Faculty Bibliography
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146
Single-Cell RNA Sequencing Reveals Tissue Compartment-Specific Plasticity of Mycosis Fungoides Tumor Cells
Mycosis fungoides (MF) is the most common primary cutaneous T-cell lymphoma. While initially restricted to the skin, malignant cells can appear in blood, bone marrow and secondary lymphoid organs in later disease stages. However, only little is known about phenotypic and functional properties of malignant T cells in relationship to tissue environments over the course of disease progression. We thus profiled the tumor micromilieu in skin, blood and lymph node in a patient with advanced MF using single-cell RNA sequencing combined with V-D-J T-cell receptor sequencing. In skin, we identified clonally expanded T-cells with characteristic features of tissue-resident memory T-cells (TRM, CD69+CD27-NR4A1+RGS1+AHR+
PMCID:8097053
PMID: 33968070
ISSN: 1664-3224
CID: 5596212
Correction to: Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers
PMID: 33693997
ISSN: 1432-1084
CID: 5596192
CXCR4 PET imaging of mantle cell lymphoma using [68Ga]Pentixafor: comparison with [18F]FDG-PET
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 (SUVmax and SUVmean) and tumor-to-background ratios (TBRblood and TBRliver) 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 TBRblood, with 4.9 for [68Ga]Pentixafor-PET and 2.0 for [18F]FDG-PET. For bone marrow involvement, [68Ga]Pentixafor-PET SUVmean showed an AUC of 0.92; and for splenic involvement, TBRblood 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.
PMCID:7738870
PMID: 33391493
ISSN: 1838-7640
CID: 5596182
Minimal residual disease in multiple myeloma: defining the role of next generation sequencing and flow cytometry in routine diagnostic use
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.
PMID: 33674146
ISSN: 1465-3931
CID: 5596172
Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers
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.
PMID: 33492473
ISSN: 1432-1084
CID: 5596112
Assessment of Central Nervous System Lymphoma Based on CXCR4 Expression In Vivo Using 68Ga-Pentixafor PET/MRI
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.
PMCID:8385649
PMID: 33208624
ISSN: 1536-0229
CID: 5596082
European journal of nuclear medicine & molecular imaging. 2021:48(10):3268-3276.DOI: 10.1007/s00259-021-05193-7
18F FDG PET/MRI with hepatocyte-specific contrast agent for M staging of rectal cancer: a primary economic evaluation
PURPOSE:F FDG PET/MRI as an alternative imaging method to standard diagnostic workup for initial staging of rectal cancer. METHODS:F FDG PET/MRI with a hepatocyte-specific contrast agent and pelvic MRI + chest and abdominopelvic CT was created based on Markov simulations. For obtaining model input parameters, review of recent literature was performed. Willingness to pay (WTP) was set to $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was applied, and probabilistic sensitivity was determined using Monte Carlo modeling. RESULTS:F FDG PET/MRI was identified as an adequate diagnostic alternative to SCI with high robustness of results to variation of input parameters. CONCLUSION:F FDG PET/MRI was identified as a feasible diagnostic strategy for initial staging of rectal cancer from a cost-effectiveness perspective.
PMCID:8426298
PMID: 33686457
ISSN: 1619-7089
CID: 5595572
Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer
OBJECTIVES:To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. MATERIALS AND METHODS:Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis-by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis-was performed to predict clinical outcomes. RESULTS:The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28-79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7-76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. CONCLUSIONS:The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.
PMCID:8396356
PMID: 34449713
ISSN: 2379-139x
CID: 5595602
Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper
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.
PMCID:8784734
PMID: 35083155
ISSN: 2234-943x
CID: 5595712
Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: effects of post-reconstruction methods in a dual-center study
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.
PMCID:8128805
PMID: 33277670
ISSN: 1432-1084
CID: 5475812
