment options, more data are required to support these findings. Furthermore, a consensus on several critical steps in the radiomics workflow -most importantly, image reconstruction and post processing, lesion segmentation, and choice of classification algorithm- is desirable to ensure comparability of results between research institutions.

PURPOSE OF THE REPORT/OBJECTIVE:F-FDG PET is limited for assessment of central nervous system lymphoma (CNSL) due to physiologic tracer accumulation in the brain. We prospectively evaluated the novel PET tracer Ga-pentixafor, which targets the C-X-C chemokine receptor 4 (CXCR4), for lesion visualization and response assessment of CNSL. MATERIALS AND METHODS/METHODS:Seven CNSL patients underwent Ga-pentixafor PET/MRI with contrast enhancement (CE-MRI) and diffusion-weighted sequences. The accuracy of Ga-pentixafor PET for CNSL lesion detection relative to the CE-MRI reference standard was determined. Standardized uptake values (SUVmean and SUVmax), PET-based (PTV) and MRI-based (VOLMRI) tumor volumes, and apparent diffusion coefficients (ADCs) were assessed, and correlation coefficients were calculated. Three SUVmax thresholds (41%, 50%, and 70%) were evaluated for PTV definitions (PTV41%, PTV50%, and PTV70%) and tested against VOLMRI using paired sample t tests. RESULTS:Twelve Ga-pentixafor PET/MRI examinations (including 5 follow-up scans) of 7 patients were evaluated. Ga-pentixafor PET demonstrated 18 lesions, all of which were confirmed by CE-MRI; there were no false-positive lesions on PET (accuracy, 100%). PTV41% showed the highest concordance with lesion morphology, with no significant difference compared with VOLMRI (mean difference, -0.24 cm; P = 0.45). The correlation between ADCmean and SUVmean41% (r = 0.68) was moderate. Changes in PTV41% on follow-up PET/MRI showed the same trend as VOLMRI changes, including progression of 1 lesion each in patient 1 (+456.0% PTV41% and +350.8% VOLMRI) and patient 3 (+110.4% PTV41% and +85.1% VOLMRI). CONCLUSIONS:Ga-pentixafor PET may be feasible for assessment and follow-up of CNSL. Future studies need to focus on testing its clinical value to distinguish between glioma and CNSL, and between radiation-induced inflammation and viable residual tumor.

For PET imaging of mantle cell lymphoma (MCL), [¹⁸F]FDG (2-deoxy-2-[¹⁸F]fluoro-D-glucose) is the currently recommended radiotracer, although uptake is variable and bone marrow evaluation is limited. In this prospective study, we evaluated the novel CXCR4 (G-protein-coupled C-X-C chemokine receptor type 4) tracer [⁶⁸Ga]Pentixafor in MCL patients, and compared it to [¹⁸F]FDG. Methods: MCL patients underwent [⁶⁸Ga]Pentixafor-PET/MRI, and, if required for routine purposes, also [¹⁸F]FDG-PET/MRI, before treatment. PET was evaluated separately for 23 anatomic regions (12 lymph node stations and 11 organs/tissues), using MRI as the main reference standard. Standardized uptake values (SUV_max and SUV_mean) and tumor-to-background ratios (TBR_blood and TBR_liver) were calculated. General Estimation Equations (GEE) were used to compare [⁶⁸Ga]Pentixafor-PET and [¹⁸F]FDG-PET sensitivities and positive predictive values (PPV). For bone marrow involvement, where biopsy served as the main reference standard, and splenic involvement, receiver operating characteristic curves were used to determine the optimal SUV and TBR cut-off values, and areas under the curve (AUC) were calculated. Results: Twenty-two MCL patients were included. [⁶⁸Ga]Pentixafor-PET sensitivity (100%) was significantly higher than for [¹⁸F]FDG-PET (75.2%) (P<0.001), and PPV was slightly, but not significantly lower (94.0%.vs. 96.5%; P=0.21). SUVs and TBRs were significantly higher for [⁶⁸Ga]Pentixafor-PET than for [¹⁸F]FDG-PET (P<0.001 in all cases); the greatest difference was observed for mean TBR_blood, with 4.9 for [⁶⁸Ga]Pentixafor-PET and 2.0 for [¹⁸F]FDG-PET. For bone marrow involvement, [⁶⁸Ga]Pentixafor-PET SUV_mean showed an AUC of 0.92; and for splenic involvement, TBR_blood showed an AUC of 0.81. Conclusion: [⁶⁸Ga]Pentixafor-PET may become an alternative to [¹⁸F]FDG-PET in MCL patients, showing clearly higher detection rates and better tumor-to-background contrast.

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.